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Chinese automation Congress (CAC)

作者： Genghui Pei Chi Xu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Engineering Research Center of Intelligent Technology for Geo-Exploration Ministy of Education Wuhan China Engineering Research Center of Intelligent Technology for Geo-Geo-Exploration Ministy of Education Wuhan China

Hand detection and gesture recognition are an important branch of artificial intelligence, which are widely used in daily life, such as interaction between human and machines, communication using signals, virtual reality and so on. In order to realize gesture recognition in unconstrained situations, we propose a novel end-to-end model based on Transformer, which can detect multiple hands in a picture. Our network can be roughly divided into the following three stages. In the first stage, we will extract feature maps of different layers by convolutional network from monocular RGB images. In the second stage, features are sent into the codec module based on Transformer, and hand detection is further carried out through multi-layer perceptrons with the decoding vectors as the input. In the third stage, we combine the attention weight maps obtained in the second step with the features of different layers in the first step to complete the gesture recognition task. From the experiment results, our network accurately identifies the positions of multiple hands in the picture and correctly recognizes hand gestures.

关键词： Solid modeling Codecs automation Gesture recognition Virtual reality Transformers Feature extraction

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Fast control Strategy of Grid-forming Inverter Based on Power Feedforward

Fast Control Strategy of Grid-forming Inverter Based on Powe...

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Chinese control Conference (CCC)

作者： Sheng Zhang Zhijian Fang Qine Liu Muchao Xiang Dingbang Huang 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 State Grid Hubei Electric Power Co. Ltd Xiangyang Power Supply Company Xiangyang China Electric Power Scientific Research Institute of State Grid Hubei Electric Power Co. Ltd Wuhan China

Grid-forming inverter is widely used in grid-connected systems of distributed generation because of its frequency and voltage support capacity and good stability in microgrid, but its large inertia will affect the dynamic response speed of grid-forming inverter. In order to solve this problem, this paper introduces the loop that affects the dynamic response of grid-forming inverter, and carries out small signal modeling for active loop, analyzes the dynamic performance indicators and determinants of typical second-order system. Thus, a method of adding power feedforward coefficient to the forward channel of the power loop is designed, and the response speed of the system with or without feedforward coefficient under the unit step response is compared. Finally, the simulation results show that adding the power feedforward coefficient can improve the response speed of the grid-forming inverter during startup and power switching, then achieves the effect of fast control.

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Circulating Load Ratio Prediction Model Based on Clustering Sag Milling Operating Conditions

SSRN

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SSRN 2024年

作者： Liao, Zhenhong Xu, Ce Chen, Wen Wang, Feng 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 Wuhan430074 China Department of Mineral Resources Development and Utilization Changsha Research Institute of Mining and Metallurgy CO. LTD. Changsha410012 China School of Engineering Tokyo University of Technology Tokyo192-0982 Japan

Grinding in mineral processing is used to control the ore at the technically feasible and economically optimum particle size to achieve mineral liberation for separation. A circulating-load ratio (CLR) during a semi-autogenous grinding (SAG) milling process is critical for controlling particle size and energy consumption. This paper presents a CLR-prediction model based on clustering SAG milling operating conditions. First, operating parameters affecting the CLR are identified by comprehensively analyzing the complex mechanism and characteristics of a typical industrial SAG milling ***, a method is developed to cluster operating conditions of the SAG milling process based on the power consumption and CLR of the process. The method reveals the actual physical significance of each operating condition. Then, the support vector regression (SVR) is used to model the CLR in each operating condition. After that, a distance-based model integration strategy is designed to determine the weights of each SVR model to predict the CLR. Finally, integrating the SVR submodels yields a CLR prediction model. Actual run data demonstrated the accuracy and effectiveness of the model in predicting CLR. This method has significant practical value for improving SAG milling efficiency and is applicable to other complex industrial processes. © 2024, The Authors. All rights reserved.

关键词： Particle size

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Fast accurate position control of light-responsive liquid crystal elastomer actuator

Fast accurate position control of light-responsive liquid cr...

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Asian control Conference

作者： Xinyu Nie Jundong Wu Yawu Wang Chun-Yi Su Daiki Sato 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 Gina Cody School of Engineering and Computer Science Concordia University Montreal Quebec Canada Laboratory for Future Interdisciplinary Research of Science and Technology (FIRST) Tokyo Institute of Technology Yokohama Kanagawa Japan The School of Engineering Tokyo University of Technology Tokyo Japan

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Light-responsive liquid crystal elastomers (LCEs) are promising for soft robot actuators due to their ability to respond to light stimuli and produce deformation. In order to realize their application in soft robots, fast and accurate position control is essential. Based on the intrinsic physical nature of LCE actuator's deformation, a terminal sliding mode controller (TSMC) is designed to efficiently adjust the temperature of the actuator, thus directly controlling the actuator deformation. In order to achieve the target temperature, a prediction model is established to predict the target temperature corresponding to the target displacement. Moreover, a proportional integration controller is applied to eliminate model errors and disturbances. The feasibility of the control strategy is experimentally verified, and compared with conventional PID controller. The results prove that the proposed control strategy can achieve fast and accurate control of displacement.

关键词： Heating systems Actuators Accuracy Deformation Position control Process control Elastomers

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Chinese Character Style Transfer Based on the Fusion of Multi-scale Content and Style Features

Chinese Character Style Transfer Based on the Fusion of Mult...

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第40届中国控制会议

作者： Xiaoxue Zhou Ziying Zhang Xin Chen Mengxi Qin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Arts and Communication China University of Geosciences

Style transfer has received much attention, and Chinese character style transfer is an important application. In the previous Chinese character style transfer methods, most of the methods separately establish a mapping from source font to target Chinese character for each specific target font. It is impossible to extend a single model to other styles. The model training is based on the amount of Chinese character data from a single font, and the demand of the character is greater. In fact, different font style transfer tasks have certain commonalities. Therefore, we use multiple fonts in the data set and combine these fonts into multiple sets of Chinese character style transfer tasks. These tasks will be trained at the same time, instead of that only one font style transfer task training is achieved at a time like the previous work. In order to achieve the purpose of extracting and fusing the content features and style feature, we propose a style transfer network composed of content encoder, style encoder, feature fusion module, and decoder, based on the fusion of multi-scale content and style features. Through the experimental results, it can be found that, using our method, training the style transfer of multiple fonts at the same time can help any one of them to generate better results with only a few samples.

关键词： Chinese characters style transfer Generative adversarial networks Content and style features

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A Swing-Up and Stable control Strategy for Pendubot Based on Nonlinear Model Predictive Method

A Swing-Up and Stable Control Strategy for Pendubot Based on...

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Chinese automation Congress (CAC)

作者： Lejun Wang Yiqin Liu Ruihuan Zhang Zixin Huang Zhen Cai School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing 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 Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China School of Information Engineering Wuhan City Polytechnic Wuhan China

The control objective of Pendubot is to stabilise the system in a vertically upward position by swinging up from a vertically downward position. Facing various physical constraints in practical applications, this paper proposes a swing-up stabil-isation control strategy based on a nonlinear model prediction method to accomplish the above control objective. On the basis of the system dynamics model, the forward Euler method is used to construct a nonlinear prediction model of the system. According to the control objectives and physical constraints of the system, the performance index function is designed, and then the control inputs of each sampling cycle are obtained through rolling optimisation and feedback correction, so that the system state gradually converges to the target trajectory, thus achieving the motion control objective. Based on the simulation results, it can be seen that the strategy proposed in this paper is efficient and robust under external disturbances.

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Noise Reduction Method of Transient Electromagnetic Signal Based on VMD-ICA

Noise Reduction Method of Transient Electromagnetic Signal B...

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第40届中国控制会议

作者： Guangjun Wang Zhaohui Wang Hui Jiang Lan Cui Chengxiang Ding Xinzhao Qi Yutao Cheng School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Law Zhongnan University of Economics and Law

Aiming at the problems of traditional transient electromagnetic signal noise reduction methods that have poor noise reduction effects and excessive noise reduction to varying degrees,a transient electromagnetic signal noise reduction algorithm based on VMD and ICA is *** use VMD decomposition to adaptively decompose the original signal into multiple intrinsic mode functions（IMF）,select the noise component according to the autocorrelation criterion,and then use the independent component analysis algorithm based on kurtosis maximization to achieve transient electromagnetic signal best *** with the traditional EMD noise reduction method,we found that the method proposed in this paper not only has a good noise reduction effect,but also retains more signal *** algorithm proposed in this paper is an effective method for noise reduction of transient electromagnetic signals,and it has important value for transient electromagnetic detection to improve detection accuracy.

关键词： TEM noise reduction VMD ICA autocorrelation

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Reconstruction of Spectral Reflectance Based on Fusion Convolution Neural Network 2020

Reconstruction of Spectral Reflectance Based on Fusion Convo...

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Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

作者： Lijun Zhang Xiangguo Wang Jingjing Zhang Jinlong Qiao Penghui Li School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781450387835

An improved method for spectral reflectance reconstruction from the RGB response of the digital camera is proposed by deep convolution neural network. The proposed method learns a fusion mapping theory that represents the basic characteristics of spectral reflectance, including spectral nonlinear mapping and spatial similarity. The deep residual network greatly deepens the network structure and improves the performance of the model, enabling the recovery network to make full use of the sample structure information to accurately recover the spectral reflectance. The performance of the method is compared with several existing methods in the cases of the same experimental data set. Experimental results show that the proposed method has the best accuracy in estimating spectral reflectance and colorimetric values in most cases in comparison with existing methods.

关键词： Spectral reflectance reconstruction spatial similarity deep convolution neural network spectral nonlinear mapping

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A Face Mask Detection Model Based on Improved Genetic Algorithm-Optimized Convolution Neural Network in Covid-19 Pandemic

SSRN

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SSRN 2023年

作者： Liu, Lulin Wang, Xu Du, Sheng School of Future Technology China University of Geosciences Wuhan430074 China College of Communication Engineering Jilin University Changchun130000 China 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

Wearing masks is an essential means of ensuring public safety under the pandemic of COVID-19. Detecting whether people wear masks correctly is vital for preventing and controlling new crown epidemics. In response to the poor effectiveness of current basic deep learning methods for mask-wearing detection, this paper proposes a convolutional neural network optimized by an improved genetic algorithm to detect face masks. An improved genetic algorithm is used to optimize the parameters in the convolutional neural network, and then the effect is compared with those of other papers. The result shows that the proposed method is more effective in detecting the wearing of masks with a correct rate of up to 99.125% and can provide practical help and guidance for preventing and controlling the COVID-19 epidemic. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention 26

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Tokyo Institute of Technology Yokohama226-8502 Japan Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798331521950

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method. © 2025 IEEE.

关键词： Modal analysis

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