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作者： Hou, Chunyan Chen, Chen Wang, Jinsong School of Computer and Communication Engineering Tianjin University of Technology Tianjin China College of Computer and Control Engineering Nankai University Tianjin China

In the era of e-commerce, purchase behavior prediction is one of the most important issues to promote both online companies' sales and the consumers' experience. The previous researches usually use the feature engineering and ensemble machine learning algorithms for the prediction. The performance really depends on designed features and the scalability of algorithms because the large-scale data and a lot of categorical features lead to huge samples and the high-dimensional feature. In this study, we explore an alternative to use tree-based Feature Transformation (FT) and simple machine learning algorithms (e.g. Logistic Regression). Random Forest (RF) and Gradient Boosting decision tree (GB) are used for FT. Then, the simple algorithm, rather than ensemble algorithms, is used to predict purchase behavior based on transformed features. Tree-based FT regards the leaves of trees as transformed features, and can learn high-order interactions among original features. Compared with RF, if GB is used for FT, simple algorithms are enough to achieve better performance. © 2018 The Institute of Electronics, Information and communication Engineers.

关键词： Decision trees

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Sensing Matrix Design for MMV Compressive Sensing: An MVDR Approach

Sensing Matrix Design for MMV Compressive Sensing: An MVDR A...

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作者： Zhang, Liang Huang, Lei Li, Bo Yin, Jingwei Bao, Weimin School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Nuance Communication Inc. MontrealQCH3A 3S7 Canada Key Laboratory of Marine Information Acquisition and Security Ministry of Industry and Information Technology Harbin150001 China Science and Technology Council China Aerospace Science and Technology Cooperation Beijing100048 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin150001 China

Compressive sensing (CS) has been widely used in vehicular technology including compressive spectrum sensing, sparse channel estimation, and vehicular communications. The complete procedure of CS consists of sparse representation, sparse measurement, and sparse recovery. In the CS, measurement matrix Φ is utilized to sample a sparse signal, while sensing matrix Ψ is exploited for sparse recovery. It is important to properly design the sensing matrix that significantly affects the signal recovery performance. This paper addresses the issue of the sensing matrix design for the CS with multiple measurement vectors (MMV). We first study sufficient conditions for the MMV-CS and then develop three sensing matrix design approaches in different situations. The proposed methods have their roots in the principle of minimum variance distortionless response (MVDR) beamformer. This allows us to optimally determine the sensing matrix in the framework of the MVDR, which amounts to minimizing local cumulative cross-coherence between Φ and Ψ while keeping the columns of Ψ highly correlated to their counterparts of Φ. Simulation results are presented to verify the effectiveness of the proposed methods. © 1967-2012 IEEE.

关键词： Compressed sensing

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Improvement for Testing the Gravitational Inverse-Square Law at the Submillimeter Range

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Physical Review Letters 2020年第5期124卷 051301-051301页

作者： Wen-Hai Tan An-Bin Du Wen-Can Dong Shan-Qing Yang Cheng-Gang Shao Sheng-Guo Guan Qing-Lan Wang Bi-Fu Zhan Peng-Shun Luo Liang-Cheng Tu Jun Luo MOE Key Laboratory of Fundamental Physical Quantities Measurement & Hubei Key Laboratory of Gravitation and Quantum Physics PGMF and School of Physics Huazhong University of Science and Technology Wuhan 430074 People’s Republic of China TianQin Research Center for Gravitational Physics and School of Physics and Astronomy Sun Yat-sen University (Zhuhai Campus) Zhuhai 519082 People’s Republic of China College of Physics and Communication Electronics Jiangxi Normal University Nanchang 330022 People’s Republic of China School of Science Hubei University of Automotive Technology Shiyan 442002 China School of Electrical and Electronic Engineering Wuhan Polytechnic University Wuhan 430023 People’s Republic of China

We improve the test of the gravitational inverse-square law at the submillimeter range by suppressing the vibration of the electrostatic shielding membrane to reduce the disturbance coupled from the residual surface potential. The result shows that, at a 95% confidence level, the gravitational inverse-square law holds (|α|≤1) down to a length scale λ=48 μm. This work establishes the strongest bound on the magnitude α of the Yukawa violation in the range of 40–350 μm, and improves the previous bounds by up to a factor of 3 at the length scale λ≈70 μm. Furthermore, the constraints on the power-law potentials are improved by about a factor of 2 for k=4 and 5.

关键词： Experimental studies of gravity

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Position tracking control of magnetic levitation system based on continuous sliding mode control*

Position tracking control of magnetic levitation system base...

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Chinese Automation Congress (CAC)

作者： Lei Zhao Linjie Chen Junxiao Wang Li Yu College of Information Zhejiang University of Technology Hangzhou China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing Chian

In this paper, aiming at the problem of position tracking control of magnetic levitation system under parameter uncertainties and unknown external disturbance, a continuous sliding mode control(CSMC) method based on extended state observer(ESO) is proposed. Firstly, in order to suppress the adverse effects of disturbance and system unmeasurable states on control performance, an ESO for the system was designed to estimate system disturbances and unmeasured states. Meanwhile, in order to improve the robustness of the system and eliminate the chattering phenomenon caused by the high-frequency switching function in the sliding mode control law, a position tracking controller based on continuous sliding mode control is designed. Then combined with disturbance estimation technology and continuous sliding mode control technology, a composite position tracking controller(CSMC+ESO) is designed for the magnetic levitation system. Simulation and experimental results shows that compared with the general continuous sliding mode control(CSMC), the proposed control method improves the tracking accuracy, convergence rate, obtain better anti-interference capability and noise filtering performance for control input.

关键词： Magnetic levitation Observers Sliding mode control Mathematical model Uncertain systems Robustness

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Fuzzy control of dc microgrid

Fuzzy control of dc microgrid

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IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Yinping An Xiangping Meng Hui Wang Qi Yao Chunhui Liang Changchun University of Technology College of Electrical and Electronic Engineering Changchun China National Local Joint Engineering Research Center for Intelligent Distribution Network Measurement and control with Safety Operation Technology School of Electrical Engineering and Information Technology Changchun China Changchun Institute of Technology School of Electrical Engineering and Information Technology Changchun China

ISBN: (纸本)9781728137216

Aiming at the problem of large overshoot and long response time of bidirectional DC/DC in the energy storage part of DC micro-grid, a fuzzy self-tuning PI controller is designed in this paper to realize online setting of control parameters and achieve the purpose of optimizing traditional PI control. The results show that fuzzy self-tuning PI control can effectively solve the problem of slow tracking voltage and fluctuation in dc micro-grid.

关键词： Voltage control Batteries Supercapacitors PI control Photovoltaic systems

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Nonlinear Virtual Inertia control of WTGs for Enhancing Primary Frequency Response and Suppressing Drive-Train Torsional Oscillations

arXiv

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arXiv 2020年

作者： Liu, Bi Huang, Qi Zhao, Junbo Milano, Federico Zhang, Yingchen Sichuan Provincial Key Lab of Power System Wide-area Measurement and Control University of Electronic Science and Technology of China Chengdu Sichuan611731 China Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States School of Electrical and Electronic Engineering University College Dublin Dublin Ireland National Renewable Energy Laboratory GoldenCO80401 United States

Virtual inertia controllers (VICs) for wind turbine generators (WTGs) have been recently developed to compensate the reduction of inertia in power systems. However, VICs can induce low frequency torsional oscillations of the drive train of WTGs. This paper addresses this issue and develops a new nonlinear VIC based on objective holographic feedbacks theory. This approach allows transforming the original nonlinear control into a completely controllable system of Brunovsky’s type. Simulation results under various scenarios demonstrate that the proposed method outperforms existing VICs in terms of suppression of low frequency drive-train torsional oscillations, enhancement of system frequency nadir as well as fast and smooth recovery of WTG rotor speed to the original MPP point before the disturbance. The proposed method is also able to coordinate multiple WTGs. Copyright © 2020, The Authors. All rights reserved.

关键词： Frequency response

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Active disturbance rejection control design with suppression of sensor noise effects in application to DC-DC buck power converter

arXiv

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arXiv 2020年

作者： Lakomy, Krzysztof Madonski, Rafal Dai, Bin Yang, Jun Kicki, Piotr Ansari, Maral Li, Shihua The Poznaá Universitńy of Technology Poznan60-965 Poland The Energy Electricity Research Center International Energy College Jinan University Zhuhai519070 China The School of Automation Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education Nanjing210096 China The Institute of Robotics and Machine Intelligence Poznań University of Technology Poznań60-965 Poland The Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia

The performance of active disturbance rejection control (ADRC) algorithms can be limited in practice by high-frequency measurement noise. In this work, this problem is addressed by transforming the high-gain extended state observer (ESO), which is the inherent element of ADRC, into a new cascade observer structure. Set of experiments, performed on a DC-DC buck power converter system, show that the new cascade ESO design, compared to the conventional approach, effectively suppresses the detrimental effect of sensor noise over-amplification while increasing the estimation/control performance. The proposed design is also analyzed with a low-pass filter at the converter output, which is a common technique for reducing measurement noise in industrial applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Spurious signal noise

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Mesomechanics coal experiment and an elastic-brittle damage model based on texture features

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International Journal of Mining Science and technology 2018年第4期28卷 634-642页

作者： Sun Chuanmeng Cao Shugang Li Yong School of Electrical and Control Engineering North University of ChinaTaiyuan 030051China National Key Laboratory of Electronic Measurement Technology North University of ChinaTaiyuan 030051China State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing UniversityChongqing 400044China College of Resources and Environmental Science Chongqi ng UniversityChongqing 400044China

To accurately describe damage within coal, digital image processing technology was used to determine texture parameters and obtain quantitative information related to coal meso-cracks. The relationship between damage and mesoscopic information for coal under compression was then analysed. The shape and distribution of damage were comprehensively considered in a defined damage variable, which was based on the texture characteristic. An elastic-brittle damage model based on the mesostructure information of coal was established. As a result, the damage model can appropriately and reliably replicate the processes of initiation, expansion, cut-through and eventual destruction of microscopic damage to coal under compression. After comparison, it was proved that the predicted overall stress-strain response of the model was comparable to the experimental result.

关键词： Mesomechanics experiment Image processing Texture feature Damage variable Damage model

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Research on Deep Reinforcement Learning Exploration Strategy in Wargame Deduction

Research on Deep Reinforcement Learning Exploration Strategy...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Tongfei Shang Haoyang Dong Jing Liu Yuan Yu College of information and communication national university of defense technology Xi’an China School of electronic and information engineering Xi’an Jiaotong University Xi’an China College of command and control engineering Army engineering university of PLA Nanjing China Xi’an satellite control center Xi’an China

ISBN: (数字)9781728130668

ISBN: (纸本)9781728130675

The wargame deduction is the application of wargame. It refers to the use of boards and wargame pieces representing the battlefield and its military power, or based on the virtual battlefield environment and force of the computer, based on military rules and probability theory, simulating war confrontation. The process deduction and evaluation optimization of the combat plan is an activity that uses wargame to simulate war and is a tool for combat simulation. In this paper, the actual or assumed situation is used to simulate the military actions of two or more opposing parties. The deep reinforcement learning and exploration strategy is used to improve the realistic function of the wargame simulation and the deductive pushability function, and systematically study the wargame technology. Advancing operational decision-making and military training has certain practical and strategic significance.

关键词： Machine learning Learning (artificial intelligence) Decision making Military computing Optimization Training Conferences

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