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Chinese automation Congress (CAC)

作者： Chenghao Zhao Shiqi Zheng Haiming Li Haitao Jian Shihao Wang School of Automation China University of Geosciences Wuhan Campus Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems Wuhan China

ISBN: (纸本)9781665426480

This paper studies the mobile robots with multiple constraints based on path planning of A-star algorithm. A hierarchical adaptive control method is presented to handle multiple constaints. On the upper layer, a Astar path planning algorithm is designed. On the lower layer, a new controller is provided. Then, we design barrier Lyapunov function and adaptive RBF neural network control method to make sure that tracking error and velocity error converges to the specified set within a finite amount of time. Compared with other existing works, this system considers multiple consraints include tracking error, constraints velocity constraints and output saturation constraint, provides guarantee for the safe operation of the mobile robot. In addition, because of the unknown conditions on the ground, system also considers the impact of slipping. Computer simulation result demonstrates the effectiveness of control method. In the experimental verification, we use A-star algorithm to generate the appropriate path as the desired trajectory of the mobile robot. The experiment proved the stability of control method.

关键词： automation Computer simulation Neural networks Approximation algorithms Stability analysis Trajectory Planning

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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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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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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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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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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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Optimization of the Landslide Susceptibility Model based on Deep Forest and Twice-Sample

Optimization of the Landslide Susceptibility Model based on ...

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Chinese automation Congress (CAC)

作者： Yuelin Yang Wanke Yu Zepeng Liu Lingran Zhao Chengda Lu Min Wu Ruiqing Niu 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 Institute of Geophysics and Geomatics China University of Geosciences Wuhan China

In the analysis of regional landslide susceptibility, the geological data used usually have the characteristics of multiplicity and nonlinearity. At the same time, in the past researches, the selection of non-landslide units in the training set is usually random or subjective, and there is no guarantee sample accuracy. In this paper, the deep forest algorithm combined with the twice-sample method is used to evaluate the landslide susceptibility in the Zigui Badong area of the Three Gorges Reservoir area. First, 11 landslide impact factors were selected according to the geological data to initially construct a sample set, and the cleaned data set was obtained by twice-sample. After dividing the training set and the test set, the deep forest model was used to predict the landslide susceptibility and evaluate the accuracy. The results show that the AUC of the susceptibility prediction model built using the deep forest is 1%-10% higher than that of GBDT, MLP and other methods, while the Twice-Sample method improves the AUC by 2%.

关键词： Training Geology Predictive models Feature extraction Reservoirs Prediction algorithms Terrain factors

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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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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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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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