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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Gong, Cheng Ding, Zhaoyi Lam, Chi-Seng University of Macau State Key Laboratory of Analog and Mixed-Signal Vlsi China University of Macau Institute of Microelectronics China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350316445

Mathematical model of the inductive power transfer (IPT) systems is essential for stability analysis and control design. Conventional modeling approaches for IPT systems result in high-order models, as each resonant voltage or current is represented by two slowly changing real-valued variables. Alternative modeling methods may decrease the model order, but they are often plagued by drawbacks such as the absence of analytical expressions, reduced generality, and incomplete modeling. Moreover, the harmonic AC components of the IPT system are not well modeled in the existing methods, which affects the analysis accuracy and control optimality. In this paper, a harmonic transfer function (HTF) modeling of the IPT system is proposed, which has good mathematical properties and improved accuracy. First, the circuit configuration and the linear time-varying (LTV) large-signal model are proposed. Then, the derivation of the proposed HTF is comprehensively discussed. Finally, the accuracy of the proposed HTF is verified by simulation and experimental results in comparison with other representative modeling methods, which shows an improved accuracy. © 2023 IEEE.

关键词： harmonics transfer function inductive power transfer system modeling

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Optimal Linear Deception Attacks on Remote state Estimation with Constrained Alarm Rates: A Low-Dimensional Case

Optimal Linear Deception Attacks on Remote State Estimation ...

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IEEE Conference on Decision and Control

作者： Jun Shang Hanwen Zhang Jing Zhou Tongwen Chen Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack’s stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback–Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples.

关键词： Wireless communication Measurement Technological innovation System performance Kalman filters state estimation intelligent sensors

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Dimension Dropout for Evolutionary High-Dimensional Expensive Multiobjective Optimization 11th

Dimension Dropout for Evolutionary High-Dimensional Expensiv...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Lin, Jianqing He, Cheng Cheng, Ran Shenzhen Key Laboratory of Computational Intelligence University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783030720612

In the past decades, a number of surrogate-assisted evolutionary algorithms (SAEAs) have been developed to solve expensive multiobjective optimization problems (EMOPs). However, most existing SAEAs focus on low-dimensional optimization problems, since a large number of training samples are required (which is unrealistic for EMOPs) to build an accurate surrogate model for high-dimensional problems. In this paper, an SAEA with Dimension Dropout is proposed to solve high-dimensional EMOPs. At each iteration of the proposed algorithm, it randomly selects a part of the decision variables by Dimension Dropout, and then optimizes the selected decision variables with the assistance of surrogate models. To balance the convergence and diversity, those candidate solutions with good diversity are modified by replacing the selected decision variables with those optimized ones (i.e., decision variables from some better-converged candidate solutions). Eventually, the new candidate solutions are evaluated using expensive functions to update the archive. Empirical studies on ten benchmark problems with up to 200 decision variables demonstrate the competitiveness of the proposed algorithm. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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CADEC: A Combinatorial Auction for Dynamic Distributed DNN Inference Scheduling in Edge-Cloud Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Xu, Xiaolong Hu, Yuhao Cui, Guangming Qi, Lianyong Dou, Wanchun Nanjing University of Information Science and Technology School of Software Nanjing210044 China Nanjing University of Information Science and Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China College of Computer science and technology Qingdao China Georgia State University Department of Computer Science United States

Deep Neural Network (DNN) Inference, as a key enabler of intelligent applications, is often computation-intensive and latency-sensitive. Combining the advantages of cloud computing (abundant computing resources) and edge computing (fast transmission), edge-cloud collaborative DNN inference is a powerful solution to these problems. However, in edge-cloud networks with heterogeneous resources, how to obtain reasonable decisions on server selection, model partition and resource allocation for efficient distributed DNN inference is a hard challenge. Furthermore, it is non-trivial to design suitable resource prices to maximize the social welfare. These challenges even escalate in dynamic edge-cloud networks where decisions should be generated as soon as each user arrives without future information. Therefore, we design a combinatorial auction for dynamic distributed DNN inference scheduling, named CADEC. CADEC first constructs a bid set for each user based on convex optimization theory for optimal solution searching. Next, prices of resources in the edge-cloud network are adjusted according to changes in supply-demand relationship, and whether to admit the request of each user is decided. Finally, the dynamic distributed inference scheduling decisions are generated through the primal-dual algorithm to maximize the social welfare. Theoretical analysis shows the good competitive ratio and polynomial time complexity of CADEC. Results of simulation experiments present that CADEC improves social welfare by up to 46% compared with state-of-the-art distributed DNN inference schemes. © 2002-2012 IEEE.

关键词： Convex optimization

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MMAL: Multi-Modal Analytic Learning for Exemplar-Free Audio-Visual Class Incremental Tasks 24

MMAL: Multi-Modal Analytic Learning for Exemplar-Free Audio-...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yue, Xianghu Zhang, Xueyi Chen, Yiming Zhang, Chengwei Lao, Mingrui Zhuang, Huiping Qian, Xinyuan Li, Haizhou Department of Electrical and Computer and Engineering National University of Singapore Singapore Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha China School of Electronic Electrical and Communication Engineering University of the Chinese Academy of Sciences Beijing China National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha China Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangdong Guangzhou China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shenzhen China

ISBN: (纸本)9798400706868

Class-incremental learning poses a significant challenge under an exemplar-free constraint, leading to catastrophic forgetting and sub-par incremental accuracy. Previous attempts have focused primarily on single-modality tasks, such as image classification or audio event classification. However, in the context of Audio-Visual Class-Incremental Learning (AVCIL), the effective integration and utilization of heterogeneous modalities, with their complementary and enhancing characteristics, remains largely unexplored. To bridge this gap, we propose the Multi-Modal Analytic Learning (MMAL) framework, an exemplar-free solution for AVCIL that employs a closed-form, linear approach. To be specific, MMAL introduces a modality fusion module that re-formulates the AVCIL problem through a Recursive Least-Square (RLS) perspective. Complementing this, a Modality-Specific Knowledge Compensation (MSKC) module is designed to further alleviate the under-fitting limitation intrinsic to analytic learning by harnessing individual knowledge from audio and visual modality in tandem. Comprehensive experimental comparisons with existing methods show that our proposed MMAL demonstrates superior performance with the accuracy of 76.71%, 78.98%, and 76.19% on AVE, Kinetics-Sounds, and VGGSounds100 datasets, respectively, setting new state-of-the-art AVCIL performance. Notably, compared to those memory-based methods, our MMAL, being an exemplar-free approach, provides good data privacy and can better leverage multi-modal information for improved incremental accuracy. © 2024 ACM.

关键词： Contrastive Learning

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Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene Graphs

Cross-Modality Time-Variant Relation Learning for Generating...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jingyi Wang Jinfa Huang Can Zhang Zhidong Deng Department of Computer Science Tsinghua University Beijing China School of Electronic and Computer Engineering Peking University China Department of Computer Science Beijing National Research Center for Information Science and Technology (BNRist) THUAI State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China

Dynamic scene graphs generated from video clips could help enhance the semantic visual understanding in a wide range of challenging tasks such as environmental perception, autonomous navigation, and task planning of self-driving vehicles and mobile robots. In the process of temporal and spatial modeling during dynamic scene graph generation, it is particularly intractable to learn time-variant relations in dynamic scene graphs among frames. In this paper, we propose a Time-variant Relation-aware TRansformer (TR 2 ), which aims to model the temporal change of relations in dynamic scene graphs. Explicitly, we leverage the difference of text embeddings of prompted sentences about relation labels as the supervision signal for relations. In this way, cross-modality feature guidance is realized for the learning of time-variant relations. Implicitly, we design a relation feature fusion module with a transformer and an additional message token that describes the difference between adjacent frames. Extensive experiments on the Action Genome dataset prove that our TR2 can effectively model the time-variant relations. TR2 significantly outperforms previous state-of-the-art methods under two different settings by 2.1 % and 2.6% respectively.

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Mutural and deep features driven recommender system

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Cluster Computing 2025年第6期28卷 1-16页

作者： Yu, Aihua Wang, Yisong Chen, Panfeng Wang, Qi Li, Hui Wang, Xibin Zhou, Xin Chen, Guojun Deng, Xingzhi Department of Computer Science and Technology Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China School of Data Science Guizhou Institute of Technology Guiyang China

Knowledge graph (KG) is a highly structural knowledge system, which is widely used for reinforcing the performance of downstream applications. Recently, to enhance recommendation performance, knowledge-driven recommender system has emerged as a research hot-spot in KG applications. However, the existing models are lack of the mutual and deep features mining from it, thus it has underused. For this research, two components are formulated to manage it. They are mutual information captor (MIC) and knowledge aware learner (KAL) in our model, respectively. In MIC, an attention mechanism is used for mutual information between entities in KG and items in recommander system capturing to obtain more features in the form of multi-task feature learning methods; In KAL, a neural network model based on deep factorization is proposed to further mine the deep features between users and items, facilitating knowledge extension for recommendations. The extensive experiments demonstrate that the proposed model has achieved promising performance over almost all the state-of-the-art existing models in this research line.

关键词： Knowledge graph Knowledge representation Multi-task learning Recommender system

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Fine-grained Semantic Alignment with Transferred Person-SAM for Text-based Person Retrieval 24

Fine-grained Semantic Alignment with Transferred Person-SAM ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Yihao Yang, Meng Cao, Rui School of Computer Science and Engineering Sun Yat-Sen University Guangdong Guangzhou China State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Xi'an China Ministry of Education Guangdong Guangzhou China School of Information Science and Technology State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services Northwest University Shaanxi Xi'an China

ISBN: (纸本)9798400706868

Addressing the disparity in description granularity and information gap between images and text has long been a formidable challenge in text-based person retrieval (TBPR) tasks. Recent researchers tried to solve this problem by random local alignment. However, they failed to capture the fine-grained relationships between images and text, so the information and modality gaps remain on the table. We align image regions and text phrases at the same semantic granularity to address the semantic atomicity gap. Our idea is first to extract and then exploit the relationships between fine-grained locals. We introduce a novel Fine-grained Semantic Alignment with Transferred Person-SAM (SAP-SAM) approach. By distilling and transferring knowledge, we propose a Person-SAM model to extract fine-grained semantic concepts at the same granularity from images and texts of TBPR and its relationships. With the extracted knowledge, we optimize the fine-grained matching via Explicit Local Concept Alignment and Attentive Cross-modal Decoding to discriminate fine-grained image and text features at the same granularity level and represent the important semantic concepts from both modalities, effectively alleviating the granularity and information gaps. We evaluate our proposed approach on three popular TBPR datasets, demonstrating that SAP-SAM achieves state-of-the-art results and underscores the effectiveness of end-to-end fine-grained local alignment in TBPR tasks. © 2024 ACM.

关键词： Visual languages

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis 4

Personalized Federated Learning with Collaborative Aggregati...

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4th International Conference on Industrial Automation, Robotics and Control Engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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GSLB: The Graph Structure Learning Benchmark 37

GSLB: The Graph Structure Learning Benchmark

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37th Conference on Neural Information Processing systems, NeurIPS 2023

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9781713899921

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Graphic methods

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