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Application of the Dynamic Iterative Learning control to the Heteroplanar Active Magnetic Bearing

IFAC-PapersOnLine 2020年第2期53卷 1511-1516页

作者： Lukasz Hladowski Arkadiusz Mystkowski Krzysztof Galkowski Eric Rogers Bing Chu Institute of Control and Computation Engineering University of Zielona Góra ul. Podgórna 50 65-246 Zielona Góra Poland Faculty of Electrical Engineering Bialystok University of Technology ul.Wiejska 45c 15-351 Bialystok Poland School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK

Heteroplanar active magnetic bearings have numerous applications, where one example is a high-temperature gas-cooled reactors. Rotor imbalance, however, may cause problems for critical parts of the system in the form of repetitive periodic vibrations. This is known problem and periodic component extraction is widely used in active magnetic bearing unbalance control laws. More recently, iterative learning control has been considered as an alternative and this paper gives new results on this approach. In particular, a new control law in the 2D systems setting is developed and the results of a simulation based study using the model of a test rig are given, where such a study is an essential step prior to experimental validation.

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A Non-Stop Double Buffering Mechanism for Dataflow Architecture

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Journal of computer science & Technology 2018年第1期33卷 145-157页

作者： Xu Tan Xiao-Wei Shen Xiao-Chun Ye Da Wang Dong-Rui Fan Lunkai Zhang Wen-Ming Li Zhi-Min Zhang Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

Double buffering is an effective mechanism to hide the latency of data transfers between on-chip and off-chip memory. However, in dataflow architecture, the swapping of two buffers during the execution of many tiles decreases the performance because of repetitive filling and draining of the dataflow accelerator. In this work, we propose a non-stop double buffering mechanism for dataflow architecture. The proposed non-stop mechanism assigns tiles to the processing element array without stopping the execution of processing elements through optimizing control logic in dataflow architecture. Moreover, we propose a work-flow program to cooperate with the non-stop double buffering mechanism. After optimizations both on control logic and on work-flow program, the filling and draining of the array needs to be done only once across the execution of all tiles belonging to the same dataflow graph. Experimental results show that the proposed double buffering mechanism for dataftow architecture achieves a 16.2% average efficiency improvement over that without the optimization.

关键词： non-stop double buffering dataflow architecture high-performance computing

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Verification of AA-Diagnosability in Probabilistic Finite Automata is PSPACE-Hard

Verification of AA-Diagnosability in Probabilistic Finite Au...

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IEEE Annual Conference on Decision and control

作者： Christoforos Keroglou Christoforos N. Hadjicostis Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

In this paper we consider the complexity of verifying the property of AA-diagnosability in probabilistic finite automata and establish that AA-diagnosability is, in general, a PSPACE-hard problem. In deterministic and nondeterministic finite automata, the property of diagnosability captures our ability to determine, based on our observation of the activity in a given finite automaton, the occurrence of any fault event, at least if we wait for at most a finite number of events (following the occurrence of the unobservable fault event). In stochastic settings where the underlying system is a probabilistic finite automaton under partial observation, there is not a prevalent notion of diagnosability and many variations have been proposed, including A-diagnosability and AA-diagnosability. Earlier work has shown that the verification of A-diagnosability (also referred to as strong stochastic diagnosability) for a given probabilistic finite automaton is a PSPACE-hard problem. In this paper, we establish that the verification of AA-diagnosability (also referred to as stochastic diagnosability) is a PSPACE-hard problem.

关键词： Automata Hidden Markov models Probabilistic logic Markov processes Complexity theory Fault diagnosis

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A Markov Traffic Model for Signalized Traffic Networks Based on Bayesian Estimation

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IFAC-PapersOnLine 2020年第2期53卷 15029-15034页

作者： S.Y. Liu S. Lin Y.B. Wang B. De Schutter W.H.K. Lam School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100089 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences College of Civil Engineering and Architecture Zhejiang University PR China Delft Center for Systems and Control Delft University of Technology The Netherlands Department of Civil and Structural Engineering The Hong Kong Polytechnic University Hong Kong SAR China

In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion levels of the link. Each link can only be in one of the four link state modes at any time, and the transition probability from one state to the other state is estimated by Bayesian estimation based on the distributions of the dynamic traffic flow densities, and the posterior probabilities. Therefore, we use a first-order Markov Chain Model to describe the dynamics of the traffic flow evolution process. We illustrate our approach for a small traffic network. Compared with the data from the microscopic traffic simulator SUMO, the proposed model can estimate the link traffic densities accurately and can give a reliable estimation of the uncertainties in the dynamic process of signalized traffic networks.

关键词： Markov traffic model Traffic signals Bayesian Urban traffic network

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A Green Paradigm for Internet of Things: Ambient Backscatter Communications

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China Communications 2019年第7期16卷 109-119页

作者： Wei Zhang Yao Qin Wenjing Zhao Minzheng Jia Qiang Liu Ruisi He Bo Ai Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology and also with Institute of Telecommunication Satellites China Academy of Space Technology (CAST) Beijing Key Lab of Transportation Data Analysis and Mining Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Department of Information Engineering Beijing Polytechnic College Beijing 100042 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing 100044 China

Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT generally faces the challenges from energy constraint and implementation cost. In this paper, we will introduce a new green communication paradigm, the ambient backscatter (AmBC), that could utilize the environmental wireless signals for both powering a tiny-cost device and backscattering the information symbols. Specifically, we will present the basic principles of AmBC, analyze its features and advantages, suggest its open problems, and predict its potential applications for our future IoT.

关键词： ambient backscatter (AmBC) RF-powered device Internet of Things (IoT) battery-free tag wire-less sensor

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Convolutional Neural Network applied in mime speech recognition using sEMG data

Convolutional Neural Network applied in mime speech recognit...

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Chinese Automation Congress (CAC)

作者： Qing Ai Wei Zhang Bixuan Zhang Guang Li Meng Yang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Computer Science and Technology China University of Mining and Technology Beijing China

Decoding speaking intention from the cervical and facial muscle activity enables speech recognition independent of acoustic signals, thus allowing silent communication between human and computing devices. This research aims to design and optimize the convolutional network classifier for sEMG mime speech signals. The muscles involved in the vocalization and timbre modulation are found according to the anatomical map. Six-channel signals have been collected from the corresponding position using the sEMG signal acquisition device. The original signals are subjected to pre-processing, including noise reduction, active segment detection and interpolation, to form training and testing sets. Convolutional network models are applied to Figure out the influence of structural parameters, such as convolution kernel size, the number of convolution kernels and the depth of network, on the recognition accuracy. Based on repeated trials, the optimal convolution network is provided, which provide above 80% accuracy rate.

关键词： Speech recognition Convolution Electromyography Task analysis Training Kernel Interference

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Spectroscopic detection of pigments in tissues: correlation with tissue aging and cancer development

Spectroscopic detection of pigments in tissues: correlation ...

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International Conference on Laser Optics

作者： L. M. Oliveira T. M. Gonç alves A. R. Botelho I. S. Martins H. F. Silva I. Carneiro S. Carvalho R. Henrique V. V. Tuchin ISEP Center of Innovation in Engineering and Industrial Technology Porto Portugal Physics Department Polytechnic of Porto - School of Engineering (ISEP) Porto Portugal Department of Electrical and Computer Engineering Porto University - School of Engineering Porto Portugal Department of Pathology and Cancer Biology and Epigenetics Group Portuguese Oncology Institute of Porto Porto Portugal Dept. of Pathological Cytological and Thanatological Anatomy Polytechnic of Porto - School of Health (ESS) Porto Portugal Deptartment of Pathology Santa Luzia Hospital (ULSAM) Viana do Castelo Portugal Dept. of Pat hol. and Molecular Immunology Porto University - Institute of Biomedical Sciences Abel Salazar Porto Portugal Science Medical Center Saratov State University Saratov Russia Laboratory of Laser Molecular Imaging and Machine Learning National Research Tomsk State University Tomsk Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control FRC &#x2018 Saratov Scientific Centre&#x2019 of the Russian Academy of Sciences Saratov Russia

ISBN: (数字)9781665466646

ISBN: (纸本)9781665466653

The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melanin content is explained by the neuron degeneration process. Similar melanin and lipofuscin contents were found in the rabbit pancreas as a result of the tissue aging process. The conversion of 83 % of the melanin in the human normal kidney into lipofuscin in the cancer kidney indicates that lipofuscin can be considered a kidney cancer marker in humans.

关键词： Rabbits Absorption Neurons Lasers Aging Pigments Optics

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Silent Speech Recognition Based on Surface Electromyography

Silent Speech Recognition Based on Surface Electromyography

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Chinese Automation Congress (CAC)

作者： Siyuan Ma Dantong Jin Ming Zhang Bixuan Zhang You Wang Guang Li Meng Yang Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Department of Computer Science and Technology China University of Mining and Technology Beijing China

Silent speech recognition (SSR) can decode the activities of electromyography (EMG) from surface of articulatory muscles to the according brain information. As the essentials in SSR, feature extraction and recognition are directly related to the final decoding effects, which requires careful research, especially on small datasets. This paper collected the surface EMG (sEMG) of 10 isolated Chinese words by 6 surface electrodes on face and around, and based on which sEMG features were extracted by wavelet packet transform, principal component analysis and XGBoost (an implementation of gradient boosted decision trees) respectively. Then Support Vector Machine, K-Nearest Neighbor and Random Forest were explored for recognition on the 10 words. The results indicated that the highest accuracy of 72% was achieved by the method of Random Forest based on XGBoost.

关键词： Feature extraction Principal component analysis Support vector machines Training Speech recognition Electromyography Forestry

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Molecular Diagnostic and Using Deep Learning Techniques for Predict Functional Recovery of Patients Treated of Cardiovascular Disease

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IEEE Access 2019年 7卷 120315-120325页

作者： Ar Junejo Yin Shen Asif Ali Laghari Xiaobo Zhang Hao Luo School of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Computer Science Sindh Madressatul Islam University Karachi Pakistan School of Automation Guandong University of Technology Guangzhou China

Today, with the development of industry and mechanized life style, the prevalence of the disease is rising steadily as well. Observing at the trend and lifecycle style, its predict that after ten years around 23.6 million people die because of Cardiovascular Disease (CVD). For that reason, aim to use Deep Learning Techniques (DLTs), to analysis stable CVD that would give valuable awareness to decrease misdiagnosis in the Robust Healthcare Industry (RHI). An objective of this paper is first, Molecular diagnosis (MD), and second using Deep Learning Techniques DLTs, to synthesis and characterize to accumulate (raw information) from CVD patients, those who admitted the emergency section between January (2018 to December 2019). We are using Artificial Neural Network (ANN), model characterize to predict CVD patients and configuration, Feature selection (FS), Mean Square Error (MSE), accuracy, sensitivity. The ANN accuracy is 98.4, K-nearest neighbor (KNN) accuracy is 98.01%, Naïve Bayes (NB), accuracy is 96.99%. Decision tree (DT), accuracy is 87.81%. Our robust data driven model explore the efficient accuracy rate to predict CVD patients. The ANN model in term of their efficient in disease analysis, and prognosis of the RHI.

关键词： Heart Blood Diseases Arteries Mathematical model Artificial neural networks

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Review of renewable energy curtailment in China: Current situation and solutions

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AIP Conference Proceedings 2022年第1期2681卷

作者： Yingai Jin Yiwen Zhang Nan Jiang Mingjie Lv Hang Yin Wenyuan Sun Liang Li 1State Key Laboratory of Automotive Simulation and Control Jilin University Changchun 130025 China 2College of Automotive Engineering Jilin University Changchun 130025 China 3School of Physics Engineering and Computer Science University of Hertfordshire Hatfield AL10 9AB UK

In recent decades, China's renewable power generation has developed quickly. However, the randomness of output power causes wind and photovoltaic power curtailment. With the rapid development of renewable energy, renewable energy consumption has gradually become the focus of research. This article comprehensively reviews the current situation and practices of reducing the curtailment of renewable energy in China. From the perspective of methods used to stabilize the fluctuation characteristics of generation output, two modes are outlined: based on energy storage devices and complementary wind-solar system. Then, an Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) system simulation model is proposed as a crucial solution to reduce renewable energy waste. Finally, the application and development trend of renewable energy and energy storage technology has prospected.

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