The matrix factorisation approach computes a low-rank approximation of the incomplete user-item rating matrix. Existing approaches suffer from under-fitting due to the use of global information for all users and items. In this study, the authors propose a three-way recommendation model that integrates global and local information. This new model has a number of main aspects. The first is rating prediction with global and local information. A clustering and two matrix factorisation algorithms are employed for this purpose. The second is the computation of recommendation thresholds based on the decision-theoretic rough set model. Misclassification and promotion costs are considered simultaneously to build the cost matrix. The last is the determination of the recommender actions based on the prediction and thresholds. Experimental results on the well-known datasets show that authors’ proposed model improves recommendation quality in terms of average cost.

Steganography can hide secret information in an innocent cover medium. Its opponent is steganalysis, which is used to discriminate whether a suspicious carrier contains a hidden message or not. With the rapid development of deep-learning frameworks, deep-learning-based steganalytic models have hold the dominant position in the field of steganalysis. In recent years, some scholars have successfully utilised model compression methods in the field of image classification. However, as far as the authors know, no prior works are devoted to the application of model compression methods in the field of deep-learning-based steganalysis. In this study, the authors explore the effect of two quantisation schemes, namely 8-bit calculation and floating-point calculation, on the performance of XuNet, a state-of-the-art deep-learning steganalytic model. The experimental results show that the two deep-learning model quantisation schemes are applicable to steganalysis. It is even possible to compress the network size while retaining satisfactory performance.

Text clustering is an important method for effectively organising, summarising, and navigating text information. However, in the absence of labels, the text data to be clustered cannot be used to train the text representation model based on deep learning. To address the problem, an algorithm of text clustering based on deep representation learning is proposed using the transfer learning domain adaptation and the parameters update during cluster iteration. First, source domain data is used to perform the pre-training of the deep learning classification model. This procedure acts as an initialisation of the model parameters. Then, the domain discriminator is added to the model, to domain-divide the input sample. If the discriminator cannot distinguish which domain the data belongs to, the common feature space of two domains is obtained, so the domain adaptation problem is solved. Finally, the text feature vectors obtained by the model are clustered with MCSKM++ algorithm. The algorithm not only resolves the model pre-training problem in unsupervised clustering, but also has a good clustering effect on the transfer problem caused by different numbers of domain labels. Experiments suggest that the clustering accuracy of the algorithm is superior to other similar algorithms.

Robust and precise defect detection is of great significance in the production of the high-quality printed circuit board (PCB). However, due to the complexity of PCB production environments, most previous works still utilise traditional image processing and matching algorithms to detect PCB defects. In this work, an improved bare PCB defect detection approach is proposed by learning deep discriminative features, which also greatly reduced the high requirement of a large dataset for the deep learning method. First, the authors extend an existing PCB defect dataset with some artificial defect data and affine transformations to increase the quantity and diversity of defect data. Then, a deep pre-trained convolutional neural network is employed to learn high-level discriminative features of defects. They fine-tune the base model on the extended dataset by freezing all the convolutional layers and training the top layers. Finally, the sliding window approach is adopted to further localise the defects. Extensive comparisons with three traditional shallow feature-based methods demonstrate that the proposed approach is more feasible and effective in PCB defect detection area.

Pedestrian detection has vital value in many areas such as driver assistance systems, driverless cars, intelligent tourism systems etc., but there are some difficulties that need to be solved. The algorithm with high detection rate is complex and requires substantial time. Therefore, how to improve the detection accuracy and speed has become the key of pedestrian detection. For these reasons, firstly, an improved algorithm, called hash and window enhancement of binarised normed gradients (HWEBING), based on binarised normed gradients feature is proposed. Subsequently, the authors present an improved local texture feature, namely mean of local binary pattern (MLBP), based on uniform pattern local binary pattern (ULBP) for increasing the detection rate. Finally, after using the HWEBING algorithm to get the candidate windows, the combination of MLBP feature and histograms of oriented gradients feature is extracted from these windows to further enhance the detection accuracy. Experimental results reveal that speed of using the HWEBING algorithm for pre-detection is 5.5 times faster than the traditional method of pedestrian detection. Furthermore, the detection rate of MLBP feature is 3.5 and 2.1% higher than those of ULBP and basic pattern local binary pattern (Basic-LBP), respectively.

Magnetic induction tomography is a contactless technique that can measure conductivity distribution in biological tissues. This study proposed that Helmholtz coils as the excitation coils will generate a set of uniform excitation fields. The system model consists of a circular background area, a circular disturbance object, a set of Helmholtz coils, and eight magnetic detection coils. The diameter of the Helmholtz coil is 200 mm. The detection coil is a square, and its side length is 12 mm. The excitation current of the system is 30 mA, and the frequency is 10 MHz. This study discusses the reconstructed images of different locations, different volumes, different distances, and different electrical conductivities of perturbation bodies. The anti-noise ability of different noise signals is also analysed. Finally, the image differences are evaluated through structural similarity index (SSIM). The results show that the algorithm can distinguish the position and volume characteristics of the target object. However, when the volume of the target object is low, the position will be deviated. This algorithm can distinguish the position of the two, which is far away from each other.

In this study, the authors present an incremental multivariate Markov (IMM) chain model. Moreover, the estimation method of the parameters in IMM is proposed. Numerical experiments illustrate the effectiveness of IMM.

The absolute and relative quantifications between the equivalence class and the target concept are the two important research endeavours in rough set theory. Double-quantitative decision-theoretic rough set (Dq-DTRS) models utilise both absolute quantification and relative quantification in their upper and lower approximations to reflect the distinctive degrees of quantitative information. Herein, the authors apply the information theory to Dq-DTRS model to characterise and measure these two types of quantitative information. The expressions of the information entropy with regard to the two quantifications and their corresponding information co-entropy are presented in DqI-DTRS model and DqII-DTRSmodel, respectively. This work makes a further study of Dq-DTRS models by discussing the information measures with respect to absolute and relative quantification.

High-precision, low-cost three-dimensional (3D) space measurement and positioning technology is desperately needed in wide applications. This study analyses the key technologies in the recognition of the devices to achieve the requirement of device recognition and capture on the production line. A 3D measurement algorithm for the small devices based on the consumer-level sensors is proposed in this study. Histogram of gradients feature is used to classify the devices, and structure light is used to get the depth data of the devices. Object extraction and Euclidean cluster segmentation are used to analyse the depth data, in order to determine their positions and orientations. In the database built on iPhone X, the accuracy of category identification reached 0.97, and the measurement error of angle is small. The results show that the proposed method is feasible and can be applied to the recognition and position of the devices.

According to the operational requirements of citrus harvesting robot, a new harvesting sequence planning method and its intelligent optimization algorithm are proposed based on the existing harvesting sequence planning methods, which realized dynamic planning for inverse kinematics solution selection and sequence planning. The simulation results show that the energy consumption is reduced by the proposed method. By means of harvesting robot, the harvesting sequence planning based on the principle of ‘minimum energy consumption’ and principle of ‘shortest path’ are carried out respectively, and the energy consumption is counted. The results show, compared with the multi-citrus harvesting algorithm based on the principle of ‘shortest path’, the proposed method based on the principle of ‘minimum energy consumption’ and dual intelligent optimization algorithm can realize the continuous multi-citrus harvesting, and reduce the energy and time consumption as a whole. The energy consumed by the proposed algorithm is reduced by about 12.5% when the number of harvesting points is 3–8, and about 23.0% when the number of harvesting points is 9–12. The average time consumed by the proposed algorithm is reduced by about 13.0% when the number of harvesting points is 3–8, and about 22.0% when the number of harvesting points is 9–12.

Collaborative representation-based classification (CRC) has become a breakthrough in face classification due to its distinguished collaborative capacity. Nevertheless, insufficient observations of per subject are usually offered by few or even a single gallery image for face classification, which lead to a sensitive response to the variations from the original data set. In this study, the authors present a bi-directional CRC algorithm using convolutional neural network-based features for face classification. They first employ a deep convolutional neural network to extract facial features from the original gallery and query sets, and then develop a fast reverse representation model to obtain the auxiliary residual information between each training sample and the reconstructed one that is achieved from the test sample. Secondly, they offer a new solution to the bi-directional optimisation problem by which the input sample is well represented by the forward linear combination and the reverse one, respectively. The last contribution is to utilise a competitive fusion method for robust face recognition, which weighted reconstructed residuals from the bi-directional representation model. Experimental results obtained from a set of well-known face databases including AR, FERET, and ORL verify the validity of the proposed method, especially in the robustness to small sample size problem.

In this study, a deeply supervised end-to-end model is presented for fully automated segmentation for cardiac magnetic resonance imaging (MRI) images. Firstly, the mechanism of deep neural network (DNN)-based segmentation is discussed with the relationship of channels and their distribution in network in depth. Following this idea, a U-Net-based model, namely an invert-U-Net model is presented with an innovative filter number structure. Based on the invert-U-Net model, the experiment is carefully designed, and the hyper-parameters are considerately arranged. Finally, the model is applied and evaluated using Sunnybrook MR datasets from the MICCAI 2009 LV segmentation challenge and the experimental result shows that it outperforms the state-of-the-art methods.

In the design of intelligent driving systems, reliable and accurate trajectory prediction of pedestrians is necessary. With the prediction of pedestrians’ trajectory, the possible collisions can be avoided or warned as early as possible by changing the behaviour of intelligent vehicles. The trajectory prediction problem can be considered as a sequence learning problem, in which one of the recurrent neural network (RNN) models called long short term memory (LSTM) has been regarded as a promising method. The authors present a new method for predicting the pedestrian's trajectory, which is called Social-Grid LSTM based on RNN architecture. The proposed method combines the human–human interaction model called social pooling and the Grid LSTM network model. The performance of the proposed method is demonstrated on two available public datasets, and compared with two baseline methods (LSTM and Social LSTM). The experimental results indicate that the authors’ proposed method outperforms previous prediction approaches.

There are two different theory methods that are rough set theory and evidence theory, but these two theories can both handle some incomplete and uncertain information. In this study, these two models are combined in the interval-valued fuzzy ordered information system (IVFOIS). Belief functions and plausibility functions are proposed based on dominance relations in IVFOISs. The belief and plausibility reducts are defined in interval-valued fuzzy ordered decision tables (IVFODTs) and the attribute reduction of IVFODTs based on evidence theory is established. Finally, the authors use an instance to verify the above argument.

For the problem of local equilibrium point in local path planning, a local equilibrium point smooth escape algorithm is proposed. In this method, a confirmation rule for obstructing obstacles is established to confirm obstacles obstructing routes of an unmanned surface vehicle and causing local equilibrium point from multiple detecting targets. A model for tracking point confirmation is constructed to guide the unmanned surface vehicle to bypass obstructing obstacles safely while tracking points. A tracking point smooth switching model is constructed to ensure smooth change of heading planned by the algorithm in tracking point switching. The idea of ‘wall-following’ algorithm and curve smoothing idea are integrated in the model and then a heading planning method escaping local equilibrium point is given. Simulation results show that the above algorithm can guide the unmanned surface vehicle out of the area where it is trapped when it falls into a local equilibrium point. The algorithm can improve the traceability of planned paths with a heading change rate much lower than that of conventional algorithms.

China has stepped into middle-income status, which is characterised by demographic imbalance and huge discrepancy of resource utilisation, resulting in an imbalance of regional development. Using term frequency–inverse document frequency algorithm, the authors extract the resource keywords and construct the resource utilisation index. The authors then explore the impact of demographic structure on resource utilisation. The main conclusions are as follows: (i) according to the Moran Index, there are strong spatial autocorrelations in resource utilisation at the provincial level in China; (ii) the empirical results demonstrate that the regression coefficients of spatial lags are significant. This provides compelling evidences that the neighbouring regions have obvious spatial spillover effects on local resource utilisation; (iii) the authors’ findings also reveal that the total dependency ratio which reflects the demographic structure has a negative and significant effect on resource utilisation. With other explanatory variables held constant, the child-age dependency ratio and old-age dependency ratio perform differently in the robustness testing. Ultimately, targeted and systematic policy suggestions are proposed to improve resource utilisation and adjust the demographic structure.

In the field of computer vision research, the research on human action recognition of depth video sequence is an important research direction. Herein, considering the characteristics of temporal and spatial depth video sequence, the authors propose a framework of the consultation model of several action sequence features to solve the classification problem in-depth video sequence. According to the characteristics of the 3D human action space, a variety of action sequence feature data is obtained, and then these data is projected to three coordinate planes, the acquired fusion features are used to train the consultation model, and finally the model is validated through the data. The authors have achieved good results by comparing the two publicly available datasets with the other methods. Experimental results demonstrate that the model performs well in existing identification methods.

Renewable energy has great significance in environmental protection, economical conservation, and energy utilisation. Under the premise of making full use of available energy, the rational allocation and dispatch of distributed energy is the prerequisite for reliable and stable operation of urban power grid. According to the types of energy, this study divides into energy supply agent, energy demand agent, and energy dispatch agent, and establishes the multi-agent system (MAS) model of urban power grid. Use knowledge discovery algorithms to formulate scheduling plans to achieve economic development, environment-friendly, and other multi-objective optimisation. The simulation results show that the use of knowledge discovery algorithm to solve the MAS model for emergency dispatch of electric energy can effectively guarantee the scheduling request and energy supply of the lean areas in various situations.

The authors present PTEAR_VLSNR (Pitch Tracking basing on Evolutionary Algorithm with Regularization at Very Low SNR), a pitch tracking algorithm for speech in strong noise. The algorithm builds a pitch enhancement and extraction model, which enhance the pitch by a matched filter, and to further deal with strong noise, the optimal factor was proposed, which can be optimised globally by the evolutionary computing. Specially, regularisation constraint of fitness function was applied to enhance the generalisation ability. Temporal dynamics constraints are used to improve the tracking rate and the voicing decision can be optimal by evolutionary computing similarly. In addition, the balance of optimisation accuracy and time cost were considered. In experiments, genetic algorithm and particle swarm optimisation with two-norm term were represented as evolutionary algorithms with regularisation. At last, they compare the performance of the algorithm and other representative algorithms. The experimental results show that this proposed algorithm performs well in both high and low signal-to-noise ratios (SNRs).

In the area of human–computer interaction (HCI) and computer vision, gesture recognition has always been a research hotspot. With the appearance of depth camera, gesture recognition using RGB-D camera has gradually become mainstream in this field. However, how to effectively use depth information to construct a robust gesture recognition system is still a problem. In this paper, an RGB-D static gesture recognition method based on fine-tuning Inception V3 is proposed, which can eliminate the steps of gesture segmentation and feature extraction in traditional algorithms. Compared with general CNN algorithms, the authors adopt a two-stage training strategy to fine-tune the model. This method sets a feature concatenate layer of RGB and depth images in the CNN structure, using depth information to promote the performance of gesture recognition. Finally, on the American Sign Language (ASL) Recognition dataset, the authors compared their method with other traditional machine learning methods, CNN algorithms, and the RGB input only method. Among three groups of comparative experiments, the authors’ method reached the highest accuracy of 91.35%, reaching the state-of-the-art currently on ASL dataset.

Considering that the social spider algorithm is still unable to solve the multi-objective optimisation problem, this study presents a multi-objective social spider optimisation algorithm. Firstly, a new normalised fitness value formula is proposed based on the multi-objective optimisation purposes, which is able to trade off the non-dominated rankings and crowded distances and evaluate individual strengths and weaknesses effectively; secondly, the gravitational factor is used in order to balance the impact of individual fitness and distance to individual performance, which improves the vibration perception ability of the calculation method as well; once again, the renewal pattern of the female and male population is improved in order to balance the convergence rate and population diversity of the algorithm; lastly, the environmental selection strategy which is based on cosine distance is proposed for female and male population renewal. Testing on the ZDT test set, experimental results show that, compared with the six representative multi-objective evolutionary algorithms, the proposed algorithm in this study has better distribution and better convergence performance.

Printed circuit board (PCB) inspection is an essential part of PCB production process. Traditional PCB bare board defect detection methods have their own defects. However, the PCB bare board defect detection method based on automatic optic inspection is a feasible and effective method, and it is having more and more application in industry. Based on the idea of the reference comparison method, this study aims at studying the classification of defects. First of all, the method of extracting defect areas using morphology is studied; meanwhile, a data set containing 1818 images with 6 different detailed defect area image parts are produced. Then, in order to classify defects accurately, a traditional classification algorithm based on digital image processing was attempted, and a defect classification algorithm based on convolutional neural network was proposed. After experimental demonstration, in the actual results, the defect classification algorithm based on convolutional neural network can achieve a fairly high classification accuracy (95.7%), which is much higher than the traditional method, and the new method has stronger stability than the traditional one.

In order to overcome the disadvantages of existing handover approaches in cellular communication networks, a new handover detection approach based on trajectory data mining techniques is presented. It discovers frequent trajectory patterns from massive historical trajectories of moving objects by applying the improved Apriori algorithm and determines whether or not to perform handover in a cellular network based on the discovered prediction results, according to the coverage area of cellular networks. Experiments were conducted on synthetic datasets and the results show that: the proposed frequent-trajectory-based handover detection model can successfully avoid the ping-pong effect caused by unnecessary frequent handover operations, greatly reduce the error rate of handover, and the accuracy of handover detection is very high.

Approximation computation is a significant issue when the rough set model is applied. However, few authors focus on how to calculate approximations of multigranulation rough set (MGRS). Herein, the authors clarify a fact that only a part of elements in the universe need to be judged whether they belong to approximations of MGRS. If X is a target concept which is approximated by approximations in MGRS, then the element whose equivalence class does not intersect with X is of no need to be judged. Based on the fact, the authors clarify that they proposed a vector-based algorithm to compute approximations in MGRS. Time complexity of the proposed algorithm is .

There are two shortcomings in Brzostowski's method of modelling for fitness running. One is the low precision of speed acquisition by smartphones. The other is the limited search ability of modelling algorithms. As a result, it is difficult to obtain a highly precise model of fitness running. Aiming at the above two problems, a method for multiple sensors of smartphone and median filter for speed data acquisition in fitness running (MM4SA) is proposed. MM4SA can remove the impulse noise of three-axis acceleration signal, which is generated by the intermittent gesture changes of smartphones, and filter out the gravitational acceleration from the three-axis acceleration signal with the help of the orientation sensor. Secondly, the differential evolution modelling algorithm (DEMA) is applied to enlarge search space and find out a better model for fitness running. The experimental results show that the proposed method can obtain more accurate speed and better model for fitness running than Brzostowski's method.

An intelligent scheduling method for energy saving operation of multi-train is proposed based on genetic algorithm and regenerative kinetic energy. Considering the morning and evening peak, departure time, and total vehicle number, the energy consumption optimisation model of multidimensional state vector subspace of train is established for the train departure interval with the lowest total energy consumption. Two intelligent control models are used to formulate the time interval scheduling plan depending on whether to consider the morning and evening peak. The train operation diagram is solved by Matlab, and the validity and feasibility of the proposed algorithm are verified. This study provides an intelligent and efficient algorithm and solution for multi-train intelligent scheduling problem.

Since the node energy is limited, wireless sensor networks need efficient routing algorithms to reduce energy consumption. The authors proposed a multiple layer uneven clustering algorithm. This algorithm is used for single-hop network. Nodes submit data to the cluster heads, and then all cluster heads submit data to the sink node by single hop. The authors divide the whole network into several layers with different reign. The inner diameter and outer diameter of each layer are calculated by a sink node to meet the aim of balancing energy consumption. The algorithm chooses cluster heads according to the nodes’ residual energy in each layer. According to the reign of each layer, from the perspective of reducing energy consumption, the authors limit the minimum distance between two cluster heads, so that the cluster heads can be distributed evenly in one layer. Simulation results show that the algorithm can promote the lifetime of networks.

With the increasing demand for location-based services, indoor positioning technology has become one of the most attractive areas of research. Microelectromechanical systems sensors in smart terminals are used to realise a pedestrian dead reckoning algorithm. Owing to the accumulated error increased with time, the results of positioning will produce a large error, and an indoor positioning method based on the perception and constraint of map information is designed including straight path constraint and inflection point constraint. In the method of inflection point constraint, several common machine learning algorithms are compared through the experiments, and the secondary discriminant method is utilised to detect the inflection point with a detection accuracy of 97.62%. Finally, the performances of the improved algorithm and the traditional dead reckoning algorithm are compared in the experiments. The results show that the average positioning accuracy of the improved algorithm is 0.073 m, the positioning accuracy within 1 m reaches 100%, it is obviously higher than that of the traditional positioning algorithm and the effectiveness of the algorithm is verified.

In recent years, deep reinforcement learning (DRL) has made impressive achievements in many fields. However, existing DRL algorithms usually require a large amount of exploration to obtain a good action policy. In addition, in many complex situations, the reward function cannot be well designed to meet task requirements. These two problems will make it difficult for DRL to learn a good action policy within a relatively short period. The use of expert data can provide effective guidance and avoid unnecessary exploration. This study proposes a deep imitation reinforcement learning (DIRL) algorithm that uses a certain amount of expert demonstration data to speed up the training of DRL. In the proposed method, the learning agent imitates the expert's action policy by learning from demonstration data. After imitation learning, DRL is used to optimise the action policy in a self-learning way. By experimental comparison on a video game called the Mario racing game, it is shown that the proposed DIRL algorithm with expert demonstration data can obtain much better performance than previous DRL algorithms without expert guidance.

The accurate prediction of the pedestrian trajectory is necessary to endow automatic guided vehicle with the capabilities to adjust velocity and path dynamically for the navigation in real pedestrian scenes. For this purpose, this study presents a social conscious prediction model considering two main factors that affect the pedestrians’ walking in the crowd – relative distance and moving direction. To form an effective model, the authors’ conscious pooling layer is added to the Long Shot Term Memory network (LTSM) model to build the relationship between pedestrians, learning the current position m and movement trend. The experiments are conducted to compare the proposed model with the previous state-of-the-art model on several public datasets. The experimental results show that the proposed model predicts pedestrian trajectories more accurately.

Aiming at the problem that modular mechanical arm may collide with the obstacles in working space at runtime, a path planning algorithm of obstacle avoidance is proposed based on a genetic algorithm (GA). Firstly, the Denavit–Hartenberg method is applied to the modelling of the mechanical arm; then, the kinematic and kinetic analysis is conducted and the kinematic and kinetic equations of the mechanical arm are established. On this basis, the time, spatial distance and path length of the motion are regarded as optimising indices and the optimisation of path planning of obstacle avoidance is achieved using GA for mechanical arm under a working condition with single obstacle or multiple obstacles. Through simulation, the effectiveness and feasibility of a path planning algorithm of mechanical arm obstacle avoidance based on GA is verified. This algorithm improves the efficiency of a mechanical arm to avoid the obstacles in working space at runtime.

It is of great significance that making quantitative description and analysis of the cell morphological change to explore physiological or pathological status of the life. To achieve the cells morphological changes of quantitative description, the authors constructed a cell deformation model based on microscopic image sequence here. Based on the graph regularisation and structured matrix decomposition, the high-dimensional shape space is represented by the linear combination of the low-dimension subshape space, so that the authors get a quantitative indicator which represents the degree of cell deformation–deformation factor. In order to verify the validity of the authors’ model, a deformation feature extraction experiment was performed on three groups of stem cell image sequence with different deformation degree. Compared with other three common quantitative methods of deformation, the authors’ model describes the cell morphological changes more comprehensively, and has better adaptability and stability for describing the diversity of cell movements.

In recent years, deep learning has developed rapidly and gradually infiltrated into various fields. As a rookie, generation-based confrontation networks based on deep learning show excellent characteristics in many aspects. This study presents two innovative ideas, the same card force and generation cards algorithm. The generation of the equivalent cards force based on the generation of confrontation networks is studied. For the same period of time in the regular game, players will be issued different types of cards with similar card force, so as to distinguish the player level, the theory of the equal force is proposed. Based on the average and variance of scores obtained after the completion of a game, the cards are divided into ten different types of cards. On this basis, the use of generating a counterfeit network Generative Adversarial Nets generates a large number of cards of the equal card force. In the actual game, a network model is generated to generate a large number of game cards with the same force and distributed to different table numbers, so that the points scored by the landlords in different matches can have the characteristics of mutual appraisal.

For non-linear suspension, vehicle passes through a continuous speed bump, the chaos that may occur under the combined excitation of the speed bump and the engine. This study takes the five-degree-of-freedom vehicle model as the object of research, through the vehicle body poincaré section and the maximum Lyapunov index to identify the chaos produced by the vehicle under joint excitation, and utilises the feedback control of the optimal feedback gain coefficient based on the particle swarm optimisation (PSO) algorithm to suppress vehicle chaos. The results indicate that the vehicle is in a chaotic state in all speed range. Under low and medium speeds, the route to the chaos of the vehicle is the system coupling vibration under multi-frequency excitation, whereas in the high-speed condition, the vehicle approaches the chaos through the bifurcation. The chaos of the vehicle can be effectively suppressed by feedback control with the global optimal feedback gain searched by PSO. This study reveals the chaotic characteristics of non-linear suspension vehicles under combined excitation, which provides a new method for intelligent suppression of chaos.

A portfolio model is established after analysing the investment environment of the artificial intelligence concept stocks in China. To reduce the risk of investment, the beetle swarm optimisation (BSO) is proposed. BSO, based on the beetle antennae search (BAS) and the standard particle swarm optimisation (PSO), is derived from the standard PSO but the update rules of each particle originate from BAS. In global searching, BSO, making the model get a lower value at risk, is more capable than standard PSO, which is easily trapped in local optimal defects. This study tries to solve portfolio model by using BSO algorithm. The results prove that BSO can do better in dealing with optimisation problems of constrained multi-dimensional functions.

Combining multiple features and enforcing joint sparsity have proven to be beneficial for robust tracking. In this study, a novel stereo vision and two-stage sparse representation-based method is presented. First, the colouring information-based features are augmented with a depth view in the appearance modelling of a target object. Unreliable features are then dynamically removed for robust feature-level fusion in the first stage of sparse optimisation. Next, the low rank constraint is imposed onto the objective function, which facilitates a more robust representation of the ensemble of particles over the pruned views. Finally, the authors propose to detect occlusion via depth-based histogram analysis to guarantee the effectiveness of the template update. Experiments are performed on two large-scale benchmark datasets: KITTI and Princeton. Authors’ approach achieves state-of-the-art results in the aspect of robustness and accuracy.

Due to the rapid development of printed circuit board (PCB) design technology, inspection of PCB surface defects has become an increasingly critical issue. The classification of PCB defects facilitates the root causes of detects’ identification. As PCB defects may be intensive, the actual PCB classification should not be considered as a binary or multi-category problem. This type of problem is called multi-label classification problem. Recently, as one of the deep learning frameworks, a convolutional neural network (CNN) has a major breakthrough in many areas of image processing, especially in the image classification. This study proposes a multi-task CNN model to handle the multi-label learning problem by defining each label learning as a binary classification task. In this study, the multi-label learning is transformed into multiple binary classification tasks by customising the loss function. Extensive experiments demonstrate that the proposed method achieves great performance on the dataset of defects.

The tracking–learning–detection (TLD) algorithm applied in the home environment can effectively improve the tracking robustness. However, it has the problems of single target tracking and poor selection of feature points. This study proposed a dynamic target tracking method based on corner enhancement with Markov decision process (MDP) model. The MDP target tracking method is adopted to change a multi-target tracking problem into a strategy problem based on MDP model, in which one MDP model represents the life cycle of a target, and multiple targets are represented by multiple MDP models. In the tracking process, the strong corners generated by the Shi-Tomasi corner method are used to replace the feature points generated by the traditional TLD algorithm at intermediate intervals, which makes the target feature points more stable during the tracking process. The similarity function learning for data association is equivalent to the learning of the MDP strategy, in which the reinforcement learning method is used and has double advantages of both online and offline learning. The tracking experiments with different data sets are performed, and the results show that dynamic target tracking algorithm based on the corner enhancement with MDP has both good tracking performance and good anti-interference capability.

This study addresses the problem of holistic road scene understanding based on the integration of visual and range data. To achieve the grand goal, the authors propose an approach that jointly tackles object-level image segmentation and semantic region labelling within a conditional random field (CRF) framework. Specifically, the authors first generate semantic object hypotheses by clustering 3D points, learning their prior appearance models, and using a deep learning method for reasoning their semantic categories. The learned priors, together with spatial and geometric contexts, are incorporated in CRF. With this formulation, visual and range data are fused thoroughly, and moreover, the coupled segmentation and semantic labelling problem can be inferred via graph cuts. The authors’ approach is validated on the challenging KITTI dataset that contains diverse complicated road scenarios. Both quantitative and qualitative evaluations demonstrate its effectiveness.

Due to the impact of the vehicle engine jitter, unevenly inflated tires and people moving, the attitude initial alignment accuracy is not high and the repetition is poor. The author proposed the two-channel ten state variable error model for land vehicle application, the system for error equation and measurement equation are established to solve the initial alignment problem of strapdown inertial navigation system, and the simulation experiment and the initial comparison of the system static base are carried out, the calculation method of strapdown attitude is optimised and the initial alignment problem in complex environment is solved, which provides a reference for fast initial alignment of the vehicle strapdown inertial navigation system.

The combination of fuzzy information systems (ISs) and multi-adjoint theory has become a hot issue in the study and applications of artificial intelligence. An intuitionistic fuzzy set has more flexible and practical ability to represent information and is better in dealing with ambiguity and uncertainty when compared with the fuzzy set. Multi- adjoint intuitionistic fuzzy rough sets are constructed by using adjoint triples under intuitionistic fuzzy IS. For this purpose, the authors propose intuitionistic fuzzy indiscernibility relation and multi-adjoint approximation operators. The basic results in the multi-adjoint fuzzy rough set model are generalised to multi-adjoint intuitionistic fuzzy rough set model. The analogous results are also verified. After that, a novel approach of attribute reduction is proposed. First, a kind of approximate reduction to keep the dependence of the positive region to a degree is formulated. Second, they propose a heuristic algorithm to compute the attribute reduction. At last, they employ an example to describe the processing of the algorithm.

For intelligent and accurate identification of the gas anomalous area, two geophysical exploration methods, namely, the direct current (DC) method and the transient electromagnetic method, were adopted for advance detection of the underground roadway before the joint inversion of the detection data to obtain the fusion image of apparent resistivity. Based on the high-resistivity area in the image, the range of 13–65 m ahead of the driving face is the gas anomalous area. According to the test results of gas prediction indexes q and Δh ₂, there is an area with high prediction values in front of the roadway, which is consistent with that determined by geophysical exploration. From the analysis of the thickness of exposed coal, there is an area with large coal thickness ahead of the roadway where there is an increase in gas content. The electrical characteristics of the coal are changed in the area, contributing to the anomalous resistivity, which keeps in consistency with the results of geophysical exploration. Therefore, the electro-magnetic joint exploration technology can realize the intelligent identification of gas anomalous areas, which is of great significance for the efficient and accurate detection of gas anomalous area in the coal seam.

Remote sensing image scene classification is an important method for remote sensing image analysis and interpretation and plays an important role in civil and military fields. In this study, a scene classification method of remote sensing images based on hierarchical sparse coding is proposed. This method is essentially a kind of multi-layer, multi-scale, and multi-path sparse coding. It can extract features of optical remote sensing images more effectively, so that the features of the remote sensing images can be represented more sufficiently. The obtained codes are further used for spatial pyramid pooling (SPP) operation, and the corresponding SPP representation is obtained. SPP representations in different paths are combined and outputted to the support vector machine classifier, and the final classification results are obtained. Experiments on two data sets show that the proposed method can obtain better scene classification accuracy.

A hysteretic chaotic neural network is proposed to solve the crossbar switch problem effectively. The chaotic neural network structure with hysteresis and its set computation characteristics are carried out. The simulation results show that the theory is corrected by simulating the chaotic neural network with randomly generated neurons. The network architecture is applied to the crossbar switch problem, and the results of the computer simulation are given to illustrate the computational capability of the network architecture. The simulation results show that the chaotic neural network structure with hysteresis neurons is better than the previous network structure for the crossbar switch problem in terms of cost, time, and optimal solution rate.

Image registration technology has been widely used in many parts of the computer vision system such as the automatic optical inspection system which is used to detect the printed circuit board (PCB) defects. The accuracy of the image registration will deeply influence the system's performance, so this study proposed an accurate image registration algorithm and applied it to the PCB defect detection. Good features to track feature detector and speeded up robust feature descriptor are combined to extract efficient features to achieve the first accurate image registration. Afterwards, cross-correlation functions were used to compute the shift between the reference image and the first-registered image for further accurate registration. Experimental results show that the authors’ algorithm performs a much better registration, with a lower root-mean-square error value between the reference image and transformed image. What is more, they applied it to detect the defects of PCB with a high accuracy.

The authors argue that the mean of discriminant features calculated across the samples of a class (intra-class samples) cannot perform well for the classification task. The main reason is that the mean feature ignores intra-class membership's different responses to their own class for a query sample. Meanwhile, they present that the discriminant features of a test sample can also be well-linear approximated by the discriminant features of intra-class memberships. The adaptive weighted intra-class features will be more suitable for the identification ability of a class than original samples via a regression algorithm. To verify this, a new linear representation-based classification method using Fisher discriminant features (LRFC) is suggested. To be more specific, they first extract Fisher discriminant features of all face samples. Second linear regression (LR) algorithm is exploited to obtain weight coefficients of intra-class feature information for the feature representation of a query sample. At last, the weighted intra-class features are re-combined as an agent of each class and the test sample is identified as the class with the maximum similarity. The method is simple but particularly effective. Experimental results on benchmark face databases verify improvements of LRFC over its original methods.

A lily flower is a medicinal plant, which has been widely used in the Chinese medicine industry in recent years. According to the rapid picking of the lily flower, a scheme of the mechanical arm picking structure was designed, and the system with an end effector, manipulator and control system was used. The mechanical arm adopts a three-stage connecting rod structure. Through the lily plant growth of more than about 85% <50 cm characteristics of the mechanical arm were picked up from the top to bottom operation strategy. Based on this, the kinematics model of the picking robotic manipulator is designed. The kinematic equation of the manipulator is demonstrated by a DH deducing method. The kinematics simulation of the manipulator is carried out by MATLAB. The mechanical arm kinematics and picking experiments were carried out in the experimental field in a natural environment by the robotic physical machine platform. The results showed that the manipulator position error from the end of the arm was <12 mm and the picking success rate was 83.33%.

In the process of supply chain risk management, selecting the right supplier quickly and accurately is one of the most critical issues for the company. Due to the influence of various risks, improper selection or time-consuming evaluation may lead to the company losing the best time, cost and market share. For the multi-criteria decision-making problem of supplier selection, this study proposes an intelligent method combining the combination of triangular fuzzy number and network analysis method, evaluates the alternative solutions, obtains the supplier ranking result and proves the effectiveness of the method by case studies of the electronics industry. A scientific reference is provided by this method for the development of supplier-selected intelligent systems.

In order to improve the robustness of the similarity metric method of image classification, and reduce the complexity of the measure function, the Pearson correlation coefficient is introduced to improve the zero-shot image classification. Firstly, the mapping matrix from visual space to semantic space is learned by the training dataset, and the visual feature is aligned by it. Then in the semantic space, the similarity between features is calculated by the metric function, predicting the label of unseen class by the nearest neighbours. Experiments show that zero-shot image classification based on Pearson correlation coefficient is better than Euclidean distance and cosine similarity.

Spiking neuron network is generally considered as the third generation of neural networks. This type of network is a class of machine learning techniques that exploit many layers of non-linear information processing for supervised or unsupervised feature extraction, image classification, texture segmentation, and image recognition. On the other hand, the grey-level co-occurrence matrix algorithm is widely used in visual images for texture feature extraction and image structure characterisation analysis. For those buttons with the same size, same shape, similar colours, and analogous textures, they cannot be effectively identified by conventional methods. At this time, the spiking neural network trained with the improved GLCM algorithm can be used to achieve button image feature extraction, classification, and recognition. Experiments show that the method proposed here can effectively segment the button images with their texture features.

In the era of big data, the data scale of landslide monitoring could reach above terabyte level, the traditional database and data mining technology could no longer meet the requirements of intelligent monitoring and early warning. To obtain early warning information with high reliability and real time by applying big data theory, mechanisms, models and methods as well as machine learning methods are the inevitable trends in the future. This study aimed to realise a real time and precise mid-long prediction of landslide displacement, proposed two distributed landslide displacement prediction models: DLDP-GBTs (distributed landslide displacement prediction with Gradient Boosted Trees algorithm) and DLDP-RF (distributed landslide displacement prediction with Random algorithm); the cross-validation method was also adopted to evaluate and adjust parameters to reduce the root mean squared error of the model predicted results. In addition, this study proposed the rapid selection of features by using XGboost model in distributed situations can improve the Model training efficiency under distributed condition. By comparing different regression algorithms models, it was found that the DLDP-GBTs model based on the gradient optimisation decision tree was better than the other two models in terms of accuracy and real-time performance, which meets the requirements under the big data background.

Spectral clustering has been widely used for image segmentation recently. There are certain issues when using spectral clustering for image segmentation, such as a high complexity. Moreover, it commonly similarity measure is a Gaussian kernel function. However, spectral clustering is very sensitive to the scale parameters in this similarity measure, which is difficult to determine a suitable parameter. For these problems, a modified superpixel segmentation method and a new similarity measure for improving Ng-Jordan-Weiss (NJW) method are presented in this study. Then the improved NJW method is applied to image segmentation. In the authors’ scheme, their modified superpixel segmentation method will be utilised to divide the image into several small regions, which are called superpixels. Then, the NJW method is used to cluster these superpixels into some meaningful regions. In NJW, the similarity between two adjacent superpixels is measured by a kernel fuzzy similarity measure. The improving NJW method for image segmentation not only has lower complexity but also not sensitivity to scale parameters. Experimental results have demonstrated that their method visible improvement both in diminishing segmentation error, and also it has a higher efficiency.

Face alignment could be widely used in face recognition, expression recognition, face-based AR applications etc. Cascaded-regression-based face alignment algorithms have been popular in recent years for their low computational costs and impressive results in uncontrolled scenarios. Unfortunately, the size of the trained model is quite large for cascaded-regression-based methods which makes it unsuitable for commercial applications on mobile phones. In this study, the authors proposed a data compression method for the trained model of the supervised descent method (SDM). Firstly, the distribution of the model data was estimated using a non-parametric method. Then an adaptive quantisation algorithm was proposed to quantise the model data. Finally, their adaptive quantisation algorithm was tightly coupled with the SDM training process to fine tune the results. The quantitative experimental results proved that their proposed method could compress the data model to <20% of its original size without hurting the performances. The proposed method has been integrated into a mobile AR application, subjective evaluations proved that the proposed compression method could provide similar visual effects compared with the uncompressed counterpart.

Text detection is the premise of semantic recognition of natural scenes. At present, many studies only focus on print fonts or hand-written fonts. There are many stone texts in China that reflect historical and cultural values but are difficult to identify. Detecting these stone carving texts in natural scenes is more difficult than detecting them in handwritten or printed texts. In this study, the entropy-based feature extraction algorithm is proposed to extract stone texts in natural scenes. Compared with other excellent machine learning algorithms, it has achieved better results.

The driving assistant system is conducive to reduce the accidents caused by operational mistakes in the complex environment of lane-change on the highway. However, the adaptability between the system and drivers has not been taken into consideration. To solve this problem, this study put forward a personalised driving assistant strategy in the process of highway lane-change. In the study, as a methodology based on the measured data of vehicle dynamic characteristics and personalised driving characteristics, it established a criterion for the personalised driving model, dissected the factors affecting driving safety and put forward a risk assessment method for individualised driving according to the criterion. Taken Matlab/Simulink software as a simulation experiment platform, it verified the rationality and feasibility of the individualised driving-aid strategy of highway lane-change with the aid of the adaptive model predictive control algorithm. The study results show that the personalised driving criterion and risk assessment method proposed in this study can effectively distinguish the driving styles and driving risks of different drivers, and the personalised driving assistance strategy not only respects different drivers’ individualised operation styles but also effectively controls driving risks.

In the light of an electromagnetic motor driving system, electromagnetic conversion characteristics and structural principles were described. Electromagnetic drive and hybrid-driven method were proposed; the mathematical models describing the two drive systems which have adopted the magnetic circuit analysis method were established, starting with a discussion on how the drive system affects the electromagnetic force and air-gap magnetic flux density. Under the same conditions, the electromagnetic drive and hybrid-driven model were, respectively, simulated to reveal the impact on the electromagnetic force and air-gap magnetic flux density due to the different driving modes. Variation of the electromagnetic force with an increasing coil current of two types of drive systems under motor speed control is studied preliminarily. The results have shown that compared to that of the pure electromagnetic drive system, the electromagnetic force of the hybrid drive system was raised from 2198 to 4097 N, and the air-gap magnetic flux density was increased from 1.048 to 1.393 T; the electromagnetic characteristics of the magnetically driven motor were improved effectively.

Opening Book is a kind of assistive technologies to enhance the performance of computer games. The opening stages of the game method generally used to query the database generated. This method improving search efficiency and avoids the missing strategic of traditional evaluation systems. This paper studies the technical problems of using the Opening Book of draughts, and introduces the generation and usage of the Opening Book. The authors also discussed the detail of statistics in Opening Book. Besides, this paper presents a new idea of introducing the information of the Opening Book into the traditional Alpha-Beta valuation system. The experiment proved that the method proposed in this paper can effectively solve the issues of the start of the draughts and improve the game level of the draughts.

With the continuous development of the electronics industry, the number of printed circuit board (PCB) has grown at a rapid rate, and the requirements for the detection systems of PCB have also continuously increased. In the traditional PCB detection, the main reference is the comparison method. However, in a real scene, there are a series of problems such as non-uniform illumination, tilting of the camera angle, and the like, resulting in a less satisfactory effect of the reference comparison method. So, the authors proposed a non-reference comparison framework of PCB defects detection. This framework has achieved good results in speed and accuracy. The authors extract the histogram of oriented gradients and local binary pattern features for each PCB image, respectively, put into the support vector machine to get two independent models. Then, according to Bayes fusion theory, the authors fuse two models for defects classification. The authors have established a PCB data set that includes both defective and defect-free. It has been verified that the accuracy of the verification set is improved compared to the individual features using the fused features. The authors also illustrate the effectiveness of Bayes feature fusion in terms of speed.

Given the complex situation of traffic at sea and the large amount of data that grows with the information technology, the ship behaviour cannot be precise recognised or fully expressed based on the raw and noisy data itself. A semantic model of ship behaviour based on ontology engineering is developed in this study. First, an interrelated semantic network is created, which allows the easy annotation, expression, and acquisition of information about ship trajectory. Then, the time-series compression techniques are used to recognise behaviour and event in the semantic model, which, respectively, indicate the states of track points and trajectory segments, and which also can be represented as a time series. Finally, the accuracy and practicality of the model is verified by automated identification system data and geographic data; the result shows that the semantic model solves the conflict between expressiveness of ship behaviour and complexity of the situation, and reduces the amount of data by building semantic representation in higher abstraction levels.

Cylindrical end cam mechanisms are widely used in many engineering machineries, which mainly contribute to the transmission of movement and power. However, due to the stress of time variation and instability in the operating process, it generally causes vibrations and impacts of the mechanical equipment, and leads to failures or even damage to the machine. Aiming at the issue about the excessive lateral impact forces of support and anti-twist roller shaft, a new optimisation design method of the end cam curve is proposed. The simulation results show that the proposed method can effectively reduce the lateral impact, and solve the failure problem caused by excessive impact forces in the application of the non-continuous profile of the cam mechanism. In addition, multi-body dynamic analysis is used to verify the effectiveness of the optimisation method. Meanwhile, this optimisation method also has a certain reference significance to solve similar problems.

Dictionary learning serves as a considerable role in image processing and pattern recognition. However, when applied to face classification, it may suffer from the issue of the limited quantity of training samples. Therefore, it becomes a challenge to obtain a robust and discriminative dictionary. Recently, locality-constrained and label embedding dictionary learning (LCLE-DL) takes the locality and label information of atoms into account to achieve an effective performance in image classification. In this study, the authors exploit a new approach which uses synthetic training samples to enhance this dictionary learning algorithm, so they name it STS-DL. Firstly, they strengthen the diversities of training samples by producing virtual samples. Secondly, the LCLE-DL algorithm is used to calculate two deviations on the basis of the original training samples and the authors’ newly synthetic samples, respectively. Finally, they integrate them together to perform the classification task, which produces a more promising performance for image recognition. Abundant experiments have been conducted on several benchmark databases, the experimental results illustrate that the proposed STS-DL shows a higher accuracy than the LCLE-DL method, as well as some state-of-the-art dictionary learning and sparse representation algorithms in image classification.

In order to mitigate risks from road tests for autonomous-driving vehicles, reduce costs and accelerate development, a virtual reality (VR)-based test platform for autonomous-driving vehicles was built combined with the AirSim system and the UE4 engine by establishing a model library which contains the vehicle dynamics model, sensor models and traffic environment model. The controller-in-the-loop simulation method was implemented to complete the simulation test for autonomous vehicles under different driving conditions and the simulation results were used to optimise the autonomous-driving control system. The actual autonomous driving road test can now be done in an immersive VR simulation environment where autonomous-driving road-testing is done safely and cost-effectively. This plays a significant role in the future development of autonomous-driving vehicles.

When a fracturing vehicle is working, it generally needs to bear high loads, media corrosion and erosion. For this special working environment, this study proposes a rolling bearing fault diagnosis method based on stack marginalised sparse denoising auto-encoder (SDAE). This method combines the sparse auto-encoder (SAE) and the denoising auto-encoder (DAE) and combines the characteristics of dimensionality reduction and robustness. The method adds marginalisation to optimise the SDAE. Finally, it uses a two-layer stacking method. The output results of the second marginalised SDAE are used as input to the softmax classifier for learning training and classification testing. This improved method (stack SDAE) improves the denoising ability, reduces the computational complexity, solves the problems of difficult parameter adjustment and slows training convergence. The experimental tests were carried out on the failure of pitting corrosion of the outer ring of the bearing, pitting failure of the inner ring, and cracking of the rolling element. The results show that the algorithm can effectively improve the accuracy of fault diagnosis of rolling bearings, and it has greatly improved than the algorithms of SAEs and DAE.

The research of network science is of great significance to the study of human society. The discovery of community structure in a network is an important research direction in the network science. Based on the research of traditional community discovery, this study found that sporadic nodes at the edge of a network only belong to the community to which the node is connected. Therefore, the community attributions of these sporadic edge nodes are stronger than that of the network central nodes. Based on this finding, the authors proposed a community discovery method based on the calculation of network node belongingness. In this proposed method, the community detection is first carried out based on the node attributions. Second, a simple method that determines the number of communities is defined. At last, the communities are optimally combined according to the average node attributions of the network so as to realise the community discovery of the network. This proposed algorithm has low time complexity and high detection accuracy in low coverage networks.

The coverage holes in wireless sensor networks (WSNs) have severe negative effects on their life cycle and reliability. This study describes a study of WSN coverage holes based on the community percolation theory in complex networks. In the absence of information about node locations, detection algorithms and parameter optimisation with boundaries covering holes and the outer boundary of the network as the subject were studied. According to the percolation theory, changes in the state of any node in the network may cause changes in the entire network. Based on that, changes in the states of different nodes corresponding to changes in the state of a specific node were investigated to detect coverage holes in the network. Analogue simulations indicated detection of over 90% boundary nodes.

Printed circuit board (PCB) layout is becoming high density, high performance, light, and short. In the automatic PCB defect detection system, image registration of PCB plays an important role. However, most of the traditional registration methods are inefficient, and cannot cope with the problems of image distortion, affine, noise, and so on. To address this issue, the authors propose an improved scale invariant feature transform (SIFT) feature extraction algorithm combined with particle swarm optimisation (PSO) to register the images of PCB which placed on a conveyor belt. The advantage of the presented approach is that the registration results are more robust and efficient by optimising the existing PCB image matching framework. The experimental results on the proposed PCB datasets show that the speed of the proposed method (improved SIFT-PSO) is faster than the traditional SIFT feature registration method, and the average computing time of processing single picture can be improved by 10 s, the registration accuracy can be improved by 3–4%. Compared with the experimental results of other algorithms, the root-mean-square error can be reduced to 0.5146 by using the proposed method. Thus, the proposed method (improved SIFT-PSO) is more accurate and robust in real-time inspection system of PCB.