A human in-hand motion (HIM) recognition system based on multi-modal perception information fusion is proposed in this paper, which can observe the state information between the object and the hand by using customized ten kinds of HIM manipulation in order to recognize the complex HIMs. First, combined with the characteristics of HIM capture, ten kinds of HIM sets are designed, and finger trajectory, contact force and electromyographic signal data are acquired synchronously through the multi-modal data acquisition platform; second, motion segmentation is realized through the threshold segmentation method, the multi-modal signal preprocessing is realized by Empirical Mode Decomposition (EMD), and multi-modal signal feature extraction is realized by Maximum Lyapunov Exponent (MLE); then, a detailed non-linear data analysis is carried out. A detailed analysis and discussion are presented from the results of the Random Forest (RF) recognizing HIMs, the comparison results of motion recognition rates of different subjects, the comparison results of motion recognition rates of different perceptrons, and the comparison results of the motion recognition rates of different machine learning methods. The experimental results show that the multi-modal perception information based HIM recognition system proposed in this paper can effectively recognize ten different HIMs, with an accuracy rate of 93.72%.

Partial discharge (PD) pattern recognition is a critical indicator for evaluating the insulation state of gas-insulated switchgear (GIS). Aiming at the disadvantage of traditional PD pattern recognition methods, such as single feature extraction and low recognition accuracy, a pattern recognition method of PD based on multi-feature information fusion is proposed in this paper. Firstly, a recognition model based on quasi-Hausdorff distance is established according to the statistical characteristics of the phase-resolved partial discharge (PRPD) image, and then a modified convolutional neural network recognition model is established according to the image features of the PRPD image. Finally, Dempster–Shafer (D–S) evidence theory is used to fuse the two pattern recognition results and complement the advantages of the two approaches to improve the accuracy of partial discharge pattern recognition. The experimental results show that the total recognition accuracy rate of this method for four typical PD is more than 94.00%, and the recognition rate is significantly improved compared to support vector machine and normal convolution neural network. Maintaining stability in typical bipedal robots is challenging due to two main reasons.

The natural neuromuscular model has greatly inspired the development of control mechanisms in addressing the uncertainty challenges in robotic systems. Although the underpinning neural reaction of posture control remains unknown, recent studies suggest that muscle activation driven by the nervous system plays a key role in human postural responses to environmental disturbance. Given that the human calf is mainly formed by two muscles, this paper presents an integrated calf control model with the two comprising components representing the activations of the two calf muscles. The contributions of each component towards the artificial control of the calf are determined by their weights, which are carefully designed to simulate the natural biological calf. The proposed calf modelling has also been applied to robotic ankle exoskeleton control. The proposed work was validated and evaluated by both biological and engineering simulation approaches, and the experimental results revealed that the proposed model successfully performed over 92% of the muscle activation naturally made by human participants, and the actions led by the simulated ankle exoskeleton wearers were overall consistent with that by the natural biological response.

Power electronics-based apparatuses absorb non-sinusoidal currents. These are considered non-linear and non-symmetrical loads for the power grid, and they generate a harmonic current. The dynamic tuning passive filter (DTPF) is one of the best solutions for improving power quality and filtering out harmonic currents to get a symmetrical current waveform. The electrical parameters of DTPF can influence its absorbing harmonic current, tuning performance, and cost. In this paper, a method for designing and optimizing the electrical parameters of dynamic tuning passive filter is proposed in order to improve the effectiveness of DTPF and the symmetry level of the power source. First, according to the characteristics of the harmonic source, the design technical indicators of DTPF, and its topology, the design procedure for the electrical parameters of DTPF is proposed. Second, based on detailed analysis of the test results, the range of the harmonic current absorption coefficient is determined. Third, the range of the relationship coefficient is determined by analyzing the impact of the filter capacitor’s capacity on the filter performance. Fourth, the calculation method for the electrical parameters of DTPF is devised. Finally, the validity of this method is verified by several engineering cases, and the electrical parameters of the filter capacitor and electromagnetic coupling reactance converter (ECRC) under the lowest total cost are simulated and optimized. Our approach can optimize the electrical parameters of DTPF and improve the harmonic suppression effectiveness, thus leading to a more symmetrical waveform and successfully avoiding power grid problems. The research results of this study not only provide a basis for the design of ECRC, but also lay a foundation for the machining DTPF.

Partial discharge (PD) localization in an air-insulated substation (AIS) can be used to assess insulation conditions efficiently. Many localization methods have been reported during the past few years. However, the error of the localization results has been large or the localization algorithm too inefficient. The reason is that the localization equation set is nonlinear and non-symmetrical. In this paper, an improved particle swarm optimization (PSO) algorithm is proposed to improve the localization accuracy in 3D. Firstly, the proposed localization method is based on the symmetrical antenna array and the location error distribution is analyzed. Secondly, the objective function of PSO is constructed using the error distribution. Specifically, the 3D location target is divided into two steps—plane coordinates and height. The two targets are optimized respectively. To verify the method, a test is carried out by a prefabricated fault bushing in the laboratory to compare with the existing methods. According to the results, the localization error is 0.21 m, which can locate the PD source accurately. A complete calculation takes 42.29 s, and the efficiency is increased by 16.13 times under the same accuracy. The comparison results show that the proposed method can greatly improve the efficiency while ensuring accuracy.

Due to conflicts among objectives of multi-objective optimization (MO) problems, it remains challenging to gain high-quality Pareto fronts for different MO issues. Attempt to handle this challenge and obtain high-performance Pareto fronts, this paper proposes a novel MO optimizer via leveraging particle swarm optimization (PSO) with evolutionary game theory (EGT). Firstly, a modified self-adaptive PSO (MSAPSO) adopting a novel self-adaptive parameter adaption rule determined by evolutionary strategy of EGT to tune the three key parameters of each particle is proposed in order to well balance the exploration and exploitation abilities of MSAPSO. Then, a parameter selection principle is provided to sufficiently guarantee convergence of MSAPSO followed after the analytical convergence investigation of this optimizer so as to assure convergence of the searched Pareto front toward the true Pareto front as far as possible. Subsequently, a MSAPSO-based MO optimizer is developed, in which an external archive is applied to preserve the searched non-dominated solutions and a circular sorting method is amalgamated with the elitist-saving method to update the external archive. Lastly, the performance of the proposed method is examined by 16 benchmark test functions against 4 well-known MOO methods. The simulation results reveal that the proposed method dominates its peers regarding to the quality of the Pareto fronts for most of the studied benchmarks. Furthermore, the results of the non-parametric analysis confirm that the proposed method significantly outperforms its contenders at the confidential level of 95% over the 16 benchmarks.