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IFAC-PapersOnLine 2022年第4期55卷 393-398页

作者： Aleksandra Kawala-Sterniuk Amir F. Al-Bakri Mariusz Pelc Katarzyna Cichoń Wojciech Chlewicki Stepan Ozana Radek Martinek Jakub Możaryn Volodymyr Khoma Halyna Kenyo Edward Jacek Gorzelańczyk Michał Podpora Jarosław Zygarlicki Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics 45-758 Opole Poland Department of Biomedical Engineering College of Engineering University of Babylon 51001 Babylon Iraq University of Greenwich School of Computing and Mathematical Sciences SE10 9LS London UK West Pomeranian University of Technology in Szczecin Faculty of Electrical Engineering 70-313 Szczecin Poland Faculty of Mechatronics Warsaw University of Technology 02-525 Warsaw Poland Lviv Polytechnic National University Institute of Computer Technologies Automation and Metrology Lviv Ukraine Institute of Philosophy Kazimierz Wielki University 85-092 Bydgoszcz Poland Faculty of Mathematics and Computer Science Adam Mickiewicz University in Poznań 61-614 Poznań Poland

Bioimpedance is a commonly used method for various conditions monitoring. In this paper, the authors carried out some research where they implemented various smoothing filters to enable identification of the bioimpedance spectroscopy parameters. The proposed filtering methods may also be used for application on embedded systems, which have smaller computing power but have become recently very popular. The obtained results with the implementation of smoothing filters were promising, however, some of the obtained results were unsatisfactory. This work also contains a brief introduction to bioimpedance spectroscopy and smoothing filters.

关键词： Bioimpedance Spectroscopy smoothing-filtering Savitzky-Golay Filter Median Filtering Moving-Average Filters

Train Trajectory Optimization based on Quadratic Programming for Virtual Coupling Concepts *

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Train Trajectory Optimization based on Quadratic Programming...

International Conference on Intelligent Transportation

作者： Jiahao Lu Debiao Lu Baigen Cai Bin Chen Jürgen Pannek Susu Huang Frontiers Science Center for Smart High-speed Railway System Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transport School of Electronic and Information Engineering Beijing Jiaotong Universit Beijing China Frontiers Science Center for Smart High-speed Railway System Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transport School of Computer and Information Technology Beijing Jiaotong Universit Beijing China Traffic Control Technology Co. Ltd Beijing China Institute for Intermodal Transportation and Logistic System Technical University of Braunschweig Braunschweig Germany China Academy of Railway Science Corporation Limited Signal & Communication Research Institute Beijing China

The concept of virtual coupling train convoys shows promise for improving railway capacity. This approach typically involves a “leader-follower” control strategy for multiple objects, such as vehicles and drones. In order to allow followers to enter the convoy, the operation status and trajectory of the leader vehicle must be optimized. This paper examines field data collected on Guangzhou Metro Line 22, and the velocity-distance curve shows consistency among the test runs, enabling coupling possibilities. Considering constraints such as safety constraints, train dynamic model constraints, and velocity limitation constraints, when the train operating trajectory expressed by polynomial curves is incorporated into the objective function, the resulting optimization strategy can be solved using quadratic programming methods. With the optimized trajectory by the proposed method, the average acceleration decreased by 0.009 m/s 2 to 0.024 m/s 2 , and the standard deviation decreased by 0.024 m/s 2 to 0.181 m/s 2 , Using the field collected data, the paper simulates train operations under virtual coupling, leader train using the optimized train trajectory, follower train employing the pure pursuit. The correlation coefficient of the velocity time series between the leader train and the follower train is 0.9996. The headway can also be shortened to 193.15 m average and a standard deviation of 6.44 m.

关键词：

Robust Transmission Scheduling for UAV-assisted Millimeter-Wave Train-Ground Communication System

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arXiv 2022年

作者： Ma, Yunhan Niu, Yong Han, Zhu Ai, Bo Li, Kai Zhong, Zhangdui Wang, Ning The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of Porto Portugal The School of Information Engineering Zhengzhou University Zhengzhou450001 China

With the explosive growth of mobile data, the demand of high-speed railway (HSR) passengers for broadband wireless access services urgently needs the support of ultra-high-speed scenario broadband wireless communication. Millimeter-wave (mmWave) can achieve high data transmission rates, but it is accompanied by high propagation loss and vulnerability to blockage. To address this issue, developments of directional antennas and unmanned aerial vehicles (UAVs) enhance the robustness of the mmWave train-ground communication system. In this paper, we propose a UAV and MRs relay assistance (UMRA) algorithm to effectively overcome link blockage, which can maximize the number of transmission flows on the premise of meeting QoS requirements and channel qualities. First, we formulate a mixed integer nonlinear programming (MINLP) problem for UAV trajectory design and transmission scheduling in the full-duplex (FD) mode. Then, in UMRA, the relay decision algorithm and transmission scheduling algorithm based on graph theory are proposed, which make a good tradeoff between computation complexity and system performance. Extensive simulation results show that a suitable UAV position will greatly improve the performance of the UMRA algorithm and make it close to the optimal solution. Compared with the other two existing benchmark schemes, with the high channel quality requirements and large-area blockage, UMRA can greatly improve the number of completed flows and system throughput. Copyright © 2022, The Authors. All rights reserved.

关键词： Scheduling

When autonomous systems meet accuracy and transferability through AI: A survey

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arXiv 2020年

作者： Zhang, Chongzhen Wang, Jianrui Yen, Gary G. Zhao, Chaoqiang Sun, Qiyu Tang, Yang Qian, Feng Kurths, Jürgen Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74075 United States Potsdam Institute for Climate Impact Research Potsdam14473 Germany Institute of Physics Humboldt University of Berlin Berlin12489 Germany

With widespread applications of artificial intelligence (AI), the capabilities of the perception, understanding, decision-making and control for autonomous systems have improved significantly in the past years. When autonomous systems consider the performance of accuracy and transferability, several AI methods, like adversarial learning, reinforcement learning (RL) and meta-learning, show their powerful performance. Here, we review the learning-based approaches in autonomous systems from the perspectives of accuracy and transferability. Accuracy means that a well-trained model shows good results during the testing phase, in which the testing set shares a same task or a data distribution with the training set. Transferability means that when a well-trained model is transferred to other testing domains, the accuracy is still good. Firstly, we introduce some basic concepts of transfer learning and then present some preliminaries of adversarial learning, RL and meta-learning. Secondly, we focus on reviewing the accuracy or transferability or both of them to show the advantages of adversarial learning, like generative adversarial networks (GANs), in typical computer vision tasks in autonomous systems, including image style transfer, image super-resolution, image deblurring/dehazing/rain removal, semantic segmentation, depth estimation, pedestrian detection and person re-identification (re-ID). Then, we further review the performance of RL and meta-learning from the aspects of accuracy or transferability or both of them in autonomous systems, involving pedestrian tracking, robot navigation and robotic manipulation. Finally, we discuss several challenges and future topics for using adversarial learning, RL and meta-learning in autonomous systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

2D nanomaterials for realization of flexible and wearable gas sensors: A review

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Chemosphere 2024年 352卷 141234页

作者： Pawar, Krishna Kiran Kumar, Ashok Mirzaei, Ali Kumar, Mahesh Kim, Hyoun Woo Kim, Sang Sub Division of Materials Science and Engineering Hanyang University Seoul 04763 South Korea The Research Institute of Industrial Science Hanyang University Seoul 04763 South Korea School of Nanoscience and Technology Shivaji University Kolhapur 416004 India Department of Electrical Engineering Indian Institute of Technology Jodhpur 342030 India Department of Materials Science and Engineering Shiraz University of Technology Shiraz 715557-13876 Iran Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice 44-100 Poland Department of Materials Science and Engineering Inha University Incheon 22212 South Korea

Gas sensors are extensively employed for monitoring and detection of hazardous gases and vapors. Many of them are produced on rigid substrates, but flexible and wearable gas sensors are needed for intriguing usage including the internet of things (IoT) and medical devices. The materials with the greatest potential for the fabrication of flexible and wearable gas sensing devices are two-dimensional (2D) semiconducting nanomaterials, which consist of graphene and its substitutes, transition metal dichalcogenides, and MXenes. These types of materials have good mechanical flexibility, high charge carrier mobility, a large area of surface, an abundance of defects and dangling bonds, and, in certain instances adequate transparency and ease of synthesis. In this review, we have addressed the different 2D nonmaterial properties for gas sensing in the context of fabrication of flexible/wearable gas sensors. We have discussed the sensing performance of flexible/wearable gas sensors in various forms such as pristine, composite and noble metal decorated. We believe that content of this review paper is greatly useful for the researchers working in the research area of fabrication of flexible/wearable gas sensors. © 2024 Elsevier Ltd

关键词： 2D nanomaterials Flexible/wearable gas sensor Graphene MXenes Sensing mechanism TMDs Toxic gas

Sparse system identification for stochastic feedback control systems

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arXiv 2019年

作者： Zhao, Wenxiao Yin, George Bai, Er-Wei Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 Department of Mathematics Wayne State University DetroitMI48202 United States Department of Electrical and Computer Engineering University of Iowa Iowa CityIA52242 United States

Focusing on identification, this paper develops techniques to reconstruct zero and nonzero elements of a sparse parameter vector θ of a stochastic dynamic system under feedback control, for which the current input may depend on the past inputs and outputs, system noises as well as exogenous dithers. First, a sparse parameter identification algorithm is introduced based on L2norm with L1regularization, where the adaptive weights are adopted in the optimization variables of L1term. Second, estimates generated by the algorithm are shown to have both set and parameter convergence. That is, sets of the zero and nonzero elements in the parameter θ can be correctly identified with probability one using a finite number of observations, and estimates of the nonzero elements converge to the true values almost surely. Third, it is shown that the results are applicable to a large number of applications, including variable selection, open-loop identification, and closed-loop control of stochastic systems. Finally, numerical examples are given to support the theoretical analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

Identification of Abnormal Operating Conditions in Fused Magnesium Furnaces Using a Deep Multiview TSK Fuzzy System

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Identification of Abnormal Operating Conditions in Fused Mag...

Chinese Automation Congress (CAC)

作者： Chao Xi Gaochang Wu Zizhu Fan Feng Xu Hui Wang Xiaokun Tang School of Electrical and Automation Engineering East China Jiaotong University Nanchang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China College of Computer Science and Technology Shanghai University of Electric Power Shanghai China Key Laboratory of Advanced Control and Optimization of Jiangxi Province East China Jiaotong University Nanchang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The smelting process of the fused magnesium furnace(FMF) is prone to semi-molten abnormal conditions, which may lead to low product quality and production efficiency, and is also prone to safety accidents. At present, the operating conditions of FMF mainly rely on manual observation of the furnace shell features for diagnosis, and it is hard to timely and effectively identify abnormalities in the smelting process. Given the difficulty in identifying abnormal operating conditions of FMF, this paper proposes a deep multiview fuzzy classification method(DMFC) that can automatically identify abnormal operating conditions. First, based on the three-phase current data, CNN and Transformer are used to extract features as multiple views. Secondly, the Takagi-Sugeno-Kang(TSK) fuzzy model is used as a single-view model, and the evidence theory is used for dynamic fusion of multiple views. This method can effectively fuse multiple deep features, and has strong scalability and a certain degree of interpretability. Through verification of the real historical data of multiple FMFs, our model has relatively high accuracy, false detection rate(FDR), and missed detection rate(MDR).

关键词： Fuses Heuristic algorithms Furnaces Smelting Production Feature extraction Transformers Magnesium Classification algorithms Unsupervised learning

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FIRE MAIN control

NAVAL ENGINEERS JOURNAL 1985年第6期97卷 35-48页

作者： MALKOFF, DB MOY, MC WILLIAMS, HL Dr. Donald B. Malkoff majored in physics as an undergraduate at Harvard University. He received an M.D. degree from the University of Pittsburgh School of Medicine in 1960. This was followed by an internship and residency in neurology at University Hospital in Ann Arbor Michigan. He spent several years at the National Institutes of Health engaged in gerontology research has practiced and taught clinical neurology and in 1983 received an M.S. degree in computer science at the University of California San Diego. Currently Dr. Malkoff is employed by the Navy Personnel Research and Development Center in San Diego California where he is senior investigator in a human factor/computer display-and-control project involving the DDG-51 gas turbine propulsion unit. He is a member of the American Academy of Neurology the Society for Neuroscience the American Association for Artificial Intelligence and the Association for Computing Machinery. Dr. Malkoff is certified by the American Board of Psychiatry and Neurology has taught computer science at UCSD and published in several research areas including magnetoencephalography and electron microscopy. His basic interests are in the areas of artificial intelligence and learning expert systems particularly as they apply to the problems of fault-detection and control. Dr. Melvyn C. Moy received his undergraduate training in mathematics and chemistry at the University of Texas Austin. He studied experimental psychology at the University of Wisconsin Madison receiving his M.S. in 1970 and Ph.D. in 1972. He served as an assistant professor at the University of South Dakota where he taught experimental design and methodology for a year before joining the Navy Personnel Research and Development Center in 1973. His work and research since then spans across many application areas such as the development of manpower planning models for the Navy the design of operational decision aids the human engineering of interactive large-scale war gaming systems and the evaluation o

The ship fire main has undergone considerable development throughout the past 2,000 years, resulting in a system that is critical both for normal ship function and for ship survivability in emergencies. Because of its complexity, the modern firemain system is highly vulnerable to malfunction and to damage during combat. Firemain fault detection and fault recovery are currently conducted manually by damage control teams. The advantages and disadvantages of this method are discussed, and alternative methods of fault detection and recovery are explored. An interactive computer program is introduced which uses central control over remotely situated valves to facilitate fault detection and recovery, significantly reducing recovery-time and manpower requirements. These reductions may result in savings of lives, ship systems, and ships themselves. The computer program is based upon an algorithm which is, in effect, a prescription that can be followed manually by the operator or be completely automated. The color graphic display which is used for monitoring can also be utilized for the training of damage control operators or for the evaluation of other algorithms for firemain control. Alternative firemain hardware and configurations could lead to even more efficient methods of fault detection and recovery as well as improved firemain water supply management in general.

关键词： NAVAL VESSELS

Single-shot fringe projection profilometry based on deep learning and computer graphics

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arXiv 2021年

作者： Wang, Fanzhou Wang, Chenxing Guan, Qingze School of Automation Southeast University 2# Sipailou Xuanwu Nanjing210096 China Key laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Department of Computer Science University College London Gower St Bloomsbury LondonWC1E 6BT United Kingdom

Multiple works have applied deep learning to fringe projection profilometry (FPP) in recent years. However, to obtain a large amount of data from actual systems for training is still a tricky problem, and moreover, the network design and optimization still worth exploring. In this paper, we introduce computer graphics to build virtual FPP systems in order to generate the desired datasets conveniently and simply. The way of constructing a virtual FPP system is described in detail firstly, and then some key factors to set the virtual FPP system much close to the reality are analyzed. With the aim of accurately estimating the depth image from only one fringe image, we also design a new loss function to enhance the quality of the overall and detailed information restored. And two representative networks, U-Net and pix2pix, are compared in multiple aspects. The real experiments prove the good accuracy and generalization of the network trained by the data from our virtual systems and the designed loss, implying the potential of our method for applications. Copyright © 2021, The Authors. All rights reserved.

关键词： computer graphics

Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features

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arXiv 2022年

作者： Xue, Jun Fan, Cunhang Lv, Zhao Tao, Jianhua Yi, Jiangyan Zheng, Chengshi Wen, Zhengqi Yuan, Minmin Shao, Shegang School of Computer Science and Technology Anhui University Hefei China NLPR Institute of Automation Chinese Academy of Sciences Beijing China Institute of Acoustics Chinese Academy of Sciences Beijing China Qiyuan Laboratory Beijing China National Environmental Protection Engineering and Technology Center Road Traffic Noise Control Research Institute of Highway Ministry of Transport Beijing China

Recently, pioneer research works have proposed a large number of acoustic features (log power spectrogram, linear frequency cepstral coefficients, constant Q cepstral coefficients, etc.) for audio deepfake detection, obtaining good performance, and showing that different subbands have different contributions to audio deepfake detection. However, this lacks an explanation of the specific information in the subband, and these features also lose information such as phase. Inspired by the mechanism of synthetic speech, the fundamental frequency (F0) information is used to improve the quality of synthetic speech, while the F0 of synthetic speech is still too average, which differs significantly from that of real speech. It is expected that F0 can be used as important information to discriminate between bonafide and fake speech, while this information cannot be used directly due to the irregular distribution of F0. Insteadly, the frequency band containing most of F0 is selected as the input feature. Meanwhile, to make full use of the phase and full-band information, we also propose to use real and imaginary spectrogram features as complementary input features and model the disjoint subbands separately. Finally, the results of F0, real and imaginary spectrogram features are fused. Experimental results on the ASVspoof 2019 LA dataset show that our proposed system is very effective for the audio deepfake detection task, achieving an equivalent error rate (EER) of 0.43%, which surpasses almost all systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature extraction