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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271-280 订阅

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Incremental Gaussian Mixture Model for Time-varying Process Monitoring 9

Incremental Gaussian Mixture Model for Time-varying Process ...

引用

9th ieee data driven control and learning systems conference (ddcls)

作者： Dai, Qingyang Zhao, Chunhui Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Beijing Key Lab Proc Automat Min & Met State Key Lab Proc Automat Min & Met Beijing 102600 Peoples R China

ISBN: (纸本)9781728159225

With the increasing complexity of industrial production, data-driven based monitoring methods attract more attention. However, the conventional static process monitoring methods may show poor performance for the time-varying processes since they fail to track the time-varying characteristics. As Gaussian Mixture Model (GMM) has been widely used for process monitoring, this paper presents a new incremental GMM model for monitoring time-varying processes. First, an incremental GMM (IGMM) model is proposed, which can recursively update model parameters, adaptively add new Gaussian components and discard the irrelevant component based on the shifting samples online. then the Bayesian Inference Probability (BIP) is introduced for monitoring statistics and a two-level partition strategy that can separate normal shifting samples from fault samples is proposed, which reduces the possibility of adding fault samples to the model. On the basis of IGMM model, an adaptive monitoring scheme is developed, which can track the time-varying characteristics of processes. Finally, a time-varying numerical example and the Tennessee Eastman process are adopted to validate the feasibility of the proposed monitoring model. Experimental results clearly demonstrate the adaptiveness of the monitoring model to time-varying processes and the ability to avoid false updates.

关键词： Gaussian mixture model online (recursive) estimation time-varying processes incremental learning approach process monitoring

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Yarn-dyed Fabric Defect Detection using U-shaped De-noising Convolutional Auto-Encoder 9

Yarn-dyed Fabric Defect Detection using U-shaped De-noising ...

引用

9th ieee data driven control and learning systems conference (ddcls)

作者： Zhang, Hong-wei Tan, Quan-lu Lu, Shuai Ge, Zhi-qiang Gu, De Xian Polytech Univ Sch Elect Informat Xian 710048 Peoples R China Zhejiang Univ Inst Ind Proc Control Hangzhou 310027 Peoples R China Beijing Univ Chem Technol Inst Math Beijing 100029 Peoples R China Jiangnan Univ Inst Automat Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9781728159225

Practical factors such as high labor cost of labelling defect samples and scarcity of defect samples make it difficult for supervised machine learning models to solve the problem of yam-dyed fabric defect detection. To solve this problem, this paper proposes an unsupervised yam-dyed fabric defect detection method based on U-shaped de-noising convolutional auto-encoder (UDCAE). Firstly, for tested samples of yam-dyed fabric, the training dataset was constructed by collecting the non-defect yarn-dyed fabric samples. then, the non-defect dataset is utilized to model and train the proposed UDCAE model. Finally, the defective area can be quickly detected by calculating the residual between the original tested yarn-dyed fabric image and its reconstructed item correspondingly. the experiment results show that the proposed method can accurately detect defects of yarn-dyed fabrics with different patterns.

关键词： yam-dyed fabric fabric defect detection de-noising auto-encoder convolutional auto-encoder Unet

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A Spatial-information-based Semi-supervised Soft Sensor for f-CaO Content Prediction in Cement Industry 9

A Spatial-information-based Semi-supervised Soft Sensor for ...

引用

9th ieee data driven control and learning systems conference (ddcls)

作者： Jiang, Xiaoyu Yao, Le Huang, Gaopan Qian, Jinchuan Shen, Bingbing Xu, Lu Ge, Zhigiang Zhejiang Univ Coll Control Sci & Engn Inst Ind Proc Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Ali Ctr Alibaba Grp Nanjing 210000 Peoples R China Shandong Donghua Cement Co Ltd Zibo 255000 Peoples R China

ISBN: (纸本)9781728159225

f-CaO is a key factor affecting the quality of cement in production. In this paper, the cement clink production process is introduced and discussed in detail. the time delay between the variables leads to an inaccurate matching relationship with each other and defects the performance of traditional soft sensors. To this end, a semi-supervised spatial-information-based soft sensor for f-CaO content is proposed. First, we analyzed the relationship between process variables and quality variable and then reconstruct the input of samples into data matrix by stitching unlabeled process data together. the semi-supervised structure helps retain process information in the data. then, an end-to-end soft sensor based on CNN is established: convolution and pooling operations are used to extract the features of two-dimensional data containing spatial information;a multi-layer perceptron models the extracted features regressively. Further, in order to solve the defect of insufficient generalization ability of the CNN-based model, a framework for spatial feature extracting and transferring is proposed. Compared with the multilayer perceptron, strong regression models with spatial features get better prediction accuracy. An actual cement production case is used to verify the effectiveness of the proposed method.

关键词： semi-supervised CNN soft sensor f-CaO cement industry

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Adaptive SMC-based Trajectory Tracking control of Underactuated Overhead Cranes 9

Adaptive SMC-based Trajectory Tracking Control of Underactua...

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9th ieee data driven control and learning systems conference, ddcls 2020

作者： Zhang, Shengzeng He, Xiongxiong Zhu, Haiyue Feng, Yuanjing Chen, Qiang Zhu, Zhengyang Li, Xiaocong Zhejiang University of Technology College of Information Engineering Hangzhou310023 China Mechatronics Group Singapore Institute of Manufacturing Technology A∗star Singapore638075 Singapore

ISBN: (纸本)9781728159225

Overhead cranes, which are typically underactuated, are studied systematically nowadays. While, the model widely used in research is ideal. thus, the corresponding controllers may react badly under external disturbances, unmodeled dynamics and input constraints. To tackle this issue, this paper develops an adaptive version of anti-sway trajectory tracking controller for overhead cranes. First, as to constrained input, we perform a mapping action from the system input to the hyperbolic tangent function. then adaptation mechanisms are proposed to adjust the modified inputs and the system uncertainty. Such a controller achieves precise positioning and swing suppression despite input saturation, system uncertainty and external disturbances. the crane system proves to be dissipative with the proposed controller. the experiments accomplished on a laboratory-size bridge crane reveal that the proposed controller asymptotically stabilizes all system states. © 2020 ieee.

关键词： controllers

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Continuous Estimation of Human Knee-Joint Angles from SEMG UsingWavelet Neural Network 9

Continuous Estimation of Human Knee-Joint Angles from SEMG U...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Li, Wanting Liu, Keping Sun, Zhongbo Wang, Gang Li, Feng Zhang, Xin Zhou, Yanpeng Changchun Univ Technol Dept Control Engn Changchun 130025 Peoples R China Jilin Univ Key Lab Bion Engn Minist Educ Changchun 130025 Peoples R China

Surface electromyography (sEMG) signals contain a wealth of information associated with human9;s movement. In this paper, a wavelet neural network (WNN) model is proposed and implemented to estimate human knee-join... 详细信息

ISBN: (纸本)9781728159225

Surface electromyography (sEMG) signals contain a wealth of information associated with human's movement. In this paper, a wavelet neural network (WNN) model is proposed and implemented to estimate human knee-joint angles from the sEMG signals. With the processed signals as input, the WNN model is trained to estimate the knee-joint angles in continuous motion. To validate the effectiveness of the WNN model, one able-bodied person sit in a chair and accomplish leg stretching in the experiment, and simultaneously record the sEMG signals from the vastus rectus (VR) and the angles of the knee joint. then, the estimation results of the WNN model are compared with the RBF neural network and the BP neural network. the experimental results show that the WNN model has the best performance in the knee-joint angles estimation than the other two neural network models. the root mean square (RMS) error of the knee-joint angles is 6.5054 degrees and the time is 5.3271 seconds. the proposed method can be applied to rehabilitation robots or assisted exoskeleton.

关键词： Joint angles estimation Surface electronyogram (sEMG) Rehabilitation Wavelet neural network (WNN)

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Attention M-net for Automatic Pixel-Level Micro-crack Detection of Photovoltaic Module Cells in Electroluminescence Images 9

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

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9th ieee data driven control and learning systems conference (ddcls)

作者： Jiang, Yu Zhao, Chunhui Ding, Waner Hong, Ling Shen, Qu Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Zhejiang Energy Grp R&D Zhejiang Prov Key Lab Solar Energy Utilizat & Ene Hangzhou 310027 Peoples R China

ISBN: (纸本)9781728159225

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： Photovoltaic modules Micro-crack detection M-net "All Black" issue Attention mechanism

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Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned

Soft Sensing Model Visualization: Fine-tuning Neural Network...

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9th ieee International conference on Big data (ieee Bigdata)

作者： Qian, Xiaoye Zhang, Chao Yella, Jaswanth Huang, Yu Huang, Ming-Chun Bom, Sthitie Seagate Technol Bloomington MN 55435 USA Case Western Reserve Univ Cleveland OH 44106 USA Univ Chicago Chicago IL 60637 USA Univ Cincinnati Cincinnati OH USA Florida Atlantic Univ Boca Raton FL 33431 USA Duke Kunshan Univ Suzhou Peoples R China

ISBN: (纸本)9781665439022

the growing availability of the data collected from smart manufacturing is changing the paradigms of production monitoring and control. the increasing complexity and content of the wafer manufacturing process in addition to the time-varying unexpected disturbances and uncertainties, make it infeasible to do the control process with model-based approaches. As a result, data-driven soft-sensing modeling has become more prevalent in wafer process diagnostics. Recently, deep learning has been utilized in soft sensing system with promising performance on highly nonlinear and dynamic time-series data. Despite its successes in soft-sensing systems, however, the underlying logic of the deep learning framework is hard to understand. In this paper, we propose a deep learning-based model for defective wafer detection using a highly imbalanced dataset. To understand how the proposed model works, the deep visualization approach is applied. Additionally, the model is then fine-tuned guided by the deep visualization. Extensive experiments are performed to validate the effectiveness of the proposed system. the results provide an interpretation of how the model works and an instructive fine-tuning method based on the interpretation.

关键词： Deep learning Deep Visualization Defective wafer detection

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Prediction of the Li-Ion Battery Capacity by Using Event-driven Acquisition and Machine learning 7

Prediction of the Li-Ion Battery Capacity by Using Event-Dri...

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7th International conference on Event-Based control, Communication and Signal Processing (EBCCSP)

作者： Qaisar, Saeed Mian AbdelGawad, Amal Essam ElDin Effat Univ Coll Engn Jeddah 22332 Saudi Arabia Effat Univ Energy & Technol Ctr Commun & Signal Proc Lab Jeddah 22332 Saudi Arabia

ISBN: (纸本)9781665436977

the battery is a crucial element of modern power systems and it is utilized habitually in different vital applications such as electric vehicles, drones, avionics and mobile phones. Among various batteries technologies the Li-Ion batteries are widely used. It is mainly because of their compactness, longer life and high power capacity. On the other hand, due to the disadvantage of Li-ion batteries being expensive, their use is monitored using battery management systems (BMSs) to optimize their performance and ensure they last longer. the extensive processing resources that modern BMSs need can result in higher overhead power consumption. this study focuses on upgrading the present Li-ion BMSs through redesigning their associative data acquisition and processing chains differently. It aims at enhancing the data acquisition and estimation mechanisms for the Li-ion batteries' capacities. It utilizes a novel event-driven mechanism for extracting the intended Li-Ion cell parameters. the event-driven approach brings notable compression gain compared to fix-rate conventional counterparts. the mined attributes are onward conveyed to the robust machine learning algorithms for prediction. the 5-fold cross-validation approach is used for prediction performance evaluation. the achieved correlation coefficient and minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are respectively 0.9996, 0.0038 and 0.0054 respectively. It shows the feasibility of incorporating the proposed approach in contemporary BMSs.

关键词： Event-driven processing Li-Ion battery Battery capacity Features extraction Compression Approximation error

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ddcls 2020 Index

DDCLS 2020 Index

引用

data driven control and learning systems (ddcls)

Presents an index of the authors whose articles are published in the conference proceedings record.

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Presents an index of the authors whose articles are published in the conference proceedings record.

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A Traction Load Forecasting Method of Electrified Railway Based on LSTM 9

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

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ieee 9th International Power Electronics and Motion control conference (IPEMC-ECCE Asia)

作者： Ma, Qian Peng, Yishuang Luo, Pei Li, Qianru Wang, Hao Yu, Jing Xiangtan Univ Coll Automat & Elect Informat Xiangtan Peoples R China

ISBN: (纸本)9781728153018

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： electrified railway traction load forecasting deep learning LSTM

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