检索结果-内蒙古大学图书馆

Domain-Adaptive Clustered Federated Transfer Learning for EV Charging Demand Forecasting

IEEE Transactions on Power Systems 2025年第2期40卷 1241-1254页

作者： Wang, Tianjing Ren, Chao Dong, Zhao Yang Yip, Christine Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore KTH Royal Institute of Technology Wallenberg-NTU Presidential Researcher School of Electrical Engineering and Computer Science StockholmSE-100 44 Sweden City University of Hong Kong Department of Electrical Engineering Hong Kong999077 Hong Kong The University of Sydney CAFEA Smart City Limited Hong Kong and School of Civil Engineering SydneyNSW2006 Australia

To address the privacy concerns for state-of-the-art cutting-edge centralized machine learning in electric vehicle (EV) charging demand forecasting applications, federated learning (FL) has been employed to transfer training processes from the cloud server to edge devices. Nevertheless, traditional FL still grapples with several challenges in terms of personalization, transferability, feature extraction, and data security. This study proposes a domain-adaptive clustered federated transfer learning (FTL) scheme for EV charging demand forecasting. This scheme combines the principles of transfer learning (TL) with FL by utilizing maximum mean discrepancy to measure the differences between local features and cluster them, weight local model parameters in the global model aggregation, and realize domain adaptation for projecting local data and new data to the trained FL model. A multi-head attention-based transformer is leveraged to construct a forecasting model to focus on the most relevant spatio-temporal features. Under multi-stage differential privacy protections, Laplace noise is injected into the local feature, model update and local model during the clustering, FL and TL processes, respectively. The case study demonstrates that the proposed domain-adaptive clustered FTL outperforms the conventional FTL and FL, local training, and other domain shift handling techniques in predictive accuracy and operational risk. © 1969-2012 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A Subspace Projective Clustering Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3497-3509页

作者： Wang, Yue Li, Wenqing Sarkar, Esha Shafique, Muhammad Maniatakos, Michail Jabari, Saif Eddin New York University Abu Dhabi Division of Engineering Abu Dhabi129188 United Arab Emirates New York University Department of Electrical and Computer Engineering Tandon School of Engineering BrooklynNY11201 United States New York University Department of Civil and Urban Engineering Tandon School of Engineering BrooklynNY11201 United States

Backdoor attacks in deep neural networks (DNNs) involve an attacker inserting a backdoor into the network by manipulating the training dataset, which causes misclassification of inputs that contain a specific trigger. Detecting and mitigating such attacks are challenging, as only the attacker knows the trigger and target class. Our study demonstrates that the representations, i.e., the neuron activations for a given DNN, of poisoned and genuine data lie in different subspaces, which implies that there exists a certain subspace where the difference of projections from different data can be manifested. To this end, we propose a method based on subspace projective clustering (SPC), which learns a subspace as well as a projection-based weight vector by solving a projection maximization program, and the optimized weight vector can be utilized in a clustering framework to infer the group of data. Based on our theoretical analysis and experimental results, we demonstrate the effectiveness of our method in defending against backdoor attacks that use different settings of poisoned samples on GTSRB, Imagenet, VGGFace2, and PubFig datasets in comparison with the state-of-the-art methods. Our algorithm can detect more than 90% of the infected classes and identify 95% of the poisoned samples. © 2020 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A hybrid machine-learning model for solar irradiance forecasting

引用

Clean Energy 2024年第1期8卷 100-110页

作者： Ameera M.Almarzooqi Maher Maalouf Tarek H.M.El-Fouly Vasileios E.Katzourakis Mohamed S.El Moursi Constantinos V.Chrysikopoulos Department of Industrial and Systems Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE Electrical Engineering and Computer Science Department Khalifa University Abu DhabiUAE Department of Civil Infrastructure and Environmental Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE School of Chemical and Environmental Engineering Technical University of CreteChaniaGreece

Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power *** plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power *** the generation capacity within the electric grid increases,accurately predicting this output becomes increasingly essential,especially given the random and non-linear characteristics of solar irradiance under variable weather *** study presents a novel prediction method for solar irradiance,which is directly in correlation with PV power output,targeting both short-term and medium-term forecast *** proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression *** proposed method excels in forecasting solar irradiance,especially during highly intermittent weather periods.A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable *** evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford,USA and compared it against three forecasting models:persistence,modified 24-hour persistence and least *** on three widely accepted statistical performance metrics(root mean squared error,mean absolute error and coefficient of determination),our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.

关键词： solar power generation kernel ridge regression hybrid model forecasting

来源：评论

学校读者我要写书评

暂无评论

Interval Signal Temporal Logic for Robust Optimal Control 63

Interval Signal Temporal Logic for Robust Optimal Control

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Baird, Luke Coogan, Samuel Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30318 United States School of Civil and Environmental Engineering United States

ISBN: (纸本)9798350316339

We propose a robust optimal control strategy for linear systems subject to bounded disturbances constrained to satisfy a Signal Temporal Logic (STL) formula with uncertain predicates. We encode such constraints using Interval STL (I-STL), an extension of STL to interval signals and predicates that accommodates efficient numerical implementations for verification and synthesis using interval arithmetic methods. Given an I-STL constraint, a quadratic cost function, and a bounded hyper-rectangular disturbance set, we construct a second robust optimal control problem using an embedding system with double the state dimension and the same cost function such that a solution to this second problem is feasible for the original problem. Moreover, owing to the numerical efficiencies of I-STL and the embedding, the computational complexity of this problem is, at worst, approximately equivalent to solving a non-robust optimal STL synthesis problem with double the state dimension, and we solve this problem as a mixed-integer quadratic program. We present a case study of a miniature blimp modeled as a 12 -dimensional linear system subject to disturbances and tasked with a mission specified in I-STL with multiple nested temporal operators. © 2024 IEEE.

关键词： Cost functions

来源：评论

学校读者我要写书评

暂无评论

Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

引用

IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1331-1341页

作者： Zhiming Zhang Shangce Gao MengChu Zhou Mengtao Yan Shuyang Cao Faculty of Engineering University of ToyamaToyama 930-8555Japan Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Gaoling School of Artificial Intelligence Renmin University of ChinaBeijing 100872China College of Civil Engineering Tongji UniversityShanghai 200092China IEEE

Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering ***,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow *** this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity ***-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored *** our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the *** method significantly reduces the workload of data measure-ment in practical engineering *** experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual ***,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field *** with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence *** confirmation underscores the considerable potential of MSU for practical applications in real engineering *** code is available at https://***/zhangzm0128/MSU.

关键词： Convolutional neural network deep learning recurrent neural network turbulence prediction wind load predic-tion.

来源：评论

学校读者我要写书评

暂无评论

Solar-Powered UV-LEDs for Microbial Disinfection: Design, Challenges and Applications 21

Solar-Powered UV-LEDs for Microbial Disinfection: Design, Ch...

引用

21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Bhattarai, Trailokya Khanal, Nita Ebong, Abasifreke Munir, Mariya Raja, M.Yasin Akhtar UNC Charlotte Department of Electrical and Computer Engineering Charlotte United States UNC Charlotte Department of Civil and Environment Engineering CharlotteNC United States UNC Charlotte Department of Electrical and Computer Engineering and Phys and OSE CharlotteNC United States

ISBN: (纸本)9798350378078

Ultraviolet-Light Emitting Diodes (UV-LEDs) application as sustainable and effective microbial deactivation in healthcare, water treatment, food safety, and surface disinfection has increased recently. This paper explores the feasibility, design, and performance of a solar-powered UV-LED system for microbial deactivation. It emphasizes the use of UV-C LEDs (200-280 nm) for their efficacy in reducing pathogens. Our study addresses the design considerations for integrating solar panels, charge controllers, batteries, UV-LEDs, and sensors to develop an efficient and safe disinfection system. Recent test-results from a 275 nm UV LED system show promising reductions in bacteriophages Phi6 and MS2. That signifies potential applications in water purification, surface sterilization, and emergency settings. However, challenges such as low wall plug efficiency and fabrication difficulties for short-wavelength LEDs need to be addressed to realize the full potential of such technology. © 2024 IEEE.

关键词： Bacteriophages

来源：评论

学校读者我要写书评

暂无评论

WCM-WTrA: A Cross-Project Defect Prediction Method Based on Feature Selection and Distance-Weight Transfer Learning

引用

Chinese Journal of Electronics 2022年第2期31卷 354-366页

作者： LEI Tianwei XUE Jingfeng WANG Yong NIU Zequn SHI Zhiwei ZHANG Yu School of Computer Science and Technology Beijing Institute of Technology China Information Technology Security Evaluation Center School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

Cross-project defect prediction is a hot topic in the field of defect prediction. How to reduce the difference between projects and make the model have better accuracy is the core problem. This paper starts from two perspectives: feature selection and distance-weight instance transfer. We reduce the differences between projects from the perspective of feature engineering and introduce the transfer learning technology to construct a cross-project defect prediction model WCM-WTrA and multi-source model Multi-WCM-WTrA. We have tested on AEEEM and ReLink datasets, and the results show that our method has an average improvement of 23%compared with TCA+ algorithm on AEEEM datasets,and an average improvement of 5% on ReLink datasets.

关键词： feature engineering cross-project defect prediction software defect prediction project management feature selection learning (artificial intelligence) software maintenance WCM-WTrA distance-weight transfer learning software quality distance-weight instance transfer multisource model MultiWCM-WTrA

来源：评论

学校读者我要写书评

暂无评论

Multiclass anomaly detection in imbalanced structural health monitoring data using convolutional neural network

引用

Journal of Infrastructure Preservation and Resilience 2022年第1期3卷 1-15页

作者： Zhao, Mengchen Sadhu, Ayan Capretz, Miriam Department of Electrical and Computer Engineering Western University LondonON Canada Department of Civil and Environmental Engineering Western University LondonON Canada

Structural health monitoring (SHM) system aims to monitor the in-service condition of civil infrastructures, incorporate proactive maintenance, and avoid potential safety risks. An SHM system involves the collection of large amounts of data and data transmission. However, due to the normal aging of sensors, exposure to outdoor weather conditions, accidental incidences, and various operational factors, sensors installed on civil infrastructures can get malfunctioned. A malfunctioned sensor induces significant multiclass anomalies in measured SHM data, requiring robust anomaly detection techniques as an essential data cleaning process. Moreover, civil infrastructure often has imbalanced anomaly data where most of the SHM data remain biased to a certain type of anomalies. This imbalanced time-series data causes significant challenges to the existing anomaly detection methods. Without proper data cleaning processes, the SHM technology does not provide useful insights even if advanced damage diagnostic techniques are applied. This paper proposes a hyperparameter-tuned convolutional neural network (CNN) for multiclass imbalanced anomaly detection (CNN-MIAD) modelling. The hyperparameters of the proposed model are tuned through a random search algorithm to optimize the performance. The effect of balancing the database is considered by augmenting the dataset. The proposed CNN-MIAD model is demonstrated with a multiclass time-series of anomaly data obtained from a real-life cable-stayed bridge under various cases of data imbalances. The study concludes that balancing the database with a time shift window to increase the database has generated the optimum results, with an overall accuracy of 97.74%. © The Author(s) 2022.

关键词： Structural health monitoring

来源：评论

学校读者我要写书评

暂无评论

Assessing the operational design domain of lane support system for automated vehicles in different weather and road conditions

引用

Journal of Traffic and Transportation engineering(English Edition) 2022年第4期9卷 631-644页

作者： Giuseppina Pappalardo Riccardo Caponetto Rosario Varrica Salvatore Cafiso Department of Civil Engineering and Architecture University of CataniaCatania 95125Italy Department of Electrical Electronic and Computer EngineeringUniversity of CataniaCatania 95125Italy

With the growing rate of automated vehicles(AVs)at the lower level of automation,the experimental tests are also in progress with vehicles at higher *** the absence of extended digital infrastructures and deployment of level 5 full automated vehicles,the physical infrastructure is required to maintain a fundamental role to enable their introduction in public *** paper focuses on lane support system(LSS)whose operational design domain(ODD)is strongly connected to the road characteristics and *** experimental test was carried out with a state of the art,and LSS and advanced technologies were used for road monitoring on different roads under various environmental conditions including dry,wet pavements and *** applied the generalized estimation equation for logistic regression to account within-cluster homogeneity which is induced by repeated measures on the same road *** models allow the identification of variables that are significant for the LSS fault probability among various effects of road features including marking,pavement distress,weather conditions,horizontal curvature,and cross *** pointed out the relevance of the wet retro-reflection of marking(RLw)and the horizontal curvature in the definition of ODD for *** values have been proposed for the tested *** pavement doesn’t affect the LSS performance when compared to the dry *** was shown to be critical even with very good road characteristics.

关键词： Lane support system Physical infrastructure Operational design domain Weather condition

来源：评论

学校读者我要写书评

暂无评论

Preface to the Proceedings of the 8th Smart Data Smart Cities (SDSC) conference - ISPRS Archives

International Archives of the Photogrammetry, Remote Sensing...

引用

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 2024年第4/W10-2024期48卷 1-4页

作者： Pantazis, Dimos N. Karanikolas, Nikitas N. Vassilakopoulos, Michael Coors, Volker Voyiatzis, Ioannis Ellul, Claire Department of Surveying and Geoinformatics Engineering Research Laboratory S.O.C.R.A.T.E.S. University of West Attica Greece Department of Informatics and Computer Engineering University of West Attica Greece Department of Electrical and Computer Engineering University of Thessaly Greece Germany Department of Civil Environmental & Geomatic Engineering University College London United Kingdom

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：