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Domain generalization with semi-supervised learning for people-centric activity recognition

Science China(Information Sciences) 2025年第1期68卷 171-188页

作者： Jing LIU Wei ZHU Di LI Xing HU Liang SONG Academy for Engineering & Technology Fudan University Shanghai East-bund Research Institute on Networking Systems of AI School of Optoelectronic Information and Computer Engineering University of Shanghai for Science & Technology

People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.

关键词： activity recognition deep learning domain generalization semi-supervised learning adversarial training

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Data-driven output regulation control for constrained linear systems

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Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and Technology Department of Electrical and Computer Engineering University of Alberta Systems Research Institute Polish Academy of Sciences Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University

Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means(FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree(MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field (AEF) Thunderstorm Fuzzy C-means (FCM) Attribute

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Reviewing metal supported solid oxide fuel cells for efficient electricity generation with biofuels for mobility

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Journal of Energy Chemistry 2025年第04期103卷 106-153页

作者： Fábio C.Antunes Jo?o P.J.de Oliveira Ricardo S.de Abreu Thiago Dias Bruno B.N.S.Brand?o Josué M.Gon?alves Josimar Ribeiro Julian Hunt Hudson Zanin Gustavo Doubek School of Electrical and Computer Engineering University of Campinas School of Food Engineering University of Campinas School of Chemical Engineering University of Campinas Mackenzie Institute for Research in Graphene and Nanotechnologies (Mack Graphe) Mackenzie Presbyterian Institute Electrochemistry Research and Development Laboratory (LPDE) Department of Chemistry Center of Exact Sciences Federal University of Espírito Santo Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology

Metal-Supported Solid Oxide Fuel Cells（MS-SOFCs） hold significant potential for driving the energy *** electrochemical devices represent the most advanced generation of Solid Oxide Fuel Cell（SOFCs） and can pave the way for mass production and wider adoption than Proton Exchange Membrane Fuel Cells（PEMFCs） due to their fuel flexibility,higher power density and the absence of noble metals in the fabrication *** review examines the state-of-the-art of SOFCs and MS-SOFCs,presenting perspectives and research directions for these key technological devices,highlighting novel materials,techniques,architectures,devices,and degradation mechanisms to address current challenges and future *** such as infiltration/impregnation,ex-solution catalyst synthesis,and the use of a pre-catalytic reformer layer are discussed as their impact on efficiency and prolonged *** concepts are also described and connected with well-dispersed nano particles,hindrance of coarsening,and an increased number of Triple Phase Boundaries（TPBs）.This review also describes the synergistic use of reformers with MS-SOFCs to compose solutions in energy generation from readily available ***,the End-of-Life（EoL）,recycling,and life-cycle assessments（LCAs） of the Fuel Cell Hybrid Electric Vehicles（FCHEVs） were *** comparing Fuel Cell Electric Vehicles（FCEVs）equipped with（PEMFCs） and FCHEVs equipped with MS-SOFCs,both powered with hydrogen（H2） generated by different routes were *** review aims to provide valuable insights into these key technological devices,emphasizing the importance of robust research and development to enhance performance and lifespan while reducing costs and environmental impact.

关键词： Biofuel Fuel cell Hybrid electric vehicle Metal-supported solid oxide fuel cell Powertrain systems Reform

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Flexiswap: A Cross-Chain Decentralized Exchange Protocol 17

Flexiswap: A Cross-Chain Decentralized Exchange Protocol

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17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Singh, Kirti Ribeiro, Vinay J. Mandal, Susmita Indian Institute of Technology Bombay Department of Computer Science and Engineering Mumbai India Institute for Development and Research in Banking Technology Hyderabad India

ISBN: (纸本)9798331531195

Decentralized exchanges (DEXs) have emerged as a promising solution to enhance trustlessness in blockchain ecosystems and mitigate security threats associated with centralized exchanges. While platforms like Uniswap offer certain advantages, their limitation to exchanging tokens within a single blockchain restricts their utility. Moreover, exchanges relying on atomic swaps, such as AtomicDEX, are susceptible to grief attacks and exhibit low success ratios. In response to these challenges, we introduce FlexiSwap, an auction-based decentralized exchange protocol that leverages atomic swaps for exchanging native tokens between smart contract-compatible blockchains. FlexiSwap stands out by combining multiple bids to facilitate one-to-many exchanges, resulting in improved exchange values, successful execution of large-volume exchanges, and an improved success ratio. The protocol incorporates a penalization mechanism to deter grief attacks, ensuring that participants cannot withdraw from an exchange without incurring a loss once the counterparty has committed its assets. We also prove the protocol's safety, liveness, and grief prevention. Our simulation results demonstrate that combining multiple bids helps achieve better exchange values and order-matching success rates. Additionally, we implemented our protocol on Ethereum-based blockchains. The experimental results show that combining bids increases overall gas usage, but the increase in the average gas user per bid is marginal. © 2025 IEEE.

关键词： Kyoto Protocol

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Blur-Deblur Algorithm for Pressure-Sensitive Paint Image Based on Variable Attention Convolution

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computers, Materials & Continua 2025年第3期82卷 5239-5256页

作者： Ruizhe Yu Tingrui Yue Lei Liang Zhisheng Gao School of Computer and Software Engineering Xihua UniversityChengdu610039China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing210016China Low Speed Aerodynamics Institute China Aerodynamics Research and Development CenterMianyang621000China

In the PSP(Pressure-Sensitive Paint),image deblurring is essential due to factors such as prolonged camera exposure times and highmodel velocities,which can lead to significant image *** deblurring methods applied to PSP images often suffer from limited accuracy and require extensive computational *** address these issues,this study proposes a deep learning-based approach tailored for PSP image *** that PSP applications primarily involve the accurate pressure measurements of complex geometries,the images captured under such conditions exhibit distinctive non-uniform motion blur,presenting challenges for standard deep learning models utilizing convolutional or attention-based *** this paper,we introduce a novel deblurring architecture featuring multiple DAAM(Deformable Ack Attention Module).These modules provide enhanced flexibility for end-to-end deblurring,leveraging irregular convolution operations for efficient feature extraction while employing attention mechanisms interpreted as multiple 1×1 convolutions,subsequently reassembled to enhance ***,we incorporate a RSC(Residual Shortcut Convolution)module for initial feature processing,aimed at reducing redundant computations and improving the learning capacity for representative shallow *** preserve critical spatial information during upsampling and downsampling,we replace conventional convolutions with wt(Haar wavelet downsampling)and dysample(Upsampling by Dynamic Sampling).This modification significantly enhances high-precision image *** integrating these advanced modules within an encoder-decoder framework,we present the DFDNet(Deformable Fusion Deblurring Network)for image blur removal,providing robust technical support for subsequent PSP data *** evaluations on the FY dataset demonstrate the superior performance of our model,achieving competitive results on the GOPRO and HIDE datasets.

关键词： Pressure-sensitive paint deep learning image deblurring typeset variable attention convolution

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Voltage and power control for distributed generation units in islanded microgrids 6th

Voltage and power control for distributed generation units i...

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6th International Conference on Renewable Energies Offshore, RENEW 2024

作者： Rouch, T. Bagheri M’zoughi, F. Aboutalebi, P. Ahmad, I. Garrido, A.J. Garrido, I. Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Faculty of Engineering of Bilbao University of the Basque Country Bilbao Spain

ISBN: (纸本)9781032905570

This article presents an optimized control strategy tailored for DC islanded microgrids, featuring a voltage controller based on mixed H2/H∞ state feedback using linear matrix inequalities (LMIs) and a power controller guided by droop characteristics. A polytopic modeling approach addresses system uncertainties and nonlinearities. The controller aims to stabilize the system, reject disturbances, equitably distribute power among distributed generation units (DGUs), and minimize energy consumption. Regional pole placement ensures welldamped transient responses. Simulation results demonstrate effective management of load perturbations and DGU disconnections, with a comparative analysis against an H∞ controller offering additional insights into performance. © 2025 The Author(s).

关键词： Microgrids

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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Science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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