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A Multi-label learning Method to Detect Arrhythmia Based on 12-Lead ECGs 1st

A Multi-label Learning Method to Detect Arrhythmia Based on ...

引用

1st international Workshop on Machine learning and Medical engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th international Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Zhu, Jinjing Xin, Kaifa Zhao, Qingqing Zhang, Yue Division of Information Science and Technology Tsinghua University Beijing China Renmin Hospital of Wuhan University Wuhan University Wuhan China

ISBN: (纸本)9783030333263

Cardiovascular disease (CVD) is one of the most serious diseases that harm human life and gives a huge burden to the health care system. Recent advances in deep learning have achieved great success in object detection, speech and image recognition. Although deep learning has been applied to the detection of arrhythmia, detection accuracy is limited because of three major issues: 1. Each ECG signal maybe contains more than one-label information;2. It is hard to classify ECG with different lengths;3. data imbalance problem is severe for arrhythmia detection. In this paper, we present a multi-label learning algorithm to address the class imbalance and detection on ECGs with different durations. We utilize Deep Convolutional Generative Adversarial Networks (DCGANs) and Wasserstein GAN-Gradient Penalty (WGAN-GP) to generate new positive samples and use two losses to balance the importance between positive samples and negative samples. Moreover, we construct a Squeeze and Excitation-ResNet (SE-ResNet) module for normal rhythm and arrhythmia detection. In order to solve the multi-label classification problem, we train nine different binary classifiers for each category and determine which types of rhythm the ECG signals belong to. Experimental results on The ECG Intelligence Challenge 2019 dataset demonstrate that our multi-label learning method achieves competitive performance in multi-label ECGs classification. © 2019, Springer Nature Switzerland AG.

关键词： Electrocardiograms

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A comparative study of machine learning algorithms for physiological signal classification 22

A comparative study of machine learning algorithms for physi...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Biagetti, Giorgio Crippa, Paolo Falaschetti, Laura Tanoni, Giulia Turchetti, Claudio Univ Politecn Marche DII Dept Informat Engn Via Brecce Bianche12 I-60131 Ancona Italy

The present work aims at the evaluation of the effectiveness of different machine learning algorithms on a variety of clinical data, derived from small, medium, and large publicly available databases. To this end, several algorithms were tested, and their performance, both in terms of accuracy and time required for the training and testing phases, are here reported. Sometimes a data preprocessing phase was also deemed necessary to improve the performance of the machine learning procedures, in order to reduce the problem size. In such cases a detailed analysis of the compression strategy and results is also presented. 2018 The Authors. Published by Elsevier Ltd.

关键词： Machine learning classification biosignal analysis

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Research on moving target statistic algorithm based on image processing

Research on moving target statistic algorithm based on image...

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international conference on Electrical Machines and Systems (ICEMS)

作者： Dong Li Xin Chen Department of Mechanical and Electrical Engineering Wuhan City Vocational University Wuhan China Department of Mechatronics Engineering and Automotive Services Wuhan Business University Wuhan China

ISBN: (纸本)9781728133997

With the development of intelligent manufacturing, image processing technology is becoming more and more close to life. Image processing measurement gains momentum development in the field of ITS. Vehicle detection system aims at obtaining data of traffic, length of models of vehicle. Virtual detection loop system is poor in accuracy,the author of this paper puts forward the improvement of Kalman algorithm, improving the accuracy of vehicle *** the same time,reducing false detection rate of vehicle information.

关键词： Target tracking Kalman filters Streaming media conferences Mathematical model Image processing

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Preface

2022 Sardar Patel International Conference on Industry 4.0 -...

引用

2022 Sardar Patel international conference on Industry 4.0 - Nascent Technologies and Sustainability for 'Make in India' Initiative, SPICON 2022 2022年

作者： Rane, Santosh Sardar Patel International Conference SPICON 2022 India

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Diagnosis of Electric Motor Using Acoustic noise Based on CNN

Diagnosis of Electric Motor Using Acoustic noise Based on CN...

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international conference on Electrical Machines and Systems (ICEMS)

作者： Dong-Jin Choi Ji-Hoon Han Sang-Uk Park Sun-Ki Hong Department of Information Control Engineering Hoseo Univeristy Asan Korea Department of Digital Control Engineering Hoseo Univeristy Asan Korea

ISBN: (纸本)9781728133997

It is very important to diagnose the mechanical failure of the motor in the industry. The conventional method is difficult to include both the various motors and the driving environment. In order to solve these problems, researches have been actively conducted to apply deep learning to fault diagnosis. Many studies focus on vibration data. Noise data can be collected easily and inexpensively compared to vibration data. In this paper, vibration data and performance were compared using noise data as training data from the diagnosis of motor failure. In the first experiment, vibration and noise data were collected in the same experimental environment. The collected data were compared through the same algorithm. In the second experiment, we compared the performance of the three deep learning models. As a result, both the noise and vibration data showed high accuracy and sufficient fault diagnosis was possible considering the vulnerability of the disturbance.

关键词： Vibrations Fault diagnosis Induction motors Machine learning Convolution Gears Classification algorithms

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data-Consistent Toolchain for a requirements-based Specification with the Interdisciplinary Modeling Language (IML) 22

Data-Consistent Toolchain for a requirements-based Specifica...

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22nd IEEE international conference on intelligent engineering Systems (INES)

作者： Herfs, Werner Flender, Jerome Storms, Simon Witte, Martin RWTH Aachen Aachen Univ Technol WZL Lab Machine Tools & Prod Engn Steinbachstr 19 D-52074 Aachen Germany Siemens AG Gleiwitzer Str 555 D-90475 Nurnberg Germany

ISBN: (纸本)9781538611227

Disruptions within engineering toolchains as well as scattered system specifications still impede the integration of phase-spanning system design approaches for production machines. Aside of the necessary high efforts to gather the essential requirements for the implementation of a component or process, this also inhibits documenting the reasons for a specific choice of a particular machine concept. Though to achieve reasonable system designs, it is necessary to link and track requirements with the implementing machine structures - consistently and synchronized across all design phases. Therefore, this article presents an approach for a requirements-based toolchain, which enables the data-consistent use of requirements in the system design phase of production machines. Based on the specification language IML (Interdisciplinary Modeling Language) and its central interdisciplinary system model, intuitive operation principles shall support the definition of component-specific requirements sets while round-trip strategies realize synchronized requirement profiles and histories.

关键词： System analysis and design Modeling Production Synchronization Requirements management Stakeholders History

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data Fusion for MaaS: Opportunities and Challenges 22

Data Fusion for MaaS: Opportunities and Challenges

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22nd IEEE international conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Wu, Jianqing Zhou, Luping Cai, Chen Shen, Jun Lau, Sim Kim Yong, Jianming Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia CSIRO Data 61 Adv Data Analyt Transport Sydney NSW Australia Univ Southern Queensland Sch Management & Enterprise Toowoomba Qld Australia

ISBN: (纸本)9781538614822

Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling.

关键词： data fusion machine learning mobility as a service

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Research on Interturn Short Circuit Fault Identification Method of PMSM based on Deep learning

Research on Interturn Short Circuit Fault Identification Met...

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international conference on Electrical Machines and Systems (ICEMS)

作者： Sen Wang Jieqiu Bao Siyang Li Hongkui Yan Tianyao Tang Di Tang Department of College of Automation Shenyang Institute of Engineering Shenyang China China electric power equipment and technology co. Ltd Beijing China

ISBN: (纸本)9781728133997

Based on neural network deep learning, the fault diagnosis of permanent magnet synchronous motor was *** creating different sample labels for neural network deep learning training, diagnosis of interturn short circuit fault cause;By comparing the accuracy of different neural networks, the deep learning neural network has the highest accuracy in PMSM fault *** with the traditional motor fault diagnosis method, the deep learning method of genetic neural network can solve the problems of large knowledge base, low fault search efficiency, effectively shorten the fault diagnosis time and maintain the stable operation of the PMSM.

关键词： Permanent magnet motors Circuit faults Synchronous motors Fault diagnosis data models Machine learning Neural networks

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Application of CBR for intelligent Process Control of a WWTP 22

Application of CBR for Intelligent Process Control of a WWTP

引用

22nd international conference of the Catalan-Association-for-Artificial-Intelligence (CCIA)

作者： Pascual-Panach, Josep Angel Cuguero-Escofet, Miguel Sanchez-Marre, Miguel Aguilo-Martos, Pere CCB Serv Mediambientals SAU Barcelona Spain Consorci Besos Tordera Barcelona Spain Univ Politecn Cataluna Knowledge Engn & Machine Learning Grp KEMLG Intelligent Data Sci & Artificial Intelligence Re BarcelonaTech Barcelona Spain Consorci Besos Tordera R&D Dept 241 St Julia Ave Granollers 08103 Barcelona Spain

ISBN: (纸本)9781643680156;9781643680149

This paper proposes the use of a Case-Based Reasoning (CBR) system for the control and the supervision of a real wastewater treatment plant (WWTP). A WWTP is a critical system which aims to ensure the quality of the water discharged to the receiving bodies, stablished by applicable regulations. At the current stage the proposed methodology has been tested off-line on a real system for the control of the aeration process in the biological treatment of a WWTP within the ambit of Consorci Besos Tordera (CBT), a local water administration in the area of Barcelona. For this purpose, data mining methods are considered to extract the available knowledge from historical data to find a useful case base to be able to generate set-points for the local controllers in the WWTP. The results presented in this work are evaluated taking into account the performance of the CBR method e.g. case base size, CBR cycle time or number of cases resolved satisfactorily (forthcoming steps will include on-line tests). For this purpose, some Key Performance Indicators (KPI) are designed together with the plant manager and process experts, in order to monitor key parameters of the WWTP which are representative of the performance of the control and supervision system. Hence, these KPI are related with water quality regulations -e.g. ammonia concentration in the WWTP effluent- and the economic cost efficiency -e.g. electrical consumption of the installation. In order to evaluate the results, different flat-based memory organizations (i.e. cases are stored sequentially in a list) for the case base are considered. First, a unique case base is used. At the current stage and for the results shown in this work, this case base is divided in multiple libraries depending on a case classification. Finally, the combination of this approach with Rule-Based Reasoning (RBR) methods is proposed for the next stages of the work.

关键词： Case-Based Reasoning intelligent Process Control Wastewater Treatment Plant data Mining

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Noise and Vibration Analysis of Dry-Type Power Transformer for Monitoring and data Mining Applications

Noise and Vibration Analysis of Dry-Type Power Transformer f...

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international conference on Electrical Machines and Systems (ICEMS)

作者： C. Boonseng R. Boonseng K. Kularbphettong Faculty of Engineering King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand Faculty of Science and Technology Suan Sunandha Rajabhat University Bangkok Thailand

ISBN: (纸本)9781728133997

An important source of transformer noise is magnetostrictive vibration of the magnetic core. This paper outlines some important effects of vibration on aging due to the deterioration of insulation by on-line measurement. The acceleration in the XYZ axis is telling the model of the vibrational force. A large amount of data can be collected as a database for maintenance decisions and take care of electrical insulation. Finally, collect data into the data management process using data mining. Storage data is much smaller approximately 1.17% as compared to all data.

关键词： data models Acceleration Vibrations Machine learning data mining Magnetic cores Power transformers

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