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Adaptive inter-intradomain alignment network with class-aware sampling strategy for rolling bearing fault diagnosis

science China(Technological sciences) 2023年第10期66卷 2862-2870页

作者： GAO QinHe HUANG Tong ZHAO Ke SHAO HaiDong JIN Bo LIU ZhiHao WANG Dong National Key Discipline Laboratory of Armament Launch Theory&Technology Rocket Force University of EngineeringXi’an 710025China Key Laboratory of Road Construction Technology&Equipment Ministry of Education Chang’an UniversityXi’an 710054China College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China Institute of Systems and Robotics Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbra 3030-290Portugal

Existing unsupervised domain adaptation approaches primarily focus on reducing the data distribution gap between the source and target domains,often neglecting the influence of class information,leading to inaccurate alignment *** by this observation,this paper proposes an adaptive inter-intra-domain discrepancy method to quantify the intra-class and inter-class discrepancies between the source and target ***,an adaptive factor is introduced to dynamically assess their relative *** upon the proposed adaptive inter-intradomain discrepancy approach,we develop an inter-intradomain alignment network with a class-aware sampling strategy(IDAN-CSS)to distill the feature *** classaware sampling strategy,integrated within IDAN-CSS,facilitates more efficient *** multiple transfer diagnosis cases,we comprehensively demonstrate the feasibility and effectiveness of the proposed IDAN-CSS model.

关键词： unsupervised domain adaptation inter-class domain discrepancy intra-class domain discrepancy class-aware sampling strategy

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On Security-Constrained Bidding of Shared Transportation Hubs for Renewable Fuels and Refined Oil Participating Within Energy, Regulation and Pipeline Logistics Markets 7

On Security-Constrained Bidding of Shared Transportation Hub...

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7th IEEE Student Conference on Electric Machines and systems, SCEMS 2024

作者： Wang, Shengshi Li, Hongyi Huang, Danji Li, Kun Cui, Shichang Ai, Xiaomeng Fang, Jiakun Wen, Jinyu School of Electrical and Electronic Engineering Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology Wuhan China School of Engineering Cardiff University Cardiff United Kingdom Iowa State University Department of Electrical and Computer Engineering Ames United States University of Macau Department of Electrical and Computer Engineering China PetroChina ShenZhen New Energy Research Institute Co. Ltd. Shenzhen China

ISBN: (纸本)9798350366945

This paper investigates the security-constrained bidding strategy of the emerging shared transportation hubs for renewable fuels and refined oil (STH-RRs) participating within energy, regulation and pipeline logistics markets. Firstly, to excavate more flexible capability for the power system as well as to increase the revenue of the STH-RR operation company, the model for STH-RR participating in regulation market is established. Secondly, the security-constrained two-stage bidding model is first proposed for STH-RRs to balance the revenue across energy, regulation and pipeline logistics markets. Particularly, to guarantee secure operation of STH-RRs and meet the delivery demand, the rigorous multi-product transportation constraints are considered in both stages. Case studies based on the modified real-world multi-product pipelines in South China validate the proposed method, with the revenue and turnover increase of 14% and 4.7%, respectively, compared with conventional methods. © 2024 IEEE.

关键词： Pipelines

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Gram-Schmidt Methods for Unsupervised Feature Selection 63

Gram-Schmidt Methods for Unsupervised Feature Selection

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Yaghooti, Bahram Raviv, Netanel Sinopoli, Bruno Washington University in St. Louis Department of Electrical and Systems Engineering St. LouisMO United States Washington University in St. Louis Department of Computer Science and Engineering St. LouisMO United States

ISBN: (纸本)9798350316339

Unsupervised feature selection is a critical task in data analysis, particularly when faced with high-dimensional datasets and complex and nonlinear dependencies among features. In this paper, we propose a family of Gram-Schmidt feature selection approaches to unsupervised feature selection that addresses the challenge of identifying non-redundant features in the presence of nonlinear dependencies. Our method leverages probabilistic Gram-Schmidt (GS) orthogonalization to detect and map out redundant features within the data. By applying the GS process to capture nonlinear dependencies through a pre-defined, fixed family of functions, we construct variance vectors that facilitate the identification of high-variance features, or the removal of these dependencies from the feature space. In the first case, we provide information-theoretic guarantees in terms of entropy reduction. In the second case, we demonstrate the efficacy of our approach by proving theoretical guarantees under certain assumptions, showcasing its ability to detect and remove nonlinear dependencies. To support our theoretical findings, we experiment over various synthetic and real-world datasets, showing superior performance in terms of classification accuracy over state-of-the-art methods. Further, our information-theoretic feature selection algorithm strictly generalizes a recently proposed Fourier-based feature selection mechanism at significantly reduced complexity. © 2024 IEEE.

关键词： Feature Selection

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Enhancing Power Quality through Harmonic Reduction: A Triple-Memristor Hopfield Neural Network Approach 8

Enhancing Power Quality through Harmonic Reduction: A Triple...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Varaprasad, K.S.B. Sukhi, Y. Subramaniyan, Shanmugapriya Dhandapani, Ramya Rajendiran, M. Kaliappan, S. Visakha Institute of Engineering & Technology Department of Electrical and Electronics Engineering Andhra Pradesh Visakhapatnam530027 India R.M.K. Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Tiruvallur601206 India Srm Institute of Science & Technology Department of Electrical and Electronics Engineering Tamil Nadu Kattankulathur603203 India Sri Eshwar College of Engineering Coimbatore Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600123 India Lovely Professional University Division of Research and Development Delhi Jalandhar144411 India

ISBN: (纸本)9798350376425

Harmonic distortion in power systems is a significant concern that impacts the efficiency, reliability, and safety of electrical networks. Implementing effective harmonic distortion mitigation solutions, such as filters or advanced control systems, can be expensive and may necessitate substantial changes to existing infrastructure. This paper proposes a Triple-Memristor Hopfield Neural Network (TMHNN) as a novel method for mitigating harmonic distortion in power systems. The main objective of the proposed approach is to improve the quality of power by mitigating the harmonics. The TMHNN is used to predict the actual harmonic current injected by specific harmonic sources within a power system. The results demonstrate the significant advantages of the proposed TMHNN method over existing techniques such as Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Recurrent Neural Network (RNN). TMHNN achieves the highest efficiency at 96.3%, lowest error rate at 4.16% and minimizing total harmonic distortion (THD), with the lowest value of 3.74%. These results highlight TMHNN's superior performance in terms of efficiency, harmonic distortion reduction, and accuracy. © 2024 IEEE.

关键词： Harmonic distortion

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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GACP:graph neural networks with ARMA flters and a parallel CNN for hyperspectral image classification

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International Journal of Digital Earth 2023年第1期16卷 1770-1800页

作者： Jing Yang Jie Sun Yaping Ren Shaobo Li Shujie Ding Jianjun Hu School of Mechanical Engineering Guizhou UniversityGuiyangPeople’s Republic of China State Key Laboratory of Public Big Data Guizhou UniversityGuiyangPeople’s Republic of China School of Intelligent Systems Science and Engineering Jinan UniversityZhuhaiPeople’s Republic of China Department of Computer Science and Engineering University of South CarolinaColumbiaSCUSA

In recent years,the use of convolutional neural networks(CNNs)and graph neural networks(GNNs)to identify hyperspectral images(HSIs)has achieved excellent results,and such methods are widely used in agricultural remote sensing,geological exploration,and marine remote *** many generalization classification algorithms are designed for the purpose of learning a small number of samples,there is often a problem of a low utilization rate of position information in the empty spectral *** on this,a GNN with an autoregressive moving average(ARMA)-based smoothingfilter samples the node information in the null spectral domain and then captures the spatial information at the pixel level via spatial feature convolution;then,the null spectral domain position information lost by the CNN is located by a coordinate attention(CA)***,autoregressive,spatial convolution,and CA mechanisms are combined into multiscale features to enhance the learning capacity of the network for tiny *** conducted on the widely used Indian Pines(IP)dataset,the Botswana(BS)dataset,Houton 2013(H2013),and the WHU-Hi-HongHu(WHU)benchmark HSI dataset demonstrate that the proposed GACP technique can perform classification work with good accuracy even with a small number of training examples.

关键词： Graph neural networks word convolutional neural networks hyperspectral image classification attention mechanisms

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Generation of Squeezed Light in Silicon Nitride Photonic Integrated Chips

Generation of Squeezed Light in Silicon Nitride Photonic Int...

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Liu, Shuai Hariri, Abdulkarim Wu, Bo-Han Zhang, Yuheng Zhang, Zheshen Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States

ISBN: (纸本)9781957171395

关键词： Photonic integrated circuits

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Bio-Inspired Intelligence-Based Multiagent Navigation with Safety-Aware Considerations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2946-2961页

作者： Lei, Tingjun Luo, Chaomin Yang, Simon X. Carruth, Daniel W. Bi, Zhuming Mississippi State University Department of Electrical and Computer Engineering MS39762 United States Laboratory School of Engineering GuelphONN1G 2W1 Canada Mississippi State University Center for Advanced Vehicular Systems MS39762 United States Purdue University Fort Wayne Department of Civil and Mechanical Engineering Fort WayneIN46805 United States

Multiple autonomous vehicles (MAVs) enhance efficiency and task execution compared to a single vehicle. Real-world applications necessitate MAVs to safely navigate in dynamic formation along planned trajectories, while sensing, mapping, and avoiding obstacles. Addressing the need for trajectory adaptation amidst real-world scenarios, a safety-aware bio-inspired framework is proposed in this article. Our approach employs a chaotic gravitational search algorithm (CGSA) for global trajectory generation in a predefined formation. A quadtree-driven variable resolution (QVR) algorithm using monocular cameras provides occupancy grid maps (OGMs) at different resolutions. A formation control with target tracking minimizes a potential function for MAVs to follow the CGSA trajectory. Additionally, a bio-inspired neural network (BNN) local navigator coupled with dynamic moving windows (DMW) advances obstacle avoidance and refines safe trajectories using QVR and OGMs. Simulation and comparative studies validate the framework's robustness and effectiveness for MAVs. © 2020 IEEE.

关键词： Trajectories

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A Mobile Visitor Management System Using a QR Code and PIN for Access Control 2nd

A Mobile Visitor Management System Using a QR Code and PIN f...

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2nd International Conference on Connected Objects and Artificial Intelligence, COCIA 2024

作者： Awotunde, Joseph Bamidele Adeniyi, Abidemi Emmanuel Imoize, Agbotiname Lucky Mejdoub, Youssef Abdualazizu, Zakariyya Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Computer Science Bowen University Osun State Iwo Nigeria Department of Electrical and Electronics Engineering University of Lagos Akoka Nigeria RITM Laboratory Higher School of Technology ESTC Hassan II University Casablanca Morocco

ISBN: (纸本)9783031704109

Visitor management is a common activity that happens every day all around the world. Accepting visitors at home or in the office, going to the bank, or visiting a private residential area;some sort of visitor management goes on in all those activities to ensure access control and security. Insecurity is a big challenge that comes with receiving visitors and that is why visitor management is important. Traditionally, visitor management is handled physically by recording visitors’ information into a log book and asking visitors to contact their hosts before gaining access to a secured environment but this process is time-consuming and the visitor’s record is exposed to exploitation. Automated Visitor Management systems (VMS) overcome the shortcomings of the traditional method of visitor management. VMS automates the process of recording visitors’ information, keeping a log of all visits, and ensuring some sort of access control or visitor authentication just in case of an imposter or if anything ever goes wrong. This study aims to design and model a flexible Mobile Visitor Management system that can be used to record visitors’ information and authenticate visitors for access control purposes. The study was achieved using Figma software to design and prototype the system and then using React Native and SQLite for the development of the system. The system has proven to be more convenient than the common traditional way of recording visitors’ information into a physical log book. The system protects its users from loss of information, waste of time, and insecurity. The flexible and inexpensive nature of the system makes it adaptable to almost any organization or property owner, this gives the general public a great opportunity to try out Visitor Management Technology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Authentication

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Seg-UResNet: A Deep Hybrid Convolutional Neural Network of Road Scenes for semantic segmentation

Seg-UResNet: A Deep Hybrid Convolutional Neural Network of R...

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2024 International Conference on Circuit, systems and Communication, ICCSC 2024

作者： Faska, Zahra Khrissi, Lahbib Haddouch, Khalid El Akkad, Nabil Sidi Mohamed Ben Abdellah University Laboratory of Engineering Systems and Applications Ensa Fez Morocco Sidi Mohamed Ben Abdellah University Lisac Department of Computer Science Fsdm Fez Morocco

ISBN: (纸本)9798350365306

Comprehending the circumstances is an essential element of every autonomous driving system. computer vision harbors the task of semantic segmentation as one of its core components wherein each pixel in an image is labeled with precise labels. The task has found significant applications in areas like autonomous driving plus analysis of images and videos, not to mention medical imaging and scene understanding. The progress made in semantic segmentation through the utilization of deep learning techniques has been truly extraordinary. Our study introduces a groundbreaking approach known as hybrid evolutionary weighted ensemble, which combines the strengths of two deep models. Two distinct segmentation models will be developed using U-Net and ResNet34 as the decoder and the encoder respectively for the first model using transfer learning technique, we are able to create individual segmentation models. Additionally, we consider SegNet another highly regarded convolutional neural network for image segmentation as our second deep model. With the goal of enhancing the accuracy of the deep learning model, we propose an evolutionary leap towards the convergence of the DL model accuracy will be achieved through the introduction of a hybrid weighted ensemble model. This shall be done by combining ResNet34, an individual U-Net-based DTL model, with SegNet model and then executing fine-tuning. To assess the effectiveness of the hybrid network, we conducted an evaluation using the widely recognized and demanding CamVid dataset. Through our experiments, we were able to showcase that the hybrid network outperforms other popular semantic segmentation algorithms by a significant margin, solidifying its position as a cutting-edge solution. © 2024 IEEE.

关键词： Semantic Segmentation

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