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arXiv 2020年

作者： Liang, Chang-Duo Ge, Ming-Feng Liu, Zhi-Wei Wang, Yan-Wu Karimi, Hamid Reza School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical Engineering Politecnico di Milano Milan20156 Italy

In this paper, we present a new terminal sliding mode control to achieve predefined-time stability of robot manipulators. The proposed control is developed based on a novel predefined-time terminal sliding mode (PTSM) surface, on which the states are forced to reach the origin in a predefined time, i.e., the settling time is independent to the initial condition and can be explicitly user-defined via adjusting some specific parameters called the predefined-time parameters. It is also demonstrated that the proposed control can provide satisfactory steady-state performance in the case of both external disturbances and parametric uncertainties. Besides, we present a formal systemic analysis method to derive the sufficient conditions for guaranteeing the predefined-time convergence of the closed-loop system. Finally, the effectiveness and performance of the presented control scheme are illustrated through both theoretical comparisons and numerical simulations. Copyright © 2020, The Authors. All rights reserved.

关键词： Sliding mode control

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Finite-size scaling of geometric renormalization flows in complex networks

arXiv

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arXiv 2020年

作者： Chen, Dan Su, Housheng Wang, Xiaofan Pan, Gui-Jun Chen, Guanrong School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China Department of Automation Shanghai University Shanghai200072 China Faculty of Physics and Electronic Science Hubei University Wuhan430062 China Department of Electrical Engineering City University of Hong Kong Hong Kong Hong Kong

Recently, the concept of geometric renormalization group provides a good approach for studying the structural symmetry and functional invariance of complex networks. Along this line, we systematically investigate the finite-size scaling of structural and dynamical observables in geometric renormalization flows of synthetic and real evolutionary networks. Our results show that these observables can be well characterized by a certain scaling function. Specifically, we show that the critical exponent implied by the scaling function is independent of these observables but only depends on the small-world properties of the network, namely, all networks located in the small-world phase have a uniform scaling exponent, while those located in the non-small-world phase and in their critical regions have another uniform scaling. More importantly, we perform extensive experiments on real evolutionary networks with small-world characteristics, and our results show that these observables also have uniform scaling in their geometric renormalization flows. Therefore, in a sense this exponent can be used as an effective measure for classifying universal small-world and non-small-world network models. © 2020, CC BY-SA.

关键词： Geometry

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control-oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

Control-oriented fault detection of solid oxide fuel cell sy...

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作者： Wu, Xiao-long Xu, Yuan-wu Xue, Tao Shuai, Junchao Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Wuhan Industrial Economic Development Research Center Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

As the solid oxide fuel cell (SOFC) system work environment is a high-temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer-based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault. © 2018 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Fault detection

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Development of an MEMS based biomimetic whisker sensor for tactile sensing

Development of an MEMS based biomimetic whisker sensor for t...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Zihou Wei Qing Shi Chang Li Shurui Yan Guanglu Jia Zhigang Zeng Qiang Huang Toshio Fukuda Key Laboratory of Biomimetic Robots and Systems Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Ministry of Education Beijing China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control the School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728150734

ISBN: (纸本)9781728150741

It has been demonstrated that biological whisker sensing system has excellent capacity to achieve the pattern recognition, and whisker sensor based robot tactile perception has caused widespread concern. However, most of current existed whisker sensors are too large in size, and are difficult to be integrated into a small robot. In this paper, we proposed a MEMS based biomimetic whisker sensor inspired by rats, which integrates four sensing units on a square silicon wafer with a side length of 6.8mm and makes it possible to be installed on a small robot with similar size to a real rat. Our sensor measure the bending moment at the root of the whisker based on the piezoresistive effect. We have explored the range of functions that whisker arrays can serve on and it is shown that our whisker sensor can determine obstacle distance, and is also capable of gathering surface information including shape and texture.

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DAmageNet: A universal adversarial dataset

arXiv

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arXiv 2019年

作者： Chen, Sizhe Huang, Xiaolin He, Zhengbao Sun, Chengjin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China

It is now well known that deep neural networks (DNNs) are vulnerable to adversarial attack. Adversarial samples are similar to the clean ones, but are able to cheat the attacked DNN to produce incorrect predictions in high confidence. But most of the existing adversarial attacks have high success rate only when the information of the attacked DNN is well-known or could be estimated by massive queries. A promising way is to generate adversarial samples with high transferability. By this way, we generate 96020 transferable adversarial samples from original ones in imageNet. The average difference, measured by root means squared deviation, is only around 3.8 on average. However, the adversarial samples are misclassified by various models with an error rate up to 90%. Since the images are generated independently with the attacked DNNs, this is essentially zero-query adversarial attack. We call the dataset DAmageNet, which is the first universal adversarial dataset that beats many models trained in imageNet. By finding the drawbacks, DAmageNet could serve as a benchmark to study and improve robustness of DNNs. DAmageNet could be downloaded in http://***/Show/56/122 Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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A layered parallel algorithm for data clustering via fuzzy c-means technique

Research Square

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Research Square 2021年

作者： Mu, Yashuang Wei, Wei Guo, Hongyue Sun, Lijun Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou450001 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China School of Maritime Economics and Management Dalian Maritime University Dalian116026 China Collaborative Innovation Center for Transport Studies Dalian Maritime University Dalian116026 China

In this study, a layered parallel algorithm via fuzzy c-means (FCM) technique, called LP-FCM, is proposed in the framework of Map-Reduce for data clustering problems. The LP-FCM mainly contains three layers. The first layer follows a parallel data partitioning method which is developed to randomly divide the original dataset into several subdatasets. The second layer uses a parallel cluster centers searching method based on Map-Reduce, where the classic FCM algorithm is applied to search the cluster centers for each subdataset in Map phases, and all the centers are gathered to the Reduce phase for the confirmation of the final cluster centers through FCM technique again. The third layer implements a parallel data clustering method based on the final cluster centers. After comparing with some famous classic random initialization sequential clustering algorithms which include K-means, K-medoids, FCM and MinMaxK-means on 20 benchmark datasets, the feasibility in terms of clustering accuracy is evaluated. Furthermore, the clustering time and the parallel performance are also tested on some generated large-scale datasets for the parallelism. © 2021, CC BY.

关键词： K-means clustering

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Metric Learning Based Rolling Bearing Faults Diagnosis with Curvelet Transform

Metric Learning Based Rolling Bearing Faults Diagnosis with ...

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2019 3rd International Conference on Machine Vision and Information Technology (CMVIT 2019)

作者： Ziming Lu Jiang-Wen Xiao Zhengyi Huang School of Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control(Huazhong University of Science and Technology) Ministry of Education

Rolling bearing faults are among the primary causes of breakdown in mechanical equipment. Aiming at the vibration signals of rolling bearing which are non-stationary and easy to be disturbed by noise, a novel fault diagnosis method based on curvelet transform and metric learning is proposed. This method consists of 3 parts. The first one is feature engineering which includes reshaping the original timing features of rolling bearings, employing curvelet transform to transform reshaped features and making its coefficients as the new features. Curvelet transform can analyse the original signal from many angles. The second one is employing metric learning to map these new features into special embedding space. The last one is applying KNN classifier to detect the rolling bearing faults. Metric learning can effectively improve the performance of KNN by learning a mapping matrix to modify the distribution of samples. The proposed method overcomes the problems such as the subjectivity and blindness of manual feature extraction, poor coupling in each stage and sensitive to the effect of noise. Extensive simulations based on several data-sets show that the our method has better performance on bearing fault diagnosis than traditional methods.

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Existence theorem of finite krasner hyperfields

arXiv

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arXiv 2021年

作者： Tsafack, Surdive Atamewoue Amassayoga, Ogadoa Onasanya, Babatunde O. Feng, Yuming School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Chongqing404100 China Department of Mathematics Higher Teacher Training College University of Yaounde 1 Cameroon Department of Mathematics Faculty of Science University of Yaounde 1 Cameroon Key Laboratory of Intelligent Information Processing and Control Chongqing Three China Gorges University Chongqing404100 China Department of Mathematics Uni-versity of Ibadan Nigeria School of Computer Science and Engineering Chongqing Three Gorges University China

关键词： Algebra

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Residential virtual power plant with photovoltaic output forecasting and demand response

Residential virtual power plant with photovoltaic output for...

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作者： Cui, Shichang Wang, Yan-Wu Lin, Xiangning Xiao, Jiang-Wen School of Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China Hubei Provincial Collaborative Innovation Center for New Energy Microgrid China Three Gorges University Yichang China State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan China

Decreasing conventional power supply is promoting the development of the distributed renewable energy sources, such as solar power and wind power. Recently rooftop photovoltaic has been widely applied, and accordingly efficient energy management is getting increasingly important for fully use of renewable energy and the peak shaving of the main grid. This paper investigates the residential energy management as a small-scale virtual power plant (VPP) connected to the main grid includes distributed energy resources, energy storages and residential loads. The self-organizing map (SOM) and the radical basis function (RBF) networks are adopted to classify the weather types and predict hourly photovoltaic output precisely. In a time-of-use electricity market, price-based demand response is applied to adjust the demand. The residential VPP has two goals: maximum profit by selling surplus power to grid and minimum power purchased from grid. The two goals are integrated as an optimization object by introducing a weight parameter. The algorithm combining receding horizon optimization and linear programming is proposed to solve the optimization problem in residential VPP. Numerical simulation tests can help to find the most suitable value of the weight parameter. Different scenarios are simulated and discussed to demonstrate the performance of the VPP and the proposed algorithm. © 2019 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Energy management

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A protocol for structured illumination microscopy with minimal reconstruction artifacts

引用

生物物理学报 2019年第2期5卷 80-90页

作者： Junchao Fan Xiaoshuai Huang Liuju Li Shan Tan Liangyi Chen Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of China School of Automation Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Membrane Biology Beijing Key Laboratory of Cardiometabolic Molecular MedicineInstitute of Molecular Medicine Peking University Beijing 100084 China

The imaging rate of structured illumination microscopy (SIM) reached 188 Hz *** the exposure time decreases,the camera detects fewer virtual photons,while the noise level remains the *** a result,the signal-to-noise ratio (SNR) decreases ***,the SNR decreases further because of photobleaching and *** decreased quality of SIM raw data may lead to surprising artifacts with various causes,which may confuse a new user of SIM *** summarize three significant possible sources of severe artifacts in reconstructed super-resolution (SR) *** motion of a biological sample or an uneven illumination pattern is the most difficult to be *** estimated parameter could also be incorrect,leading to artifact of regular ***,reconstruction with the Wiener method generates stochastic artifacts due to the amplification of noise during the deconvolution *** deal with these problems,we have established a protocol to reconstruct ultrafast SIM raw data obtained in low SNR ***,we checked the quality of the raw data with the imageJ plugin SIMcheck before ***,a modified parameter estimation method was used to improve the precision of the ***,an iterative algorithm was used for SIM reconstruction under low signal-to-noise ratio *** procedure effectively suppressed the artifacts in the super-resolution images reconstructed from raw data of low signal-to-noise ratio.

关键词： Ultrafast SIM Low signal-to-noise ratio Reconstruction

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