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检索条件"任意字段=9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020"

共 506 条记录，以下是481-490 订阅

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A Novel Local Selective Ensemble-based AdaBoost Method for Fault Detection of Industrial Process

A Novel Local Selective Ensemble-based AdaBoost Method for F...

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data driven control and learning systems (ddcls)

作者： Yuan Xu Cuicui Zhang Qunxiong Zhu Yanlin He College of Information Science and Technology Beijing University of Chemical Technology Beijing China Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

For the sake of guaranteeing the security of complex industrial system, it is important to accurately and efficiently detect the faults. AdaBoost algorithm is an effective fault detection method. It can generate a large number of weak classifiers in iterations and combine many of these weak classifiers into the strong classifier to solve the classification problem for fault detection. For the traditional AdaBoost, several of these poor weak classifiers are often ignored and not fully used. However, the weak classifiers with poor performance may store the significant information and pay more attention to the difficult samples. To solve these problems, we propose a local selective ensemble-based AdaBoost (AdaBoost-LSE) in this article. Firstly, error feedback ELM (EFELM) is introduced to establish the basic weak classifier. through the iteration of AdaBoost, these weak classifiers based on EFELM are generated. Secondly, these weak classifiers are divided into good weak classifiers and bad weak classifiers based on the classification accuracy. the poor weak classifiers with good performance are selected by calculating the classification accuracy for the targeted samples. thirdly, the strong classifier of AdaBoost-LSE is constructed by integrating the original good weak classifiers and some of these poor weak classifiers with good performance. To verify the efficiency of AdaBoost-LSE, the Tennessee Eastman (TE) simulation process is used. the experimental results reveal that the proposed AdaBoost-LSE can greatly improve the accuracy of fault detection.

关键词： Fault detection Classification algorithms Support vector machines Process control Market research Electrical fault detection Valves

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Early Fault Feature Extraction of Nuclear Main Pump Based on MEMD-1.5 dimensional Teager Energy Spectrum

Early Fault Feature Extraction of Nuclear Main Pump Based on...

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data driven control and learning systems (ddcls)

作者： Shule Li Jie Ma Institute of Automation Beijing Information Science & Technology University Beijing Institute of electrical and mechanical Beijing Information Science & Technology University Beijing

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

For the weak component in the early failure of the nuclear main pump, it is easy to be masked by strong faults or overwhelmed by strong noise to cause leakage diagnosis, and in actual working condition measurement, multiple sensors are usually used to synchronize the signals. the existing traditional feature analysis methods are only the single-channel vibration signal measured by multi-sensors is processed, and the multi-channel data fusion is not performed at the later stage to achieve the multi-channel synchronization correlation analysis. A multi-dimensional empirical mode decomposition (Multivariate Empirical Mode Decomposition, MEMD)-1.5-dimensional Teager energy spectrum is proposed for the extraction of micro-fault features. Firstly, use the MEMD to adaptively decompose the multi-channel vibration signals on the collected multi-channel fault characteristic signals under the same state to obtain the Intrinsic Mode Functions(IMF) components of each channel, and then calculate the kurtosis value and correlation coefficient of each channel IMF component to select the best IMF component containing the main information of the fault. Finally, the 1.5-dimensional Teager energy spectrum is used to obtain the fault characteristic information of the signal to achieve the extraction of minor fault features. In order to verify the feasibility of the theory, simulation tests are carried out and the method is applied to the early failure of the outer ring of the bearing, and compared with EMD and envelope demodulation, it is verified that this method can effectively deal with early multi-channel failure information of rotating machinery. It has theoretical guidance significance for early diagnosis of small faults of nuclear main pump.

关键词： Feature extraction Vibrations Fault diagnosis Empirical mode decomposition Correlation Rolling bearings data mining

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Attention M-net for Automatic Pixel-Level Micro-crack Detection of Photovoltaic Module Cells in Electroluminescence Images

Attention M-net for Automatic Pixel-Level Micro-crack Detect...

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data driven control and learning systems (ddcls)

作者： Yu Jiang Chunhui Zhao Waner Ding Ling Hong Qu Shen State Key Laboratory of Industrial Control Technology and College of Control Science and Engineering Zhejiang University Hangzhou P.R. China Zhejiang Provincial Key Laboratory of Solar Energy Utilization & Energy Saving Technology Zhejiang Energy Group R&D Hangzhou P.R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In the solar power industry, quality inspection of solar cells is a very important part of the production and application process. Micro-crack is a type of common defect that may be present in photovoltaic (PV) module cells which can reduce the power generation efficiency and service lifetime. However, it is difficult to identify micro-crack by the naked eye because it is readily confused with non-uniform backgrounds and complex textures in Electroluminescence (EL) images. In this paper, we propose Attention M-net which combines efficient segmentation model structure and attention mechanism. It is a novel micro-crack detection model for automated pixel-level micro-crack detection of PV module cells. the M-shaped structure solves "All Black" issue that is easy to occur due to the severe imbalance of the micro-crack segmentation dataset. And integration of attention mechanism into the network significantly improves the accuracy of segmentation. Because of the above two advantages, the proposed model can be accurately learned from a small annotated dataset, thereby saving the time of micro-crack EL images collection and pixel-level annotations. We extensively trained the proposed model on a small dataset of 20 annotated EL images which include 10 monocrystalline and 10 polycrystalline. the evaluated results on the test dataset demonstrate the high efficiency and accuracy of Attention M-net for pixel-level micro-crack detection of PV module cells in EL images.

关键词： Feature extraction Image segmentation Photovoltaic cells Decoding Task analysis Silicon Convolution

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A Combined Policy Gradient and Q-learning Method for data-driven Optimal control Problems 9

A Combined Policy Gradient and Q-learning Method for Data-dr...

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9th International conference on Information Science and Technology (ICIST)

作者： Lin, Mingduo Liu, Derong Zhao, Bo Dai, Qionghai Dong, Yi Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Beijing Normal Univ Sch Syst Sci Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Inst Technol Sch Opt & Photon Beijing Peoples R China

ISBN: (纸本)9781728121062

this paper focuses on the data-driven controller design for optimal control problems of nonlinear nonaffine discrete-time systems. A novel policy gradient and Q-learning (PGQL) adaptive algorithm which learns the optimal control policy from real empirical data is developed without requiring system dynamics. A policy iteration scheme is designed to iteratively update the approximate Q-function, and the control policy is improved via gradient method until they converge to the bounded regions of the optimal Q-function and the optimal control policy, respectively. Two neural networks (NNs) are employed to realize the developed algorithm. Moreover, the convergence analysis of approximate Q-function is established. Since the control policy is parameterized, it can be upgraded through updating the actor-NN parameters in the direction of the performance gradient. Finally, the simulation results are given to verify the performance of the developed PGQL adaptive algorithm.

关键词： adaptive dynamic programming optimal control reinforcement learning policy gradient Q-learning data-driven

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Keynote Talk 3: Security and Privacy Challenges in learning learning-enabled IoT systems

Keynote Talk 3: Security and Privacy Challenges in Learning ...

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International conference on Informatics, Electronics & Vision (ICIEV)

作者： Mani Srivastava University of California LA LA

ISBN: (数字)9781728193311

ISBN: (纸本)9781728193328

Innovative edge devices, pervasive wireless connectivity, and powerful cloud computing are leading to a new generation of learning learning-enabled IoT systems. Unlike their precursors that primarily focused on collecting sensor data for off off-line knowledge discovery and simple control, this new generation of IoT systems harness machine learning (ML) to make rich inferences about the state of natural, eng ineered, and human systems; to comprehend and project it to the future; and to decide on actions that influence and nudge that state in a desired and timely manner. While data data-driven ML algorithms are helping equip these emerging IoT systems with intellig ence and autonomy, they also introduce vulnerabilities to a variety of security and privacy problems. this talk will cover research in my group on (i) efficient black black-box attacks on sensor data and machine learning models that cause these systems to make i ncorrect inferences and control actions, (ii) defense mechanisms that help detect and mitigate these attacks, and (iii) protecting against privacy loss arising from inference attacks on high high-dimensional sensory data being shared by differentially differentially-private s ynthetic sensor data generation generation.

关键词： Security data privacy Machine learning Wireless sensor networks Wireless communication Privacy Machine learning algorithms

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A Traction Load Forecasting Method of Electrified Railway Based on LSTM

A Traction Load Forecasting Method of Electrified Railway Ba...

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International conference on Power Electronics and Motion control (IPEMC)

作者： Qian Ma Yishuang Peng Pei Luo Qianru Li Hao Wang Jing Yu College of Automation and Electronic Information Xiangtan University Xiangtan China

ISBN: (数字)9781728153018

ISBN: (纸本)9781728153025

A data-driven traction load forecasting method is proposed to better evaluate the impact of electrified railways on power quality of power grids. By analyzing the characteristics of a large number of traction load measured data, this paper combines the idea of deep learning to build a traction load forecasting model using long short-term memory (LSTM) network, including the preprocessing of input data, the construction of the deep LSTM network, the training of the model and the search of hyper-parameters. the simulation results show that the proposed method can achieve good forecasting effect, and confirm the ability of LSTM to learn the characteristics of input traction load data.

关键词： Analytical models Load forecasting Simulation Predictive models Rail transportation data models Load modeling

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Deep learning for massive MIMO: Challenges and Future prospects

Deep Learning for massive MIMO: Challenges and Future prospe...

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International conference on Communication systems and Network Technologies (CSNT)

作者： Vandana Bhatia Malay Ranjan Tripathy Priya Ranjan Department of Computer Science ASET AUUP NOIDA Uttar Pradesh India Department of ECE ASET AUUP NOIDA Uttar Pradesh India Department of ECE SEAS SRM University Amarawathi Andhra Pradesh India

ISBN: (数字)9781728149769

ISBN: (纸本)9781728149776

the wireless networks today are complex, massive and have dynamic capacity demands. Increase in demand resulted into trouble in managing and monitoring the network components. thus, intelligent data-driven designs and approaches are required so that the 5th generation (5G) of mobile systems can be reformed for enabling self-organizing capabilities. thus, in the last decade, mathematical models are designed and adapted among modems. this paper presents a comprehensive outline of the emerging research on deep learning-based models for massive MIMO systems. In most of the work, Deep learning models are used for redesigning the conventional communication system. It may involve channel encoding, decoding, detection, recognition, antenna selection, modulation, etc. It will be interesting to comprehend that replacement of the communication system with a profoundly new architecture such as deep learning based autoencoder, convolutional neural network, etc. these Deep learning-based models show promising performance enhancements with a few limitations and can be efficiently used with massive MIMO.

关键词： Wireless transmission Deep learning MIMO Channel Matrix multiple input multiple output systems WIRELESS TRANSMISSION channel matrix COMMUNICATION systems Network components Enabling Channel coding

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learning from Noisy Labeled data Using Symmetric Cross-Entropy Loss for Image Classification

Learning from Noisy Labeled Data Using Symmetric Cross-Entro...

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ieee Global conference on Consumer Electronics (GCCE)

作者： Hiroshi Takeda Soh Yoshida Mitsuji Muneyasu Graduate School of Science and Engineering Kansai University 3-3-35 Yamate-cho Suita-shi Osaka Japan

ISBN: (数字)9781728198026

ISBN: (纸本)9781728198033

While image classification with deep neural networks (DNNs) has made remarkable progress, learning from noisy labeled data degrades performance, and this problem remains challenging. Some previous methods, such as Co-teaching and MentorNet, use the memorization effects, which are characteristics of DNN training that first learn easy (most likely clean) samples and then difficult (most likely noisy) samples. these methods treat samples with small training loss as clean, and we call this idea the “small-loss criterion”. However, when there is a difference in learning speed between samples, a class including easy samples tends towards overfitting. In this paper, we propose a robust learning method by introducing a symmetric cross-entropy (SCE) loss to control the difference in learning speed between all classes. Experimental results on the CIFAR-100 image classification dataset containing 45% and 50% of noisy labels show the robustness of the proposed method.

关键词： Training learning systems Neural networks Robustness Noise measurement Consumer electronics Image classification

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IoT Based CNC Machine Condition Monitoring System Using Machine learning Techniques

IoT Based CNC Machine Condition Monitoring System Using Mach...

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International conference on Communication systems and Network Technologies (CSNT)

作者： Mohan Krishna K Prashanth Kannadaguli Bengaluru India Dhaarini Academy of Technical Education Bengaluru India

We developed a CNC machine9;s condition monitoring system based on Artificial Neural Network (ANN) and correlate the same with the real-time CNC machine data. the classification of the condition of a CNC machine wa... 详细信息

ISBN: (数字)9781728149769

ISBN: (纸本)9781728149776

We developed a CNC machine's condition monitoring system based on Artificial Neural Network (ANN) and correlate the same with the real-time CNC machine data. the classification of the condition of a CNC machine was done by deciding whether it is a fresh machine or worn machine using machine learning techniques. In consideration of real time data loaded from a CNC machine we built a database and then modelled an ANN. Based on this approach of machine learning which implements pattern recognition and probabilistic modelling of the CNC machine data, classification of the condition of a CNC machine was done successfully. Finally, performance analysis of this machine model prevail in terms of Machine Error Rate (MER) upholds the impressive fact that modeling using the ANN yields better results over another alternative modeling techniques and can be used for developing Automatic CNC machine condition monitoring and recognition system.

关键词： ANN Machine learning CNC Machine database Modeling MER Artificial neural networks Computer numerical control MAXIMUM EFFICIENT RATE Methanol extraction residue machine Artificial Intelligence Machine learning database models learning methods Condition monitoring

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Towards a data-driven predictive-reactive production scheduling approach based on inventory availability 9

Towards a data-driven predictive-reactive production schedul...

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9th IFAC/IFIP/IFORS/IISE/INFORMS conference on Manufacturing Modelling, Management and control (IFAC MIM)

作者： Takeda Berger, Satie Ledoux Zanella, Renata Mariani Frazzon, Enzo Morosini Univ Fed Santa Catarina Ind & Syst Engn Dept Ctr Technol Florianopolis SC Brazil

To survive in a competitive business environment, manufacturing systems require the proper deployment of advanced technologies coming from Industry 4.0. these technologies allow access to quasi real-time data that provide a continuously updated picture of the production system, including the state of available inventory. data-driven predictive-reactive production scheduling has the potential to support the anticipation and prompt reaction to overcome different kinds of disruptions that occur in production execution nowadays. this research paper aims to propose a conceptual model for a data-driven predictive reactive production scheduling approach combining machine learning and simulation-based optimization, considering current inventory of raw material, work in process and final products inventory to characterize a job-shop production execution state. the approach supports decision-making in dynamic situations related to inventory availability that can affect production schedules. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Predictive-reactive scheduling manufacturing system data-driven machine learning simulation-based optimization

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