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A multi-sensor based online tool condition monitoring system for milling process

Procedia CIRP 2018年 72卷 1136-1141页

作者： X.Y. Zhang X. Lu S. Wang W. Wang W.D. Li Faculty of Engineering Environment and Computing Coventry University Coventry CV1 5FB UK Department of Production and Automation Engineering University of Skövde Skövde 541 28 Sweden

Tool condition monitoring has been considered as one of the key enabling technologies for manufacturing optimization. Due to the high cost and limited system openness, the relevant developed systems have not been widely adopted by industries, especially Small and Medium-sized Enterprises. In this research, a cost-effective, wireless communication enabled, multi-sensor based tool condition monitoring system has been developed. Various sensor data, such as vibration, cutting force and power data, as well as actual machining parameters, have been collected to support efficient tool condition monitoring and life estimation. The effectiveness of the developed system has been validated via machining cases. The system can be extended to wide manufacturing applications.

关键词： Tool condition monitoring Multipe sensors Vibration

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Sleep Disorder Classification Method based on Logistic Regression with Apnea-ECG Dataset 2019

Sleep Disorder Classification Method based on Logistic Regre...

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2019 International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2019

作者： Cai, Liangming Chen, Chunqiang Wang, Xiaoling Yang, Xiong Lin, Shurui Huang, Jiefeng Jiang, Jinping Datta, Rituparna Du, Min Jiang, Haiyan Zhu, Minchen Huang, Jingshan College of Physics and Information Engineering Fuzhou University Fuzhou China Zhicheng College University of South Alabama United States HaiXi Institutes Chinese Academy of Science Fuzhou China Zhicheng College Fuzhou University Fuzhou China Department of Respiratory Medicine First Affiliated Hospital of Fujian Medical University Fuzhou China College of Electrical Engineering and Automation Fuzhou University Fuzhou China School of Computing University of South Alabama Mobile United States Provincial Key Laboratory of Eco-industrial Green Technology Wuyi College Wuyishan China College of Mathematics and Computer Science Fuzhou University Fuzhou China Fuzhou University Fuzhou China University of South Alabama United States

ISBN: (纸本)9781450372022

Adequate sleep is significant for human to actively pursue daily activity. On the other hand, insomnia is directly proportional to aging and health deterioration. Sleep disorder classification is important for medical scientists as well as machine learning researchers. In the paper, we have developed a sleep disorder classification method for Electrocardiogram (ECG) data. The data set have two labels;sleep disorder or not, which can be categorized as binary output. As a result, logistic regression is used as classification technique. For initial experimentation, an existing data set is used. The data set consists of every 10 second data up to 6000 seconds for 35 patients. We have used 70% data for training, and 30% data for testing purpose. Our results from logistic regression show that the logistic regression method is efficient to detect sleep disorders. The prediction result is found to be 59%. The future research in this direction would be to take data from hospitals and use our developed algorithm for sleep disorder classification and prediction. © 2019 Association for computing Machinery.

关键词： Electrocardiograms

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AbdomenCT-1K: Is abdominal organ segmentation a solved problem?

arXiv

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arXiv 2020年

作者： Ma, Jun Zhang, Yao Gu, Song Zhu, Cheng Ge, Cheng Zhang, Yichi An, Xingle Wang, Congcong Wang, Qiyuan Liu, Xin Cao, Shucheng Zhang, Qi Liu, Shangqing Wang, Yunpeng Li, Yuhui He, Jian Yang, Xiaoping Department of Mathematics Nanjing University of Science and Technology China Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China AI Lab Lenovo Research School of Automation Nanjing University of Information Science and Technology China Shenzhen Haichuang Medical CO. LTD. China Institute of Bioinformatics and Medical Engineering Jiangsu University of Technology China School of Biological Science and Medical Engineering Beihang University China Beijing Infervision Technology CO. LTD. China School of Computer Science and Engineering Tianjin University of Technology China Department of Computer Science Norwegian University of Science and Technology Norway School of Electronic Science and Engineering Nanjing University China Suzhou LungCare Medical Technology Co. Ltd China Bioengineering Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Department of Computer and Information Science Faculty of Science and Technology University of Macau China School of Biomedical Engineering Southern Medical University China Institutes of Biomedical Sciences Fudan University China Computational Biology University of Southern California United States Department of Radiology Nanjing Drum Tower Hospital The Affiliated Hospital Nanjing University Medical School China Department of Mathematics Nanjing University China

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods. © 2020, CC BY-NC-SA.

关键词： Computerized tomography

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Adaptive Neural Network Control for Consensus of Nonlinear Multi-Agent Systems with Actuator Faults

Adaptive Neural Network Control for Consensus of Nonlinear M...

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International Conference on Information Science and Technology (ICIST)

作者： Gaosheng Zhang Qichao Ma Jiahu Qin Yu Kang Wei Xing Zheng Department of Automation University of Science and Technology of China Hefei China School of Computing Engineering and Mathematics Western Sydney University Sydney Australia

This paper investigates the fault tolerant consensus problem for a class of nonlinear multi-agent systems with actuator faults. The dynamics of the multi-agent systems are unknown nonlinear and nonidentical. The types of actuator fault include partial loss of effectiveness fault and biased fault. The main idea of the fault tolerant control adopted in this paper is the adaptive control. The control method used is a neural network based adaptive control which has a better adaptability than the traditional adaptive control. The developed adaptive neural network consensus protocol is proved to perform well with respect to the system nonlinear dynamics and actuator faults of the agent. Finally, numerical simulation on multi-agent system of four Chen's chaotic systems is performed to illustrate the effectiveness of the investigated adaptive neural network consensus protocol.

关键词： Actuators Protocols Fault tolerance Fault tolerant systems Adaptive control Biological neural networks

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Mobile Application Helps Planning Activities during Pregnancy

Mobile Application Helps Planning Activities during Pregnanc...

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Igor Efrem Magnar Høyvik Ilona Heldal Carsten Helgesen Attila Kovari Jozsef Katona Cristina Costescu Adrian Rosan Andrea Hathazi Robert Demeter Serge Thill Avans AS Bergen Area Norway Department of Computing Mathematics and Physics Western Norway University of Applied Sciences Bergen Norway CogInfoCom Based LearnAbility Research Team Institute of Engineering Dunaujvaros Hungary Special Education Department Babes-Bolyai University Cluj-Napoca Romania Department of Automation and Information Technology Transilvania University of Brasov Brasov Romania Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherland

ISBN: (数字)9781728147932

ISBN: (纸本)9781728147949

The demo describes a mobile application called ELISA made for the new info-communication purpose of supporting prospective parents, that helps planning activities following the recognition of pregnancy. The key features of the application are focusing on the identification and sharing of the relevant data between both prospective parents, healthcare institutes, and supporting social groups. The prototype of the application has been worked out to implement key features along with the planning of the critical tasks concerning the pregnancy.

关键词： Pregnancy Mobile applications Planning Conferences STEM

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Systematic survey on evolution of cloud architectures

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International Journal of Autonomous and Adaptive Communications Systems 2018年第1期11卷 14-14页

作者： Bhushan, S. Bharath Reddy, Pradeep Subramanian, Dhenesh V. Gao, X.Z. School of Information Technology and Engineering VIT University Vellore India School of Computing Information and Mathematical Sciences University of the South Pacific Suva Fiji Department of Electrical Engineering and Automation Aalto University School of Electrical Engineering Aalto Finland

Cloud architectures are becoming an active area of research. The quality and durability of a software system are defined by its architecture. The architecture approaches that are used to build cloud-based systems are not available in a blended fashion to achieve an effective universal architecture solution. The paper aims to contribute to the systematic literature review (SLR) to assist researchers who are striving to contribute in this area. The main objective of this review is to systematically identify and analyse the recently published research topics related to software architecture for cloud with regard to research activity, used tools and techniques, proposed approaches, domains. The applied method is SLR based on four selected electronic databases proposed by Kitchenham and Charters (2007). Out of 400 classified publications, we regard 121 as relevant for our research domain. We outline taxonomy of their topics and domains, provide lists of used methods and proposed approaches. At present, there is little research coverage on software architectures for cloud, while other disciplines have become more active. The future work is to develop a secure architecture to achieve quality of service and service level agreements. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Quality of service

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Contract Based Energy Blockchain for Secure Electric Vehicles Charging in Smart Community

Contract Based Energy Blockchain for Secure Electric Vehicle...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Yuntao Wang Zhou Su Qichao Xu Ning Zhang School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Computing Science Texas A&M University at Corpus Christi TX USA

ISBN: (纸本)9781538675199

The smart community (SC), as an important part of the Internet of energy (IoE), can facilitate integration of distributed renewable energy sources (RES) and electric vehicles (EVs) in the smart grid. However, due to the potential security and privacy issues caused by untrusted and opaque energy markets, it becomes a great challenge to optimally schedule the charging behaviors of EVs with distinct energy consumption preferences in SC. In this paper, we propose a contract based energy blockchain for secure EV charging in SC. Firstly, a permissioned energy blockchain system is introduced to implement secure charging services for EVs with the execution of smart contracts. Secondly, a reputation based delegated Byzantine fault tolerance (DBFT) consensus algorithm is proposed to eficiently achieve the consensus in the permissioned blockchain. Furthermore, based on the contract theory, the optimal contracts are analyzed and designed to satisfy EVs' individual needs from energy sources while maximizing the operator's utility. Finally, numerical results validate the effectiveness and ef?ciency of the proposed scheme through comparison with other conventional schemes.

关键词： Energy consumption Electric vehicle charging Power grids

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Master stability functions for complete, intralayer, and interlayer synchronization in multiplex networks of coupled Rössler oscillators

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Physical Review E 2019年第1期99卷 012304-012304页

作者： Longkun Tang Xiaoqun Wu Jinhu Lü Jun-an Lu Raissa M. D'Souza Fujian Province University Key Laboratory of Computation Science School of Mathematical Science Huaqiao University Quanzhou 362021 China School of Mathematics and Statistics Wuhan University Wuhan 430072 China Department of Computer Science University of California Davis California 95616 USA School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

Synchronization phenomena are of broad interest across disciplines and increasingly of interest in a multiplex network setting. For the multiplex network of coupled Rössler oscillators, here we show how the master stability function, a celebrated framework for analyzing synchronization on a single network, can be extended to certain classes of multiplex networks with different intralayer and interlayer coupling functions. We derive three master stability equations that determine, respectively, the necessary regions of complete synchronization, intralayer synchronization, and interlayer synchronization. We calculate these three regions explicitly for the case of a two-layer network of Rössler oscillators and show that the overlap of the regions determines the type of synchronization achieved. In particular, if the interlayer or intralayer coupling function is such that the interlayer or intralayer synchronization region is empty, complete synchronization cannot be achieved regardless of the coupling strength. Furthermore, for any network structure, the occurrence of intralayer and interlayer synchronization depends mainly on the coupling functions of nodes within a layer and across layers, respectively. Our mathematical analysis requires that the intralayer and interlayer supra-Laplacians commute. But, we show this is only a sufficient, and not necessary, condition and that the results can be applied more generally.

关键词： Collective dynamics Synchronization Synchronization transition

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The medical segmentation decathlon

arXiv

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arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, Keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation

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Examination of Gaze Fixations Recorded during the Trail Making Test

Examination of Gaze Fixations Recorded during the Trail Maki...

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Attila Kovari Jozsef Katona Ilona Heldal Carsten Helgesen Cristina Costescu Adrian Rosan Andrea Hathazi Serge Thill Robert Demeter CogInfoCom Based LearnAbility Research Team Institute of Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Computing Mathematics and Physics Western Norway University of Applied Sciences Bergen Norway Special Education Department Faculty of Psychology and Educational Sciences Babes-Bolyai University Cluj-Napoca Romania Donders Centre for Cognition Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherland Department of Automation and Information Technology Faculty of Electrical Engineering and Computer Science Transilvania University of Brasov Brasov Romania

ISBN: (数字)9781728147932

ISBN: (纸本)9781728147949

The continuous visual tracking of activity performed in perception and action activities requires continuous eye motion. The visual computer monitoring, recording and evaluation of human motion provides many opportunities in the development of human-computer interfaces. In most activities performed by humans, eye-hand coordination plays a significant role. From the eye-hand coordination aspect, the fixation's direction is an essential factor, since before touching an object, the look is aimed at the object to be touched for a longer period. In this paper, the fixation playing a major role in eye-hand coordination is being analyzed during the Trail Making Test. The test primarily aims to measure visual searching or scanning, visual-spatial sequencing; however, the eye-hand coordination's speed plays a key role in the quick and accurate execution of the test. During the test, eye motion parameters were recorded and determined, where further analyses make certain human cognitive factors indirectly able to be analyzed.

关键词： Tracking Task analysis Conferences Visualization Software Testing Cognition

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