检索结果-内蒙古大学图书馆

第42届中国控制会议

作者： Feng Liu Feifan Zhou Wei Wang Gang Cai Hua O Wang Xiuqin Yang School of Automation China University of Geosciences 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 Department of Mechanical Engineering Boston University School of Education Minzu University of China

This article investigates a fractional-order coupled Hindmarsh-Rose neural networks ***,the existence and stability of an equilibrium point in the system are ***,the periodic bifurcation behavior of the system on a twoparameter plane is studied,and numerical simulations show the existence of both non-chaotic and chaotic plus periodic bifurcation behavior on the two-parameter ***,a feedback controller was designed to stabilize the bifurcation point of the delayed system and increase the stable range of the system.

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Fault diagnosis of drilling process based on multi-scale decomposition and decision fusion

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IFAC-PapersOnLine 2023年第2期56卷 8079-8084页

作者： Aoxue Yang Min Wu Wanke Yu Jie Hu Chengda Lu Yosuke Nakanishi School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China Graduate School of Environment and Energy Engineering Waseda University Tokyo 169-8555 Japan

Data-driven fault diagnosis methods have been widely applied at present. In the drilling process, there usually exists multiple failure modes resulting in the multi-scale of drilling fault data, which would bring challenges for the data-driven methods application in drilling. By considering the characteristics of multi-scale and multivariate, an original scheme based on mode decomposition and decision fusion is proposed for fault diagnosis of drilling process. Firstly, the raw data are decomposed and reconstructed into multiple groups of series. Then, for each group, the diagnosis model is established using the convolutional neural network (CNN), and several diagnostic results are obtained. Finally, all diagnostic results are fused by the Dempster-Shafer (D-S) evidence theory, and the fused result is taken as the final diagnostic result. The actual data based experiments illustrate the effectiveness of proposed method for improving the performance of drilling fault diagnosis.

关键词： Fault diagnosis drilling process multivariate variational mode decomposition decision fusion

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Large-Scale Hyperspectral Image Clustering Using Contrastive Learning

Large-Scale Hyperspectral Image Clustering Using Contrastive...

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2021 International Conference on Information and Knowledge Management Workshops, CIKMW 2021

作者： Cai, Yaoming Liu, Yan Zhang, Zijia Cai, Zhihua Liu, Xiaobo School of Computer Science 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 China

Unsupervised hyperspectral image (HSI) classification is an important but challenging task in the hyperspectral processing community. Despite great success, previous HSI clustering approaches belong to offline clustering which is often performed in a transductive scheme, thus failing to generalize to large-scale and unseen scenes. In this paper, we propose an online and deep clustering model for large-scale HSI clustering, termed spectral-spatial contrastive clustering (SSCC). Specifically, SSCC performs contrastive learning based on a series of semantic-preserving spectral-spatial augmentation to simultaneously maximize the intra-class agreement and inter-class variation, which are implemented by an instance-level contrastive loss and a cluster-level contrastive loss, respectively. The SSCC model is trained in an end-to-end fashion with minibatch, allowing it to efficiently handle large-scale HSIs. We assess the performance of SSCC on real HSI and show that SSCC significantly advances the state-of-the-art results with 8.41% improvement on accuracy. © 2021 CEUR-WS. All rights reserved.

关键词： Spectroscopy

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Finite-Time Synchronization of Flux-controlled complex-Valued Memristive Neural Networks

Finite-Time Synchronization of Flux-Controlled Complex-Value...

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Neuromorphic Computing (ICNC), International Conference on

作者： Yonghuan Wang Leimin Wang Genping Wu 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 Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China

In this paper, by using the flux-controlled memristor model, the finite-time synchronization problem of delayed complex-valued memristive neural networks (MCNNs) is studied. Firstly, according to the proposed memristor model, we model the MCNNs as continuous systems on voltage-flux-time $(\wp, \varpi, t)$ domain. Then, we design a class of approaches to realize state synchronization between the drive and response systems, and the corresponding synchronization conditions are achieved. Finally, the effectiveness of results is illustrated with the simulations.

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Temperature Compensation Method of Fluxgate Sensor Based on Polynomial Fitting

arXiv

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arXiv 2024年

作者： Yang, Ruiping Liu, Huan Ge, Jian Chugo, Daisuke Dong, Haobin 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 Faculty of Engineering Kwansai Gakuin University Osaka669-1330 Japan

Fluxgate sensors are widely used in the field of low frequency and weak vector magnetic field measurement because of their good performance, such as high resolution and low power consumption. However, during the long-term continuous observation, the drift errors of the fluxgate sensor will occur due to the variable ambient temperature. This paper proposes a temperature compensation method for fluxgate sensors based on polynomial fitting. First, a physical model of the temperature & fluxgate sensor was established on the COMSOL Multiphysics simulation platform, and the influence of temperature on the measurement performance of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature compensation model of the fluxgate sensor was constructed. And compared it with other temperature compensation method, the result shows that the proposed temperature compensation method is relatively simple and can better achieve real-time compensation for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were *** temperature drift is reduced from more than 500 nT before compensation to about 1 nT. The results show that the proposed method has a good temperature compensation effect on the data measured by the fluxgate sensor within a variable temperature background. Copyright © 2024, The Authors. All rights reserved.

关键词： Simulation platform

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Optimal consensus control for partially unknown multi-agent systems with switching communication topology*

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IFAC-PapersOnLine 2023年第2期56卷 1210-1216页

作者： Wenpeng He Xin Chen Menglin Zhang Yipu Sun Akinori Sekiguchi Jinhua She School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China School of Engineering Tokyo University of Technology Hachioji Tokyo 192-0982 Japan

Since the local neighborhood tracking error system for switching multi-agent systems (MASs) is time-varying, the existing optimal consensus method for fixed MASs cannot be used in switching MASs. This paper proposes a new approach to solve the optimal consensus problems of linear switching MASs. Firstly, a new augment local neighborhood tracking error (ALNTE) system, which is based on the joint communication topology, is developed to construct the control object, and the open-loop estimation method is used to estimate the ALNTE for individuals. Secondly, based on the ALNTE system, the performance index is designed, and the value iteration method is used to solve the coupled Hamilton-Jacobi-Bellman equations and obtain the optimal control. Finally, theoretical analysis is given to prove the learning stability and control stability of the value iteration method, and a numerical simulation is used to verify the effectiveness of the proposed method.

关键词： Switching topology multi-agent systems (MASs) optimal control value iteration augment local neighborhood tracking error (ALNTE)

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Current-Induced Magnetic Switching in an L1_(0) FePt Single Layer with Large Perpendicular Anisotropy Through Spin–Orbit Torque

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Engineering 2022年第5期8卷 55-61页

作者： Kaifeng Dong Chao Sun Laizhe Zhu Yiyi Jiao Ying Tao Xin Hu Ruofan Li Shuai Zhang Zhe Guo Shijiang Luo Xiaofei Yang Shaoping Li Long You School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of GeosciencesWuhan 430074China School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China China Resources Microelectronics Limited Shanghai 200072China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518000China Wuhan National High Magnetic Field Center Huazhong University of Science and TechnologyWuhan 430074China

In this study,current-induced partial magnetization-based switching was realized through the spin–orbit torque(SOT)in single-layer L1_(0) FePt with a perpendicular anisotropy(K_(u⊥))of 1.19×10^(7) erg·cm^(-3)(1 erg·cm^(-3)=0.1 J·m^(-3)),and its corresponding SOT efficiency(βDL)was 8×10^(-6) Oe·(A·cm^(-2))^(-1)(1 Oe=79.57747 A·m^(-1)),which is several times higher than that of the traditional Ta/CoFeB/MgO structure reported in past *** SOT in the FePt films originated from the structural inversion asymmetry in the FePt films since the dislocations and defects were inhomogeneously distributed within the ***,the FePt grown on MgO with a granular structure had a larger effective SOT field and effi-ciency than that grown on SrTiO_(3)(STO)with a continuous *** SOT efficiency was found to be considerably dependent on not only the sputtering temperature-induced chemical ordering but also the lattice mismatch-induced evolution of the *** findings can provide a useful means of efficiently electrically controlling a magnetic bit that is highly thermally stable via SOT.

关键词： L1_(0)FePt SOT Inversion asymmetry Magnetic switching Perpendicular anisotropy

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An Attention Detection System Based on Gaze Estimation Using Self-supervised Learning 8th

An Attention Detection System Based on Gaze Estimation Usin...

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8th International Workshop on advanced Computational Intelligence and intelligent Informatics, IWACIII 2023

作者： Zeng, Xiang-Yu Zhang, Bo-Yang Liu, Zhen-Tao 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

ISBN: (纸本)9789819975891

With the rapid development of online education around world, it becomes difficult for teachers to evaluate the effect of learning during the online class. To solve this problem, an attention detection system based on gaze estimation using self-supervised learning is proposed. Self-supervised learning is used to train the gaze estimation network and it achieved an average gaze angle error of 6. 5∘ on the MPIIFaceGaze dataset. Cross dataset testing was performed by 5 test subjects in the RT-GENE dataset and all samples in the Columbia dataset, in which achieved average gaze angle error 13. 8∘ and 5. 5∘. It is found that the network has good cross dataset generalization performance, but it is susceptible to factors such as feature occlusion and low image clarity. Then, we developed an attention detection system which has functions of unmanned detection, eye state detection, and gaze detection. Online classroom experiments were conducted to verify the effectiveness of the proposed method, in which the average accuracy of the detection of distraction is around 60%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Supervised learning

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Disturbance Observer-Based control of Weight on Bit in Drilling Process with Formation Variation

Disturbance Observer-Based Control of Weight on Bit in Drill...

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Asian control Conference

作者： Shipeng Chen Wangnian Li Chengda Lu Yang Zhou Weihua Cao 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 CCTEG Xi'an Research Institute (Group) Co. Ltd. Xi'an China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In deep geological drilling, encountering various formations is inevitable. Formation changes can lead to fluc-tuations in the weight on bit due to the bit-rock interaction. This paper proposes a disturbance observer-based weight on bit control system to mitigate the adverse effects of formation changes on system performance. First, an axis-torsion coupled dynamic model is established, and the relationship between the two dimensions is analyzed. The validity of the model is confirmed through comparison with field data collected from a geothermal well drilling operation. Then, a disturbance observer is designed to estimate and compensate for the disturbance at the control input, thereby reducing weight on bit fluctuations caused by formation variations. Simulation results considering formation variation during drilling verify the effectiveness of the proposed method.

关键词： Drilling Simulation System performance Geology Process control control systems Disturbance observers

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Cascade conditional generative adversarial nets for spatial-spectral hyperspectral sample generation

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Science China(Information Sciences) 2020年第4期63卷 81-96页

作者： Xiaobo LIU Yulin QIAO Yonghua XIONG Zhihua CAI Peng LIU School of Automation China University of Geosciences (Wuhan) Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Computer Science China University of Geosciences (Wuhan) The Beibu Gulf Big Data Resources Utilization Laboratory Beibu Gulf University

Sample generation is an effective way to solve the problem of the insufficiency of training data for hyperspectral image classification. The generative adversarial network（GAN） is one of the popular deep learning methods, which utilizes adversarial training to generate the region of samples based on the required class label. In this paper, we propose cascade conditional generative adversarial nets for hyperspectral image complete spatial-spectral sample generation, named C;GAN. The C;GAN includes two stages. The stageone model consists of the spatial information generation with a window size that entails feeding random noise and the required class label. The second stage is the spatial-spectral information generation that generates spectral information of all bands in the spatial region by feeding the label regions. The visualization and verification of generated samples based on the Pavia University and Salinas datasets show superior performance, which demonstrates that our method is useful for hyperspectral image classification.

关键词： hyperspectral image sample generation generative adversarial nets(GAN) deep learning spatial-spectral information

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