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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Multi-view hybrid embedding: A divide-and-conquer approach

arXiv

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arXiv 2018年

作者： Xu, Jiamiao Yu, Shujian You, Xinge Leng, Mengjun Jing, Xiao-Yuan Chen, C. L. Philip School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Science University of Houston HoustonTX77204 United States State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau Macau99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multi-view data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multi-view data is sampled from nonlinear manifolds or suffers from heavy outliers. To circumvent this drawback, motivated by the Divideand-Conquer strategy, we propose Multi-view Hybrid Embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase discriminability in intra-view and interview samples respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate overwhelming advantages against the stateof-the-art MvSL based cross-view classification approaches in terms of classification accuracy and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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A Future Outlook for Cyber-Physical Power System

A Future Outlook for Cyber-Physical Power System

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IEEE Conference on Energy Internet and Energy System Integration

作者： Zhuo Su Luo Xu Shujun Xin Weijian Li Zhan Shi Qinglai Guo Guangdong Province Power Dispatching and Control Center China Southern Power Grid State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University

ISBN: (纸本)9781538614280

The energy management system (EMS) is an indispensable advanced information system for smart grids. EMS makes the power system efficient and safe operation, but also makes the power system more dependent on it. This paper analyzes the related research of CPS modeling and CPS assessment for power systems. Besides, with the development of smart grid and Energy Internet, integrated energy management system (IEMS), distributed EMS and cloud EMS are developed. Meanwhile, the research on information masking algorithm for data protection of cloud EMS is analyzed. Finally, a future outlook for cyber-physical power system in CPS modeling, assessment and information protection is given.

关键词： Cyber-physical system (CPS) EMS Smart grid CPS modeling CPS assessment Information protection

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automatic Sleep Spindle Detection with EEG Based on Complex Demodulation Method and Decision Tree Model

Automatic Sleep Spindle Detection with EEG Based on Complex ...

引用

The 11th International Conference on Bioinformatics and Biomedical engineering (ICBBE 2017)

作者： Jiabin Li Bei Wang Takenao Sugi Yu Zhang Masatoshi Nakamura Department of Automation East University of Science and Technology Department of Electrical and Electronic Engineering Saga University Research Institute of Systems Control Institute for Advanced Research and Education Saga University

Sleep spindle is the characteristic waveform of electroencephalogram(EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were recorded based on the standard polysomnogram(PSG) measurement. A preprocessing procedure is introduced to exclude the unnecessary data segments and normalized the necessary data segments. Complex demodulation method is adopted to detect the candidate sleep spindle waveforms and calculate the features. The sleep spindles are recognized based on a decision tree model. Finally, the detected sleep spindles were utilized to amend the sleep stage recognition results. The sleep EEG data from 3 patients with sleep disorders were analyzed. The obtained results showed that the detected sleep spindles in EEG signal improved the accuracy of sleep stage recognition.

关键词： Sleep Spindle Detection Complex Demodulation Method Decision Tree Model EEG

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Radiation Pattern Synthesis Using Hybrid Fourier-Woodward-Lawson-Neural Networks for Reliable MIMO Antenna systems

arXiv

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arXiv 2017年

作者： Ghayoula, Elies Ghayoula, Ridha Fattahi, Jaouhar Pricop, Emil Chouinard, Jean-Yves Bouallegue, Ammar Sys’Com Laboratory National Engineering School of Tunis ENIT Tunis El Manar University Tunisia Department of Electrical and Computer Engineering Laval University Quebec Canada Department of Computer Science and Software Engineering Laval University Quebec Canada Automatic Control Computers and Electronics Department Petroleum-Gas University of Ploiesti Romania

In this paper, we implement hybrid Woodward-Lawson-Neural Networks and weighted Fourier method to synthesize antenna arrays. The neural networks (NN) is applied here to simplify the modeling of MIMO antenna arrays by assessing phases. The main problem is obviously to find optimal weights of the linear antenna array elements giving radiation pattern with minimum sidelobe level (SLL) and hence ameliorating the antenna array performance. To attain this purpose, an antenna array for reliable Multiple-Input Multiple-Output (MIMO) applications with frequency at 2.45 GHz is implemented. To validate the suggested method, many examples of uniformly excited array patterns with the main beam are put in the direction of the useful signal. The Woodward-Lawson-Neural Networks synthesis method permits to find out interesting analytical equations for the synthesis of an antenna array and highlights the flexibility between the system parameters in input and those in output. The performance of this hybrid optimization underlines how well the system is suitable for a wireless communication and how it participates in reducing interference, as well. Copyright © 2017, The Authors. All rights reserved.

关键词： Antenna arrays

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Location prediction optimisation in WSNs using kriging interpolation 9

Location prediction optimisation in WSNs using kriging inter...

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9th IEEE/IET International Symposium on Communications systems, Networks and Digital Signal Processing, CSNDSP 2014

作者： Ali, Arshad Ikpehai, Augustine Adebisi, Bamidele Mihaylova, Lyudmila Islamic University Al Madinah Al Munawarah Saudi Arabia Division of Electrical and Electronic Engineering Manchester Metropolitan University Manchester United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom

Many wireless sensor network (WSN) applications rely on precise location or distance information. Despite the potentials of WSNs, efficient location prediction is one of the subsisting challenges. This study presents novel prediction algorithms based on a Kriging interpolation technique. Given that each sensor is aware of its location only, the aims of this work are to accurately predict the temperature at uncovered areas and estimate positions of heat sources. By taking few measurements within the field of interest and by using Kriging interpolation to iteratively enhance predictions of temperature and location of heat sources in uncovered regions, the degree of accuracy is significantly improved. Following a range of independent Monte Carlo runs in different experiments, it is shown through a comparative analysis that the proposed algorithm delivers approximately 98% prediction accuracy. © The Institution of engineering and Technology 2016.

关键词： Location

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Retracted: Improved and adaptive model of optimum droop management for multi-terminal DC systems throughout line outages

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IET Generation, Transmission & Distribution 2019年第21期14卷 4974-4974页

作者： Arslan Habib Adesh Ananta Rabeh Abbassi Abdelkader Abbassi Andrés Julián Aristizábal Faculty of Engineering University of Strathclyde Glasgow G1 1XQ UK State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems Beijing People's Republic of China Department of Electrical Engineering College of Engineering University of Hail Hail City Saudi Arabia LaTICE Laboratory Higher National Engineering School of Tunis (ENSIT) University of Tunis 5 Avenue Taha Hussein P.O. Box 56 1008 Tunis Tunisia Institute of Applied Sciences and Technology of Kasserine (ISSATKas) University of Kairouan P.O. Box 471 1200 Kasserine Tunisia Engineering Department Universidad de Bogota Jorge Tadeo Lozano Cr. 4 #22-61 Bogota Colombia

The following article published in IET Generation Transmission and Distribution entitled ‘Improved and adaptive model of optimum droop management for multi-terminal DC systems throughout line outages' (DOI: 10.1049/iet-gtd.2018.5704) has been retracted due to a breach of the IET's Policy in Relation to Plagiarism, Infringement of Copyright and Infringement of Moral Rights and Submission to Multiple Publications, and concerns over the authorship of the paper.

关键词： power transmission control distributed power generation power system control DC power transmission wind power adaptive model optimum droop management line outages droop control plan multiterminal DC systems 6-bus MTdc system system components variable droop control gains technique wind power

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Linear Quadratic control for Sampled-data systems with Stochastic Delays

Linear Quadratic Control for Sampled-data Systems with Stoch...

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American control Conference

作者： Masashi Wakaiki Masaki Ogura Joao P. Hespanha the Department of Electrical and Electronic Engineering Chiba University 1-33 Yayoi-cho Inage-ku Chiba 263-8522 Japan the Department of Electrical and Systems Engineering at the University of Pennsylvania Philadelphia PA 19104 USA the Center for Control Dynamical-systems and Computation (CCDC) University of California Santa Barbara CA 93106 USA

ISBN: (纸本)9781509045839

We study optimal control for sampled-data systems with stochastic delays. Assuming that the delays can be modeled by a Markov chain and can be measured by controllers, we design a control law that minimizes an infinite-horizon continuous-time quadratic cost function. The resulting optimal control law can be efficiently computed offline by the iteration of a certain Riccati difference equation. We also obtain sufficient conditions in terms of linear matrix inequalities for stochastic stabilizability and detectability, which are used for the optimal controller design.

关键词： optimal controller design controllers data systems delay control law Data sampling linear quadratic control Markov chain Optimal Optimal control controller design Stochastic Difference equation linear matrix inequalities detectability iterative methods

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String stability and a delay-based spacing policy for vehicle platoons subject to disturbances

arXiv

引用

arXiv 2017年

作者： Besselink, B. Johansson, K.H. Johann Bernoulli Institute for Mathematics and Computer Science University of Groningen Groningen Netherlands ACCESS Linnaeus Centre Department of Automatic Control School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden

A novel delay-based spacing policy for the control of vehicle platoons is introduced together with a notion of disturbance string stability. The delay-based spacing policy specifies the desired inter-vehicular distance between vehicles and guarantees that all vehicles track the same spatially varying reference velocity profile, as is for example required for heavy-duty vehicles driving over hilly terrain. Disturbance string stability is a notion of string stability of vehicle platoons subject to external disturbances on all vehicles that guarantees that perturbations do not grow unbounded as they propagate through the platoon. Specifically, a control design approach in the spatial domain is presented that achieves tracking of the desired spacing policy and guarantees disturbance string stability with respect to a spatially varying reference velocity. The results are illustrated by means of simulations. Copyright © 2017, The Authors. All rights reserved.

关键词： Vehicles

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An experimental study of modeling and self-tuning regulator design for an electro-hydro servo-system

An experimental study of modeling and self-tuning regulator ...

引用

International Conference on control, Instrumentation, and Automation

作者： P. Anahita Moradmand Hamid Khaloozadeh Center of Excellence in Control and Robotics Department of Electrical Engineering Amirkabir University of Technology(AUT) Tehran Iran +98 Department of Systems and Control K. N. Toosi University of Technology(KNTU) Tehran Iran

ISBN: (纸本)9781538621356

To design model-based controllers for industrial systems, an accurate mathematical model is necessary. Over time, most of these models are changed and the former controllers should be modified to achieve good performance. Hydro systems, according to their wide application over last forty years, are an interesting field of research. In this paper, based on the system identification and adaptive control theory, a special scheme for modeling and controller design of an old ElectroHydro servo-system is proposed. Industrial process control suffering from noise and saturation as two main problematic factors. Here by conducting an experiment, the identification and control are employed in linear period of the nonlinear system. To specify the order of system, Matlab Ident toolbox, Hankel matrix and Hankel singular values are used. Recursive Least Square (RLS) method is implemented to determine estimated parameters. Designing a practical self-tuning regulator is a significant task too. Due to the inappropriate results of controlled system, a corrected regulator is generated. Finally, acceptable simulations are recorded and demonstrated.

关键词： Mathematical model Adaptation models control systems System identification Adaptive control MATLAB Hydraulic systems

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