检索结果-内蒙古大学图书馆

International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Qianyu Li Bei Wang Jing Jin Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China

ISBN: (数字)9781728180229

ISBN: (纸本)9781728180236

Sleep staging is an effective method for diagnosing sleep disorder and monitoring sleep quality. With the rapid development of machine learning technology, the automatic staging methods of sleep gradually replace the traditional manual interpretation which can improve the efficiency on sleep staging for medical research. LSTM networks can save the historical information as a reference for the current moment, which is undoubtedly a good way to improve sleep staging performance. In this paper, a convolutional neural network (CNN) is constructed to extract the features from a single-channel EEG. The Uni-directional Long Short-Term Memory (Uni-LSTM) network and Bi-directional Long Short-Term Memory (Bi-LSTM) network are combined with CNN to realize automatic sleep staging. The obtained results showed that the two presented network frameworks are effective and feasible on sleep staging. The Bi-LSTM which has more enriched sequence information got better classification performance than the Uni-LSTM.

关键词： Sleep Manuals Machine learning Feature extraction Electroencephalography Monitoring Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Representation evaluation block-based teacher-student network for the industrial quality-relevant performance modeling and monitoring

arXiv

引用

arXiv 2021年

作者： Yang, Dan Peng, Xin Lu, Yusheng Huang, Haojie Zhong, Weimin Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

Quality-relevant fault detection plays an important role in industrial processes, while the current quality-related fault detection methods based on neural networks main concentrate on process-relevant variables and ignore quality-relevant variables, which restrict the application of process monitoring. Therefore, in this paper, a fault detection scheme based on the improved teacher-student network is proposed for quality-relevant fault detection. In the traditional teacher-student network, as the features differences between the teacher network and the student network will cause performance degradation on the student network, representation evaluation block (REB) is proposed to quantify the features differences between the teacher and the student networks, and uncertainty modeling is used to add this difference in modeling process, which are beneficial to reduce the features differences and improve the performance of the student network. Accordingly, REB and uncertainty modeling is applied in the teacher-student network named as uncertainty modeling teacher-student uncertainty autoencoder (TSUAE). Then, the proposed TSUAE is applied to process monitoring, which can effectively detect faults in the process-relevant subspace and quality-relevant subspace simultaneously. The proposed TSUAE-based fault detection method is verified in two simulation experiments illustrating that it has satisfactory fault detection performance compared to other fault detection methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Wastewater treatment

来源：评论

学校读者我要写书评

暂无评论

Distribution Adaptation Local Outlier Factor for Multimode Process monitoring

Distribution Adaptation Local Outlier Factor for Multimode P...

引用

第三十九届中国控制会议

作者： Yutang Xiao Yang Tao Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education East China University of Science and Technology

In modern industrial processes, the production process includes multiple operating modes, due to changes in production goals and conditions. And the data generated in this process is a mixture of Gaussian and non-Gaussian distributions. Therefore, the data distribution of multimode processes is uncertain and complex. It is difficult to monitor multimode processes accurately by using traditional Multivariate Statistical Process Monitoring（MSPM） methods. In this case, the absence of labeled data is considered more valuable. This paper proposes a new unsupervised domain adaptive method called the Distribution Adaptation Local Outlier Factor（DALOF）. This method can extract useful information from multimode data through domain adaptation, which can reduce the distribution difference of training data and improve the accuracy of modeling. In the DALOF model, data from different modes in the training set are defined as the source domain and the target domain. Weight the training data according to their correlation, and then project these data into a low-dimensional subspace. In this space, the distribution distance between the source and target domain is minimum. To solve the nonlinear characteristics of multimode data, this paper uses LOF to build a model and formed a density-based monitoring index. Numerical examples and Tennessee Eastman（TE） process simulation demonstrate the effectiveness of DALOF.

关键词： Distribution Adaptation Local Outlier Factor Multimode Process Process monitoring

来源：评论

学校读者我要写书评

暂无评论

Data-driven Scheduling optimization of Ethylene Cracking Furnace System

Data-driven Scheduling Optimization of Ethylene Cracking Fur...

引用

第32届中国控制与决策会议

作者： Xinwei Lin Liang Zhao Wenli Du Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology

The ethylene cracking furnace system convert a variety of hydrocarbon feeds into products such as ethylene and propylene in parallel. During the cracking process, the coke produced by the cracking will degrade the performance of the cracking furnace, so the furnace should be cleaned periodically. Scheduling of the cracking furnace system involves various raw materials, multiple furnaces, and multiple constraints, so it is a difficult mathematic programming problem. Previous studies have simplified the optimization model to obtain a corresponding mixed integer nonlinear programming（MINLP） model. In order to make the scheduling more suitable for the actual process, this paper develops the transfer-line exchanger（TLE） model, fuel consumption model, and super-high pressure steam（SS） model by using the actual plant data. A case study is put forward to demonstrate the effectiveness of the proposed method.

关键词： Ethylene cracking furnace system scheduling MINLP TLE

来源：评论

学校读者我要写书评

暂无评论

Modeling of oil near-infrared spectroscopy based on similarity and transfer learning algorithm

引用

Frontiers of Chemical Science and Engineering 2019年第3期13卷 599-607页

作者： Yifei Wang Kai Wang Zhao Zhou Wenli Du Key Laboratory of Advanced Control and Optimization for Chemical Processes (Ministry of Education) East China University of Science and TechnologyShanghai 200237China School of information science and engineering East China University of Science and TechnologyShanghai 200237China

Near-infrared spectroscopy mainly reflects the frequency-doubled and total-frequency absorption information of hydrogen-containing groups (0 -H, C-H, N-H, S-H) in organic molecules for near-infrared lights with different wavelengths, so it is applicable to testing of most raw materials and products in the field of petrochemicals. However, the modeling process needs to collect a large number of laboratory analysis data. There are many oil sources in China, and oil properties change frequently. Modeling of each raw material is not only unfeasible but also will affect its engineering application efficiency. In order to achieve rapid modeling of near-infrared spectroscopy and based on historical data of different crude oils under different detection conditions, this paper discusses about the feasibility of the application of transfer learning algorithm and makes it possible that transfer learning can assist in rapid modeling using certain historical data under similar distributions under a small quantity of new data. In consideration of the requirement of transfer learning for certain similarity of different datasets, a transfer learning method based on local similarity feature selection is proposed. The simulation verification of spectral data of 13 crude oils measured by three different probe detection methods is performed. The effectiveness and application scope of the transfer modeling method under different similarity conditions are analyzed.

关键词： near-infrared spectroscopy transfer learning similarity modeling

来源：评论

学校读者我要写书评

暂无评论

Labeled Graph Partitioning Scheme for Distributed Edge Caching

SSRN

引用

SSRN 2023年

作者： Wang, Pengfei Li, Shiqi Sun, Geng Zhou, Changjun Gao, Chengxi Qiu, Sen Tao, Tiwei Zhang, Qiang School of Computer Science and Technology Dalian University of Technology Dalian116024 China Changchun130012 China School of Computer Science and Technology Zhejiang Normal University Jinhua321000 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China School of Artificial Intelligence Dalian University of Technology Dalian116024 China

Distributed edge caching could address latency and congestion problems in large-scale data access effectively, improving system throughput and performance. However, the lack of specialized edge caching solutions for graph data results in low cache hit rates and high data access latency. This limitation stems from existing graph partitioning schemes that overlook the specific requirements of edge caching, such as communication overhead between edge servers, data query frequency, and query patterns. To overcome these challenges, we propose LGPE, a labeled graph partitioning scheme for distributed edge caching based on frequent query patterns. LGPE generates frequent query pattern according to the user’s historical query subgraphs, and then performs labeled graph partitioning based on the frequent pattern, ensuring that the labeled graph divided into edge servers has a high hit rate for frequent user queries while satisfying minimum edge-cut. Evaluation on real datasets from various application domains demonstrates that LGPE achieves approximately 40% higher cache hit rates compared to benchmarks like METIS and SCOTCH, and it also performs well in terms of edge cuts in special graph cases. © 2023, The Authors. All rights reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Phase composition and microstructure formation mechanism of in-situ Cu-Fe micro-composites 1

Phase composition and microstructure formation mechanism of ...

引用

1st International Conference on Metals and Alloys, CMA 2019

作者： Liu, K.M. Jin, Y. Sheng, X.C. Huang, Z.K. Zou, J. Chen, W. Zhang, M.C. Huang, H.M. Lu, D.P. Jiangxi Key Laboratory for Precision Drive and Control Nanchang Institute of Technology Nanchang330099 China Jiangxi Key Laboratory for Advanced Copper and Tungsten Materials Jiangxi Academy of Sciences Nanchang330029 China

The phase composition and microstructure formation mechanism of in-situ Cu-Fe micro-composites were investigated. The microstructures of longitudinal and transverse sections were analyzed by light microscopy and scanning electron microscopy. The phase analysis was executed by X-ray diffraction. The common microstructure characteristic of Cu-XFe (X = 11, 14 and 17) alloys was that the second phase α-Fe dendrites were uniformly distributed in the Cu matrix. The disorderly distributed Fe dendrites of Cu-14Fe alloy underwent initial inhomogeneous deformation and then were gradually changed into the directionally arranged Fe fibers of in-situ Cu-14Fe micro-composite in the longitudinal section, and were gradually transformed into the irregular V-shaped Fe fibers in the transverse section. The initial inhomogeneous deformation and the irregular V-shaped Fe fibers in the transverse section are closely related to the formation of texture. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Phase composition

来源：评论

学校读者我要写书评

暂无评论

Automatic detection of awake state during sleep based on the approximate entropy of EMG

Automatic detection of awake state during sleep based on the...

引用

International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Qihui Ai Bei Wang Jing Jin Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai China

ISBN: (数字)9781728180229

ISBN: (纸本)9781728180236

Sleep is important to human beings. A good sleep can bring sufficient energy and keep healthy. Sleep process contains several sleep stages. There are also several short-duration awake states appeared in sleep process. The amount of those awake states during sleep would be related to evaluate the sleep quality. In this study, the automatic detection method of awake state is investigated on the sleep recording of patient with sleep disorder. Two nonlinear features of ApEN and Mc-ApEN are calculated based on the EMG signal recorded during one's overnight sleep process. The detection performance is analyzed and compared. The obtained results showed that the presented method is feasible for awake state detection during sleep, where Mc-ApEN is slightly better than ApEN. It can be an assistant tool for sleep evaluation.

关键词： Tools Electromyography Entropy Informatics

来源：评论

学校读者我要写书评

暂无评论

Distribution Adaptation Local Outlier Factor for Multimode Process monitoring

Distribution Adaptation Local Outlier Factor for Multimode P...

引用

Chinese control Conference (CCC)

作者： Yutang Xiao Yang Tao Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In modern industrial processes, the production process includes multiple operating modes, due to changes in production goals and conditions. And the data generated in this process is a mixture of Gaussian and non-Gaussian distributions. Therefore, the data distribution of multimode processes is uncertain and complex. It is difficult to monitor multimode processes accurately by using traditional Multivariate Statistical Process Monitoring (MSPM) methods. In this case, the absence of labeled data is considered more valuable. This paper proposes a new unsupervised domain adaptive method called the Distribution Adaptation Local Outlier Factor (DALOF). This method can extract useful information from multimode data through domain adaptation, which can reduce the distribution difference of training data and improve the accuracy of modeling. In the DALOF model, data from different modes in the training set are defined as the source domain and the target domain. Weight the training data according to their correlation, and then project these data into a low-dimensional subspace. In this space, the distribution distance between the source and target domain is minimum. To solve the nonlinear characteristics of multimode data, this paper uses LOF to build a model and formed a density-based monitoring index. Numerical examples and Tennessee Eastman (TE) process simulation demonstrate the effectiveness of DALOF.

关键词： Monitoring Training data Adaptation models Data models Training Production Process control

来源：评论

学校读者我要写书评

暂无评论

Data-driven Scheduling optimization of Ethylene Cracking Furnace System

Data-driven Scheduling Optimization of Ethylene Cracking Fur...

引用

Chinese control and Decision Conference, CCDC

作者： Xinwei Lin Liang Zhao Wenli Du Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

The ethylene cracking furnace system convert a variety of hydrocarbon feeds into products such as ethylene and propylene in parallel. During the cracking process, the coke produced by the cracking will degrade the performance of the cracking furnace, so the furnace should be cleaned periodically. Scheduling of the cracking furnace system involves various raw materials, multiple furnaces, and multiple constraints, so it is a difficult mathematic programming problem. Previous studies have simplified the optimization model to obtain a corresponding mixed integer nonlinear programming (MINLP) model. In order to make the scheduling more suitable for the actual process, this paper develops the transfer-line exchanger (TLE) model, fuel consumption model, and super-high pressure steam (SS) model by using the actual plant data. A case study is put forward to demonstrate the effectiveness of the proposed method.

关键词： Furnaces Feeds Mathematical model Job shop scheduling Raw materials optimization Fuels

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：