Sunshine duration is an important variable in surface meteorological measurement. In this article, two methods for measuring sunshine duration are described, one is photovoltaic-based instrument, which ranges from 400 to 1100 nm, and the other is thermopile-based pyrheliometer, which ranges from 300 to 3000 nm. Due to the spectrum is more representative and accuracy for solar radiation, the pyrheliometer is recommended as the reference sensor for the detection of the threshold irradiance for defining the sunshine duration. The comparison between the photovoltaic- and thermopile-based instruments is carried out to uncover the spectral influence on sunshine duration measurement, analysis by the SMARTS tool is presented to calculate irradiance with different spectral ranges under different altitudes.

It is necessary to test the reliability of the operation of the machine itself for meeting the high-speed, ultra-precision, flexible, and other modern manufacturing and processing needs. In practical applications, the obtained reliability detection information presents a variety of uncertainties such as randomness, imprecision, incompleteness and so on, due to the complexity of the machining environment, the limitations of the sensor itself and the imperfections of the information acquisition technology. The uncertainty reasoning method is proposed to test reliability and accuracy precision. Based on the multi-sensor information characteristic, the rule of the credibility sets is established to achieve the operational reliability analysis. It can realise the uncertainty reasoning for the modal and reliability rules. Examples of analysis show that during the high-end CNC machine tools operational reliability analysis, it has a practical effect to study the uncertainty reasoning method of the modal and reliability propositional rule and the analysis basis can be provided for the precision degradation and fault diagnosis.

Objected to the planetary gear transmission, research on the fault diagnosis of gear cracks is mainly carried out with method of EEMD decomposition and Envelope demodulation. During the process, EEMD decomposition is used to the experiment data and continuously parameters of the root mean square (RMS) value, peak-to-peak (PtP) value, kurtosis (Kr), and other parameters of the IMF components are computed. According those parameters, the signal is reconstructed and after band-pass filtering the Hilbert transform and the envelope demodulation are carried out. The results showed that the power spectrum information obtained by using the EEMD decomposition and Hilbert transform could effectively response fault characteristics of the gear crack in the planetary transmission. The research is beneficial to the gear meshing fault diagnosis of the planetary transmission quickly and accurately and has a positive engineering value.

Online oil monitoring technique is now an important development means to monitor wear condition and diagnose wear fault real time. Inductive wear debris monitoring is the research hotspot in the current online oil monitoring for its unique characteristics. Based on the current domestic and international online inductive wear debris monitoring, the relevant comments to the online inductive wear debris monitoring technology were given. Then, the positive and inverse problems of numerical analysis of inductive wear debris monitoring are analysed. Based on the above, the difficulties and shortages of online inductive wear debris monitoring are studied and the development trend and research focus of online inductive wear debris monitoring are discussion to provide for the oil online technology research.

Integrated test architecture (ITA) is a testing infrastructure developed for overcoming the design deficiency of stovepipe system based on current construction situation of underwater vehicle equipment. The general infrastructure of ITA is given at first, and then the design of ITA-based foundation platform infrastructure (IFPI) of underwater vehicle testing (UVT) is presented with details, mainly including development of middleware and the design of ITA testing process tool (TTCT). Moreover, an integrated test system for underwater vehicle testing (ITSUVT) is constructed by developing or integrating testing resource on IFPI. Finally, the performance of VBFPI is tested according to the designed testing schemes, and the function of ITSUVT is verified by field and virtual UVT.

The electronics system is a crucial component of a nuclear radiation detector whose performance directly affects the accuracy of nuclear radiation monitoring. The design of a neutron detector's electronics system is introduced here. Two sets of preamplifiers are designed, the amplitude-versus-frequency curves of the preamplifiers are compared using Multisim simulation software, and a better preamplifier is determined through the comparison. A similar approach is used in the design of main amplifier, the amplitude-versus-frequency curves of four operational amplifiers are simulated by Multisim, and the best ones are chosen. The discriminator and trigger are designed in the electronics after the main amplifier, which are used for signal screening and shaping. The neutron detector is tested by cosmic neutrons in environmental background, which is composed of the designed electronic system and a proportional counter tube. Experiments show that the electronics is suitable for neutron detector.

This article studies a variational Bayesian method to fix the linear regression (LR) model of which regressors are Gaussian distributed with non-zero prior means, and then apply the method to the linear state space (LSS) model. Here, we innovatively transform the LSS model into a special LR model: In each state, the value obtained from the predict step can be seen as the prior mean of the regressors, and the update step can be viewed as the iterative solving in LR model with non-zero prior means. We simulate the proposed algorithm with high-dimensional discrete LSS models where most states are prior zeros; simulation results show that the proposed algorithm and its applications in LSS are both effective and reliable.

Compared with the traditional electrical resistance strain gauge, the fibre Bragg grating (FBG) is a new material that can be used to produce the strain sensor, which has many advantages such as convenient installation, low temperature drifting, less signal interference and higher transmitted quality. In this study, FBG sensor system configuration, design principle, numerical simulation and loading tests for the FBG strain gauge are expatiated extensively. At the last, one-axial and three-axial strain gauges are produced and some conclusions have been put forward. So the FBG strain gauge can meet the precision of the structure monitoring and replace the traditional sensors.

For the challenges of filter bank multi-carrier (FBMC) test applications in the fifth-generation mobile communication network (5G) system, presented is a time domain synchronisation estimation algorithm based on power synchronisation in frequency domain. Through MATLAB simulation, the proposed algorithm is verified to achieve good synchronisation at low signal-to-noise ratio (SNR) and large frequency offset without signal quality changes. As a result, it can be applied in FBMC signal vector analysers, promoting FBMC signal vector analysis functions used in 5G test applications.

The energy calibration and efficiency calibration of one silicon drift detector (SDD) are introduced here. The energy calibration is achieved using four radioactive sources, and the linear correlation coefficient is close to one. The efficiency curve of the SDD is obtained by Monte–Carlo simulations from 0.5 keV to 40 keV. Then, the efficiency of the SDD is calibrated using two radioactive sources. The results of experiments are compared with the calculated and the relative deviation are within 5.6%.

In order to deal with the shortcomings of a photoelectric mode cardiotachometer, such as wasting energy and slowly refreshing results, this study mainly focuses on a real-time displayed, portable, low-cost, and energy-saving cardiotachometer. The design is based on the Mbed, which is the core microprocessor, controlled and dispatched by the built-in function. Before filtering and amplifying the electronic signal, the pulse signal is collected by the photoelectronic sensor at the beginning of the whole process of the cardiotachometer. The energy-saving function can be achieved through audio p-type metal oxide semiconductor to switch on and off the light-emitting diode (LED) which is in the sensor. Eventually, the real-time ratio of the heartbeats and the waveforms can be displayed on the two 8 × 8 LED modules at the same time with overall energy-saving of about 50%. This design can be applied in many fields focusing on long battery life and portable, especially as a part of a wearable device.

In storage environment, aiming at the problem of goods positioning when picking, the pallet is firstly recognised based on deep learning. Then, algorithm of obtaining the pose of the pallet by the image processing and Kinect sensor is proposed in this study. The pallet is recognised and its selected box is obtained by deep learning. On this basis, the position and the angle of the pallet are obtained by the image processing method, and then RGB-D transforms the position and posture of the pallet into the three-dimensional (3D) coordinate for three-dimensional positioning. The experiment results show that the algorithm can obtain real-time pallet position with the success rate of 81.02%. Thus, the algorithm can meet the requirements of the efficiency and accuracy location requirements of the storage of goods when picking.

Here, the auxiliary robot for distribution network without power failure is designed. The robot includes the design of mobile chassis, turntable, tops, and legs. The mobile chassis is responsible for walking, power supply, and work safety support platform. It can walk flexibly and has a certain ability to overcome obstacles. The turntable not only can withstand axial and radial loads, but also can withstand tilting moments. Tops can provide reliable support and good insulation guarantee for work buckets. Outriggers serve to level out and ensure the stability of the vehicle. The experimental results show that the design of the network-assisted robot without power failure can meet the actual operation requirements.

This article mainly introduces the motion control system of high-altitude live working vehicle and the design of the hydraulic drive system of the trolley. Motion control system is the nerve centre of auxiliary robot for power distribution without interruption. It plays a key role. The hydraulic drive system with trolley provides power support for the insulated bucket of the trolley car. The experimental results show that the design scheme of the control system and the hydraulic drive system can meet the actual operation requirements of the electric car.

In view of the defects such as long time-consuming and large limitation in the detection of pesticide resistance of crop pathogens at present. A novel photoelectric detection method based on enriched microfluidic chips is proposed for the detection of pesticide resistance of crop fungal disease. First, according to the aerodynamics characteristics of microbial aerosol particles and optical detection technology, a special enrichment microfluidic chip was designed to achieve high efficiency enrichment and high precision concentration detection of fungal spores. On this basis, based on the theory of pesticide resistance sensitivity baseline, the relationship among spore germination, systematic enrichment concentration, and optical detection concentration of Botrytis cinerea in strawberry treated with Pyrimethanil was studied, and the pesticide resistance detection model of uncultured pathogens was established. The light intensity of light source was 1.1 × 10³ cd, and the wavelength was 620 nm as the optimum parameters for optical detection. The correlation coefficient between the microscopic examination of spores’ concentration and the results of optical detection is 0.9792. It has good linearity and reliability, which provides a theoretical basis for the research and development of portable equipment for detecting pesticide resistance of crop diseases.

Many key parts of mechanical equipment gradually enter failure period with service, and failures happen in some of them, it will lead to serious consequences with economic loss and crash of machine and death of human if weak failures cannot be identified in time, which might degrade to major failure. It would be of benefit to prevent the fault deterioration if faults could be identified in early period, with the states of key parts obtained. Locomotive gear was chosen as the research object here, a method of inhibition mode mixing and characteristic frequency acquisition based on dynamic cascade empirical mode decomposition is put forward for the weakness and aliasing out of uncertain factors. The fault features are much more obviously presented in different time scale by optimal intrinsic mode function (IMF) acquisition and the improvement of envelope solution method. The results of experiment show that weak faults can be obtained under much noise, and an effective method is provided for early fault prognosis and diagnosis.

A shock tube-based calibration installation for dynamic pressure transducers was designed and manufactured. The hardware of the calibration installation contained a shock tube, two pressure transmitters, three transducers for velocity measurement, a temperature transmitter, two data sampling cards and a industrial personal computer, etc. The software of the calibration installation was a data sampling and data processing system. The functions of the software included test conditions monitoring, performance parameters computation, and calibration reports generation, etc. The performance of the calibration installation was tested. The test result showed that the range of the step pressure was 9.60 kPa–1.101 MPa, the maximum rise time of the step pressure was no more than 1μs, the minimum duration of the step pressure plateau was longer than 4 ms, the fluctuation of the step pressure plateau was better than ± 2%, and the maximum 2nd order expand uncertainty of the step pressure was less than 4%. Thus the calibration installation was qualified to be a shock tube-based dynamic pressure standard because that the performance of the calibration installation was verified to meet the requirements of ‘Verification Regulation of Dynamic Pressure transducers’ and ‘Verification Regulation of Dynamic Pressure standards’.

Falling weight deflectometer (FWD) is widely used in traffic transport industry. In order to solve the traceability of FWD, the definition of deflection was studied, the use and measurement of FWD was thoroughly analysed, and the measurement parameters were determined. Calibration device, standard foundation, and experimental environmental conditions of FWD was set up. The measurement uncertainty of the calibration equipment has been analysed. Through the test of the typical FWD, the performance of the calibration equipment is tested. Through the establishment of the traceability chain of ‘ Absolute normal low frequency vibration standard device-FWD calibration equipment-FWD’, it can meet the calibration/verification requirements of FWD.

Owing to the limited processing speed of current digital signal processing devices, digital generated signal frequency and bandwidth based on digital up-conversion technique has long been a bottleneck. Interpolation on the baseband signal puts great pressure on the following filtering and mixing operation. In this study, an efficient parallel architecture is presented, which moves the interpolation behind the filtering and mixing, and adopts parallel numerically controlled oscillator (NCO) arrays and poly-phase low-pass filter arrays to achieve the anticipated orthogonal mixing signal and prototype filter. Parallel NCO decomposition principle and mathematical derivation are elaborated, and realisation of the poly-phase filter arrays is also discussed. The proposed post-interpolation architecture can effectively relieve the filtering and mixing pressure including operation rate and computational complexity, which will benefit the hardware implementation. In the end, verification test of signal up-conversion with 400 MHz bandwidth is launched to certify the architecture validity.

There exists few reports in the literature aimed at estimating depth from the spectrally-varying-amount-of-defocus contained in a single image generally captured by a commercial colour camera. However, the colour camera can adversely affect the depth measurement accuracy, due to the cross-talk between its wide and overlapping red/green/blue channels. In this study, the authors propose an improved method to estimate depth from non-overlapping and narrow-band spectral images captured by a snapshot narrow-band multispectral imaging (SNB-MSI) sensor. An optical lens with visible longitudinal chromatic aberration is placed in front of the SNB-MSI sensor to enlarge the spectrally-varying-focal-depth so that spectral images corresponding to different narrow bands are focused on apparently different planes. This setting not only differentiates depth ambiguity on both sides of the focal plane, that is caused by two different depths lead to the same blurred image, but also prevents cross talks between different spectral channels. Preliminary experimental data show that the central wavelength of best focused spectral band shifts with increasing depth of the target. This suggests that it is feasible to dynamically measure the depth of the scene using the combination of a longitudinal chromatic aberration optical lens with a SNB-MSI sensor.

Mercury ion (Hg²⁺) is one of the common pollutants which exists in the natural environment far and wide. The detection of Hg²⁺ usually requires large laboratory equipment, which limits their field applications. Here, a miniaturised optical fibre sensor based on quantum dots (QDs) for detection of Hg²⁺ is developed. The sensor is based on the principle of fluorescence quenching caused by Hg²⁺ and evanescent wave sensing. It is an all-fibre sensor which composed of an optical fibre probe module, an optical module and a signal acquisition module. Through miniaturisation design of optical system and the structure of the sensor, the sensor is reduced to the volume of 120 × 120 × 100 mm. The experimental results show that in the miniaturised sensor both has high sensitivity and fast response speed. Meanwhile, the ion anti-interference experiment shows that the sensor has good selectivity for Hg²⁺ detection. In general, the proposed sensor has significant potential in the field of biochemistry, environmental monitoring, and food safety.

Currently, some five-axis CNC machine tools have almost achieved the same advanced level of the western countries in terms of functionality and performance. However, the reliability of system is much lower than similar foreign products. The reliability has become one of bottlenecks to restrict the development of domestic CNC machine tools. At present, domestic reliability research of CNC machine tools developed rapidly, but the evaluation research is limited to a single indicator, lacking overall evaluation about it. To fully evaluate the reliability of five-axis CNC machine tools, the matter-element model of reliability evaluation is established with the application of matter-element in extension theory, and the reliability evaluation indices system is established comprehensively. The correlation functions are introduced to calculate the correlation degree, and the weights of each evaluation indices are determined by using AHP. The actual example and result on two types of domestic CNC machine tools show that the proposed method is effective, reasonable, and accurate in the process of reliability evaluation. The proposed methodology provides a detailed instruction for comprehensive reliability evaluation of CNC machine tools.

To minimise the torque ripple for vector control of interior permanent magnet synchronous motor (IPMSM) of railway traction system, this study proposes a new velocity, torque and current three-loop control system, and designs a velocity and torque sliding mode controller (SMC) for permanent magnet synchronous motor (PMSM) driven system. First, the mathematical model of IPMSM is described and the new velocity and torque closed-loop control system for PMSM is designed. Second, the velocity SMC and torque SMC are designed. The fast convergence of the motor velocity and torque is realised, and the torque ripple is reduced effectively. The stability of the designed SMC is analysed. Finally, the effectiveness of the proposed scheme was compared with PI by simulations and experiments. The results show that the proposed method has good control performance and strong robustness.

Temperature and humidity play an extremely important role in indoor thermal comfort and industrial production. To achieve precise control of temperature and humidity in the building space environment and reduce energy consumption, it is necessary to analyse turbulence from the perspective of energy. On the basis of the basic principle of building energy enthalpy, under the premise of reasonable assumptions of various boundary conditions, four turbulence models were established for simulation. The basic characteristics, overall distribution and local characteristics of the interior energy enthalpy were compared and analysed. The theory of building energy enthalpy not only can effectively solve the influence of latent heat on the building environment, but also can provide guidance on the selection strategy of turbulence models. The proposed theory can improve the accuracy and credibility of the simulation data of building space.

Aiming at the method of indoor calibration of a transmission visibility meter has not been established; a new calibration method by measuring the luminous transmittance of attenuator piece was established. Based on the high homogeneity stability lighting and the multi-spectral transmittance analysis technique, a calibration system of the transmission visibility meter by measuring the luminous transmittance of the attenuator piece was designed. The experiment was designed by measuring the luminous transmittance with the range of 0.2–99.2% of a series of calibrated attenuators and the visibility in the range of 40 m to 20 km, then the luminous transmittance errors, the meteorological optical range (MOR) errors and their uncertainty were obtained. The result showed that the luminous transmission error was within ±0.18%, which satisfied the calibration requirement for transmission visibility meter transmittance; the MOR calibration error met the accuracy requirements of the International Civil Aviation Organisation for visibility measurement; this work will have guiding significance in establishing a calibration procedure for the transmission visibility meter.

With the world's energy shortage, the scholars all over the world have done a lot of research work on the efficient use of energy, energy-saving emission reduction, and the development of new energy. Turbine generators have a wide range of applications in energy recovery. In this study, a new type of turbine generator structure is proposed. Four disc-type permanent magnet motors are symmetrically mounted on the shafting, and are supported by gas hydrostatic bearing. The vibration tests of the shafting were carried out. The vibration characteristics of the shafting were analysed by using the bode diagram, the time-waterfall map, the bifurcation diagram, the shaft orbits at the typical speeds, and the Poincare maps, which can provide references for developing high-speed turbine generators.

Industrial robots, as a structurally sophisticated mechatronics system, have a high cost of routine maintenance and repair. Repairs after fault require the corresponding manpower and material resources, and have hysteresis. If the fault can be predicted in a timely and accurate manner, the maintenance process can be carried out in advance, and the hidden dangers can be eliminated to fundamentally solve the fault problem. Based on the self-organised critical theory (SOC theory), this article draws lessons from its self-organisation evolution model and uses the self-organised criticality of industrial robot fault to establish an autoregressive moving average model (ARMA model) for industrial robots. According to the analysis of residual value and the explanation for the faults of industrial robots, find ways and means to prevent and reduce faults.

Vegetation chlorophyll content is very important for monitoring the growth and health status of vegetation. Remote sensing of chlorophyll content holds an important potential for evaluating crop growth status and diseases and insects. The both units can represent the chlorophyll content of leaves, but there are some differences between them. The former reflects the proportion of chlorophyll in the leaf components, while the latter displays the weight of chlorophyll in the unit leaf area. Combining ten representative hyperspectral vegetation indices, the effect of measurement units on estimating crop leaf chlorophyll content was studied with winter wheat and summer corn leaf chlorophyll content data and leaf reflectance data. First, the relationships between leaf chlorophyll content and the ten selected hyperspectral vegetation indices were analyzed. Second, the estimating models of leaf chlorophyll were built. Finally, the accuracies of estimating models were also assessed based on the validating data. The results show that: (i) due to chlorophyll absorption effect at visible bands, vegetation indices were highly correlated with chlorophyll content of crop leaves and it is feasible to estimate chlorophyll with vegetation index, for example, the relative errors for the estimating model based on MERIS Terrestrial Chlorophyll Index <14%; (ii) The different measurement units of leaf chlorophyll content can give rise to differences in estimating accuracy. Measurement units of mg g⁻¹ and μg cm⁻² can both effectively describe chlorophyll content of vegetation leaves. However, the leaf chlorophyll models based on the former were generally superior to the latter. For instance, the relative errors of summer corn for measurement units of mg g⁻¹ and μg cm⁻² are 7.6 and 13.6%, respectively. The reflectance signals of leaves at visible and near infrared regions contain various absorption information on leaf components, especially for pigments. Moreover, the measurement unit of mg g⁻¹ reflects the relative content of chlorophyll in the leaf components. Therefore, the unit of mg g⁻¹ is in agreement with the reflectance signals of leaves. However, the unit of μg cm⁻² only represents the absolute weight of chlorophyll in the unit leaf area. Therefore, it is recommended that the measurement unit of mg g⁻¹ should be used as far as possible when estimating chlorophyll of crop leaves or other vegetation leaves with remote sensing.

This article proposes an electrode channel combination method for optimisation steady-state visual evoked potentials (SSVEPs) towards a high-performance brain-computer interface (BCI). In SSVEP-based BCIs, the channels which lie in the area near the occipital and parietal lobes are always selected to improve the classification accuracy. Although the electrode channels which are selected in occipital and parietal lobes have significantly improved the accuracy and reduced the calculating time of algorithm, the effect of electrode channel combination for each subject have not been systematically explored. This study conducts a comparison of BCI performance between two subjects in different electrode channel combinations. The results show significant difference between two subjects when selecting different electrode channel combinations using unsupervised target identification methods. The results suggest that different electrode channels should be selected for different subjects in SSVEP-based BCIs.

The traditional pressure sensors are mostly based on piezoresistive, piezoelectric effect, and strain gauge, which are easily affected by ambient temperature. The structure of some sensors is rather complex to effectively acquire the signal or measure distribution force. Therefore, a pressure sensor based on the planar coil and elastic element was designed to solve this problem. The newly designed sensor has a spiral planar coil made by a printed circuit board and a polydimethylsiloxane (PDMS) thin film as the elastic element. The conductive layer is attached on the upper surface of PDMS to realise the eddy current effect. Meanwhile, a simulation model was established to find the relationship between deformation displacement and the pressure of the elastic element. By generating the experimental curves of the deformation displacement of the elastic element and the output frequency of the eddy current sensor, the pressure measurement on the surface of the elastic element can be identified. The preliminary experimental results showed that the eddy current pressure sensor based on the plane coil has good linearity and repeatability, and it can realise large area distributed pressure measurement, which is also suitable for accurate measurement of distribution force.

In order to improve the performance of motor imagery brain–computer interface (BCI) based on deep learning algorithm, here, the authors propose an electrode channel combination method. Although motor imagery electro-encephalography (EEG) signals which contain different electrode channels on the scalp surface have an effect on the classification performance, the effect of different electrode channel combinations has not been systematically explored. With the two deep learning models the authors constructed, the authors list some different electrode channel combinations to classify the left fist and right fist motor imagery EEG signals. The results show that the more the number of channels in these combinations, the higher the classification accuracy. However, when the number of channels exceeds 11, the classification accuracy increases slowly, and the classification effect is rarely improved. Therefore, the authors obtain an optimal electrode channel combination to use the electrode channels efficiently and to improve the performance of motor imagery BCI based on deep learning algorithms.

Motor imagery (MI) classification based on electroencephalogram (EEG) with high speed and accuracy is a key issue in brain–computer-interface (BCI) technology. This study compared the support vector machine (SVM) and back-propagation neural network (BP-NN) for MI classification. In this study, EEG data of four subjects provided by BCI competition 2008 was employed. For the comparison of classification accuracy (CA), there were three steps. First, EEG feature extraction for MI was implemented by using a common spatial pattern. Second, SVM and BP-NN were used to classify MI by cross-validation. Finally, the CA rate, receiver operating characteristic curve and area under the curve (AUC) were given to evaluate two classifiers. The average CA rates obtained on the four subjects using SVM and BP-NN were 75.20 and 80.73%, respectively. Furthermore, the mean AUCs of SVM and BP-NN were 0.7860 and 0.9462, respectively. Both average CA rate and AUC indicate that BP-NN has better accuracy of classification than SVM.

Image classification is a fundamental task in image analysis. Recent advances in deep learning have achieved promising results on many image classification benchmarks. However, in some particular tasks, especially in biomedical image analysis, preparing a large number of labelled images for the model's training is costly and unpractical. In this study, the authors aim to address the following questions: With limited effort (e.g. time, cost and manpower) for labelling, what instances should be chosen to annotate and how to train to model using limited annotated data. For that, they present an active learning algorithm combining with data balancing, making the model (e.g. convolutional neural network) fine-tuned continuously and incrementally to reduce the effort of labelling and making model's training process more robust and efficient in both binary and multi-class classification with high-performance. They have evaluated the authors’ method of both binary natural dataset and three classes biomedical dataset, demonstrating that active learning with data balancing could help models’ training more robust and broaden active learning's field to multi classification and more application scenarios. More significantly, their experiments showed that at least a half of effort in labelling could be saved for satisfied performance by their method.

Steady-state visual evoked potential based brain–computer interface (BCI) is now attracting growing attention for its fast and efficient information transfer rate. However, human beings show excellent response only in a limited range of frequencies. Due to the limitation of frequency bandwidth, it is difficult to obtain enough frequency for target encoding. This study presents a high-resolution steady-state visual evoked BCI whose frequency resolution reaches 0.1 Hz. Compared with the widely-used system with frequency resolution of 0.2 Hz, this system have doubled the number of evoked frequencies, and increased the information transfer rate by 27.89% when sampling time is >3 s.

The purpose of this article is to discuss the method of determining the wall correction factor of graphite cavity ionisation chamber. The method is the ‘equal effect wall thickness’ simulation and direct calculation of the wall correction factor of the std10cm³ and std30cm³ spherical graphite cavity ionisation chamber in the national air-specific kinetic energy reference group (National Institute of Standards and Technology NIST), respectively, in the NIST10cm³ and NIST30cm³ spherical graphite cavity ionisation chamber. The calculated results are in agreement with the values released by NIST in 0.1%. Conclusions of the equivalent wall thickness simulation method and the direct calculation simulation method meet each other's requirements, which provide a new idea for the determination of wall correction factor.

Since the German physicist Roentgen discovered X-rays, X-rays have been widely used in medical diagnostics, industrial non-destructive testing, and scientific research. There are four main types of single-energy X-ray generation: k-fluorescence, radioactive sources, X-ray machines (relying on monochromators), and synchrotron radiation. In view of the advantages of using X-ray machines to generate continuous energy points and non-nuclear pollution, this study uses Oxford fluorescent tubes and crystal diffraction methods to generate single-energy X-rays. The diffracted crystal uses Si111 and calculates the different energy values obtained by diffracting different Bragg angles. This study uses the silicon drift detector to measure the energy spectrum of (6–15) keV single-energy X-rays, the stability of the Oxford fluorescent tube, and spot size with a charge-coupled device detector. The obtained energy spectrum is compared with the theoretical energy calculated to verify the correctness of this method and provides important reference for crystal diffraction in low-energy sections.

Wear is one of the common faults in the mechanical transmission system. Detection of wear particles in lubricating oil can detect and evaluate the wear degree of mechanical equipment. The electromagnetic sensor for metal wear particle detection can be connected in series with the oil circuit, which has the advantages of full liquid flow, simple structure, distinguishing metal properties, and not being influenced by bubbles and vibration. In view of the shortcomings of the existing electromagnetic sensor for wear particle detection, the detection principle and the induction electromotive force model were studied, and the magnetic field change of the metal particles through three coil differential solenoids was analysed. The optimisation design method was proposed here, which was about the related parameters, such as the wire diameter, the coil turns, the coil widths and so on. The experimental analysis of the sensor was carried out based on detection hardware system that could provide the phase-locked amplification function. The 10-mm inner diameter sensor could detect the minimum 100-μm ferromagnetic metal abrasive particles after optimisation. The developed sensor could provide data information for the abrasive particle detection, fault diagnosis and early warning of wear degree of mechanical equipment.

In order to improve the operation reliability of high-grade computer numerical control (CNC) machine tools and reduce the maintenance time of failures, the remote state monitoring and intelligent maintenance system for CNC machine tools was proposed. Through collecting the basic information of equipment, maintenance information, and the information of the vibration, temperature, main current and power of the machine tool based on soft-bus technology, the system analysed the real-time state data, got the reliability parameters, extracted the fault features and established the maintenance knowledge base. Combining the dynamic monitoring data and static data, the system evaluated the key parts of machine tools. By the fault knowledge base, the system analysed the reason for failure and recommended the maintenance method. With the function modules, the system can improve the intelligent maintenance level of CNC machine tools and improve the using efficiency of machine tools.

Clustering algorithm is one of the most important algorithms in unsupervised learning. For density-based spatial clustering of applications with noise (DBSCAN) density clustering algorithm, the selection of neighborhood radius and minimum number is the key to get the best clustering results. Aiming at the problems of traditional DBSCAN algorithm, such as the neighborhood radius and the minimum number of points, this article puts forward two classifications based on K-means algorithm, and gets two clustering centers. Where calculated between two data points and the cluster center-to -center distance, clustering, distance, statistics in a distance of data points within the scope of the search, the number of data points corresponding to the maximum distance value, and thus the parameters for the DBSCAN algorithm to estimate and selection of initial radius of neighborhood with the minimum number of clustering start critical value. When the parameters are iterated and optimized continuously, the data are divided into clusters, and the most suitable neighborhood radius and the minimum point number are obtained. The experimental data analysis show that the improved algorithm reduces the human factors in the traditional algorithm and improves the efficiency, so as to get the accurate clustering results.

In view of the stability and safe service of the transmission system of the wind turbine, the trend early warning method for the stability deterioration of the electromechanical system is proposed and the level of the predictive maintenance of the complex electromechanical system is improved. It includes: establishing the dynamic model of wind power transmission system, revealing the evolution law of the system stability deterioration, putting forward the signal processing method of the weak detection information, extracting the fault features decoupling with energy and constructing the early warning methods of the system stability deterioration trend. The research results can be applied to the safety monitoring and fault warning of the wind turbine. It is beneficial to ensure the safe and reliable operation of the equipment and realise the prediction maintenance, improve the operation and management level of the equipment, reduce the economic loss caused by the failure and promote the utilisation and development of green energy.

Aiming at the problem that fault signals of rolling bearing are easily submerged by the strong background noise, which makes it difficult to extract fault information, so a method based on kurtosis criterion variational mode decomposition (VMD) and modulo square threshold is proposed and applied to fault diagnosis of rolling bearing. First, the vibration signals of rolling bearing are processed by VMD. Second, the signals are reconstructed by selecting the intrinsic mode function (IMF) components with the largest kurtosis and the second largest kurtosis. Finally, the reconstruction signals are de-noised by the modulo square threshold. Through the test of the inner ring and outer ring of the rolling bearing, the feasibility and effectiveness of the proposed method are verified.

The sine eccentric shaft is one of the most important parts for industrial robot RV reducer, and its machining quality has immediate effect on the overall device performance. The eccentric shaft X-C linkage grinding model is built based on the tangential point tracing machining principle, and the method of calculating the roundness error for eccentric circle grinding is proposed here. The research is performed on non-uniform rational B-spline (NURBS) interpolation for the coordinates’ densification of eccentric shaft X-C linkage grinding, and the algorithm process is put forward. The process of eccentric grinding is simulated and analysed by wheel inversion envelope method. The results show that the eccentric tangent point tracking model proposed is correct. Compared with the linear interpolation, NURBS interpolation can remarkably improve the accuracy of eccentric shaft tangent point tracking grinding.

A fault diagnosis method for rolling bearing based on ensemble empirical mode decomposition (EEMD) sample entropy and probabilistic neural network (PNN) is proposed for non-steady and non-linear signals. First, the rolling bearing signals are decomposed into intrinsic mode function (IMF) using EEMD. Then, the kurtosis of each component is calculated. Five components with large kurtosis are selected and the sample entropy is extracted to form the feature vectors. Finally, the feature vectors are input to the PNN for fault diagnosis. The method is used to classify the type of the rolling bearing fault. The results show that the accuracy of fault diagnosis of the proposed method is 100%, which proves the effectiveness of the proposed method.

This study first analyses the problems that the solenoid valve generates during operation. Then the electromagnetic valve was modelled and ANSYS finite element analysis simulation software was used to simulate the electromagnetic field and transient magnetic field of the solenoid valve. The magnetic induction intensity distribution diagram and magnetic field distribution diagram of the electromagnet were obtained. According to the simulation results of the solenoid valve, the magnetic flux leakage line is blocked after the magnetic isolation cover is added, the magnetic induction strength of the iron core and the valve head is increased, the leakage magnetic flux is reduced, and the structure is optimised.

The energy spectrum is one of the most effective methods to characterise the quality of reference filtered X radiations. To obtain the energy resolution, the authors choose seven sealed standard radioactive sources to carry out energy calibration for high-purity germanium detector whose energy resolution is within 3%. Then they use this spectrometer to measure the reference filtered X radiations with its energy from 55 to 125 keV of low air-kerma rate series, meanwhile the BEAMnrc is used to simulate the spectra of the same four radiation qualities. By analysing the data of spectral distribution, they can determine mean energy and spectral resolution of these reference radiation qualities. By comparing simulation results with actual measurements, the result shows the spectra stimulated by the BEAMnrc are consistent with the spectra measured by high-purity germanium detector and the deviation of the mean energy is within 4.0%. The spectral resolution of the reference filtered X radiation is 22.8, 22.4, 22.5 and 22.8%, respectively.

This article analyses the structure and working principle of the brushless DC motor, the advantages and disadvantages of PID regulation and fuzzy control are analysed as well, and puts forward the fuzzy PID motor control technology with both advantages. The speed control of the brushless DC motor is carried out and the simulation model is built. The results show that, compared with the traditional PID adjustment, the fuzzy PID makes the motor speed more stable and the speed fluctuation decreases obviously, which satisfies the requirement of the speed control of the electric bicycle.

Wear is the main influential reason for the reliability and service life of machinery and the inductive monitoring is the most research and application of online ferromagnetic debris detection for its unique characteristics. In the electromagnetic characteristics analysis of the wear debris, the numerical analysis method can directly solve the problem of electromagnetic field in the form of value, program instead of analytic form. Based on the current research on numerical analysis of electromagnetic characteristics for ferromagnetic debris, the solved problems of numerical analysis are analysed. The core and theoretical basis of numerical analysis of electromagnetic characteristics are studied and assumptions and boundary conditions are provided. Then the present problems and future development trend of the numerical analysis of electromagnetic characteristics for ferromagnetic wear debris are discussed.

Vibration sensor calibration is performed using the absolute calibration method and the comparison calibration method. The comparative calibration method is used to take a carefully calibrated benchmark to the measured sensor and the vibration sensor for a contrast test, the use of standard sensors requires regular inspection to traceability to national standards to ensure the reliability and accuracy of dissemination. From the measurement perspective, using the absolute calibration method to calibrate a laser interference vibration acceleration sensor can achieve high precision, wide field, high reliability calibration to ensure the sensor of high accuracy, vibration signal high reliability is of great significance. The vibration method of absolute calibration is based on laser interference technology research, put forward in the development of the laser absolute method based on the technology of vibration calibration device, a further optimised method of the absolute calibration device, the mathematical model is used to improve the technical indicators and to provide reliable technical guarantee for vibration transducer calibration.

Aimed at the metrology support requirements of weapons found north orientation equipment, a gyro north-seeker calibration system is designed. The system uses one plane mirror and two 1 m collimators to set up three north benchmarks at the same time, and they are assigned to the north. The accuracy of the introduced three northward azimuthal angles is as follows: ±0.20″, ±0.18″, ±0.17″. Between the three north benchmarks, the mutual check function can be realised. The system can realise the calibration of the gyro theodolite and gyro north-seeker with auto-collimation function and no auto-collimation function. The composition and working principle of the system are described in detail. After examination, the north-seeking precision accuracy of the system is 3″ (k = 2). It satisfies the metrological calibration of the gyro theodolite and gyro north-seeker whose north-seeking precision is below 12″. Through practice, the system is accurate, stable and reliable.

In pathological diagnosis of breast cancer, there are problems such as shortage of pathologists, difficulties in sample labeling, and huge workload of manual diagnosis. Therefore, deep learning-based computer-assisted pathology analysis systems have been developed to diagnose breast cancer and have achieved impressive results. However, it is difficult to obtain a large number of training sets due to the scarcity of pathological images and the huge labeling costs. Therefore, the size of the training set should be planned before building the pathology computer-assisted breast cancer analysis system. Here, the authors present a study to determine the optimal size of the training data set needed to achieve high classification accuracy when developing a pathology computer-assisted breast cancer analysis system. The authors trained two kind of CNNs using six different sizes of training data set and then tested the resulting system with a total of 10,000 images. All images were acquired from the Camelyon17 challenge. Here, the authors propose a scheme for determining the size of the training set and the size of the model in developing the pathology computer-assisted breast cancer analysis systems, which can be easily applied to develop systems for other different pathological images.

‘S’ specimen through its unique profile features makes it possible to fully mobilise the five-axis linkage function in the five-axis finishing stage. The continuous rotation direction of the rotating axis not only reflects the static error of the machine tool but also the servo system follow error, tool positioning error, profile error etc. The authors studied its influence on the accuracy of each axis of five-axis CNC machine tool by analysing the geometrical characteristics of a S specimen and using UG's CAM environment to process ‘S’ specimen. Finally, the corresponding program forming system for S specimen machining experiments is designed by LabVIEW, and the authors used macro instruction to do NC program trigger system to collect vibration signals segmentally, so that the processing and acquisition system can be synchronised. This provides a more complete basis for the test piece cutting method to evaluate performance of machine tools.

The resistive leakage current in surge protectors is an important indicator to characterise its aging degree. It is of great significance to accurately extract the resistive components from the total leakage current for online monitoring of the surge protectors. An improved phase-shift current method is proposed to extract the weak resistive components. The fundamental frequency and phase-shift angle of the total leakage current are determined by the measures of zero-crossing detection, low-pass filtering, and phase measurement on the leakage current. The resistive components extracted from simulation and experimental data by this improved method are analysed with FFT. The results show that the improved method can significantly improve the extraction accuracy of the fundamental and harmonic resistive components.

Objected to influences of impact to the whole body with sitting posture, the inverse dynamical analysis is carried out. Firstly by using the high-speed video camera, the images of the marked points on the body and the impact platform are captured and then the marked points’ displacement can be obtained. Taking those displacements as input parameters, the inverse dynamical composition are continued with AnyBody Modeling System and responses are obtained and the impact transfer performance of the vertical direction in the human body with sitting posture and the muscle activities could be confirmed. From the sitting musculoskeletal model, it is concluded that acting forces of the joints and the mechanism performance of those parts of the body could be computed with different impact loadings, which would be conducive to understand the impact influence for the most parts of the body; especially for those parts that could be not tested. The study could be better for the reasonable countermeasures with certain postures for random impacting loadings.

The Monte–Carlo (MC) program EGSnrc was used to establish the model of low-energy radiation field and low-energy free air ionisation chamber. The electron loss correction factor and the fluorescence scattering correction factor of the molybdenum-filtered X-ray radiation of 23 ∼ 35kv tungsten target were simulated. The results show that due to the low effective energy of the radiation and the energy is close, the influence of the electronic loss correction factor is negligible, and the fluorescence and scattering correction factors increase slightly with the increase of the effective energy.

As a renewable energy source, wind power has become a hot research topic, despite the lack of wind-environment simulation equipment in most laboratories. To address this shortcoming, this paper developed equipment based on a DC motor to simulate wind turbines. The reference torque is determined by the operational parameters of the wind turbine. The DC-motor speed is regulated by using DC pulse-width modulation, so that the output characteristics of the DC motor may be tuned to be equivalent to those of the wind turbine. A dsPIC30F2010-microcontroller-based hardware platform is used in experiments to verify the effectiveness of the proposed approach.

The development of space applications based on commercial system on chip (SOC) FPGA devices has become an important direction for the development of aerospace technology, but single event upsets (SEUs) in space is a difficult problem for commercial SOC FPGAs for space applications. This article presents an anti-anti method for ARM processors in SOC FPGA. This method makes full use of the hardware resources of dual-core ARM in SoC FPGA and improves the system's anti-SEU capability through dual-core mutual-check and recovery mechanisms. At the same time, the data stream and control flow fault tolerant are used to improve the anti-SEU capability within the processor. Error detection and correction (EDAC) and triple modular redundancy (TMR) are used to improve anti-SEU capability of the data flow. A two-level watchdog and ARM exception handling are used to achieve the anti-SEU capability of the control flow. Experimental results show that the two-level fault-tolerance mechanism proposed here improves the system's anti-SEU capability without adding additional hardware resources. This method is currently carrying out satellite-borne ground application verification.

By connecting the symmetrical spiral inductive sensor to the oil return pipeline of the lubricating oil, a full flow online detection of the size, quantity and other parameters of the tiny metal debris particles in the lubricating oil solution is realised. Inductive sensors are used to excite symmetrical double helix coils, and alternating electromagnetic fields are generated. Changes in induced electromotive force are detected by the centre induction coil. In order to improve the detection accuracy of tiny metal debris particles, the degree of anti-jamming of the excitation signal, and the transmission distance, a method of stimulating using sinusoidal AC steady current signals was designed. The sinusoidal signal generator is designed based on FPGA, and the sinusoidal AC steady current excitation signal is realised through differential amplification, low-pass filtering, and AC steady current amplification, and experimental verification is performed to achieve better excitation and detection results.

Gear and its transmission are widely used in different transmission systems, and its complicated and changeable condition brings a series of problems to the fault feature extraction and diagnosis. In recent years, deep learning techniques have been gradually applied to feature extraction and pattern recognition, and the features of feature extraction and fault diagnosis in complex working environments have shown certain advantages. This study is based on stacked autoencoder under deep learning model, and improve training network performance by modified activation function. Through the network training before and after the experiment done, and to extract the fault feature data comparison in testing, improving network after activation function to extract fault features showed a greater advantage, can be a very good application in practical fault feature extraction.

The space application task is to carry out various scientific experiments and applied research by using the ability of space experiment of spacecraft. In the past 20 years, >50 space application studies have been carried out in Chinese manned space flight application system, >500 units have been involved in the previous flight missions, and fruitful results have been achieved. The white paper ‘Chinese spaceflight in 2016’ pointed out that in the next 5 years, Chinese satellite system will enhance the level and basic ability to construct the satellite system. Chinese manned space station project is scheduled to be completed ∼2022 and it will plan to operate >10 years. The space station, based on the world-wide integrated information network, has a large number of payloads and will become a national space laboratory. Space activities are full of risks and challenges. On the basis of a great deal of literatures, the method of avoiding space risk in the field of spaceflight is discussed. Aiming at the fault diagnosis task for space utilisation, the intelligent methods of deep learning including deep belief network, convolutional neural network and generative adversarial network are discussed.

For the operation of the spinning frame in the spinning mill, Hall sensors are used to test the running rate of each axis of the spinning frame. In order to centrally monitor the operation of the spinning frame, analyse the production site environment, communication distance and node layout, ZigBee is used to establish a networked monitoring system to improve the stability of rate acquisition and enhance the stability of data transmission. According to the actual condition at the production site, the ZigBee traditional routing algorithm is improved to be more convenient and efficient in the textile mill environment and have the packet loss rate reduced. The rate collection node sends the collected data to the coordinator. The coordinator uploads the data to the upper computer by WI-FI module through WI-FI gateway. The results show that the improved routing algorithm greatly reduces the packet loss rate and the entire monitoring system is stable and reliable.

Since Roentgen discovered X-rays, X-rays have been widely used in medical diagnosis and industrial non-destructive testing. With the improvement of the performance of aviation technology and instrumentation, higher requirements have been set for the calibration of detectors. X-ray machine performance is very important for calibration. Tube voltage, tube current, and inherent filtration are important parameters of the X-ray machine and directly affect the image imaging quality and radiation dose. Here, the voltage performance of the X-ray machine was measured using HPGe detector. Measure the tube voltage of the X-ray machine using the end of the spectrum method, it is shown that the performance of the X-ray machine shows the tube voltage within ±2%. Measurement of the linearity of the X-ray tube current and current stability using the PTW30013 ionisation chamber. The relative deviation of the actual measured tube current is 0.08%. The inherent filtration of the X-ray machine was measured by the half-value layer method, and the inherent filtration was 0.058 mm Al. The inherent filtration of the K-fluorescent device was measured to be 1.487 mm Al.

A spherical robot is a machine with a unique appearance and novel function. The spherical robot function designed in this study is limited to the movement in all directions. The principle is that the centroid of the spherical robot deviates from the centroid to create an unbalanced state, which drives the movement of the spherical robot. The structure of the spherical robot is mainly composed of a ball screw, two self-rotating frames, and a spherical shell. Its action is similar to that of the ‘Euphorbia’ – A self-aligning frame and a ball screw form a spherical coordinate system. Through the rotation of two self-rotating frames, a spherical coordinate is formed. The two corners of the system move in a vertical direction through the ball screw to achieve the movement of the target centre of mass on the polar axis.

CNC machine tools play important role in industries. The dynamic characteristics of the machine tools are the main factor which affected the status of a machine tools. A three-dimensional model of a five-axis CNC machine tool was built by using PRO/E. First, the model was imported into ANSYS software in order to analyse the dynamic characteristics of the machine tool; the first six natural frequencies and vibration modes of the machine tool were obtained through modal analysis and harmonic response analysis. The accuracy of the finite element analysis model was verified through the experimental results. Finally, the optimisation suggestions of the machine tool structure were proposed.

Maintenance robot is used widely in industries due to its convenience. The maintenance robot system is introduced. The hardware frame is designed in detail. Based on gesture recognition, the effect of controlling the robot movement is achieved. Gesture recognition information is achieved through the acquisition of human skeleton information by a Kinect sensor. The construction of the gesture library is done. The maintenance robot control system is set up to control the omnidirectional mobile robot driven by the Mecanum wheel. Finally, the maintenance robot is developed and the design functions are carried out.

There are mechanical coupling interference and hydraulic coupling interference in the vehicle wheel bridge simulation loading system based on secondary regulation, which can cause the control accuracy of the system to decrease obviously and seriously affect the control performance of the system. In order to eliminate the coupling interference between systems, two decoupling methods are proposed, one way to do this is to join the decoupling element, and the other is to join the robust compensator. The simulation results show that the decoupling control of decoupling element has a good decoupling effect on mechanical coupling, but it has no effect on hydraulic coupling. Robust decoupling control has a good decoupling effect on mechanical coupling and hydraulic coupling.

To improve the reliability of current signal monitoring tool wear status, a method based on Mahalanobis distance to identify tool wear status is proposed. First, the obtained current signal is analysed in time domain, frequency domain and wavelet domain, and several features that have good correlation with tool wear status are selected to form the feature vector. The feature vector of the current signal in normal tool wear status is taken as the reference vector. Then calculate the Mahalanobis distance value of the feature vectors of the current signal of the tool with moderate wear and severe wear, so that two corresponding thresholds T ₁ and T ₂ can be obtained. The feature vector of an unknown wear status is calculated using the Mahalanobis distance and then compared with the two thresholds obtained previously. When the calculated value is between threshold T ₁ and T ₂, the tool is judged to be in moderate wear status. When its Mahalanobis distance value is greater than the threshold T ₂, it is judged that the tool has been seriously worn. Finally, multiple unknown wear status is identified. It is believed that the recognition method based on Mahalanobis distance can accurately determine the tool wear status.

Based on laser power meter calibration requirements, through the purchase of standard equipment and field monitoring equipment developed, standard laser detector, optical path related equipment, such as construction of laser radiation power meter calibration test system, implementation of laser power meter of laboratory test and vehicle, large equipment form a complete set of laser power meter field test and field test of the radiated power of the laser equipment. Calibration system is introduced in detail the composition and working principle of the uncertainty of the system are analysed in detail, and through the system stability test, laser power, beam monitoring and correction device performance test, ate test for power meter calibration experiment data analysis, proved in this study, the laser power meter calibration method provided will enable calibration system, the expanded uncertainty of relative reached 2% (k = 2), which can realise wavelength of 532 and 1064 nm, power measuring the range of 0.1 mW–20 W laser power meter calibration.

At present, the box type cargo picking robot mostly adopts the sucker type end-effector in the logistics industry in china. In the light of the disadvantages and limitation of the complex heavy pneumatic devices used in the existing sucker type end-effector, a new type of clamping end-effector's structure is designed here. Relying on the clamp, flip, clamping to realise the pickup of the box goods. Only by stepping motor as the driving device, the weight of the driving system is greatly reduced, so that the overall structure of the robot is lightweight and the selection efficiency is improved. The finite element analysis of key components improves the feasibility of structural design. The finite element analysis of key components verifies the feasibility of the structure design and provides references for subsequent optimisation.

When a single image segmentation method is used for image segmentation of wind turbine blade images under a complex background, the results obtained are not accurate and complete. This article proposes an image segmentation algorithm that applies morphology to Canny edge detection. It uses morphological opening to erode and dilate the binary image after Canny algorithm processing, and removes redundant edge information to obtain a complete fan blade image. Experimental results show that the results obtained by the image segmentation method proposed here have good integrity and accuracy, and can improve the segmentation effect of the image.

Combining two-dimensional code recognition technology with database technology, indoor location and navigation methods which are not easy to be interfered by other signals and have low cost are studied, and indoor location and navigation system based on two-dimensional code and database is designed. The research results show that the two-dimensional code recognition technology and database technology can achieve efficient and precise indoor location and navigation, and improve the efficiency and reduce the efficiency.

In recent years, the measurement of X-rays has attracted much attention. Especially in the aspects of environmental monitoring, radiation protection, and radiation therapy, to obtain more accurate measurement values, it is necessary to set up a corresponding reference device to trace the magnitude. This paper aims to establish a 350–450 kV wide spectrum and high air-kerma rate X-ray radiant mass. First, the Monte Carlo simulation was used to simulate the establishment of a model of the light engine, and the energy spectra of different tube voltages and different additional filters were calculated to obtain the average photon energy. Then the average photon energy was compared with the extrapolated average photon energy so that the deviation was within ±5%. Finally, half-value measurements are made on additional filters that meet the requirements.

The human–machine dialogue system allows people and the machine to communicate basically. This technology is also attracting more and more attention. It is conducive to the realisation of resource sharing, inheritance, and protection of Dongba culture worldwide to analyse the Naxi speech recognition for the protection and inheritance needs of Naxi Dongba culture. The model was established for Naxi speaker recognition, and the method is studied with text-independent speaker speech voice recognition. The practical analysis method was used to compare the application effect of Gaussian mixture model (GMM) and hidden Markov model (HMM) in the processing of speaker recognition in Naxi speaker's pronunciation. It is very useful to provide a theoretical basis for the establishment of a Naxi language-based human–computer interaction system to find out the speaker's voice characteristics, personality differences, and different regions. This is a theoretically necessary analysis of ethnic minority languages and the protection of ethnic and folk cultures. It can also provide practical significance for carrying out ethnic cultural differences analysis.

Aiming at the problem of poor classification and recognition rate for distorted texture image based on single texture feature, a classification method of texture image based on multi-features fusion is proposed. First, the corresponding GLCM features, HOG features, and HU moment features were extracted from the segmented texture images. Then, the three feature matrices were cascaded into a new feature matrix, and the principal component analysis method was used to reduce the dimension of the new feature matrix. Finally, the fused feature matrix was inputted to the support vector machine (SVM) for training, so that the final discriminant model was obtained. The model is applied to the classification of distorted texture images and compared with the single texture feature classification method through experiment. The results show that the multi-features fusion classification method improves the classification accuracy of distorted texture images and has better real-time performance.

Here, in order to calibrate the magnetostrictive liquid-level meter in situ, a set of in-situ calibration device for magnetostrictive liquid-level meter is designed. The structure and working principle of the device are described, and the experimental test and error analysis are carried out for the parts of the in-situ calibration device. Finally, according to the modern instrument and error theory, the accuracy of the calibration device is assessed, and the field calibration experiment of magnetostrictive liquid level gauge is carried out. The theoretical analysis and field experiment results show that the calibrated device can meet the in-situ calibration requirements of the magnetostrictive liquid-level meter with a range of 0–6 m and a precision of 1 mm.

The original signal of rolling bearing fault contains a large number of phase coupling components and is easily submerged in the background noise, which make the fault information difficult to be extracted accurately. Aiming at the above problems, a method of fault feature extraction for rolling bearing is proposed, which combines empirical mode decomposition (EMD) with a 1.5-dimensional spectrum. The original signal is decomposed by EMD to obtain the intrinsic modal function (IMF) of different scales. The IMF is selected by the size of a correlation coefficient and a kurtosis value to eliminate the high-frequency components, which is reconstructed to achieve the purpose of noise reduction. The reconstructed Hilbert envelope signal is analysed by the 1.5-dimensional spectrum to extract the nonlinear characteristic of two phase couplings, so that the fault characteristic frequency of the bearing is obtained. By analysing the signal of actual rolling bearings, the fault characteristic frequency of bearing inner and outer rings can be effectively extracted, and the validity and feasibility of the method are proved.

Fault diagnosis of rotating machinery plays an important role for the reliability and safety of modern industrial systems, and it is challenging to detect the weak character signal in the noisy background. This article presents an improved resonance sparse decomposition method for fault diagnosis of rolling bearings. The PSO algorithm is used to optimise the quality factor of the resonance sparse decomposition, which can overcome the deficiency caused by the manual given quality factor in the traditional resonance sparse decomposition method and achieve the effective separation of resonance components. An envelope analysis of the low-resonance decomposition including the major fault components was performed to extract the fault feature frequency. Based on this, the two decomposition methods are applied to the fault diagnosis of the inner and outer rings of the bearing, respectively. The experimental results show that the improved resonance sparse decomposition method can reduce the interference components when extracting the frequency of fault features and improve the accuracy of fault diagnosis.

To solve the problems of missed detection, repeated detection and low precision in the process of subpixel corner point detection of black-and-white checkerboard used for calibrating microscopic devices in complexed industrial environment, a new detection method based on greyscale constraint on the four-neighbourhood diagonal was proposed in the paper. By analysing geometric characteristics of the four-neighbourhood region and the grayscale features in diagonal direction, the SINC greyscale distribution was adopted to constrain corner point position, which realised rapid detection of subpixel corner point. Comparing with the existing methods, in the new method proposed the rate of repeated detection and missed detection decreased by about 20% and 2% respectively, which achieved a high rate of detecting accuracy over 99.9%. Meanwhile, the maximum error of corner point detection lowered from ±0.6pix to ±0.3pix, which showed that precision improved by about 50%. Finally, microscopic calibrating experiments used for micro-hole centring system were carried out. The results show centring error was reduced from 10 μm (before calibration) to 3 μm (after calibration), and centring precision was increased by about 75%. It demonstrated that the new method improved the accuracy and precision effectively, which verified its applicable feasibility of microscopic calibration used in industry spots.

In order to calibrate radiation protection instruments and determine radiation dose, it is critical to establish an X-ray narrow-spectrum series recommended by the standard ISO 4037-1 which is published by the International Standardisation Organisation (ISO). By experiment, the qualities of filtered X radiation of narrow-spectrum series in the range from 60 to 300 kV were established. The differences of the first half-value layer (HVL1) and the second HVL (HVL2) between experimental results and the values given in the standard ISO 4037-1 were all within 5%. What is more, the Monte Carlo code EGSnrc was used to simulate the spectra of these radiation qualities, which showed a good agreement with these spectra given in the standard ISO 4037-1. The spectral resolution, mean energy and effective energy of these reference qualities can be obtained, which are all have good agreement with the given values.

As one of the core components of the machine tool, the reliability of spindle is very important for improving machine tool quality. To effectively evaluate the performance degradation degree of the spindle, a method of degradation evaluation of the spindle performance based on variational mode decomposition (VMD) and random forests (RFs) was proposed. Firstly, VMD is used to process the current signal to obtain several modal components. Then, the time domain and frequency domain features of each modes component are calculated as eigenvalues. Finally, the RFs algorithm is used to classify the eigenvalues. The experimental results show that VMD can decompose the signal better and avoid the phenomenon of the modal mixture. The combination of VMD and RFs can accurately and effectively evaluate the performance degradation of the spindle.

In this study, a diagnostic method for gear wear fault is proposed, which is based on the ultra-complete independent component analysis. The ultra-complete analysis model is constructed, and the similar source signals more than mixing signals are separated. Based on the typical features of the fault source signal, the useful component of the mixing signals can be found. As the fault source signal and similar fault source signal are similar, the magnification range of similar shapes can be estimated by using the interval estimation method. The mapping between one-way varying magnification time domain and rotary component fault degree is identified, and the fault degree judgment standard can be established. Thus, the fault degree can be determined. The method is used to deal with the state monitoring data of the gear, and the analysis results show that the diagnostic method is of practical value.

Low altitude airspace mobile weather service is based on the short service of meteorological services in China's rapidly developing low altitude navigation industry. Through the analysis of the weather information data acquisition status and the analysis of the main meteorological service software products in the market, it is concluded that professional low altitude weather service software products are needed to meet the demand of low altitude aircraft operation. The software interface design of low altitude airspace mobile meteorological service demand function is analysed, and the Android technology is used to design and implement the client interface of meteorological services.

According to the analysis characteristic of the on-line sensor of the mechanical device, the on-line magnetic field of the double excitation sensor is analysed by using the most advanced F-30 multi-dimensional digital magnetic measurement system. Using F-30 mode scanning analysis of the dual incentive sensor of two-dimensional (2D) and 3D characteristics, a system of a 3D mesh of the sensor is obtained, the 3D graph, the 3D graphic model, at last, through export data for magnetic field distribution of more detailed information. The accuracy of the F-30 magnetic field analysis system is very high, and the field distribution information of the sensor is more practical. This analysis is of a great reference value for the experimental analysis of the sensor.

Machine chatter phenomenon has a great influence on the stability during the machining process of the machine tool. It has a great limitation on the machining efficiency of the machine tool and the machining accuracy of the product. The flutter of a machining center will appear when a certain machining center reaching a particular speed. The chatter will occur at a specific frequency. With the increase in the depth of cutting and the spindle speed, the value of chatter frequency will have complex changes; through the vibration test, it was confirmed that the two chatter frequencies are the natural frequencies of the product system and the spindle system, respectively. Improve the optimisation parameters and propose ways to suppress chatter.

Network function virtualisation (NFV) represents one of the key enablers of the next generation mobile network systems (5G). NFV allows running virtual network functions (NFs) as software components on top of a virtualisation system (i.e. virtual machines or containers) hosted in a cloud, allowing high flexibility and elasticity to deploy network services and functions. Therefore, the NFs in 5G core network can be deployed on the common hardware platform in the form of software. In this study, network repository function (NRF) is implemented in Docker-based NFV platform in the form of JSON + HTTP/2.0 that supports service discovery function. The performance of NRF is also tested on real hardware platforms, including NF Register, NF Update, and NF Deregister. From the experimental data, it can be seen that the NRF in the NFV-based 5G architecture has good performance, and Docker-based NFV platform is more flexible than traditional communication networks.

With the improvement of people's quality of life, the demand for three-dimensional (3D) video has gradually grown in recent years. However, there are many challenges to providing high-definition and high-fidelity 3D video. One of the most difficult challenges for users to provide 3D video is a large amount of energy lost during 3D video transmission. In this study, the authors present a 3D resource allocation system architecture, which can effectively alleviate the problem of large transmission energy loss. They propose that multiple users in a small cell can share video resources in the downlink from the cloud to the client. In the cloud processing, multiple users can share part of the cloud computing work cycle. In the downlink, multiple users can share part of video resources. They presume that some share output bits can be multicast to overall users, and the other bits can be sent to every user in unicast. They model this and derive the simulation results. The simulation results show that the resource allocation scheme in this study achieves greater energy savings under certain conditions than the traditional case without sharing the resource.

The resource management of virtualisation system is a core issue in the field of cloud computing. For achieving the goal of efficient manage system resource without performance loss in clouding environment, the authors propose an optimal load balancing controller to assign the resource to different virtual machines, which run on a single physical server. Here, the authors focus on dynamical web workloads that the system resource management problem can be formulated as a dynamical optimisation problem. In the face of dynamical and robust workloads, the authors present an alternative approach based on the constrained boundary linear quadratic control (cBLQC) method, the optimal resource allocation scheme is solved by minimising the quadratic cost function with constrains on the system input and output. Different with existing works that assume a particular distribution for system noise or ignore the noise directly, the authors loosen the restrictions on system noise, which simply assume that system noise belongs with some reasonable norm-bounded set. Experiments on the Xen-based platform with a set of workload patterns show that the efficiency of authors’ method in terms of control precision and stability.

Eye diseases caused by long times of use of visual display terminal (VDT) products is considered as a serious problem in recent years. The eye fatigue monitoring is significant for early warning. Here, an unconstrained measuring method is used to extract Ballistocardiogram (BCG) by acceleration sensor. The acceleration signals are acquired by installing the sensor on seat back. Adaptive threshold wavelet transform is processed for denosing non-linear noise interference such as power line interference, baseline drift, random movement of human body, and ground vibration. Frequency domain filtering and IFFT are applied to extract BCG from acceleration sensor. RR interval of ECG and JJ interval of BCG in time domain have the similar cycle, and the correlation coefficient is up to 0.9918, which prove the feasibility to extract BCG from vibration signals using unconstrained method.

This study analysed the influence of bearing preload on the stiffness of spindle bearings. MATLAB/Simulink is used to calculate the vibration of simplified spindle system. The vibration of the simplified spindle system decreases with the increase of bearing preload. A kind of force output characteristics of piezoelectric actuators test device was designed. Experimental analysis of the maximum force output characteristic, hysteresis characteristic, creep characteristic and stability characteristic of PSt 150/4/7 VS9-type piezoelectric actuators under different initial loads, voltage frequencies, cycles working and voltage travel. The evaluating indexes are the maximum output force, average hysteresis, maximum deviation, drift amount and cycle delay. In conclusion that the piezoelectric actuator has better repeatability and stability under the initial load is around 150 N and low-voltage frequency. It is not suitable for multiple cycles working. The increment of output force is proportional to starting voltage at same voltage travel. MATLAB/Simulink is used to calculate the feasibility of variable bearing preload strategy based on a piezoelectric actuator. It provided scientific references for the research of high performance and intelligent spindle.

Feature point matching plays an important role in feature-based image registration such as the scale-invariant feature transform algorithm. Feature-based image registration is widely used in visual simultaneous localisation and mapping, augmented reality, self-driving etc. The most meaningful study on feature matching is to improve the accuracy and efficiency and this study pays attention to improving the accuracy by removing the mismatching feature points. Since most of the existed feature-based image registration algorithms are not so strong and efficient enough in mismatch removing, in this study, the authors propose a novel mismatch removal algorithm by incorporating depth prediction into feature matching to improve the performance. In this approach, the depth maps are predicted in pixel-wise through the given red–green–blue images using a deep learning algorithm. Experimental results show that their method outperforms conventional ones in mismatch removing.

Unbalanced body pressure distribution caused by poor sitting posture is a significant factor causing lumbar disease. Research on the dynamic adjustment strategy for comfort sitting posture is of great significance to the prevention and improvement of lumbar disease. In this study, based on the pressure sensor array, the body pressure distribution in different sitting postures are obtained, including: back bow, waist bow, body slip, body right leaning, standard sitting position, and body left leaning. The recognition of the human body posture was achieved using the support vector machine algorithm and the accuracy rate reach to 83.33%. Furthermore, the evaluation indicators for sitting comfort of the human body are proposed using the maximum pressure, average pressure, maximum pressure gradient, average pressure gradient and asymmetry coefficient, the accuracy of the above indicators were verified using semantic differential subjective evaluation method. Experimental results show that there is a strong positive correlation between objective indicators and seat comfort. In addition, the dynamic adjustment strategy using airbag base on seat comfort evaluation is studied.

In this study, the important role of the household gas safety information terminal in the gas supply industry is introduced, and the necessity of studying the new type of the household gas safety information terminal is pointed out. Then, the function and specification of the terminal are introduced. The system design scheme of the terminal is described, including gas, photoelectric reading conversion device, valve, wireless communication module, pressure detection unit, gas leak detection unit, and control unit; the characteristics, functions, and performance indexes of each part of the system are described in detail. Finally, the practical application effect of the terminal is given.

Britain, in addition to its airports, has suffered from drone attacks in its prisons. Suspects use drones to transport contraband such as drugs or cell phones to inmates in a new challenge to British prison safety. In China, the means of manpower prevention, physical protection, technical prevention are comprehensively applied, the integrated loss prevention and crime prevention system of land-space is the guarantee of prison safety. Firstly, the integrated loss prevention and crime prevention system of air and land is devoted to the construction of intelligent video surveillance system, the personnel location management, the cooperation and information sharing between video surveillance technology and management system, and the perfection of ‘low, slow’ in the low altitude airspace of the prison. Small ‘aircraft’ and other systems, such as timely early warning, all-weather monitoring, strong expulsion and safety capture, combine the two information together to build a model of accurate information, rapid organisation, well-informed decision-making, well-managed and effective safety and prevention, to ensure the safety of the prison.

This study aims to improve the performance in solder joint image compression and reconstruction. A novel adaptive block compressive sensing with convex optimisation and Gini index (Ad_BCSGB_Gini) methodology for solder joint image compression and reconstruction is proposed. At first, the image is split into square blocks and each block is resized into a row which consists of a new image. Then, the new image is transformed into a sparse signal by an orthogonal basis matrix, and the image reconstruction is handled as a convex optimisation problem. Moreover, a gradient-based method which has fast computational speed is used to reconstruct image. There is a control factor which controls a norm l ₁ in the optimisation problem. To achieve the best performance, at last, the proposed method adaptively selects the best result by comparing Gini index of the reconstruction results based on different control factor values. Experimental results with different methods indicate that the Ad_BCSGB_Gini method is able to achieve the best performance in quantisation comparison than several classical algorithms, and Ad_BCSGB_Gini has a good robustness.

Micro-linear motor, is the key feed components which is based on macro–micro-motion control technology, the accuracy of its movement directly determines the accuracy of ultra-precision machining level. Based on the study of the open servo characteristics of POWER program multiple axis controller (PMAC), a fuzzy proportional integral derivative (PID) composite control technology is applied to the servo control of the motor. The fuzzy control is used to improve the response speed and dynamic performance of the system, and the precise PID control is used to realise the precise position control. In this study, based on the analysis of linear motor installation and load characteristics, the use of smooth switching fuzzy PID composite control algorithm is applied to the micro-feed motor control. Due to the introduction of the smoothing weighting function, the composite fuzzy PID servo control mode of dual mode switching is implemented without disturbance switching. Finally, the effect of the control algorithm is verified by experiments, especially in the case of small errors, this algorithm still reflects the ability to adapt to the impact of interference.

Aiming at the problem of the detect and rescue in the coal mine with the leaking vas, a type of pneumatic hexapod explored robot which uses a high pressure nitrogen cylinder as the power producer is designed. The leg mechanism is designed with three degrees of freedom, the straight line walking and turning gaits are planned, which are testified by the virtual prototype. The control loop of the cylinder is designed with the magnetic valve with three positions and five paths. The air bottle, the reducing valve, the general follow control part (GFC) the magnetic valve with three positions and five paths, the cylinder are selected and the air supply loop of the hole machine is designed, the 3D model of the hole machine is built which is used to confirm the installing form of the hole body. The experimental prototype is obtained at last. The power supply and control circuits of the magnetic valves are designed, the control series is written on the Arduino single chip. The experiment of the leg swing of the robot on the stage is proceeded which verifies the correctness of the design.

This study investigates the coordinated braking strategy integrated with sliding mode control for multiple high-speed trains, to solve the problem of non-linear couplers between trains and uncertainty during the braking process. First, a multi-agent model with power between trains and uncertainty is established. Secondly, a sliding mode consistency tracking controller is designed. The consensus algorithm assures the consistency convergence of speed of each carriage. Sliding mode variable structure term is used to deal with the compound interference item consisting of non-linearity and uncertainty. Finally, it is demonstrated by a simulation experiment that, the algorithm can well deal with compound disturbance term so that high accuracy is obtained during the parking process.

This study describes a method based on the measurement of a reflection coefficient to determine the dielectric constant of a sample by near-field microwave. A mathematical relation between the reflection coefficient and the parameters of a lumped circuit was derived referring to an impedance model of the probe−sample interaction. Theoretical analysis was performed to verify the equivalent model of the interaction between the probe and the sample which is equivalent to the parallel connection of multiple capacitors in a lumped circuit. By considering an equivalent model between the probe and the sample in the near field, the parameter measured by the instrument was converted into the impedance parameter between the needle tip and the sample in the near field, and the impedance parameter was used to obtain the equivalent capacitance of the sample, and the dielectric constant of the sample under testing can be deduced. Different samples under near-field probes were modelled in a high-frequency structure simulator. Using a known dielectric constant, the reflection coefficient can be calculated from the coaxial probe model, and compared with the simulation results. The feasibility of the near-field probe in determining dielectric properties is verified.

This study studies the technology of environmental monitoring and control in an intelligent Internet of Things (IoT) perception. To improve the issue of small coverage and low stability in the traditional environmental parameter testing, a new environment monitoring technique based on intelligent perception is proposed. The technical framework consists of three layers, intelligent perception layer, network communication layer, and application layer. The intelligent perception collects and transmits data to the application-level server via the network communication layer for data analysis and diversified display. Based on this technique, we constructed a smart home environment monitoring and control system. The experimental test result shows that the system can accurately collect and process the data through the server and diversified displayed, which verifies the accuracy and reliability of this technique which is potential for further application.

AD converter is one of the core components in the signal acquisition system. The quality of the signal acquisition system is mainly determined by its internal analogue-to-digital converter (ADC) characteristics. However, because of the non-linearity of the ADC itself, the dynamic acquisition range of the ADC is greatly affected. Here, the broadband dither technique in the signal acquisition process is studied. Dither signals are added to the input signal to increase the dynamic acquisition range of ADC. Here, the dithering process was first simulated through Simulink and subsequently an FPGA-based broadband dithering module experiment platform was built. The process of signal dithering was implemented. The data were analysed with Simulink and the Spurious Free Dynamic Range (SFDR) of the ADC is obtained. The broadband dither signal can randomise the harmonic distortion phenomenon, verifying that the dithering technique can improve the SFDR of ADC.

Currently, S-parameters are commonly used in the design, measurement, and modelling of microwave circuits, but it is only suitable for linear small-signal conditions and not suitable for large-signal conditions. X-parameter is more accurate than the S-parameter in characterising the non-linearity of the device or the microwave circuit, and the artificial neural network has significant advantages in predicting the non-linearity of the system. Based on theoretical analysis, this article selects the GaN HEMT device CGH40010F for X-parameter modelling. Using X-parameter generator of ADS software, the X-parameters of the device were obtained. An X-parameter model based on BP neural network was built using MATLAB neural network box. The accuracy of the model was verified by comparing the test data with the model prediction data.

The traditional satellite prognostic and health management (PHM) mostly relies on the satellite downlink telemetry data. Nowadays, the application of satellite becomes more diversification and its function becomes much more complicated. As a result, the telemetry data cannot meet the data requirements for satellite PHM due to the limited resources of telemetry channels and low sampling rate. Therefore, satellite in-orbit embedded testing was proposed. In order to satisfy the requirement of large volumes of data acquisition and transmission in satellite in-orbit embedded testing, this article proposes a new data agreement by the modification of the Modbus protocol to unified control 5-lane MLVDS bus. Therefore, a multi-channel parallel MLVDS data transmission bus is proposed to realise the data transmission from the data collection end to the information processing end of the PHM system on-board. The bus protocol designed here is implemented by using the Programmable Logic Part on the Xilinx XC7Z045.

The on-board target tracking of unmanned aerial vehicle (UAV) relies on on-board computer to process the collected ground image information, and then use the target tracking algorithm to achieve the target tracking function, which provides the user with target position, trajectory and other information. UAVs are limited by load and power consumption. Due to the high resolution and frame rate of UAV's image collection system, traditional target tracking platforms cannot meet the requirements. As a result, the real-time nature of the tracking platform urgently needs to be resolved. This study designs and implements the airborne ground target tracking accelerator for UAV. The kernelised correlation filters (KCFs) algorithm with low computational complexity and precision is selected to achieve the airborne ground target tracking function and its performance bottleneck is analysed. In order to improve the real-time performance of the tracking task processing, the ZYNQ platform based on ARM and field programmable gate arrays is chosen to achieve the hardware acceleration of the KCF algorithm. The KCF algorithm is implemented in hardware using the high-level synthesis technology, and a high-speed data transmission link is established. Experiments show that the design can achieve a tracking rate of more than 30 FPS under 960 × 540 resolution, which meets the real-time requirements of UAV target tracking task.

This article proposes the use of phase angle gradient as a feature to detect and classify different types of defects. Based on the principle of eddy-current testing (ECT), the ECT platform was designed and manufactured. Based on phase-sensitive demodulation principle, the phase angle and gradient of the detected signal are analysed. The results show that the phase angles and phase angle gradients of different types of defect detection signals show different trends; the phase angle gradient of the integrated position direction and frequency direction can effectively identify the defect type.

Radiation observation data are important basic data for assessment and research of climate change and solar energy resources. Quality control of radiation observation data is an important guarantee for accurate and reliable observation. First, the global radiation data collection method and the quality control process are introduced, and the differences and similarities between different quality control standards are compared in detail. Then, the quality control contents of the upper and lower limits and the rate of change are analysed theoretically and verified by experiments. Theoretical analysis and experimental results showed that the lower limit of the global radiation irradiance quality control should be −20 W/m², the upper limit should be 2221 W/m² or based on solar position, and the wrong change rate of sampling value should be 1000 W/m². Through the unification of the global radiation quality control standards, the difference caused by the inconsistent quality control methods can be avoided, thus improving the effectiveness of the radiation observation data and ensuring the accuracy and consistency of the radiation observation data.

In view of the actual problems existing in life-cycle health monitoring and diagnosis of large complex equipment, the machine-learning algorithm is applied to data mining of the equipment operation big data, the expert knowledge base is established, the diagnosis rules related to the fault are obtained, the intelligent online monitoring and remote diagnosis of the equipment health condition are realised. The system uses uncertain fault prediction method and hybrid intelligent algorithm to discover the hierarchical association between operation feature big data and operation faults, the feature extraction of operation faults, and the intelligent diagnosis of operation faults. It effectively improved the sensitivity, robustness, and accuracy of monitoring and diagnosis. In the cloud service platform based on the Internet of things, the system realises the intelligent fault prediction and diagnosis, establishes a proactive maintenance system, improves the production efficiency, and ensures the production safety.

Here, the authors propose an energy method to obtain the friction coefficient of a rubber block sliding on self-affine substrates via simulation. Due to the multi-scale property, the rough surfaces are modelled with four harmonic rigid frictionless roads. As the rubber block is a viscoelastic solid, energy dissipation in the contact area results in friction. The authors propose a new simulation method to obtain friction coefficient through energy dissipation. With different loads and sliding velocities, the change law of friction coefficient is calculated. The authors design a test instrument to verify the friction coefficients of rubber on some certain surface. The findings of this research constitute a theoretical basis for safe tire design and tire grip improvements.

According to the current legal metrological regulation of diaphragm gas meter, domestic gas meters in China are required to pass mandatory initial verification at 20°C, and then a 10 years' legal duration is allowed without any subsequent or in-service verifications. Here, experimental study has been implemented to investigate the metrological performance and temperature adaptability of old diaphragm gas meters with different use age. Through the introduction of weighting factors, weighted mean errors are calculated to analyse the correlation between the indication errors of gas meters and their use age, as well as the relationship between the indication errors and test temperatures. Experimental results indicate that the use age of gas meters has negligible influence on their metrological performance during the 10 years' legal duration. However, the indication errors worsen as temperature varies.

This article proposes an intelligent charge scheduling strategy for electric vehicles (EVs) in residential areas based on real-time guide (RTG) tariffs which are set dynamically according to the load information. The architecture of the system realises the interconnection of information on vehicles and distribution networks. The authors first establish a mathematical model, the objective of which is to accomplish the charging task in an economical way within a given deadline. The authors then introduce dual theory for a distributed computing to the model. EVs obtain an optimised charging plan using the intelligent charge scheduling system, which ensures the safe operation of the distribution transformer. Finally, taking the load curve of a residential area as an example, the Monte–Carlo simulation method is utilised to calculate the charging needs of customers based on actual customer charging behaviour. The distribution transformer load curve and average charge per vehicle are simulated under both uncoordinated and coordinated charging conditions, and the simulation results indicate that the scheduling strategy can reduce the required charge and achieve a smoother load curve, and is suitable for vehicle online scheduling.

To detect the joint angle of the manipulator accurately, a measuring method based on an IMU sensor is proposed. The sensor's attitude angles and corresponding rotation matrix are obtained according to the data of three-axis gyroscope and three-axis accelerometer. During the installation, the sensor's Z-axis direction is kept along with the motor's rotation axis direction, so that the angle of the sensor rotating around its Z-axis, which is the rotation angle of the motor, can be calculated by comparing the rotation relationship between the sensor's initial position and the sensor's position after the motor rotating. The largest source of result error derives from the inconsistent between the sensor's Z-axis direction and the motor's rotation axis direction. Consequently, an installation's error compensation method is designed to correct the sensor's Z-axis to the motor's rotation axis by rotating the related motor. The experiments show that the method can measure the joint angle of the manipulator accurately and calibrate the installation error effectively. The measurement result errors are confined <0.25°.

It is hard for traction control to ensure the stability of vehicles because of the non-linearity, uncertainty and parameters variation of vehicle dynamics. A novel traction control model is presented in this study to obtain the optimal traction performance by taking the advantage of fast torque response of electric vehicles (EVs) and dealing with driver operation properly. A new judgement unit is designed to combine the optimal slip ratio controller with driver operation, and the driver's input is kept when the slip ratio exceeds the reference value. Further, an engineering design method of the slip ratio controller is researched in terms of speed range and different work conditions. Two sets of controller parameters are designed since the vehicle time constant varies with wheel speed. The possibility of taking fix controller parameters value in one speed range is analysed and verified. Moreover, the stability of system and the influence of sampling frequency on system are analysed. Finally, the validity of the proposed control methodology is fully verified with simulation and experiments on the tire–road simulation test bench which adopts the asynchronous motor mechanical properties to simulate tire–road characteristics.

This article describes the simulation and experimental determination of the oscillation modes of a Coriolis Mass Flowmeter (CMF). The authors use the SolidWorks software to create a suitable model which is a double U-type of CMF. Based on SolidWorks software, the authors established the double-U tube model for CMF, adopted ANSYS-Workbench simulation software to make the modal analysis, and the exciting mode and Coriolis force mode of CMF are obtained by simulation. According to the results of modal analysis, the authors made the harmonic response analysis of the Flowmeter, got the displacement amplitude of the U-shaped tube excited by different frequency loads under the exciting force, and the maximum displacement amplitude at the resonant frequency was determined. Next, the authors use a laser vibrometer to perform an experimental modal test on the flowmeter. The result of the simulation of the finite element model was verified by modal test of the flowmeter. Provides an effective reference for CMF modelling and simulation in the future.

The atomic clock prediction is the key step for constructing atomic clock time scales and implementing atomic clocks steering, and its prediction level directly affects the accuracy and stability of atomic clock time scales. Aiming at the non-linear and non-stationary characteristics of the hydrogen atom clock, the least squares support vector machine (LSSVM) hydrogen clock prediction algorithm is proposed, which is verified by the actual hydrogen atomic clock data of the timekeeping laboratory in National Institute of Metrology, China. The results show that compared with the linear prediction algorithm and the support vector machine (SVM) prediction algorithm, the least squares SVM prediction algorithm improves the accuracy of the clock prediction, and its root mean square error is, respectively, reduced by 50% and 29%.

The rapid progress of modern science and technology promotes the continuous development of precision measurement technology and improves the accuracy and efficiency of measuring instruments. In optical precision measurement technology, grating measurement technology has the advantage of high precision and large range. This study introduces the principle, structure and data handling of grating tester for dial indicator gauges. As the grating tester for dial indicator gauges is a kind of high precision measuring standard instrument, the verification basis, verification method and uncertainty analysis are also discussed in depth in this study.

Tool failure is one of the main causes of failure in CNC machine tool machining. The real-time online monitoring technology of CNC machine tool is studied, which is helpful to improve the efficiency of failure-free operation of CNC machine tool and reduce the probability of scrap and equipment failure due to tool failure. Here, the correlation between inverter input current and tool wear condition was studied first, based on which a new method about how to calculate line current on inverter input side was defined, according to definition of current virtual value. Then, a real-time monitoring system for online tool wear is designed and developed. The experimental results show that the system can reflect the tool wear condition and remind to change tool timely.

The low energy germanium detector (LEGe) is in all aspects optimised for performance at low and moderate energies. It has specific advantages over conventional planar or coaxial detectors. LEGe is a kind of semiconductor detector commonly used in X-ray detection. It can display the spectrum of X-ray and record photon counts. Photon fluence is used to describe the radiation field by recording the number of incident photons. In order to take full advantage of the low energy response of this intrinsically thin window detector, LEGe cryostats are usually equipped with a beryllium window, thin front and side contact allowing spectroscopy from 3 keV and above. The rear contact is of less than full area which gives a lower detector capacitance compared to a planar device of similar size. It is widely used in low energy gamma spectroscopy, X-ray absorption spectroscopy, nuclear safeguards or X-ray fluorescence spectrometer and X-ray diffraction. Spectrum is an intuitional and accurate description of X-ray radiation. In this work, experiment was carried out to calibrate the detector and test the spectrum, and then to calculate the energy resolution. The experiment results about energy resolution (full width at half maximum) are 222 eV (@5.9 keV) and 519 eV (@122 keV).