检索结果-内蒙古大学图书馆

2nd International Conference on computer, control and Robotics, ICCCR 2022

作者： Wen, Gangcheng Shi, Xin Wu, Zhiyue Wang, Tingting Luo, Bing School of Control and Computer Engineering North China Electric Power University Beijing China Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9781665466745

The community integrated energy system (CIES) has become a new energy utilization approach for users to meet multi-energy supply and demand. Taking a community in China as the research object, this paper develops a two-stage optimization approach for the CIES planning. The first stage is based on the improved whale optimization approach (WOA) to optimize the equipment type and capacity of the community energy station. It is a multi-objective planning optimization problem, and its purpose is to achieve the coordinated optimization of economic costs and environmental costs. As for the multiple Pareto frontier solutions obtained in the first stage, the mixed integer linear programming (MILP) is used to separately optimize the operation cost of each planning scheme. Case studies validate that the developed approach is practical and feasible for CIES planning. © 2022 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

DPPL Hallway Tracker: Hospital Contact Tracing During the COVID-19 Pandemic 9

DPPL Hallway Tracker: Hospital Contact Tracing During the CO...

引用

9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Marinoni, Christian Ponzi, Valerio Comminiello, Danilo Dpt. of Information Engineering Electronics and Telecommunications Sapienza University of Rome Via Eudossiana 18 Roma00184 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

During the COVID-19 pandemic, the use of a people tracking system could have been crucial, particularly in sensitive environments, such as hospitals. DPPL Hallway Tracker is a framework that uses security camera footage to determine which rooms in a corridor a person has entered. It generates a database containing all the people identified and allows quick identification of potential cases of infection based on the time spent in a room and its maximum capacity. DPPL Hallway Tracker is structured in two phases: detection and re-identification. In the first phase, it exploits Mask RCNN to identify people and room doors. In the second one, it uses the deep association metric model from DeepSORT to re-identify a person as he leaves a room. © 2023 Copyright for this paper by its authors.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Based Intrusion Detection System in Cloud Environment 8

Machine Learning Based Intrusion Detection System in Cloud E...

引用

8th International Conference on Information Technology and Nanotechnology, ITNT 2022

作者： Saeed, Muhammad Salman Saurabh, Raman Bhasme, Sarang Balasaheb Nazarov, Alexey N. Moscow Institute of Physics and Technology Department of Radio Engineering and Cybernetics Moscow Russia Federal Research Center Computer Science and Control of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

Attacks and intrusions on computer networks often have different characteristics and behaviors that require professional help. The number of attacks is growing in line with the development of computer networks. In fact, expert knowledge is declining over time and should be reviewed and made available in the system, making it necessary to hire an experienced person. Cloud computing is an advancement in IT technologies that provide users with the most up-To-date, highly sought-After virtual services with high flexibility, low infrastructure costs, and minimal maintenance. Protection against network intruders is one of the most important security challenges for cloud computing, as it affects the privacy, availability, and integrity of cloud services. Because cloud computing is a shared environment, it can be vulnerable to various threats. Because building strong access systems are essential to maintaining cloud security, but it remains an obscure goal and a major challenge due to the growing number of cyber-Attacks. In this study, a machine-learning based approach was used to propose an intrusion detection system (IDS) based on a network based cloud computing model. During the previous processing phase, we used a feature selection algorithm from the CICDDoS2019 database to select features. We have used the Naive Bayes Classifier, Decision Tree Classifier, Supporting Classifier, Logistic Regression, and Random Forest Classifier, all of which are well-known categories. Simulation results when compared to these five different classifiers, random forest algorithm show improvements in overall accuracy, precision, recall and F1 score. We explain how various characteristics of machine learning techniques can be used to build efficient IDS. The goal of this research is to develop a new method based on intrusion detection systems and their varied designs for improving intrusion detection accuracy in cloud computing. © 2022 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

引用

Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

来源：评论

学校读者我要写书评

暂无评论

PSRMER: Proactive Services Recommendation Driven-by Multimodal Emotion Recognition

PSRMER: Proactive Services Recommendation Driven-by Multimod...

引用

IEEE International Conference on Web Services (ICWS)

作者： Zhizhong Liu Guangyu Huang Dianhui Chu Yuhang Sun School of Computer and Control Engineering Yantai University Yantai China School of Computer Science and Technology Harbin Institute of Technology Weihai China

In the past three years, global COVID-19 pandemic not only impacted people’s physical health but also significantly affected their mental health, which resulting in rapid increase of psychological problems. Emotions are a common manifestation of psychological changes, and some services (such as music, video, or psychological counseling services) can help users to adjust their emotions in a timely manner, thus to avoid bringing extreme events (e.g., running away from home or committing suicide). Therefore, how to perceive users’ real-time emotions and then recommend the most appropriate services to users has become a challenge. To address this issue, this work proposes an approach for proactive services recommendation driven-by multimodal emotion recognition (named as PSRMER). Specifically, PSRMER first actively identifies a user’s emotion with a multimodal emotion recognition model based on BiGRU and Transformer; Then, considering the user’s emotion and preferences, PSRMER selects the optimal services based on an index-graph linking different emotions and various services; Finally, PSRMER proactively recommends the selected optimal service to the user. Extensive experiments have been conducted and the effectiveness of our proposed method have been proved. Moreover, the proposed method can also be used in smart education, smart transportation, smart elderly care and other modern industry fields.

关键词：

来源：评论

学校读者我要写书评

暂无评论

State Derivative Normalization for Continuous-Time Deep Neural Networks

引用

IFAC-PapersOnLine 2024年第15期58卷 253-258页

作者： Jonas Weigand Gerben I. Beintema Jonas Ulmen Daniel Görges Roland Tóth Maarten Schoukens Martin Ruskowski Chair of Machine Tools and Control Systems RPTU Kaiserslautern and the German Research Center for Artificial Intelligence Kaiserslautern Germany Control Systems (CS) Group at the Department of Electrical Engineering Eindhoven University of Technology Netherlands. R. Tóth is also affiliated to the Systems and Control Laboratory at the Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature.

关键词： System Identification Continuous-Time Neural Ordinary Differential Equations Nonlinear State-Space

来源：评论

学校读者我要写书评

暂无评论

A Markov Matrix Based Method for Prediction Direction in Wind Power Forecasting Attacks 6

A Markov Matrix Based Method for Prediction Direction in Win...

引用

6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Yang, Tianming Jiao, Runhai Wei, Junqiang Wu, Xuejie North China Electric Power University School of Mathematics and Physics Beijing China North China Electric Power University School of Control and Computer Engineering Beijing China Powerchina Renewable Energy Co. Ltd Institute of Renewable Energy & Energy Storage Technologies Beijing China

ISBN: (纸本)9798350377798

Deep learning has a wide range of applications in the field of wind power prediction, but it is susceptible to attacks. The fast gradient sign method based on selected directions achieves better performance than methods relying on random direction determined by judging directions. However, this method still lacks sufficient interpretability and understanding of its underlying mechanisms. In this paper, we propose a method based on the Markov transition matrix to predict directions, which is comparable to the method using selected directions in terms of prediction effectiveness. Furthermore, this paper explains the reason behind such predictable outcomes, namely, that wind power data from the previous time step makes a significant and primary contribution to the wind power at the current time step. © 2024 IEEE.

关键词： Wind forecasting

来源：评论

学校读者我要写书评

暂无评论

Prototype Guided Personalized Federated Intrusion Detection System 10

Prototype Guided Personalized Federated Intrusion Detection ...

引用

10th IEEE Smart World Congress, SWC 2024

作者： Cheng, Long Yan, Huiru Zhou, Hanlin Wang, Ying Tang, Haichuan Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Ai Lab of Crrc Academy Beijing China

ISBN: (纸本)9798331520861

Preventing network attacks and protecting user privacy are consistently hot research topics in the Internet of Things (IoT) and edge computing fields. Recent advancements in Federated Learning (FL) have shown promise in addressing these challenges. FL allows various clients to collaboratively build an intrusion detection system (IDS) without sharing their private data. However, most existing methods train a single intrusion detection model for all clients and assume that the training and test data distribution are identical, leading to unsatisfactory detection accuracy and generalization abilities in practice. To overcome these challenges, this paper introduces a prototype-guided personalized FL approach named PG-FedIDS. We propose two novel mechanisms within this method. Firstly, we utilize class prototypes as auxiliary information carriers to generate personalized models for each client, rather than generating a single global model as in previous works. Secondly, we propose a prototype-guided ensemble learning strategy, which can leverage the global knowledge in prototypes to enhance detection accuracy and generalization abilities for each client. We conduct extensive experiments on two benchmark datasets with different evaluation test settings. The results demonstrate that our PG-FedIDS achieves promising detection accuracy and consistently outperforms other FL baselines. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A Weighted Least-Squares Method for Non-Asymptotic Identification of Markov Parameters from Multiple Trajectories

arXiv

引用

arXiv 2024年

作者： He, Jiabao Rojas, Cristian R. Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

Markov parameters play a key role in system identification. There exists many algorithms where these parameters are estimated using least-squares in a first, pre-processing, step, including subspace identification and multi-step least-squares algorithms, such as Weighted Null-Space Fitting. Recently, there has been an increasing interest in non-asymptotic analysis of estimation algorithms. In this contribution we identify the Markov parameters using weighted least-squares and present non-asymptotic analysis for such estimator. To cover both stable and unstable systems, multiple trajectories are collected. We show that with the optimal weighting matrix, weighted least-squares gives a tighter error bound than ordinary least-squares for the case of non-uniformly distributed measurement errors. Moreover, as the optimal weighting matrix depends on the system’s true parameters, we introduce two methods to consistently estimate the optimal weighting matrix, where the convergence rate of these estimates is also provided. Numerical experiments demonstrate improvements of weighted least-squares over ordinary least-squares in finite sample settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Asymptotic analysis

来源：评论

学校读者我要写书评

暂无评论

Automated Recognition and Analysis of Escape Response in Caenorhabditis Elegans

IEEE Transactions on Computational Biology and Bioinformatic...

引用

IEEE Transactions on Computational Biology and Bioinformatics 2025年第3期22卷 1201-1210页

作者： Wenyue Zhan Weiyang Chen Yi Pan School of Cyber Science and Engineering Qufu Normal University Qufu China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Significant progress in medical research in recent years has led to the successful treatment of numerous illnesses. Unfortunately, effective treatments for several neurological disorders—particularly those affecting motor neurons—remain elusive, not only significantly impairing patients' quality of life but also placing a substantial financial burden on both individuals and society. To address this gap, scientists are focusing on model organisms to unravel pathogenic mechanisms and develop treatment strategies. Among various model organisms, Caenorhabditis elegans ( C. elegans) has shown special relevance in neurological disorder research. Escape responses, including reversals and omega turns, are controlled by motor neurons. Thus, the escape response in C. elegans provides researchers with a reliable observational metric to evaluate the functional state of motor neurons. We developed a head and tail position localization model to determine the positions of the nematode's head and tail, enabling automated counting of escape responses. We further clarified the association between escape response and motor neuron function by comparing the escape response of normal nematodes to those with motor neuron deficiencies. Consequently, the automated counting of C. elegans escape responses is a useful tool for assessing the health of motor neurons. It may offer strong backing for the exploration of neurodegenerative disease therapeutics.

关键词： Videos Motors Tail Neurons Head Gray-scale Trajectory Phenotypes Computational modeling Turning

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：