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Industrial Cyber-Physical Systems (ICPS)

作者： Haiyang Wei Luefeng Chen Jie Hu Yi Ren Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan

Coke pushing current is an indicator to evaluate the difficulty of coke pushing operation. The higher coke pushing current is, the greater coke pushing resistance is, and the more difficult coke is to push out. Accurate prediction of current peak during future coke pushing operation can provide more time for production personnel to adjust production status and avoid difficult coke pushing in carbonization chamber. In this paper, a combination prediction model based on VMD (Variational Mode Decomposition) and improved ARIMA (Autoregressive Integrated Moving Average) models is proposed. Firstly, VMD algorithm is used to decompose time series of coke pushing current peak, de-noising data, and extracting main information of time series. Then, ARIMA model is used to predict mean change of linear elasticity and GARCH (Generalized Autore-gressive Conditional Heteroskedasticity) model is introduced to predict ARIMA model residual and improve heteroscedasticity of nonlinear part of time series, and then ARIMA-GARCH model is established. Finally, predicted value is obtained by the sum of each component prediction. The experimental results show that the proposed prediction model has a high prediction accuracy in the short-term prediction of coke pushing current peak. The scheme is applied to actual coking production to guide production of coke.

关键词： Resistance Ovens Time series analysis Noise reduction Production Predictive models Prediction algorithms

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Stability and Bifurcation control of Fractional BAM Neural Networks with Diffusion and Time Delays

Stability and Bifurcation control of Fractional BAM Neural N...

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Chinese control Conference (CCC)

作者： Feng Liu Wei Wang Gang Cai Feifan Zhou Hua O Wang Xiuqin Yang School of Automation China University of Geosciences Wuhan China Engineering Research Center of Ministry of education for intelligent technology of earth exploration Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Department of Mechanical Engineering Boston University Boston MA USA School of Education Minzu University of China Beijing China

Based on fractional calculus theory and reaction-diffusion equation theory, a fractional-order time-delay reaction-diffusion neural network with Neumann boundary conditions is investigated. By constructing the phase space basis based on the Laplace operator eigenvector, the system equation is linearized to obtain the characteristic equation. Then, the characteristic equation is analyzed, and the local stability of the system at the equilibrium point is discussed. And taking the time delay as the bifurcation parameter, the stability changes of the system at the equilibrium point and the generation conditions of the Hopf bifurcation are studied when the time delay changes. Moreover, a state feedback controller is designed to control the bifurcation of the system. Finally, the theoretical derivation is verified by numerical simulation.

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Dynamic Analysis and control of Fractional Order Memristor Hopfield Neural Network

Dynamic Analysis and Control of Fractional Order Memristor H...

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Chinese control Conference (CCC)

作者： Feng Liu Gang Cai Feifan Zhou Wei Wang Hua Wang Xiuqin Yang School of Automation China University of Geosciences Wuhan China Engineering Research Center of Ministry of education for intelligent technology of earth exploration Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Department of Mechanical Engineering Boston University Boston MA USA School of Education Minzu University of China Beijing China

In this paper, a novel smooth magnetron is introduced to construct a fractional memristor Hopfield neural network (fractional order M-HNN). The local stability of equilibrium point are analyzed theoretically. Taking the memristor coupling strength coefficient and the fractional order as bifurcation parameters, the phase trajectory diagram, the bifurcation diagram of the system are drawn to analyze the influence on the dynamic behavior of the neural network. When the system parameters are fixed, the hyperchaos phenomenon of the fractional order M-HNN model is revealed. Finally, the PD controller is applied to the model to enhance the stability of the system.

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Explainable Deep Learning for Semg-Based Similar Gesture Recognition: A Shapley-Value-Based Solution

SSRN

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SSRN 2023年

作者： Wang, Feng Ao, Xiaohu Wu, Min Kawata, Seiichi She, Jinhua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Engineering Tokyo University of Technology Hachioji Tokyo192-0982 Japan

Surface electromyography (sEMG) based gesture recognition shows promise in enhancing human-robot interaction. However, accurately recognizing similar gestures is a challenging task, and the underlying mechanisms of gesture recognition are not well understood. To address these issues, we developed a new solution called the Shapley-value-based similar gesture recognition (SV-SGR) method. Our solution combines deep learning and game theory to achieve high recognition accuracy and interpretability. First, we devised a data preprocessing method that converts sEMG signals into sEMG color images, which can be more effectively utilized by deep learning techniques. Next, we established a deep-neural-network-based model for gesture recognition using the processed sEMG color images. Then, we designed a global explanation approach based on Shapley values to quantify the contribution of each channel to recognizing similar gestures. Finally, we carried out an explanation analysis, which provides feedback on the recognition model to enhance the precision of gesture recognition. Extensive comparisons and interpretable analyses have been conducted on real-world datasets, and the results demonstrate that the SV-SGR method outperforms other baselines under various experimental conditions. The interpretable analysis method based on Shapley values effectively enhances the performance of recognizing similar gestures and provides valuable insights into the decision-making process of recognition models. © 2023, The Authors. All rights reserved.

关键词： Gesture recognition

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The Analysis of Muscle-Synergy Similarity between Walking and Pedaling Based on Variational Mode Decomposition and Non-Negative Matrix Factorization

The Analysis of Muscle-Synergy Similarity between Walking an...

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IEEE International Conference on Engineering, Technology and Innovation

作者： Wangyang Ge Jinhua She Shijie Guo Feng Wang Juan Zhao Seiichi Kawata School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies Hebei Key Laboratory of Robot Sensing and Human-Robot Interaction Tianjin China School of Mechanical Engineering Hebei University of Technology Tianjin China

ISBN: (数字)9798350362435

ISBN: (纸本)9798350362442

Pedaling lower-limb rehabilitation robots are widely used in various stages of lower-limb rehabilitation because of their high safety and low requirements for patients' lower-limb strength. Researchers found that the activation sequence of the surface electromyographic (sEMG) signals of the muscles of the lower limb during pedaling is very similar to that of walking, which makes it possible to simulate the muscle force generation during walking by changing the pedal load during pedaling training. However, most pedaling lower-limb rehabilitation robots use constant loads, and it is of great importance to study the relationship between load and muscle synergy and to design load phase angle trajectories with higher muscle synergy consistency in the walking-pedaling motion to optimize the training effect of pedal-type rehabilitation robots. In this paper, we used variational mode decomposition (VMD) and nonnegative matrix factorization (NMF) to extract the muscle synergy features in each band of sEMG during walking and pedaling and to study the similarity of pedaling and walking and the influence of pedal load on muscle synergy consistency.

关键词： Legged locomotion Training Technological innovation Time series analysis Muscles Assistive robots Feature extraction Trajectory Safety Matrix decomposition

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A New Total Variation Denoising Algorithm for Piecewise Constant Signals Based on Non-convex Penalty 1

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2nd International Conference on Neural Computing for advanced Applications, NCAA 2021

作者： Lv, Donghao Cao, Weihua Hu, Wenkai Wu, Min School of Automation China University of Geosciences Wuhan430074 China School of Information Engineering Inner Mongolia University of Science and Technology Baotou014010 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9789811651885

ISBN: (纸本)9789811651878

Total variation signal denoising is an effective nonlinear filtering method, which is suitable for the restoration of piecewise constant signals disturbed by white Gaussian noises. Towards efficient denoising of piecewise constant signals, a new total variation denoising algorithm based on an improved non-convex penalty function is proposed in this paper. While ensuring the objective function is convex, a new non-convex penalty is designed. The denoising efficiency is improved by updating the dynamic total variational adjustment in the penalty function to the static adjustment. Experimental results demonstrated that the proposed denoising algorithm improved the denoising efficiency without affecting the denoising effect. The overall performance was shown better than the current excellent denoising algorithm. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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Fixed-/Preassigned-Time Anti-Synchronization of Chaotic Neural Networks

Fixed-/Preassigned-Time Anti-Synchronization of Chaotic Neur...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Haoyu Li Leimin Wang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781665402460

This paper investigates a unified controller to solve the fixed-time anti-synchronization (FTAS) and preassigned-time anti-synchronization (PTAS) problems for chaotic neural networks. Under our controller, chaotic neural network can realize anti-synchronization within the fixed or preassigned time which greatly expands the practical application range of the anti-synchronization. In addition, sufficient conditions and time estimation on FTAS and PTAS are derived. Finally, the feasibility of the control scheme is proved via a numerical simulation.

关键词： Sufficient conditions Neural networks Estimation Numerical simulation Synchronization Cybernetics

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An Enhanced Fuzzy Repetitive-control Design and Optimisation Method based on 2D Performance Index 10

An Enhanced Fuzzy Repetitive-Control Design and Optimisation...

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10th IEEE Data Driven control and Learning Systems Conference, DDCLS 2021

作者： Zhang, Manli Wu, Min Tian, Shengnan She, Jinhua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Engineering Tokyo University of Technology Hachioji192-0982 Japan

ISBN: (纸本)9781665424233

This paper concerns the design and optimisation of two-dimensional (2D) repetitive control of nonlinear systems based on the Takagi-Sugeno (T-S) fuzzy model. First, a continuous-discrete 2D model of nonlinear repetitive-control systems based on T-S fuzzy model is constructed by utilizing the 2D characteristics of continuous control and discrete learning actions in the repetitive-control process. Next, a fuzzy Lyapunov-Krasovskii functional derives the linear-matrix-inequality-based stability condition with a low conservatism. Two positive and two nonzero parameters in the fuzzy Lyapunov-Krasovskii functional tune the control and learning actions. Then the particle swarm optimisation algorithm based on a 2D performance index searches for the best parameter combination, resulting in the optimal 2D controller gains. A numerical example is given to demonstrate the effectiveness of the method. © 2021 IEEE.

关键词： Linear matrix inequalities

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UAV Remote Sensing Image Stitching 15th

UAV Remote Sensing Image Stitching

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15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019

作者： Wu, Hui Chen, Jun School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9783030324551

The majority of image stitching (include UAV remote sensing images stitching) models are homography matrix transformation function, which could effectively simulate the rigid transformation of 2D images in 3D coordinate system. In order to prevent the accumulation of alignment errors (drift) and the projection distortion in the multi-image stitching task, we proposes a novel UAV remote sensing image stitching access consisting of homography transformation and Helmert transformation. In the overlapping and non-overlapping region we utility homography transformation and Helmert transformation respectively. With proposed method the UAV remote sensing image stitching result has fewer alignment errors and less projection distortion. © Springer Nature Switzerland AG, 2020.

关键词： Remote sensing

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Downhole condition identification for geological drilling processes based on qualitative trend analysis and expert rules 1

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1st International Conference on Neural Computing for advanced Applications, NCAA 2020

作者： Li, Yupeng Cao, Weihua Hu, Wenkai Wu, Min School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9789811576706

ISBN: (纸本)9789811576690

In geological drilling processes, downhole incidents usually pose a serious threat to drilling safety. In order to improve the performance of drilling safety monitoring, a systematic downhole condition identification method is proposed based on the Qualitative Trend Analysis (QTA) and expert rules. Qualitative trends directly related to downhole conditions are extracted by QTA. Then, the existing expert rules to identify downhole conditions are summarized to create a knowledge base. The qualitative trends extracted from historical drilling data serve as new rules to extend the knowledge base. For online application, with new data collected, the qualitative trends are calculated, and then the downhole condition can be identified by querying the knowledge base. The effectiveness and practicality of the proposed method are demonstrated by case studies involving real drilling processes. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Geology

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