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arXiv 2021年

作者： Cai, Yaoming Zhang, Zijia Liu, Yan Ghamisi, Pedram Li, Kun Liu, Xiaobo Cai, Zhihua School of Computer Science China University of Geosciences Wuhan430074 China Helmholtz Institute Freiberg for Resource Technology Freiberg09599 Germany Vienna1030 Austria College of Electronic Science and Technology National University of Defense Technology Changsha410073 China 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

Clustering of hyperspectral images is a fundamental but challenging task. The recent development of hyperspectral image clustering has evolved from shallow models to deep and achieved promising results in many benchmark datasets. However, their poor scalability, robustness, and generalization ability, mainly resulting from their offline clustering scenarios, greatly limit their application to large-scale hyperspectral data. To circumvent these problems, we present a scalable deep online clustering model, named Spectral-Spatial Contrastive Clustering (SSCC), based on self-supervised learning. Specifically, we exploit a symmetric twin neural network comprised of a projection head with a dimensionality of the cluster number to conduct dual contrastive learning from a spectral-spatial augmentation pool. We define the objective function by implicitly encouraging withincluster similarity and reducing between-cluster redundancy. The resulting approach is trained in an end-to-end fashion by batch-wise optimization, making it robust in large-scale data and resulting in good generalization ability for unseen data. Extensive experiments on three hyperspectral image benchmarks demonstrate the effectiveness of our approach and show that we advance the state-of-the-art approaches by large margins. Copyright © 2021, The Authors. All rights reserved.

关键词： Spectroscopy

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Voltage Sensitivity of Tag Antennas for Inductively Coupled RFID Systems

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Journal of Physics: Conference Series 2021年第1期1738卷

作者： Cheng Jin Jixuan Zhu School of Automation China University of Geosciences (Wuhan) Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China

This paper describes a parameter of voltage sensitivity to recognize the performance differences of tag antennas for inductively coupled RFID systems. Based on the equivalent circuit model of the RFID tag and reader, an expression for the voltage sensitivity is educed. Then, the design steps of measuring platform to obtain the voltage sensitivity of tag antenna are introduced in detail. Finally, the feasibility of the proposed method was verified with the market available tag antennas through the measuring platform.

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Adaptive control for Fractional-order Interconnected Systems

Adaptive Control for Fractional-order Interconnected Systems

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第三十八届中国控制会议

作者： Bingyun Liang Shiqi Zheng Zichao Yang Feng Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems

This paper focuses on a class of fractional-order interconnected system. A decentralized adaptive controller is designed based on the fractional Lyapunov direct method. In order to solve the interactions between subsystems, a compensation term is presented. In addition, the various uncertainties, such as unknown parameters, disturbances and nonlinear functions, are considered and estimated by the proposed fractional-order update laws. Especially, neural networks are adopted to approximate the unknown nonlinear functions. Furthermore, a fractional-order auxiliary system is constructed to deal with the input nonlinearities with backlash and saturation. Finally, the effectiveness of the proposed control scheme is verified by some simulation examples.

关键词： Adaptive control fractional-order system interconnected system input nonlinearity neural networks

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A Computer-Aided Tuning Method for Microwave Filters by Combing T-S Fuzzy Neural Networks and Improved Space Mapping

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Computer Modeling in Engineering & Sciences 2018年第9期116卷 433-453页

作者： Shengbiao Wu Weihua Cao Can Liu Min Wu School of Automation China University of GeoscienceWuhanHubei430074China Nanchang Institute of Science and Technology.Nanchang Jiangxi330100China The Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems WuhanHubei430074China

A computer-aided tuning method that combines T-S fuzzy neural network(TS FNN)and offers improved space mapping(SM)is presented in this *** method consists of three main ***,the coupling matrix is effectively extracted under the influence of phase shift and cavity loss after the initial ***,the surrogate model is realized by using a T-S FNN based on subspace ***,the mapping relationship between the actual and the surrogate models is established by the improved space mapping algorithm,and the optimal position of the tuning screws are found by updating the input and output parameters of the surrogate ***,the effectiveness of different methods is verified by an experiment with a nine order cross coupled *** results show that,compared to a back propagation neural network method based on electromagnetic simulation and an SM method based on a least squares support vector machine,the proposed method has obvious advantages in terms of tuning accuracy and tuning time.

关键词： Computer-aided tuning T-S FNN S-parameters coupling matrix

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A Modular Magneto-Inductive Sensor for Low Vector Magnetic Field Measurements

arXiv

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arXiv 2020年

作者： Liu, Huan Wang, Xiaobin Zhao, Changfeng Wang, Zehua Jian, Ge Dong, Haobin Liu, Zheng School of Automation China University of Geosciences Hubei Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Hubei Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Wuhan430074 China School of Engineering University of British Columbia Okanagan Campus KelownaBCV1V 1V7 Canada

The low magnetic field measurement has been utilized since ancient times in order to find economic resources, to detect magnetic anomalies, etc. In this case, the vector magnetic survey can simultaneously obtain the modulus and direction information of the magnetic field, which can contribute to obtaining more precise information and characteristics of magnetic field resources. This paper is concerned with the potential to exploit the signals of vector magnetic field measurement with a magneto-inductive (MI) sensor. To evaluate the capability of the MI sensor, a test platform is set up and its performance including the noise floor, the resolution, the sensitivity, etc., are comprehensively characterized. Further, a comparative geomagnetic observation and magnetic anomaly detection among the proposed MI sensor, a high-precision Overhauser sensor, and a commonly used and accepted commercial MI sensor are conducted. The experimental results identify the capability of the proposed MI sensor in weak magnetic detection. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic fields

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A novel online learning RECOC-KFDA method for lithologic identification in drilling process*

A novel online learning RECOC-KFDA method for lithologic ide...

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Asian control Conference

作者： Xi Chen Weihua Cao Chao Gan Min Wu School of Automation China University of Geosciences and Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781728102634

Aiming to achieve a safe and efficient drilling, this paper is concerned with identification of formation lithology, which provides critical information for drilling control. Notice that it is hard to make accurate geological prediction using conventional identification approaches, due to the data characteristics of imbalanced, multi-classification and low value density, a novel reduction error correcting output code kernel fisher discriminant analysis algorithm(RECOC-KFDA) method is developed in an online manner. It consider design optimal error correcting output code(ECOC) matrix based on a reduction algorithm, and it proposed an online method to reduce the computation complexity required for updating the kernel fisher discriminant analysis(KFDA) classifiers rather than recalibrating them. Proposed method has been applied to lithologic identification in drilling site. Simulations and comparisons demonstrate that our method is superior to the existing ones for both offline training and online prediction model.

关键词： Encoding Computational modeling Kernel Geology Drilling machines Data models Diversity reception

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CNN-based Broad Learning with Efficient Incremental Reconstruction Model for Facial Emotion Recognition ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10236-10241页

作者： Luefeng Chen Min Li Xuzhi Lai Kaoru Hirota Witold Pedrycz 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 Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

Convolutional neural network-based broad learning with efficient incremental reconstruction model (CNNBL) is proposed to recognize emotions in human-robot interaction. It aims to extract deep and abstract features from facial emotional images, and reduce the influence of the complex structure and slow network updates on facial emotion recognition in deep learning. Feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotion recognition. When the network needs to expand, the network is dynamically updated by incremental learning algorithm. We verified the experimental performance through k -fold cross validation. According to the recognition results, the accuracy on JAFFE database of our proposal is greater than that of the state of the art, such as the Local Binary Patterns with Softmax and Deep Attentive Multi-path convolutional neural network.

关键词： Convolution Neural Networks Broad Learning Emotion Recognition Human-Robot Interaction

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Signal discrimination with wavelet algorithm and Pearson correlation-based algorithm in φ-OTDR systems

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Journal of Physics: Conference Series 2021年第1期1738卷

作者： Dai Li Mo Zhi Hua Yu Yun Fei Cai Rui Liu School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan 430074 China

This paper presents a novel method of distinguishing signal, the way expected to reduce the nuisance alarm rate since the high nuisance alarm rate will restrict the capability of phase-sensitive optical time-domain reflection technology. The proposed method includes two parts: wavelet positioning mutation to obtain the perturbation area and Pearson correlation algorithm to directly convert the intensity of the perturbation into a useful amplitude. This technique avoids the use of irrelevant data in these differential signals and provides a simple and feasible new approach for distinguishing signal and optimizing the positioning speed of φ-OTDR systems.

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Design of intelligent Drilling System Software Framework and Data Architecture Based on MVC Pattern

Design of Intelligent Drilling System Software Framework and...

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第三十八届中国控制会议

作者： Peilun Ouyang Weihua Cao Min Wu Chao Gan Fawen Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Hubei Institute of Urban Geological Engineering

Drilling system software is crucial for the complex geological drilling process due to its vital role in improving the drilling efficiency and safety. In this paper, a Model-View-controller(MVC) pattern-based software framework and data architecture of an intelligent drilling system are proposed. First, four main functional requirements, namely, data fusion and visualization, safety assessment and efficiency calculation, efficiency and safety optimization, and coordination control have been analyzed. After that, a four-layers is established based on the above analysis and MVC pattern. Finally, a new online data architecture has been presented at the end of this paper. The proposed software framework and data architecture form a basis for the monitoring of the drilling process.

关键词： Model-View-controller intelligent Drilling Software Framework Data Architecture

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Two-DOF Speed control of Permanent Magnet Synchronous Machine with a novel Parameter Identification Method

Two-DOF Speed Control of Permanent Magnet Synchronous Machin...

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第三十九届中国控制会议

作者： Mingjin Hu Wei Hua Xudong Wu Chengdong Wu Dazhi Wang Zhentao Liu Xiaofang Yuan Wennong Zhang School of Electrical Engineering Southeast University Faculty of Robot Science and Engineering Northeastern University College of Information Science and Engineering Northeastern University Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems College of Electrical and Information Engineering Hunan University

In this paper, a two-degree-of-freedom（2-DOF） speed control scheme for permanent magnet synchronous machines is developed. Both the reference-tracking and disturbance rejection performance are taken into account and only a single parameter related to expected bandwidth determines the dynamics. To improve the performance of speed control, a novel identification method for mechanical parameters is also proposed. The effectiveness of the proposed method is verified by simulations.

关键词： Speed control two-degree-of-freedom permanent magnet synchronous machine parameter identification

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