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Pervasive and intelligent Computing (PICom), IEEE Conference on

作者： Xiaohu Ao Jinhua She Feng Wang Seiichi Kawata Edwardo Fumihiko Fukushima Jinseok Woo 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

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

Rehabilitation robots and human-robot interaction systems are receiving increasing attention in modern healthcare and engineering. Motion intent recognition techniques based on surface electromyography (sEMG) signals provide important support for these systems. However, motion intent recognition models rarely focus on the spatiotemporal properties of sEMG signals simultaneously. There is a lack of interpretable analysis of these spatiotemporal properties. The purpose of this study is to perform gesture recognition of sEMG signals from the Ninapro database using a temporal convolutional network (TCN). The sEMG signals are converted into sEMG images, which are used as input to the TCN model. The TCN model is utilized to perform gesture recognition on these images. The results show that after fusing the spatial and temporal features of the sEMG signal, the recognition accuracy of the TCN model is significantly higher than that of models using only spatial features or only temporal features. To understand the decision-making process of the model, we used an interpretable method based on Shapley values to perform a detailed interpretive analysis of the recognition results. The experimental results show that the TCN model has significant accuracy and stability in the sEMG signal gesture recognition task, while the Shapley value-based interpretable method can effectively reveal the contribution of different features to the prediction results, thus improving the transparency and user trust in the model. This study not only provides a new technical tool for sEMG signal gesture recognition but also demonstrates the value of interpretable methods in deep learning models.

关键词： Accuracy Intent recognition Computational modeling Gesture recognition Predictive models Stability analysis Real-time systems Spatiotemporal phenomena Convolutional neural networks Time complexity

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Causality Analysis and Modeling of Fuel Ratio Influencing Factors in Blast Furnace Ironmaking Process

Causality Analysis and Modeling of Fuel Ratio Influencing Fa...

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Asian control Conference

作者： Binjie Qian Weihua Cao Qifu Chen Min Wu 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 Future Technology China University of Geosciences Wuhan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Blast furnace ironmaking is a key process in iron and steel production, and the fuel ratio has an important impact on the smelting efficiency, cost and environmental impact of the blast furnace. This study aims to provide new ideas and method-ological support for the optimized management and control of the blast furnace ironmaking process by exploring the effects of different factors on the fuel ratio. We firstly sorted out the factors affecting the fuel ratio based on the blast furnace process mechanism, and then deeply analyzed the causal relationship of each parameter on the fuel ratio based on the transfer entropy value, and screened out the parameters that did not have a significant effect on the fuel ratio. Taking the remaining key parameters as inputs, the LSTM and GRU models, which can capture time series information, are used to predict the mean and variance of the fuel ratio, respectively, and the two are combined to compute the final fuel ratio, which overcomes the problem of poor model generalization brought by small fluctuations in the training data of fuel ratio. The experimental results show that the convergence speed of the model is significantly improved after parameter screening, and the fuel ratio can be effectively predicted within a certain error range.

关键词： Computational modeling Time series analysis Smelting Process control Training data Predictive models Blast furnaces

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Prediction of gas utilization ratio based on singular spectrum analysis, improved PSO and KELM

Prediction of gas utilization ratio based on singular spectr...

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Chinese control and Decision Conference, CCDC

作者： Zhuang Li Jianqi An Yunpeng Guo Guoyu Zhao Min Wu 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 Future Technology China University of Geosciences Wuhan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The gas utilization ratio (GUR) in a blast furnace is directly linked to the efficiency, cost-effectiveness and environmental impact on the blast furnace ironmaking process. However, The stability of the published GUR time series prediction models need to be improved. This paper presents an improved particle swarm optimization (PSO) incorporating linearly decreasing inertia weights (LDIW) to optimize the kernel-based extreme learning machine (KELM) for single-step prediction. This paper uses singular spectrum analysis (SSA) to preprocess the data and extract the key components from the GUR time series to solve the problem of high volatility of the GUR time series. In addition, this paper introduces LDIW to improve the optimization ability of particle swarm optimization algorithm, which enhances the stability of a single-step prediction model. Then this paper uses the improved PSO algorithm to extract the optimal parameters of KELM, and establishes a single-step GUR prediction model based on the improved PSO-KELM. Finally, this paper uses the actual production process data of blast furnace to verify the prediction model. The results show that the prediction accuracy of GUR and the overall stability of the model are significantly improved, providing important guidance for the blast furnace ironmaking process.

关键词： Extreme learning machines Time series analysis Production Predictive models Blast furnaces Prediction algorithms Stability analysis

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Nonlinear Underwater Robot System Environmental Disturbance Suppression Based on Feedback Linearization and EID Method

Nonlinear Underwater Robot System Environmental Disturbance ...

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Asian control Conference

作者： Chengyu Li Xin Chen Yang Zhou Wei Tan Wenpeng He School of Future Technology 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 Automation China University of Geosciences Wuhan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

A disturbance suppression approach combining feedback linearization and equivalent input disturbance (EID) method is proposed to control nonlinear underwater robots' position. Firstly, the complex nonlinear model of the underwater robot is transformed into a linear model by feedback linearization. Then, to enhance the system's ability to reject unknown disturbances in the marine environment, the EID method is utilized for estimation and compensation. This helps improve the system's ability to handle disruptions. Additionally, a linear quadratic regulator (LQR) is employed to ensure system stability and achieve rapid convergence. Finally, the proposed approach is validated by demonstrating its effectiveness through simulation results.

关键词： Autonomous underwater vehicles Regulators Simulation Position control Mathematical models Stability analysis Feedback linearization

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Valuing Equity-Linked Guaranteed Minimum Death Benefits with European-Style Asian Payoffs Under the Regime-Switching Diffusion Process with Jump

SSRN

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SSRN 2022年

作者： Wang, Yayun Liu, Shengda Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing400065 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

In this paper, we consider a novel and efficient method to price equity-linked guaranteed minimumdeath benefits (GMDB) with European-style geometric Asian and arithmetic Asian payoffs. In thesituation of continuous transition of market economy, we assume that the underlying asset price processfollows regime-switching Ĺevy model and use complex Fourier series (CFS) expansion method to derivethe approximate value of GMDB products. The error analysis shows an exponential convergence rateunder geometric Asian and arithmetic Asian payoffs. Furthermore, the CFS approach is comparedwith other Fourier transform methods and Monte Carlo simulation, the numerical experiment resultsillustrate our method has a distinct advantage over other methods in both efficiency and accuracy. © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Improving the Adaptability of sEMG-Based Gesture Recognition Using Variational Mode Decomposition and Transfer Learning

Improving the Adaptability of sEMG-Based Gesture Recognition...

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Pervasive and intelligent Computing (PICom), IEEE Conference on

作者： Hao Wu Feng Wang Juan Zhao Seiichi Kawata Jinhua She 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

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

Surface electromyography (sEMG) based gesture recognition has received broad attention and application in rehabilitation areas. sEMG signals exhibit strong user dependence properties among users with different physiology, causing the inapplicability of the recognition model on new users. Transfer learning (TL) is a representative method to reducing user gaps by utilizing features already learned by pre-trained models. However, TL uses a large amount of training data due to the discrepancy of sEMG among different users, which increases the training burden. In this paper, a multi-user adaptive network (MUAN) is devised to decompose the insensitive features among different users to improve gesture recognition accuracy for new users, which is based on variational modal decomposition (VMD), convolutional neural network (CNN), and TL. Ninapro dataset is used to evaluate the adaptability of MUAN and the training burden on new users. Experimental results show that MUAN outperforms CNN and TL, and reduces the training burden for new users. MUAN has the potential to provide a robust and generalized HMI system for clinical applications.

关键词： Training Adaptation models Adaptive systems Transfer learning Training data Gesture recognition Electromyography Physiology Convolutional neural networks Signal resolution

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A Multi-feature Extraction-based Image Identification Method for Rock Debris in The Drilling Process

A Multi-feature Extraction-based Image Identification Method...

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2022 Chinese Automation Congress, CAC 2022

作者： Xu, Pengju Gan, Chao Wang, Luzhao Cao, Weihua 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 No.3 Exploration Institute of Geology and Mineral Resources of Shandong Province Yantai264000 China

ISBN: (纸本)9781665465335

In this paper, a multi-feature extraction-based image identification method for rock debris in the drilling process is proposed, involving three main parts (trainable feature extractor, strong feature extraction, and classification). In trainable feature extractor, abstract features are obtained by extracting the full connection layer of Convolutional Neural Network (CNN). In strong feature extraction, the method uses Gray-Level Co-occurrence Matrix (GLCM) and Color Coherence Vector (CCV) to get the strong feature. In classification, the extracted abstract features and strong features are concatenated and fed into the Support Vector Machine (SVM). Comparison results with two well-known methods indicated the effectiveness of the proposed method. © 2022 IEEE.

关键词： Feature extraction

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Modeling the Kinematics and Dynamics of the Pedaling Lower-Limb Rehabilitation Robot

Modeling the Kinematics and Dynamics of the Pedaling Lower-L...

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intelligent systems, International Workshop on

作者： Hao Zhang Juan Zhao Feng Wang Seiichi Kawata Jinhua She 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

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

Lower-limb rehabilitation robots are becoming increasingly prevalent in rehabilitation training. Accurate modeling helps to develop an effective rehabilitation program. This paper analyzes the physiological structure and joint motion characteristics of the human lower limbs using a pedaling lower-limb rehabilitation robot. We establish a simplified three-link model of the human lower limbs and apply the Denavit-Hartenberg (D-H) parametric method to analyze the joint range of motion, determining geometric parameters and boundary conditions. The forward kinematics analysis derives the expression for the end position of the three-link model, and inverse kinematics calculates joint angles, velocities, and accelerations. Using Lagrange's dynamical equations, we compute the torque required by each joint for different motion states, establishing the foundation for subsequent controller design. Finally, joint angles and torques were simulated in MATLAB. The results verified the feasibility of the model.

关键词： Training Analytical models Torque Computational modeling Dynamics Kinematics Assistive robots Robot sensing systems Mathematical models optimization

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A Novel Rate of Penetration optimization Method for Vertical Wells Based on an Improved Particle Swarm optimization Algorithm

A Novel Rate of Penetration Optimization Method for Vertical...

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Asian control Conference

作者： Yang Zhou Peng Zhang Xiao Wu Xin Chen Min Wu 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 Future Technology China University of Geosciences Wuhan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The rate of penetration serves as a crucial indica-tor reflecting drilling rig efficiency. Maintaining high drilling speed is crucial in reducing drilling costs and non-drilling time. However, due to the complex nonlinearity inherent in the drilling process, optimizing and adjusting drilling speed faces high-dimensional variations and intricate constraints. This complex nonlinearity makes it challenging to obtain a suitable set of operating parameter values. To overcome these difficulties in rate of penetration optimization, we propose a novel rate of penetration optimization method aimed at addressing high-dimensional changes and complex constraints. First, the support vector regression method is introduced to formulate the drilling rate optimization problem. Then, an improved particle swarm optimization algorithm (IPSO) is developed to address the chal-lenge of high-dimensional variation in drilling rate optimization. Moreover, a method for analyzing vertical well longitudinal force is introduced to handle constraints. IPSO demonstrates superior global search capabilities compared to other algorithms in the IEEE CEC2015 benchmark functions. The developed drilling optimization method is validated using actual data. Results from two groups of experiments indicate that compared to manual adjustment, the developed method improved by 15.31 % and 15.38 %, respectively.

关键词： Drilling Support vector machines Simulation optimization methods Process control Manuals Benchmark testing

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Prediction model of photovoltaic output power based on VMD-EMD-BiLSTM

Prediction model of photovoltaic output power based on VMD-E...

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Industrial Cyber-Physical systems (ICPS)

作者： Shaolong Zheng Danyun Li 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 Wuhan China

The accuracy of photovoltaic (PV) power prediction is significantly influenced by the high complexity and volatility of the PV sequence. The existing methods for predicting photoelectric power are difficult to effectively mine and analyze the internal variation law of data. To improve the accuracy of PV power prediction, a new method is proposed that first performs variational mode decomposition (VMD) and empirical mode decomposition (EMD), and then establishes a bi-directional long and short-term memory neural network (BiLSTM) for PV output power prediction. The proposed method extracts the amplitude and frequency characteristics of the PV output power series through VMD. After that, the residual term with strong non-stationarity is generated, which still has more sequence characteristics. The residual term is then decomposed by EMD for the second time to extract more features. Finally, the BiLSTM model is established to conduct bidirectional mining for PV power data and predict PV output power. The actual PV data is used to test the experimental results, which show that the proposed VMD-EMD-BiLSTM prediction model has better prediction performance.

关键词： Photovoltaic systems Empirical mode decomposition Simulation Neural networks Predictive models Cyber-physical systems Prediction algorithms

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