检索结果-内蒙古大学图书馆

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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Load Prediction Model of Gas Boiler Generator Set Based on CNN-LSTM 11th

Load Prediction Model of Gas Boiler Generator Set Based on...

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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： Xu, Yan Wu, Min Hu, Jie Peng, Fu-Sheng Zhang, Wen Song, Wen-Shuo Li, Hui-Hang 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

ISBN: (纸本)9789819647521

With the proposal of carbon peak and carbon neutrality targets and the construction of a new power system, improving energy efficiency and reducing carbon emissions have become the key to the sustainable development of industrial boiler generator sets. However, the current load scheduling of gas boiler power plants is mainly based on manual experience, which limits achieving more efficient and stable operation. Accurate short-term load forecasting can help dispatchers to make reasonable production schedules. Therefore, this paper proposes a hybrid load forecasting model for generator sets based on convolutional neural networks combined with long short-term memory networks. First, the Isolation forest algorithm eliminates anomalies in the historical data set. Then, variables related to unit load are selected by combining mechanistic analysis with the Spearman algorithm. Considering the multiple operating conditions and high complexity of unit loads, the CNN-LSTM network is used to fully extract the spatial and temporal characteristics to build the load prediction model. Finally, experiments are conducted using actual production data, showing that the proposed method is effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric load dispatching

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Trajectory Tracking for Directional Drilling in Underground Coal Mine Using Robust Model Predictive control 26

Trajectory Tracking for Directional Drilling in Underground ...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Wu, Jiajun Lu, Chengda Li, Hao Wei, Hongchao Wu, Min School of Future Technology China University of Geosciences Wuhan430074 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 Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an710077 China China Coal Research Institute Beijing100013 China

ISBN: (纸本)9798331521950

This paper studies the problem of trajectory tracking control for directional drilling in underground coal mine by devising a robust model predictive control method. First, a directional drilling trajectory extension model is obtained by the linearization of a drilling tool kinematic model through the Taylor series expansion in the borehole depth domain. Then, in view of the influence of the uncertainty of the drilling tool slope and external interference, a robust model predictive control method is introduced in the form of an optimal control problem. A sufficient condition is obtained to calculate the corresponding controller gain matrix on the premise of satisfying the stability of the closedloop control system and the H∞ performance index. At last, the effectiveness of our method is illustrated through simulation analysis. Compared with model predictive control, robust model predictive control directly incorporates the uncertainty of the drilling tool slope and external interference into the control design, enabling it to better adapt to the complex and variable underground coal mine environment. © 2025 IEEE.

关键词： Predictive control systems

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A dulcimer keys positioning method based on improved Roberts operator

A dulcimer keys positioning method based on improved Roberts...

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2021 China automation Congress, CAC 2021

作者： Tang, De Zhang, Ziying Chen, Xin Xiao, Zhe Qin, Mengxi School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China School of Arts and Communication China University of Geosciences Wuhan430074 China

ISBN: (纸本)9781665426473

It is important for the dulcimer robot to obtain the spatial coordinates of the dulcimer phonemes. However, the traditional manual positioning method is both inefficient and does not meet the intelligence requirements of modern robot. A visual positioning method based on improved Roberts operator is proposed in this paper on account of solving these problems, which can obtain the 3D coordinates of the dulcimer keys in a short time. First, an improved Roberts edge detection operator is designed, which fully detects the edge information of the target by transforming the scale. Further, this paper proposes a dulcimer keys positioning method by fusing RGB and depth image information. This method has a better positioning effect for dulcimer keys with complex background and small size. The experimental results show that the visual positioning method proposed in this paper can effectively identify and locate the dulcimer keys, and it has better robustness to the lighting environment. © 2021 IEEE

关键词： Binocular vision

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Adaptive Sliding Mode control for Random Teleoperation System with Time-Varying Delay

Adaptive Sliding Mode Control for Random Teleoperation Syste...

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第40届中国控制会议

作者： Haitao Jian Shiqi Zheng Chenghao Zhao Haiming Li Shihao Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

This paper focuses on the control problem of a class of random teleoperation systems. To overcome the difficulties caused by the random environment, a new adaptive sliding mode control method for random teleoperation system is *** with the previous work, the model in this paper is built by random differential equations. In addition, different time-varying delays are introduced between the two communication channels. Furthermore, a new design scheme for random teleoperation system with varying-time delay is proposed. Radial Basis Function neural network（RBFNN） is introduced to deal with the unknown nonlinearities of the system. Using this method, good position tracking performance and stability can be obtained.

关键词： Adaptive control Sliding mode Teleoperation Random Lagrangian system Time-varying delay

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Design of a prediction and optimization system for plate shape in roller quenching process

Design of a prediction and optimization system for plate sha...

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第40届中国控制会议

作者： Wen Zhang Min Wu Cong Chen Sheng Du Luefeng Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Plate shape is one of the key quality indices of steel plates after *** is of great significance to realize the prediction and optimization of plate shape for obtaining high quality steel *** paper designs a prediction and optimization system for plate shape in roller quenching ***,the roller quenching process is described in detail,the design objectives are analyzed,and the architecture of the system is ***,the system is designed from four parts:the prediction model of plate shape,the comprehensive evaluation model of plate shape,the intelligent optimization model of operating parameters and the case ***,the prediction and optimization system is applied to the industrial *** results of preliminary tests show that the system improves the quality of plate shape.

关键词： Roller quenching process Plate shape prediction intelligent optimization Case matching

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