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arXiv 2024年

作者： Shi, Xiufang Zhang, Wei Wu, Mincheng Liu, Guangyi Wen, Zhenyu He, Shibo Shah, Tejal Ranjan, Rajiv The College of Information Science Zhejiang University of Technology Hangzhou310023 China The College of Control Science and Engineering Zhejiang University Hangzhou310027 China The Computing Science and Internet of Things Newcastle University NewcastleNE1 7RU United Kingdom

In federated learning, the heterogeneity of client data has a great impact on the performance of model training. Many heterogeneity issues in this process are raised by non-independently and identically distributed (Non-IID) data. This study focuses on the issue of label distribution skew. To address it, we propose a hybrid federated learning framework called HFLDD, which integrates dataset distillation to generate approximately independent and equally distributed (IID) data, thereby improving the performance of model training. Particularly, we partition the clients into heterogeneous clusters, where the data labels among different clients within a cluster are unbalanced while the data labels among different clusters are balanced. The cluster headers collect distilled data from the corresponding cluster members, and conduct model training in collaboration with the server. This training process is like traditional federated learning on IID data, and hence effectively alleviates the impact of Non-IID data on model training. Furthermore, we compare our proposed method with typical baseline methods on public datasets. Experimental results demonstrate that when the data labels are severely imbalanced, the proposed HFLDD outperforms the baseline methods in terms of both test accuracy and communication cost. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Distributed Adaptive Fault-Tolerant Output Regulation of Heterogeneous Multi-Agent Systems With Coupling Uncertainties and Actuator Faults

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1098-1106页

作者： Chao Deng Weinan Gao Weiwei Che the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the Department of Electrical and Computer Engineering Allen E.Paulson College of Engineering and ComputingGeorgia Southern UniversityStatesboroGA 30460 USA the State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027 the Institute of Complexity Science Qingdao UniversityQingdao 266071China

In this paper, we consider the distributed adaptive fault-tolerant output regulation problem for heterogeneous multiagent systems with matched system uncertainties and mismatched coupling uncertainties among subsystems under the influence of actuator faults. First, distributed finite-time observers are proposed for all subsystems to observe the state of the exosystem. Then, a novel fault-tolerant controller is designed to compensate for the influence of matched system uncertainties and actuator faults. By using the linear matrix inequality technique, a sufficient condition is provided to guarantee the solvability of the considered problem in the presence of mismatched coupling uncertainties. Moreover, it is shown that the system in closed-loop with the developed controller can achieve output regulation by using the Lyapunov stability theory and cyclic-small-gain ***, a numerical example is given to illustrate the effectiveness of the obtained result.

关键词： Adaptive control cyclic-small-gain fault-tolerant control multi-agent systems(MASs)

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Curiosity and Affect-Driven Cognitive Architecture for HRI

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IEEE Transactions on Affective computing 2025年

作者： Berto, Leticia Tanevska, Ana Cirne, Azamor Costa, Paula Simoes, Alexandre Gudwin, Ricardo Rea, Francesco Colombini, Esther Sciutti, Alessandra State University of Campinas Institute of Computing Campinas Brazil Italian Institute of Technology CONTACT Unit Genoa Italy Uppsala University Department of Information Technology Uppsala Sweden Federal University of Paraiba Department of Finance and Accounting João Pessoa Brazil State University of Campinas School of Electrical and Computer Engineering Campinas Brazil São Paulo State University Department of Control and Automation Engineering Sorocaba Brazil

This study explores how humans and cognitive robots with different value systems and motivations understand each other's needs in free-form interactions. We developed a cognitive architecture that links sensing and perception to internal motivation and an intrinsic value system for determining actions. Inspired by young children's needs, this architecture includes three drives: learning, interaction, and recharging, each with varying dependence on the human partner. We aimed to assess how experimentally changing the importance of these drives within a fixed architecture affects interaction dynamics with human partners (acting as caregivers) and their understanding of the robot's needs. By adjusting the learning and interaction drives, we created two robot profiles: Playful, which prioritizes environmental exploration and playfulness to reduce boredom, and Social, which focuses on social interaction through touch and visual contact to increase comfort. Our findings show that changing the importance of these drives produces distinct behaviors and human perceptions. Robot behaviors matched their profiles, and participants adapted their responses accordingly. Participants identified and attributed distinct traits to each robot without knowing the specific profiles. Despite variability among human partners, the robots, especially the playful one, were generally well understood by most participants. © 2025 IEEE. All rights reserved.

关键词： Robot learning

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Fatigue Load Prediction of Large Wind Turbine by Big Data and Deep Learning

Fatigue Load Prediction of Large Wind Turbine by Big Data an...

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Electrical, Automation and Computer engineering (ICEACE), International Conference on

作者： Jiayue Wu Qinmin Yang Nanlin Jin Low Carbon Electrical Power and Energy Technology xXi'an Jiaotong-Liverpool University Suzhou China College of Control Science and Engineering Zhejiang University Hangzhou China Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China

This paper introduces a fatigue load prediction framework for the nacelle of wind turbines utilizing operational data sourced from the supervisory control and data acquisition (SCADA) system of a Wind Farm. The primary objective revolves around achieving precise fatigue load predictions for the nacelle, forecasting 15 minutes in advance. Firstly, a series of data preprocessing steps were implemented on the wind turbine data procured from the SCADA system to enhance the model's generalization capability and robustness, including 3-Sigma outlier detection, sample point selection, as well as filtering and de-noising techniques. Then, the gate recurrent unit (GRU) was tuned and trained as the optimal model for long time-series forecasting of SCADA parameter channels. Subsequently, the support vector regression (SVR) model, refined with grid search methodology, was employed to establish the intricate relationship between SCADA parameters and fatigue loads. Finally, the predicted SCADA parameter values were fed into the optimized SVR model to derive fatigue load prediction results for wind turbines. Experimental results indicated that the proposed model framework can effectively predict fatigue loads on the nacelle of wind turbines utilizing the SCADA data in real scenarios.

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Natural Human Skin‑Inspired Wearable and Breathable Nanofiber‑based Sensors with Excellent Thermal Management Functionality

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Advanced Fiber Materials 2024年第6期6卷 1955-1968页

作者： Peng Wang Xiaodan Li Guifen Sun Guoqing Wang Qing Han Chuizhou Meng Zhonghe Wei Yang Li School of Mechanical Engineering University of JinanJinan 250022China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human‑Robot InteractionSchool of Mechanical EngineeringHebei University of TechnologyTianjin 300401China Shandong Provincial Key Laboratory of Network‑Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan 250022China Network Information Center Department of Computer ScienceQilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Integrated Circuits Shandong UniversityJinan 250101China

Wearable sensors have been rapidly developed for application in various human monitoring ***,the wearing comfort and thermal properties of these devices have been largely ignored,and these characteristics urgently need to be ***,we develop a wearable and breathable nanofiber-based sensor with excellent thermal management functionality based on passive heat preservation and active Joule heating *** multifunctional device consists of a micropatterned carbon nanotube(CNT)/thermoplastic polyurethane(TPU)nanofiber electrode,a microporous ionic aerogel electrolyte and a microstructured Ag/TPU nanofiber *** to the presence of a supercapacitive sensing mechanism and the appli-cation of microstructuration,the sensor shows excellent sensing performance,with a sensitivity of 24.62 ***,due to the overall porous structure and hydrophobicity of TPU,the sensor shows good breathability(62 mm/s)and water repellency,with a water contact angle of 151.2°.In addition,effective passive heat preservation is achieved by combining CNTs with high solar absorption rates(85%)as the top layer facing the outside,aerogel with a low thermal conductivity(0.063 W m-1 k-1)as the middle layer for thermal insulation,and Ag with a high infrared reflectance rate as the bottom layer facing the *** warming,this material yields a higher temperature than ***,the active Joule heat-ing effect is realized by applying current through the bottom resistive electrode,which can quickly increase the temperature to supply controlled warming on *** proposed wearable and breathable sensor with tunable thermal properties is promising for monitoring and heat therapy applications in cold environments.

关键词： Wearable sensor Breathable Personal thermal management Electrospun nanofibers Joule heating

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Nonreciprocal Bundle Emissions of Quantum Entangled Pairs

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Physical Review Letters 2024年第4期133卷 043601-043601页

作者： Qian Bin Hui Jing Ying Wu Franco Nori Xin-You Lü School of Physics and Institute for Quantum Science and Engineering College of Physics Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China Theoretical Quantum Physics Laboratory Cluster for Pioneering Research Center for Quantum Computing Physics Department

Realizing precise control over multiquanta emission is crucial for quantum information processing, especially when integrated with advanced techniques of manipulating quantum states. Here, by spinning the resonator to induce the Sagnac effect, we can obtain nonreciprocal photon-phonon and photon-magnon super-Rabi oscillations under conditions of optically driving resonance transitions. Opening dissipative channels for such super-Rabi oscillations enables the realization of directional bundle emissions of entangled photon-phonon pairs and photon-magnon pairs by transferring the pure multiquanta state to a bundled multiquanta outside of the system. This nonreciprocal emission is a flexible switch that can be controlled with precision, and simultaneous emissions of different entangled pairs (such as photon-phonon or photon-magnon pairs) can even emerge but in opposite directions by driving the resonator from different directions. This ability to flexibly manipulate the system allows us to achieve directional entangled multiquanta emitters, and has also potential applications for building hybrid quantum networks and on-chip quantum communications.

关键词： Cavity quantum electrodynamics Non-reciprocal propagation Photon statistics Quantum entanglement

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Active Power control for Off-Shore Wind Turbines Against FDI Attack

Active Power Control for Off-Shore Wind Turbines Against FDI...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Xuguo Jiao Hao Luo Liuyang Jiang Lin Wang Xin Wang Zuqiang Huang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Information and Control Engineering Qingdao University of Technology Qingdao China Zhejiang Windey Co. Ltd. Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Civil Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Recently, offshore wind power has rapidly developed due to its low environmental interference and abundant wind resources. However, due to its remote geographical location and high demand for communication infrastructure, off-shore wind turbines (OWTs) are vulnerable to potential threats from false data injection (FDI) attack. FDI attack can interfere with active power control strategies by tampering with the rotor speed data measured by sensors and pose a threat to the stability of the power grid. To ensure that OWTs can accurately track the active power instructions set by wind farm's control center under FDI attack, this paper proposes a rotor speed control (RSC) strategy based on Kalman filtering technology (RSC- KF). This strategy can estimate the rotor speed of the wind turbine in real-time, and then a residual detection and compensation mechanism is designed to effectively detect and correct attacked rotor speed information caused by FDI attack. Further, based on the corrected rotor speed, the RSC method is developed, which can flexibly switch control strategies according to different rotor speed values, ensuring the efficient operation of OWTs under different wind speed conditions. The proposed approach's effectiveness is proved via simulations conducted on the OpenFAST platform.

关键词： Filtering Velocity control Power control Rotors Power system stability Stability analysis Real-time systems Wind turbines Kalman filters Cyberattack

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Vision Transformers(ViT) Pretraining on 3D ABUS Image and Dual-CapsViT: Enhancing ViT Decoding via Dual-Channel Dynamic Routing

Vision Transformers(ViT) Pretraining on 3D ABUS Image and Du...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingwang Xu Wei Wang Kuanquan Wang Suyu Dong Pengzhong Sun Jinwei Sun Gongning Luo Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computer and Control Engineering Northeast Forestry University Harbin China School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China

Breast cancer continues to be a pressing global health concern, emphasizing the essential need for effective diagnostic techniques. Automated Breast Ultrasound Systems (ABUS) provide a promising advance in breast tumor detection, yet they require significant expertise in interpreting 3D ABUS images, a task fraught with distinctive challenges. Although Vision Transformers (ViT) display remarkable potential for image processing, their low inductive bias and significant data requirements pose obstacles, particularly in the data-constrained medical field. To mitigate these issues, we introduce a Mask-Recover strategy for pretraining Transformer models on 3D ABUS images, enhancing model adaptability and reducing the data demands of the ViT model. Moreover, recognizing the risk that ViTs’ average pooling approach may unintentionally mask small but vital features, we propose Dual-CapsViT, an inventive model combining Transformers and Capsule Networks. This integration affords efficient token routing while preserving fine-grained details. To reconcile potential inconsistencies between capsules and tokens, we engineer a novel dual-channel routing algorithm, strengthening the decoder’s performance. We benchmarked our models against well-known standards such as ResNet and ViT for classifying breast tumors in ABUS images. Our models exhibited superior performance, as evidenced by improved accuracy, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC) metrics, thereby affirming Dual-CapsViT’s potential to enhance breast cancer diagnostics.

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Triple IRS-Aided Communications: Row-Column Sparsity Enhanced Bayesian Tensor Learning for Channel Estimation

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IEEE Transactions on Communications 2025年

作者： Hu, Limei Shao, Xiaodan Qiu, Tingzhi Chen, Feng Cheng, Lei Wu, Qingqing Southwest University College of Artificial Intelligence College of Electronic and Information Engineering Brain-Inspired Computing and Intelligent Control of Chongqing Key Laboratory National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China University of Waterloo Department of Electrical and Computer Engineering Waterloo N2L 3G1 ON Canada Zhejiang University College of Information Science and Electronic Engineering Hangzhou 310058 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China

Channel acquisition presents a major challenge in deploying intelligent reflecting surfaces (IRS) aided communication systems, due to massive reflective elements that create a complex multi-path channel and increase channel dimensions. For an IRS-aided communication system, complete channel includes three parts: From the users to IRS, from the IRS to BS, and from the BS back to the IRS. Thus, a generalized multi-IRS cascaded communication system with three cascaded IRSs is considered. Unfortunately, existing channel estimation methods focus on single or double IRS cascades, which is not applicable to the case of triple cascaded IRS channel estimation directly. In this paper, we study the uplink channel estimation for triple cascaded IRSs aided single-user single-input single-output (SISO) systems. Specifically, the triple IRS cascaded channel is typically sparse. It permits us to characterize the channel estimation as a problem of sparse matrix recovery. Then, the sparse learning is explored to achieve robust channel estimation with limited training overhead. Particularly, the sparse channel matrices of the cascaded triple IRS channels have a common row-column block sparsity structure. However, a unique challenge lies in characterizing and enhancing such a common row-column sparsity. To tackle this issue, we apply a random matrix prior to promote the common row-column-wise sparsity of the channel matrix, and then an efficient Bayesian tensor inference algorithm is proposed to estimate the IRS channel. Finally, simulation results confirm that the proposed scheme outperforms traditional counterparts in terms of accuracy. © 1972-2012 IEEE.

关键词： Bayesian learning common row-column sparsity tensor modeling Triple intelligent reflecting surface (IRS)

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Cross-Layer Optimization for Fault-Tolerant Deep Learning

arXiv

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arXiv 2023年

作者： Zhang, Qing Liu, Cheng Liu, Bo Huang, Haitong Wang, Ying Li, Huawei Li, Xiaowei Both State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China Beijing Institite of Control Engineering Beijing100190 China

Fault-tolerant deep learning accelerator is the basis for highly reliable deep learning processing and critical to deploy deep learning in safety-critical applications such as avionics and robotics. Since deep learning is known to be computing- and memory-intensive, traditional fault-tolerant approaches based on redundant computing will incur substantial overhead including power consumption and chip area. To this end, we propose to characterize deep learning vulnerability difference across both neurons and bits of each neuron, and leverage the vulnerability difference to enable selective protection of the deep learning processing components from the perspective of architecture layer and circuit layer respectively. At the same time, we observe the correlation between model quantization and bit protection overhead of the underlying processing elements of deep learning accelerators, and propose to reduce the bit protection overhead by adding additional quantization constrain without compromising the model accuracy. Finally, we employ Bayesian optimization strategy to co-optimize the correlated cross-layer design parameters at algorithm layer, architecture layer, and circuit layer to minimize the hardware resource consumption while fulfilling multiple user constraints including reliability, accuracy, and performance of the deep learning processing at the same time. © 2023, CC BY.

关键词： Fault tolerance

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