检索结果-内蒙古大学图书馆

11th IEEE International Conference on System engineering and Technology, ICSET 2021

作者： Sadiq, Musaab Esam Humaidi, Amjad Jaleel Kadhim, Saleem Khalefa Mahdi, Abdulkareem Sh. Alkhayyat, Ahmed Ibraheem, Ibraheem Kasim General Company for Grain Processing Ministry of Trade Baghdad Iraq University of Technology Control and Systems Eng. Dept. Baghdad Iraq School of Computer Science and Eng. Taylor's University Malaysia College of Technical Engineering Islamic University Najaf Iraq College of Engineering University of Baghdad Baghdad Iraq

ISBN: (纸本)9781665437660

The systems actuated by Pneumatic Artificial Muscles (PAMs) are characterized by high nonlinearity and time-varying of their coefficients. Therefore, nonlinear and robust controllers are required to cope with these challenging control problems. This work presents the development of new control design for trajectory tracking of PAM-Actuated manipulator based on Sliding Mode control (SMC), Backstepping Sliding Mode control (BSMC). The control laws of controlled PAM hanging mass based on SMC and BSMC have been developed according to Lyapunov stability analysis. Additionally, the PSO technique is used to tune the design parameters of proposed controllers for further enhancement of controlled PAM system performance. A comparison study has been conducted among the proposed controllers via computer simulation using MATLAB/Simulink programming software. © 2021 IEEE.

关键词： Trajectory tracking System performance Muscles Programming Systems engineering and theory Software Nonlinear dynamical systems

来源：评论

学校读者我要写书评

暂无评论

Quantum Machine Learning for Electricity Theft Detection: an Initial Investigation

Quantum Machine Learning for Electricity Theft Detection: an...

引用

IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Lianting Xue Long Cheng Yuancheng Li Ying Mao School of Control and Computer Engineering North China Electric Power University Beijing China Fordham University New York USA

ISBN: (纸本)9781665417631

Electricity theft could result in massive economic loss and cause potential security problems to the grids. There-fore, it is of great importance to detect electricity theft from both an economic and social perspective. With the help of Advanced Metering Infrastructure (AMI) in smart grids, now it is possible to detect theft activities through analyzing the recorded electricity consuming data using advanced techniques, such as machine learning. However, training a learning model, especially for the ones with a huge amount of parameters, is always resource-consuming, and users could encounter performance bottlenecks due to hardware limitations. To improve the problem, using quantum computing for machine learning tasks has shown to be an effective approach to speed up the learning process. However, the relevant techniques have not been studied in the domain of energy yet. As a pioneering exploration, in this work, we are trying to leverage a quantum classification algorithm to classify the electricity dataset and on that basis to perform a case study on theft activity detection. Specifically, we have given the details of our design in the study, and our experimental results show that the prediction accuracy can be significantly improved with increasing training epochs, demonstrating the feasibility of quantum machine learning for energy data analysis.

关键词： Training Economics Social computing Quantum computing Data analysis Machine learning Hardware

来源：评论

学校读者我要写书评

暂无评论

Wind Turbine Abnormal Data Identification Based on MKIF Model

Wind Turbine Abnormal Data Identification Based on MKIF Mode...

引用

Chinese control and Decision Conference, CCDC

作者： Bosong Liu Bing Zhao Peng Guo School of Control and Computer Engineering North China Electric Power University Beijing China State Key Laboratory of Wind Energy Equipment and Control Technology Beijing China

ISBN: (纸本)9781665478977

The identification of wind turbine abnormal data is the basis for subsequent wind power prediction and health assessment of wind turbine. This paper analyzes the characteristics of wind turbine abnormal data in detail, and designs the MKIF (Mini batch K Means-Isolation Forest) algorithm to realize the identification of abnormal data of wind turbine. The MKIF algorithm introduces Mini batch K Means clustering into the partition process of the Isolation Forest search tree, and use Silhouette Coefficient to supervise the number and location of the split nodes of the tree. The MKIF anomaly score is defined to describe the degree of isolation of the data, which can effectively identify and eliminate abnormal data and use normal data to establish the main power band. Taking the actual operating data of two wind turbines as an example, the effectiveness of the MKIF abnormal data identification algorithm is verified.

关键词： Clustering algorithms Forestry Wind power generation Prediction algorithms Data models Wind turbines Partitioning algorithms

来源：评论

学校读者我要写书评

暂无评论

Front Cover: Micrometer-Sized Liposome-Based Systems: A Hierarchical Breakdown (ChemSystemsChem 3/2025)

ChemSystemsChem

引用

ChemSystemsChem 2025年第3期7卷

作者： Prof. Dr. Shogo Hamada Dr. Hironori Sugiyama Prof. Dr. Yiting Zhang Prof. Dr. Shoji Iwabuchi Soichiro Hiroi Toshiki Maruyama Yuktesh Balaji Sota Kumagai Prof. Dr. Satoshi Murata Prof. Dr. Taro Toyota Department of Computer Science School of Computing & Department of Systems and Control Engineering School of Engineering Institute of Science Tokyo 4259 Nagatsuta-cho Midori-ku Yokohama-city Kanagawa 226-8501 Japan These authors contributed equally. Department of Applied Chemistry Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Chemistry College of Science Rikkyo University 3-34-1 Nishi-Ikebukuro Toshima-ku Tokyo 171-8501 Japan Department of Biotechnology and Life Science Tokyo University of Agriculture and Technology 2-24-16 Naka-cho Koganei-shi Tokyo 185-8588 Japan Department of Basic Science Graduate School of Arts and Sciences The University of Tokyo 3-8-1 Komaba Meguro-ku Tokyo 153-8902 Japan Department of Robotics Graduate School of Engineering Tohoku University 6-6-01 Aramaki-Aza Aoba Aoba-ku Sendai Miyagi 980-8579 Japan Universal Biology Institute The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan

来源：评论

学校读者我要写书评

暂无评论

Reliability Prediction Based on SVR and Improved SCSO

Reliability Prediction Based on SVR and Improved SCSO

引用

International Conference on computer Network, Electronic and Automation (ICCNEA)

作者： Xiaojun Bai Hongyue Liu Gong Meng School of Computer Science and Engineering Xi’an Technological University Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Reliability prediction can provide basis for the identification of potential improvement area, cost control, and mission reliability assessment, etc. However, for complex equipment, there are many reliability influencing factors and incomplete knowledge of failure causes, which lead to a significant disparity between the predicted outcomes and the real values for traditional reliability prediction methods. To address the above issues, this research paper introduces an approach that utilizes the Support Vector Regression (SVR) model and Sand Cat Swarm Optimization (SCSO). To begin with, the sliding window technique is employed on the historical reliability data to generate time series samples, with the 5 adjacent data as sample data, and the sixth as label of the sample, and train SVR model on these samples; Second, the SVR model parameters are optimized using the ISCSO algorithm to obtain the optimal combination of parameters. In the testing stage, firstly, historical reliability data was used to predicted future data by the model, and the predicted data are then added to the sequence to form new samples, thus old data are discarded and new data are predicted continuously to realize continuous reliability prediction. Finally, the algorithm proposed in this paper is validated on a diesel engine reliability dataset. The algorithm proposed in this paper demonstrates its superiority through the Normalized Root Mean Squared Error (NRMSE) evaluation. The NRMSE of SVR-ISCSO is 8.86E-05, showcasing a remarkable 99.24% year-on-year decrease compared to the standard SVR. Additionally, it exhibits a 5.86% year-on-year decrease compared to SVR-SCSO, further validating the effectiveness of the proposed algorithm.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cross-media retrieval with semantics clustering and enhancement

Cross-media retrieval with semantics clustering and enhancem...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Minfeng Zhan Liang Li Qingming Huang Yugui Liu School of Computer and Control Engineering University of Chinese Academy of Sciences China Chinese Academy of Sciences Institute of Computing Technology China

ISBN: (纸本)9781509060689

Cross-media retrieval, which uses a text query to search for images and vice-versa, has attracted a wide attention in recent years. The mostly existing cross-media retrieval methods aim at finding a common subspace and maximizing different modalities correlations. But these approaches do not directly capture the underlying semantic information of different modalities. This paper proposes a novel cross-media retrieval by semantics clustering and enhancement, where a semantic-preserved mapping is learned from the original space to the target semantic space. Meanwhile, In order to improve the demarcation of semantic space, we enhance the semantic manifold by learning a dimension invariant matrix. Our approach not only maximizes the correlation between different modalities, but also increases the discriminative ability among different categories. Experiments show that our approach outperforms the popular methods on two real word datasets.

关键词： Semantics Manifolds Correlation Data structures Kernel Optimization Media

来源：评论

学校读者我要写书评

暂无评论

Modeling and analyzing nano-rod pushing with an AFM

Modeling and analyzing nano-rod pushing with an AFM

引用

2010 10th IEEE Conference on Nanotechnology, NANO 2010

作者： Hou, Jing Wu, Chengdong Liu, Lianqing Wang, Zhidong Dong, Zaili Dept. of Electrical and Computer Engineering Northeastern University Shenyang 110004 China State Key Laboratory of Robotics Shenyang Institute of Automation CAS Shenyang 110016 China Dept. of Advanced Robotics Chiba Institute of Technology Chiba Japan School of Information and Control Engineering Shenyang Jianzhu University Shenyang 110168 China

ISBN: (纸本)9781424470334

In developing nano-devices and nano-structures, traditional methodologies on MEMS meet the difficulty from the scale restriction. With the strategy of objects assembly, using AFM to handle nano-rods and other nano-objects is considered as an important and high potential technology in constructing nanostructures. However most of AFM only has one tip as the end effector and cannot control both translational and rotational motions during manipulation, this makes it be difficult to realize posture controllable manipulation especially in nano environment. In this paper, the behavior of nano-rod under pushing is theoretically analyzed and modeled. Viscous friction is incorporated in the model and the two situations that the pivot of the nanorod is either inside or outside the rod are addressed. By modeling, the pivot can be determined at each manipulation in real time, and the pushing points can be planned to realize posture controllable manipulation by using nano-hand strategy which is inspired by stable pushing theory of object manipulation in macro world, and to improve the reliability and the precision of manipulation. ©2010 IEEE.

关键词： Nanorods

来源：评论

学校读者我要写书评

暂无评论

A Vision-Based Recognition Method for Transformer Based on AdaBoost and Multi-Template Matching

A Vision-Based Recognition Method for Transformer Based on A...

引用

IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Yang Wu Hongkai Chen Xiaoguang Zhao Yongjie Zhai School of Control and Computer Engineering North China Electric Power University The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781479987313

With the smart monitoring being widely concerned recently, substations have been introducing smart monitoring system. In this paper, we propose a vision-based recognition method for transformers in substation via combining with AdaBoost and a multi-template matching method. The proposed method works by dividing the whole process into two parts, namely coarse detection and fine recognition. In coarse detection, haar features of training samples in each sub-region are extracted and then AdaBoost algorithm is utilized for training and detecting. After coarse detection, we then perform fine recognition using multi-template matching with histogram intersection. Experimental results demonstrate that our method has a higher recognition precision and it is superior and more effective than the conventional AdaBoost method.

关键词： Transformer recognition Haar-like features AdaBoost Multi-template matching Transformer ADABOOST Recognition (Psychology) TEMPLATE MATCHING subregion Substations monitoring systems

来源：评论

学校读者我要写书评

暂无评论

Partial discharge type recognition for transformers based on Fisher discriminant method

Partial discharge type recognition for transformers based on...

引用

International Conference on Estimation, Detection and Information Fusion (ICEDIF)

作者： Li Li Yongli Zhu Min Lu Liuwang Wang Yaqi Song School of Control and Computer Engineering North China Electric Power University Baoding China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University China Zhejiang Power Dispatching & Control Center China School of Control and Computer Engineering North China Electric Power University China

Towards the problem of low rate of partial discharge (PD) recognition caused by lack of effective train samples, Fisher discriminant method is applied to improve recognition rate of PD for transformer. The discharge data produced by four PD models is collected, from which forty-four statistical characteristics are extracted. In order to solve the problem of singular matrix due to the high dimension, an effective dimension-reduced strategy is put forward. Forty-four characteristics are divided into seven low-dimensional subgroups, which become the input data for seven classifiers constructed by Fisher discriminant method. The PD type of the test samples is identified as that voted by results of seven classifiers. Results show that, in contrast to the back-propagation network method, the proposed method is more stable and possesses higher recognition rate under the condition of limited training samples, thus with good practical values.

关键词： Partial discharges Character recognition Power transformer insulation Insulation Feature extraction Discharges (electric) Spectrogram

来源：评论

学校读者我要写书评

暂无评论

Hard Disk Failure Prediction Based on Association Analysis and Long Short-Term Memory Network

SSRN

引用

SSRN 2023年

作者： Xiaojun, Bai Pan, Zhaofeng Meng, Gong Wang, Shenhang Fu, Yanfang Xi’an Technological University School of Computer Science and Engineering Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Hard disk is the main storage device for cloud service, and there always contain massive disks deployed in a data center. Disk failure occur frequently in data center, which may lead to data loss and other disasters, so there have urgent needs for a failure prediction method of hard disk so as to ensure service reliability. This paper proposes a temporal prediction model based on LSTM. Firstly, the SMART data of the disk is analyzed, and the Pearson correlation coefficient is used to analyze the correlation between the monitoring time series data of the faulty disk and the normal disk, and the monitoring index with the lowest correlation is selected as the fault feature;secondly, for the problem of serious imbalance of positive and negative samples in the SMART dataset, the SMOTEENN algorithm is introduced for oversampling to obtain a balanced dataset of positive and negative samples. The proposed method improves accuracy by 8.268% and F1-score by 8.657% compared to the conventional method. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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：