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International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Wassim Touati Yamen El Touati Nejib Ben Hadj-Alouane Abderrazek Jemai SERCOM Laboratory Polytechnic School of Tunisia Carthage University Tunis Tunisia Department of Computer Science Faculty of Computing and Information Technology Northern Border University Kingdom of Saudi Arabia OASIS Laboratory ENIT University of Tunis EI Manar Tunisia Electrical Engineering and Computer Engineering Department American University in Dubai Dubai UAE

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Machine learning, a vital part of artificial intelli-gence, improves our ability to make predictions from complex data. The success of these predictions relies heavily on the model's fit with its data and the data's consistency. Accurate predictions depend on closely matching the model to the data patterns. However, missing data can seriously disrupt the accuracy of predictions, causing errors. To address this, we recommend adopting a proposed framework that enhances predictions by effectively filling in missing data. Our objective is to refine the accuracy of our predictions, especially in scenarios where the data might be incomplete and features exhibit patterns of dependency. In the context of missing data, our approach concentrates on enhancing regression performance to minimize the effects of missing data as effectively as possible.

关键词： Accuracy Correlation Statistical analysis Smart cities Predictive models Data models Imputation Reliability Random forests Robots

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A Meta-learning based Generalizable Indoor Localization Model using Channel State Information

A Meta-learning based Generalizable Indoor Localization Mode...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ali Owfi ChunChih Lin Linke Guo Fatemeh Afghah Jonathan Ashdown Kurt Turck Holcombe Department of Electrical and Computer Engineering Clemson University Clemson SC USA Air Force Research Laboratory Rome NY USA

Indoor localization has gained significant attention in recent years due to its various applications in smart homes, industrial automation, and healthcare, especially since more people rely on their wireless devices for location-based services. Deep learning-based solutions have shown promising results in accurately estimating the position of wireless devices in indoor environments using wireless parameters such as Channel State Information (CSI) and Received Signal Strength Indicator (RSSI). However, despite the success of deep learning-based approaches in achieving high localization accuracy, these models suffer from a lack of generalizability and can not be readily-deployed to new environments or operate in dynamic environments without retraining. In this paper, we propose meta-learning-based localization models to address the lack of generalizability that persists in conventionally trained DL-based localization models. Furthermore, since meta-learning algorithms require diverse datasets from several different scenarios, which can be hard to collect in the context of localization, we design and propose a new meta-learning algorithm, TB-MAML (Task Biased Model Agnostic Meta Learning), intended to further improve generalizability when the dataset is limited. Lastly, we evaluate the performance of TB-MAML-based localization against conventionally trained localization models and localization done using other meta-learnina algorithms.

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Analysis of a Mechanically Doubly Salient Electric Machine using FE and Lumped Parameter Magnetic Equivalent Circuit Models

Analysis of a Mechanically Doubly Salient Electric Machine u...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Goli, Chandra Sekhar Essakiappan, Somasundaram Ionel, Dan M Gafford, James Manjrekar, Madhav University of North Carolina Department of Electrical and Computer Engineering Charlotte United States University of North Carolina Energy Production and Infrastructure Center Charlotte United States Qm Power Inc Charlotte United States University of Kentucky Spark Laboratory Ece Dept Lexington United States

ISBN: (纸本)9798350316445

Analysis of a special machine topology, which features a doubly salient structure and permanent magnets in the stator and provides very high power density performance has been presented in this paper. An analytical model for the proposed machine has been developed using lumped parameter magnetic equivalent circuits (LPMEC) of the stator segments, permanent magnets (PM), toroidal windings, air gap, and reluctance-type rotor. Previously published analytical techniques have not thoroughly explored spatial harmonics, which is done in the proposed methodology, yielding a high-fidelity model. The machine analyzed is highly suited for electric vehicle traction applications and consists of 12 stator segments and coils, separated by 12 PMs in the stator, and a reluctance-type rotor with 10 protrusions. The LPMEC model has been analyzed to compute flux linkages, open circuit back EMF, and inductances with respect to the rotor position, and these results have been verified using finite element method. The analysis shows that despite its doubly salient structure, the proposed machine exhibits the characteristics of a non-salient machine, and this behavior has been validated by analyzing spatial harmonics and static torque test. © 2023 IEEE.

关键词： doubly salient machines flux switching permanent magnet machine motor drive non-salient machines Permanent magnet synchronous motors traction

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Global convergence of direct policy search for state-feedback H∞ robust control: a revisit of nonsmooth synthesis with goldstein subdifferential 22

Global convergence of direct policy search for state-feedbac...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Xingang Guo Bin Hu Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign

ISBN: (纸本)9781713871088

Direct policy search has been widely applied in modern reinforcement learning and continuous control. However, the theoretical properties of direct policy search on nonsmooth robust control synthesis have not been fully understood. The optimal H∞ control framework aims at designing a policy to minimize the closed-loop H∞ norm, and is arguably the most fundamental robust control paradigm. In this work, we show that direct policy search is guaranteed to find the global solution of the robust H∞ state-feedback control design problem. Notice that policy search for optimal H∞ control leads to a constrained nonconvex nonsmooth optimization problem, where the nonconvex feasible set consists of all the policies stabilizing the closed-loop dynamics. We show that for this nonsmooth optimization problem, all Clarke stationary points are global minimum. Next, we identify the coerciveness of the closed-loop H∞ objective function, and prove that all the sublevel sets of the resultant policy search problem are compact. Based on these properties, we show that Goldstein's subgradient method and its implementable variants can be guaranteed to stay in the nonconvex feasible set and eventually find the global optimal solution of the H∞ state-feedback synthesis problem. Our work builds a new connection between nonconvex nonsmooth optimization theory and robust control, leading to an interesting global convergence result for direct policy search on optimal H∞ synthesis.

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Open-Set Recognition Model for Distributed Fiber-Optic Vibration Sensor Based on Hierarchical Attention Network with Openmax

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IEEE Sensors Journal 2025年

作者： Zhou, Zhengxian Xie, Tianchang Wang, Xiankun Qu, Jun Shi, Jianping Han, Yashuai Yang, Xinyan Hu, Xing Zhang, Dawei Zhuang, Songlin Anhui Normal University Anhui Province Key Laboratory for Control and Applications of Optoelectronic Information Materials Wuhu241002 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

Recent advancements in deep learning have improved the signal recognition capabilities of distributed fiber-optic vibration sensors. However, most existing research focuses on closed-set scenarios, assuming that all potential events are known and accounted for during training. In practical applications, the environmental complexity surrounding sensing fibers makes it impractical to anticipate and train on every intrusion event. To address this, we propose a novel Hierarchical Attention Network with Openmax (HANO) model that integrates a hierarchical attention mechanism with the Openmax algorithm. This model effectively extracts and emphasizes discriminative features at multiple levels, enhancing the recognition of both known and unknown events. Incorporating the Openmax layer enhances the classifier’s ability to manage unknown classes, thereby facilitating open-set recognition. To validate the effectiveness of the HANO model, we conducted experiments involving human intrusion events within a fenced area. The network parameters were optimized accordingly, and HANO’s performance was compared against the Swin Transformer, as well as models utilizing Softmax and Sigmoid layers. Additionally, we benchmarked it against other open-set recognition algorithms. The experimental results demonstrate that the HANO model achieves an overall recognition accuracy exceeding 95%, outperforming the comparative methods. These results demonstrate that the HANO model enhances the recognition of open-set events, indicating its strong potential for practical applications, particularly in scenarios requiring precise intrusion detection, such as security monitoring and vibration detection. © 2001-2012 IEEE.

关键词： Convolutional neural networks

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Joint Optimization of Charging Time and Resource Allocation in Wireless Power Transfer Assisted Federated Learning

Joint Optimization of Charging Time and Resource Allocation ...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Jingjiao Wang Huan Zhou Liang Zhao Deng Meng X. Shouzhi Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China School of Computer Science Northwestern Polytechnical University Xi'an China College of Economics and Management China Three Gorges University Yichang China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

As a new distributed machine learning methodology, Federated Learning (FL) allows mobile devices (MDs) to collaboratively train a global model without sharing their raw data in a privacy-preserving manner. However, it is a great challenge to schedule each MD and allocate various resources reasonably. This paper studies the joint optimization of computing resources used by MDs for FL training, the number of local iterations as well as WPT duration of each MD in a Wireless Power Transfer (WPT) assisted FL system, with the goal of maximizing the total utility of all MDs in the entire FL training process. Furthermore, we analyze the problem by using the Karush-Kuhn-Tucker (KKT) conditions and Lagrange dual method, and propose an improved Lagrangian subgradient method to solve this problem. Finally, extensive simulation experiments are conducted under various scenarios to verify the effectiveness of the proposed algorithm. The results show that our proposed algorithm has better performance in terms of the total utility of all MDs compared with other benchmark methods.

关键词： Training Schedules Federated learning Distributed databases Wireless power transfer Benchmark testing Mobile handsets

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Security of SDN in an Adversarial Setting: The DDoS Case

Security of SDN in an Adversarial Setting: The DDoS Case

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Artificial Intelligence and Internet of Things (GCAIoT), IEEE Global Conference

作者： Ranwa Al Mallah Brian Lachine Godwin Badu-Marfo Royal Military College of Canada Electrical and Computer Engineering Kingston Canada Laboratory of Innovations in Transportation Toronto Metropolitan University Toronto Canada

In Software Defined Networking (SDN) centralization of network control creates a single point of failure and a valuable target for threat actors that wish to produce an impact on the network. Notably, network controllers can be targeted by effects such as Denial of Service (DoS) attacks that would cripple the performance of the network as a whole by making critical services unavailable if they are not detected and prevented. In Artificial Intelligence (AI), Machine Learning (ML) techniques are used to identify the presence of malicious distributed DoS activity within a sample of data collected from network activity over an interval of time. However, machine learning techniques are themselves vulnerable to attacks. We perform a detailed security analysis of a highly realistic threat model and experimentally demonstrate the effectiveness of poisoning and evasion attacks on the SDN. After this adversarial AI experiment, we propose a defense mechanism adapted to the machine learning algorithm and able to protect against those attacks on the system. This research highlights the implications of the poor management of the use of AI as a new technology in this field.

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Outgoing Editorial

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IEEE Transactions on Circuits and Systems I: Regular Papers 2023年第12期70卷 4675-4677页

作者： Zhao, Weisheng Li, Hai Helen Zito, Domenico Beihang University School of Integrated Circuit Science and Engineering Beijing100191 China Fert Beijing Institute Beihang University Beijing100191 China Beihang University Miit Key Laboratory for Spintronics Beijing100191 China Duke University Department of Electrical and Computer Engineering DurhamNC27708 United States Agh University of Science and Technology Department of Measurements and Electronics Krakow30-059 Poland

Dear TCAS-I Readers,

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Causality-inspired Unsupervised Domain Adaptation with Target Style Imitation for Medical Image Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Song, Jincai Chen, Haipeng Lyu, Yingda Nie, Weizhi Liu, An-An Jilin University College of Computer Science and Technology Jilin Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China Jilin University Public Computer Education and Research Center Jilin Changchun130012 China Tianjin University School of Electrical and Information Engineering Tianjin300072 China

Deep learning performance may decrease substantially with unseen heterogeneous data. While most unsupervised domain adaptation (UDA) methods seek to address this through image alignment, they often ignore uncertainty style fluctuations within the target domain. When testing image styles vary in both direction and intensity, such models may fail to adapt. Furthermore, existing UDA methods tend to over-reliance on domain-level entire feature alignment, resulting in potentially over-exploiting semantic content-independent cues (e.g., intensity) as shortcut features. To address these limitations, this paper introduces an innovative and model-agnostic Causality-inspired Representation Learning Based on Target Style Imitation method for UDA. Specifically, we propose a novel Target Style Imitation (TSI) data augmentation approach to diversify the training data and align training and unseen target testing image styles. TSI constructs a Gaussian distribution for the target domain style and simulates unseen testing style variations through random sampling. Additionally, inspired by the stable and generalizable causal mechanism, we propose Causality-inspired Representation Learning (CRL) based on TSI method to enforce feature representations to adhere to causal properties (i.e., Separation and Independence) essential for robust UDA, thus fostering the model to focus on the domain-invariant semantic features. Our method surpasses state-of-the-art methods on two cross-modality medical image segmentation datasets. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Distributed robust fuzzy control for mixed-order multi-agent systems ensuring global consensus

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Science China Technological Sciences 2025年第7期68卷 1-11页

作者： Yang, Xiaoli Chen, Jiaxi Gao, Xinbo Ge, Shuzhi Sam Key Laboratory of Intelligent Analysis and Decision on Complex Systems School of Mathematics and Statistics Chongqing University of Posts and Telecommunications Chongqing China School of Mathematics and Statistics Xidian University Xi’an China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

This study investigates the global consensus problem of mixed-order multi-agent systems. Firstly, a control strategy based on switching functions is proposed to ensure the effective operation of fuzzy control in the fuzzy region. Secondly, the differential equation model satisfying the global Lipschitz condition is extended, and a more universal model framework is constructed. Furthermore, online learning laws with time-varying σ-modifications are introduced to effectively guarantee the global asymptotic convergence of the closed-loop multi-agent systems. Finally, the effectiveness of the proposed algorithm is validated through designed simulation experiments.

关键词： Multi agent systems

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