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2016 IEEE International Conference on Big Data Analysis

作者： Wei Nai Fangqi Zhang Decun Dong Yijun Chen Yi Yu Wenyi Zheng Postdoctoral Research Station of Control Science and Engineering Tongji University The Cooperative Center for Maglev and Rail Transit Operation Control System Tongji University Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University School of Computer Science and Communication Engineering Jiangsu University

Pay-how-you-drive(PHYD), which is an advanced product form in usage based insurance(UBI) for vehicle, takes not only driving habit and behavior into consideration in its actuary process, but geographical factors, sociological factors, psychological factors, and even meteorological factors as well. PHYD insurance product is supported by refinement and analysis based on raw driving data of insured vehicles, and the very analysis process, which is based on big data algorithms, helps to recognize the risk level of each driving behavior by finding the relationship between them. Even if studies on risk level determination have been done adequately, research on feedback and presenting of risk evaluation results for the drivers of insured vehicles have not been reported much. In this paper, an effective presenting method for different driving styles based on hexagonal eye diagram which can be applied in PHYD insurance based driver vehicle interface(DVI) has been proposed, six key style evaluation indices have been chosen in such eye diagram. Evaluation examples have shown that by employing different colors which represent the occurrence frequency of certain evaluation results in such eye diagram, the hexagonal eye diagram proposed can describe the most representative driving style for a certain insured vehicle, even if there might be style changing from time to time.

关键词： usage based insurance(UBI) pay-how-you-drive(PHYD) risk evaluation result presenting hexagonal eye diagram driver vehicle interface(DVI)

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F-CNN: An FPGA-based framework for training Convolutional Neural Networks

F-CNN: An FPGA-based framework for training Convolutional Ne...

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International Conference on Application Specific systems (ASAP), Architectures and Processors

作者： Wenlai Zhao Haohuan Fu Wayne Luk Teng Yu Shaojun Wang Bo Feng Yuchun Ma Guangwen Yang Ministry of Education Key Laboratory for Earth System Modeling and Center for Earth System Science Tsinghua University China Tsinghua National Laboratory for Information Science and Technology China Department of Computing Imperial college London UK Department of Automatic Test and Control Harbin Institute of Technology China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9781509015047

This paper presents a novel reconfigurable framework for training Convolutional Neural Networks (CNNs). The proposed framework is based on reconfiguring a streaming datapath at runtime to cover the training cycle for the various layers in a CNN. The streaming datapath can support various parameterized modules which can be customized to produce implementations with different trade-offs in performance and resource usage. The modules follow the same input and output data layout, simplifying configuration scheduling. For different layers, instances of the modules contain different computation kernels in parallel, which can be customized with different layer configurations and data precision. The associated models on performance, resource and bandwidth can be used in deriving parameters for the datapath to guide the analysis of design trade-offs to meet application requirements or platform constraints. They enable estimation of the implementation specifications given different layer configurations, to maximize performance under the constraints on bandwidth and hardware resources. Experimental results indicate that the proposed module design targeting Maxeler technology can achieve a performance of 62.06 GFLOPS for 32-bit floating-point arithmetic, outperforming existing accelerators. Further evaluation based on training LeNet-5 shows that the proposed framework achieves about 4 times faster than CPU implementation of Caffe and about 7.5 times more energy efficient than the GPU implementation of Caffe.

关键词： Training Field programmable gate arrays Convolution Computational modeling Bandwidth Neural networks Runtime

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Author Correction: Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

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Nature genetics 2023年第3期55卷 519-520页

作者： Ceres Fernandez-Rozadilla Maria Timofeeva Zhishan Chen Philip Law Minta Thomas Stephanie Schmit Virginia Díez-Obrero Li Hsu Juan Fernandez-Tajes Claire Palles Kitty Sherwood Sarah Briggs Victoria Svinti Kevin Donnelly Susan Farrington James Blackmur Peter Vaughan-Shaw Xiao-Ou Shu Jirong Long Qiuyin Cai Xingyi Guo Yingchang Lu Peter Broderick James Studd Jeroen Huyghe Tabitha Harrison David Conti Christopher Dampier Mathew Devall Fredrick Schumacher Marilena Melas Gad Rennert Mireia Obón-Santacana Vicente Martín-Sánchez Ferran Moratalla-Navarro Jae Hwan Oh Jeongseon Kim Sun Ha Jee Keum Ji Jung Sun-Seog Kweon Min-Ho Shin Aesun Shin Yoon-Ok Ahn Dong-Hyun Kim Isao Oze Wanqing Wen Keitaro Matsuo Koichi Matsuda Chizu Tanikawa Zefang Ren Yu-Tang Gao Wei-Hua Jia John Hopper Mark Jenkins Aung Ko Win Rish Pai Jane Figueiredo Robert Haile Steven Gallinger Michael Woods Polly Newcomb David Duggan Jeremy Cheadle Richard Kaplan Timothy Maughan Rachel Kerr David Kerr Iva Kirac Jan Böhm Lukka-Pekka Mecklin Pekka Jousilahti Paul Knekt Lauri Aaltonen Harri Rissanen Eero Pukkala Johan Eriksson Tatiana Cajuso Ulrika Hänninen Johanna Kondelin Kimmo Palin Tomas Tanskanen Laura Renkonen-Sinisalo Brent Zanke Satu Männistö Demetrius Albanes Stephanie Weinstein Edward Ruiz-Narvaez Julie Palmer Daniel Buchanan Elizabeth Platz Kala Visvanathan Cornelia Ulrich Erin Siegel Stefanie Brezina Andrea Gsur Peter Campbell Jenny Chang-Claude Michael Hoffmeister Hermann Brenner Martha Slattery John Potter Konstantinos Tsilidis Matthias Schulze Marc Gunter Neil Murphy Antoni Castells Sergi Castellví-Bel Leticia Moreira Volker Arndt Anna Shcherbina Mariana Stern Bens Pardamean Timothy Bishop Graham Giles Melissa Southey Gregory Idos Kevin McDonnell Zomoroda Abu-Ful Joel Greenson Katerina Shulman Flavio Lejbkowicz Kenneth Offit Yu-Ru Su Robert Steinfelder Temitope Keku Bethany van Guelpen Thomas Hudson Heather Hampel Rachel Pearlman Sonja Berndt Richard Hayes Marie Elena Martinez Sushma Thomas Douglas Corley Paul Pharoah Susanna Larsson Yun Yen Heinz-Jo Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK. Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain. Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK. Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark. Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA. Division of Genetics and Epidemiology Institute of Cancer Research London UK. Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA. Genomic Medicine Institute Cleveland Clinic Cleveland OH USA. Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA. Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain. Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain. Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain. Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain. Department of Biostatistics School of Public Health University of Washington Seattle WA USA. Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK. Department of Public Health Richard Doll Building University of Oxford Oxford UK. Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA. Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA. Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA. De

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Multiple model predictive control for organic rankine cycle (ORC) based waste heat energy conversion systems

Multiple model predictive control for organic rankine cycle ...

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UKACC International Conference on control (control)

作者： Jianhua Zhang Ting Zhang Mingming Lin Guolian Hou Kang Li State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast UK

In this paper, a multiple model predictive control (MMPC) strategy is applied into an organic Rankine cycle (ORC) based waste heat energy conversion system (WHECS). The rotating speed of the pump and the shaft torque of the expander are manipulated simultaneously to provide the optimal (suboptimal) evaporating pressure and superheating temperature for nonlinear WHECSs under different operating conditions. Simulation results confirm the efficacy of the proposed control scheme.

关键词： Predictive models Predictive control Waste heat Torque Cooling

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An economic load dispatch of wind-thermal power system by using virtual power plants

An economic load dispatch of wind-thermal power system by us...

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Chinese control Conference (CCC)

作者： Jie Hou Junlin Guo Jizhen Liu State Key laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing Beijing CN School of Control and Computer Engineering North China Electric Power University Beijing Beijing CN

The use of renewable energy sources, such as wind power, is of major importance in future power grids for economic and environmental reasons, while the intermittent and stochastic nature of such energy brings challenges to system dispatch. Application of the virtual power plant (VPP) concept is one of the promising ways to improve utilization of renewable power. Among the many issues of practically using VPP, the aggregated output characteristics of a set of generators have not been well studied yet. In this paper, a two-phase economic dispatch model considering the aggregated VPPs is presented for wind-thermal power systems. In phase 1, outputs of VPPs consisting of several wind and thermal generators are decided by the grid operator, with characteristics of such VPPs obtained by aggregating those of individual generators. Then in phase 2, an output dispatch is performed among individual generators within each VPP by its aggregator. The model of the above two phases are both solved by using linear programming methods, and numerical tests are performed on a 3-bus system by using CPLEX. Results show that compared with conventional economic dispatch models, our model leads to substantial improvements in wind power utilization, and lower generator costs.

关键词： Generators Power systems Economics Renewable energy sources Wind farms Power generation Wind forecasting

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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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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A simulator for swarm AUVs acoustic communication networking 11

A simulator for swarm AUVs acoustic communication networking

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11th ACM International Conference on Underwater Networks and systems, WUWNet 2016

作者： Li, Guannan Liu, Jun Wang, Xue Xu, Hongli Cui, Jun-Hong Shenyang Institute of Automation CAS University of Chinese Academy of Sciences No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Changchun China Shenyang Institute of Automation CAS Northeast University No. 114 Nanta Street Shenhe District Shenyang China Shenyang Institute of Automation CAS No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University Changchun China Underwater Sensor Network Lab. University of Connecticut StorrsCT06269 United States

ISBN: (纸本)9781450346375

This paper presents a simulator for swarm operations designed to verify algorithms for a swarm of autonomous underwater robots (AUVs), specifically for constructing an underwater communication network with AUVs carrying acoustic communication devices. This simulator consists of three nodes: a virtual vehicle node (VV), a virtual environment node (VE), and a visual showing node (VS). The modular design treats AUV models as a combination of virtual equipment. An expert acoustic communication simulator is embedded in this simulator, to simulate scenarios with dynamic acoustic communication nodes. The several simulations we have performed demonstrate that this simulator is easy to use and can be further improved.

关键词： Simulators

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Improved minimum entropy control for two-input and two-output networked control systems

Improved minimum entropy control for two-input and two-outpu...

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UKACC International Conference on control (control)

作者： Jianhua Zhang Yamin Kuai Shuqing Zhou Guolian Hou Mifeng Ren State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China College of Information Engineering Taiyuan University of Technology Taiyuan China

In this paper, the problem of control algorithm design for a class of nonlinear two-input and two-output (TITO) networked control systems (NCSs) with non-Gaussian random time delays is investigated, where a general non-linear auto-regressive moving average with exogenous model is used to describe the plant. Due to the non-Gaussian random time delays involved in the systems, it is insufficient to obtain a satisfactory optimal control algorithm by only controlling the expected value of the tracking errors. The Renyi entropies of the tracking errors and control inputs are adopted to characterize the randomness of the closed-loop system. The formulations of the probability density functions (PDFs) of the tracking errors and control inputs are deduced. By minimizing the new performance index, a recursive optimal control algorithm is obtained. Furthermore, the local stability condition of the closed-loop systems is established. Finally, the simulation results are presented to illustrate the effectiveness of the proposed method.

关键词： Entropy Probability density function Performance analysis Delays Delay effects Networked control systems Stochastic processes

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Corrections to "Efficient Adaptive Turbo Equalization for Multiple-Input–Multiple-Output Underwater Acoustic Communications" [W. Duan, J. Tao, and Y. Rosa Zheng, IEEE J. Ocean. Eng ., 2017, DOI: 10.1109/JOE.2017.2707285]

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IEEE Journal of Oceanic Engineering 2017年第3期42卷 752-752页

作者： W. Duan J. Tao Y. R. Zheng Qualcomm Technologies Inc. San Diego CA USA Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University Nanjing China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

Equation In [1], (6) of the above named work is corrected.

关键词： MIMO Adaptive equalizers Underwater communication Underwater acoustics Least mean squares methods

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A Two-Step Fault Diagnosis Framework for Rolling Element Bearings with Imbalanced Data

A Two-Step Fault Diagnosis Framework for Rolling Element Bea...

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International Conference on Ubiquitous Robots and Ambient Intelligence

作者： Yuan Lan Weiwei Zong Xiaojian Ding Xiaoyan Xiong Xiaohang Han Jiahai Huang Bing Ma Key Laboratory of Ministry of Education in Advance Transducers and Intelligent Control System Institute of Mechatronics Engineering under School of Mechanical Engineering Taiyuan University of Technology Department of Computer Science Wayne State University Science and Technology on Information Systems Engineering Laboratory The Second Research Institute of China Electronic Technology Group Corporation

ISBN: (纸本)9781509008223

Rolling element bearings constitute the key parts on rotating machinery and their fault diagnosis are of great importance. In this paper, a novel Two-Step fault diagnosis framework is proposed to diagnose the status of rolling element bearings with imbalanced data. The Wavelet Packet Transform (WPT) is used to determine the feature vectors. 16-dimensional wavelet packet node energies were extracted from the original datasets as the feature vectors prepared to input to the classifiers. Next, our proposed framework consists of two steps for the fault diagnosis, where Step One makes use of Weighted Extreme Learning Machine (weighted ELM) in an effort to classify the normal or abnormal categories, and Step Two further diagnoses the underlying anomaly in details. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our Institute, and the empirical results showed that our approach is really fast and can achieve the diagnosis accuracies more than 95%.

关键词： Two-Step fault diagnosis framework Weighed ELM ELM Bearing fault diagnosis

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