This study proposes the design of two efficient nature inspired intelligent optimal controllers, flower pollination algorithm (FPA) and bat algorithm (BA) for obtaining optimal path using an autonomous mobile robot in an unknown environment. FPA is based on the pollination process of flowering plants, which transfer pollens by using different pollinators. On the contrary, BA depends on echolocation and frequency tuning to solve different types of optimisation problems in engineering. To accomplish the path-planning task of mobile robot autonomously, a fitness function has been introduced considering the distance between robot-obstacle and robot-goal to satisfy the conditions of obstacle avoidance and goal reaching behaviour of robot. Based on the values of objective function of the algorithms, mobile robot avoids obstacles in the unknown environment and moves towards the goal. In this work, the efficiency of such controllers is verified using some simulations in MATLAB environment. Further, an experimental work is carried out in real-world environment using ARDUINO Mega 2560 microcontroller to ascertain the path length, travelling time and convergence speed of the two algorithms.

The depth measurement error of bionic eyes consisting of two active cameras is derived by considering the eyes’ rotation angle errors and image feature extraction errors. The effect factors of the depth measurement are obtained from the calculation formula of depth error. Based on the analysis of these effect factors, some effective guidelines for bionic eyes are proposed to reduce the measurement errors. The guidelines suggest keeping long baseline, observing the target as nearly as possible, controlling two active cameras with the same angular velocity and keeping the target on Z _w axis if possible. The simulation experiments and practical experiments in bionic eyes platform validate the effectiveness of the proposed guidelines.

This study investigates the relationship between sampling strategy and error estimation, which is used to improve the measurement and evaluation of quadratic form error in Cartesian coordinates. This study is focused on the sampling strategy, the sampling error estimation, and the error evaluation model, and it is particularly useful when a special device measures the form characteristics of a workpiece. To obtain a reasonable sample size and an accurate error estimate for the geometric features of a quadratic form, Boltzmann curve is used to predict the reasonable sample size by fitting different evaluation results of form error. As a result, compared with earlier methods, the curve helps us to obtain a better accuracy, while the process only distinguishes a part of the form features on one or several workpieces from a mass production. In addition, a new error model based on the quadratic form is established by least squares method, which can be applied to measure and evaluate the roundness error and sphericity error. Two experiments are performed to verify the effectiveness of the new error model, and the results indicate that a systematic sampling strategy and error estimation can determine the reasonable sample size while maintaining high accuracy.

In this contribution, a novel technique for classification of electroencephalogram (EEG) signals has been presented employing generalised Stockwell ( S )-transform technique. S -transform is a technique for analysis of any non-stationary time series in joint time–frequency frame. In this work, epileptic seizure and seizure-free EEG signals have been taken from an available existing database and generalised S -transform is applied individually on different sets of EEG signals. Selective features like standard deviation and energy are evaluated from the joint time–frequency S -transform contour of the transformed signals and are eventually being classified using support vector machines (SVMs) and k-nearest neighbour (kNN) classifier. In this work, two different classification problems are addressed, namely (i) seizure and healthy (ii) seizure and inter-ictal, where both EEG signals of healthy and inter-ictal zone are considered to be in seizure-free class. For different cases investigated in this study, the highest overall classification accuracy of 98.44% is achieved using SVM classifier where as 100% accuracy is obtained using kNN classifier, which are comparable and even better than the results obtained in the existing literatures, analysed on the same dataset.

This study reports a novel technique utilising a standing-wave node as a virtual ground to implement an impedance matching network and power level tuning in a ground eliminated (GE) open-ended resonant coil structure. This technique with GE open-ended coils can potentially be used in wireless power transmission (WPT) systems, where an unknown metallic, ungrounded, arbitrary environment is used as a signal propagation medium to deliver electric power to several distributed nodes. To satisfy WPT standards the proposed resonant WPT system with the matching network is implemented and tested at 13.56 MHz. A comprehensive study of the GE open-ended resonant coil structure demonstrates ground-plane effects and the necessity of an impedance matching network in no-ground signal situations. The experimental results confirm the theoretical analysis presenting 9% improvements in mismatch efficiency, and 13.1 times in power transmission efficiency at 13.56 MHz when the matching network is deployed.

This study considers the problem of probability density estimation and model order selection in distributed sensor networks. For this purpose, a mobile-agent-based distributed variational Bayesian algorithm is proposed. It is assumed that the measurements can be statistically modelled by a common Gaussian mixture model. In the proposed algorithm, the problems of model order selection and probability density estimation will be considered simultaneously using mobile agents and the variational concept. Initially, considering a component number greater than the true one, the variational Bayesian algorithm will be executed in different nodes. In other words, the mobile agents move through different routes in the network and compute the local sufficient statistics. Afterwards, the global sufficient statistics will be updated using these values and finally the parameters of the probability density function will be calculated. This procedure will be repeated until convergence is reached. At this moment, the component whose mixture probability is lower than a threshold value will be removed. The mentioned steps will continue until the true component number is reached. Convergence of the proposed method will also be analytically studied. Finally, the proposed algorithm will be applied to synthetic and also real-world data sets to show its promising performance.

In this study, a dynamic tension test and an intelligent coordinated control system were designed for the monitoring of the dynamic tension of a heavy scraper conveyor and the realisation of coordinated control for driving scraper conveyor sprockets under a heavy load. As a subsystem, the dynamic tension test system is composed of strain gages, a wireless signal-transmitting device, and a piezoelectric energy harvester. The strain gages were installed in the chains meshed with the scrapers to measure the dynamic tension. The wireless signal-transmitting device was used to transmit a weak strain signal. The piezoelectric energy harvester was used to power both the strain gages and the wireless signal-transmitting device. In addition, the coordinated control system, which was established using a parameter self-tuning fuzzy proportion, integration and differentiation controller was used to adjust the load balance and the dynamic tension between the two driving sprockets. The tested values demonstrated the effectiveness of the proposed dynamic tension test system and its self-power stability. Furthermore, experiment results using the coordinated control system indicated its ability to balance the load and dynamic tension between the two driving sprockets. Hence, the maintenance cycle and service life of the heavy scraper conveyor were extended, and the maintenance cost and energy consumption were reduced.

This study is concerned with asynchronous data fusion problem in the non-linear multisensor multirate system, where the observation noises are coupled with the system noise of the previous step. An estimation algorithm is designed based on covariance intersection (CI) fusion algorithm. First, the system is reformulated by using multiscale system theory. The local filtering algorithm processes the cross-correlated noises by exploiting the conditional Gaussian distributions. Furthermore, the first-order Stirling's interpolation algorithm and third-degree spherical-radial rule are used for non-linear approximate estimation. The fusion of the local estimate results is based on CI fusion algorithm. This study extends the currently available asynchronous data fusion algorithm for a multirate multisensor non-linear system, and considers the asynchronous correlation, then a novel algorithm is proposed, which has the ability to deal with the correlation, and is robust to the correlation between any two local filters. The effectiveness and superiority of the proposed algorithm are indicated by the results of numerical simulation.

When a lock-in amplifier was employed in a scanning near-infrared spectrometer, there was the problem of low signal-to-noise ratio (SNR) of spectra. This study investigates the impact of unstable measured signal frequency in the lock-in amplifier. The analysis concludes that when the frequency of the measured signal and reference signal are not equal due to frequency fluctuations, the amplitude detection results are inaccurate and change periodically with the initial phase of the measured signal. According to analysis results, a zero-phase detection method is designed in the spectrometer, where the measured signal phase is judged to guarantee detection starting at the zero-phase position. The experimental results indicate that the average of the SNR of spectra is 2376 using the zero-phase detection method, which is approximately three times that of the original system and coincides with the results of the theoretical calculation (2273) and simulation analysis (2234).

A practical inertial-measurement-unit-based method is proposed to measure the angle of a single-axis rotation, where the axis rotation is invisible or inaccessible. The proposed method utilises the angular velocities of an inertial-measurement unit (IMU) to compute the orientation of the rotating axis. An equation with respect to the unknown rotation angle is first developed due to that the attitude angles of IMU vary as the measured target rotates. An interpolation algorithm is proposed to solve the equation, and simulations are performed. Finally, with a turntable and an IMU, comparative experiments are conducted to validate the proposed method. Results demonstrate the robustness to initial noise and the effectiveness under different situations.The proposed method is practical because of its simple structure, flexible installation and applicability in complex environments. Another more contributions of this study are a feasible approach to measure the orientation of the rotating axis. This method solves the problem of complex and difficult installation of angle measuring equipment in specific environments. It is simple and suited for use in the real-time calculation of angular displacement. It can be used to measure the rotation angle of rotating motors or the rotating axis of some equipment.

Potential demodulating ability in biological tissue exposed to radio-frequency (RF) signals intrinsically requires an unsymmetrical diode-like non-linear response in tissue samples. This may be investigated by observing possible generation of the second harmonic in a cavity resonator designed to have fundamental and second harmonic resonant frequencies with collocated antinodes. Such a response would be of interest as being a mechanism that could enable demodulation of information-carrying waveforms having modulating frequencies in ranges that could interfere with cellular processes. Previous studies have developed an experimental system to test for such responses: the present study reports a circuit model to facilitate calibration of any non-linear RF energy conversion occurring within a test-piece inside the cavity. The method is validated computationally and experimentally using a well-characterised non-linear device. The proposed model demonstrates that the sensitivity of the measurement equipment plays a vital role in deciding the required input power to detect any second harmonic signal, which is expected to be very weak. The model developed here provides a lookup table giving the level of the second harmonic signal in the detector as a function of the input power applied in a measurement. Experimental results are in good agreement with the simulated results.

In this study, the authors present an alternative method for poor accuracy of piezoresistive meteorology pressure sensing in low-temperature environment (down to −45°C) by incorporating external thermodynamic control. In order to use the designed sensor in such a low-temperature environment, the microelectromechanical systems (MEMS) chip itself has to be kept at 50°C, which is the most common used highest working temperature in meteorology field. The heat compensation is achieved by a pulse-width modulation controlled heating resistor based on proportional–integral–derivative algorithm. The precision of thermal control was obtained at 50 ± 0.5°C during the bench test. The simulation results from COMSOL software are consisted with lumped element model analysis. Under the condition of environmental temperature down to −45°C, system achieved a maximum absolute error within ±0.5 hPa after pressure calibration.

Global positioning system for vehicular applications in harsh urban environments faces some difficulties such as low signal intensity, intensive multipath and frequent signal blockage. These make it difficult to acquire and track signal normally by common receivers. This study proposes a high-sensitivity and high-robustness velocity tracking loop with inertial navigation system (INS) aiding. It joints the fast Fourier transform (FFT) results of all channels to estimate the velocity directly, called INS-aided joint FFT loop. Compared with conventional tracking loops, its sensitivity is improved effectively by jointing all channels in navigation domain. As this loop does not need to estimate the synchronisation parameters too, so it can avoid the influence of synchronisation parameter errors in single channel and achieve higher robustness. Simulation results show INS-aided JFFT loop has better performance in signal blockage, multipath and weak signal circumstances than some existing loops. An optimal carrier-tracking loop is also designed which combines INS-aided phase locked loop (PLL) and INS-aided JFFT. It can switch between INS-aided PLL and INS-aided JFFT by carrier-to-noise ratio (C/N ₀) estimation to achieve the optimal performance when the signal strength changes.

This study aims to explain the real-time and high-precision requirements of linear motor mover position detection. A new sub-pixel displacement estimation based on an extended phase correlation algorithm (PCA) is proposed and applied to measure the position of the linear motor mover. This extended PCA is on the basis of singular value decomposition (SVD), which is a novel application in position measurement of the linear motor mover. First, two adjacent stripe images are captured by a high-speed camera installed in the linear motor. Second, an improved Hanning (flap-top) window function is added to the input fence images to suppress the edge effect and spectral aliasing, before the discrete Fourier transform (DFT). Next, phase correlation matrix is acquired by PCA, and using SVD technique to calculate approximate rank-one matrix for subsequent phase unwrapping. Then, weighted-phase unwrap algorithm is applied to acquire the real horizontal phase data. Finally, least-squares method is utilised to linearly fit the real phase data and to obtain sub-pixel displacement of the linear motor mover. Experiments show that the detection accuracy is 1/100 pixel, and the position measurement of linear motor mover is fast and effective.

In this study, a novel robust measure of non-linearity-based cubature Kalman filter (RMoNCKF) is proposed to obtain good performance with lower computational burden. The proposed filter inherits the virtues of high accuracy of the high-degree filter and computation efficiency of the low-degree one. When the measure of non-linearity (MoN) is evaluated and compared with the threshold in the dynamic system, the cubature rules nested in the RMoNCKF can be switched autonomously to decrease the computation complexity in the low non-linear condition. Furthermore, the robust estimation technology can help to improve the value of MoN for the non-Gaussian distributed case. Simulation results of target tracking and integrated navigation system demonstrate that the RMoNCKF can have a close performance to the fifth-degree CKF with less computation time. In the circumstances of the time-varying noise and contaminated Gaussian distributed noise, the RMoNCKF outperforms the UKF, the third-degree CKF and fifth-degree CKF.

During normal operation, power transformer insulation always experiences a temperature gradient along radial direction with respect to core axis. Over time, this gradient creates non-uniform ageing within cellulosic insulation. Recent development such as Modified Debye Model (MDM) is found to be capable of modelling such non-uniformity. Owing to non-uniform ageing, measured tan δ value only provides the average of dielectric loss associated with various regions within the insulation. Researchers have observed tan δ value to be quite high (due to conductive ageing by-products) in heavily degraded areas such as regions closer to winding. Also, the rate of deterioration of various regions within the insulation cannot be assumed to be constant. In fact, rate of degradation for regions with comparatively high tan δ values are expected to be much higher. tan δ measurement fails to assess the individual condition of different regions. A technique, based on MDM, is introduced in the present work that can identify the change in ageing severity of various regions within a non-uniformly aged insulation between two consecutive testing. The proposed method is first successfully tested on multiple series connected laboratory samples. Thereafter, the methodology is applied to data recorded from real life power transformer.

Position sensors are inseparable part of motion control systems. Linear resolvers (LRs) are electromagnetic position sensors that are suitable for high vibration, polluted environments. The accuracy of their detected position can be affected by different mechanical faults. In this study, variety of possible mechanical faults along with their mathematical index in LRs is introduced. Static eccentricity, dynamic eccentricity, inclined mover and run out error are the presented mechanical faults that are discussed independently and simultaneously. Then, three-dimensional non-linear, time-stepping finite element method is employed to investigate the performance of the studied resolver under different mechanical faults. Finally, the prototype of the studied sensor is built. An experimental test setup is designed and built in order to measure the output characteristic of the LR under studied mechanical faults. Good correlation between the experimental and simulation results is obtained.

Conveyor belt longitudinal tear is one of the most serious problems in coal mining. Existing systems cannot realise lossless and real-time detection for longitudinal tear of conveyor belt. Currently, visual detecting systems are proposed by many researchers and are becoming the future trend. A visual recognition system based on using laser and area light sources is designed in this study, which can recognise and count abrasions, incomplete-tears, and complete-tears. The advantage of the system is to prevent longitudinal tear based on multi-feature information. In the process of detecting conditional characteristics, laser and area light sources are responsible for enhancing contrast between conditional features and conveyor belt surface, meanwhile false corner filtration and single-point feature identification method are designed for improving recognition accuracy of the system. Compared with several current systems, the designed system has a better performance on recognising complex tear characteristics of conveyor belt, thus the problem of starting warning only based on single feature can be effectively avoided.