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Synergetic monitoring of pressure and temperature stimulations in multisensory electronic skin based on time decoupling effect

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Carbon Energy 2025年第1期7卷 52-63页

作者： Zhiyi Gao Ye Zhang Zhenyu Hu Dongdong Zhang Shengbin Li Huiyun Xiao Ziyin Xiang Dan Xu Haifeng Zhang Yuanzhao Wu Yiwei Liu Jie Shang Runwei Li CAS Key Laboratory of Magnetic Materials and Devices Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingboChina Yunnan Key Laboratory for Micro/Nano Materials&Technology School of Materials and EnergyYunnan UniversityKunmingChina Ningbo Institute of Digital Twin Eastern Institute of TechnologyNingboChina Institute of Micro/Nano Materials and Devices Ningbo University of TechnologyNingboChina

The progress from intelligent interactions requires electronic skin(E-skin)to shift from single-functional perception to multisensory ***,the intuitive and interference-free reading of multiple sensory signals without involving complex algorithms is a critical ***,we propose a flexible multisensory E-skin by developing a highly homogeneous dispersion of BaTiO_(3)nanoparticles in polydimethylsiloxane dielectric *** E-skin is sensitive to externally applied pressure as well as temperature and can distinguish dual synergetic stimuli by the time decoupling *** pressure and temperature perception was achieved in an individual device,which greatly reduced the structural complexity compared with multifunctional integrated *** sensitivity of E-skin for pressure detection is as high as 0.0724 kPa^(−1)and the detection range reaches as wide as 15.625-10 *** sensitivity to temperature detection is as high as−1.34℃^(−1)and the detection range reaches 20-200℃.More importantly,by equipping with a multilayer neural network,the evolution from tactile perception to advanced intelligent tactile cognition is demonstrated.

关键词： E-skin intelligent tactile cognition machine learning multifunctional sensing time decoupling effect

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

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Digital Communications and Networks 2025年第2期11卷 473-481页

作者： Qi Li Gaozhan Liu Qianqian Zhang Lidong Han Wei Chen Rui Li Jinbo Xiong School of Computer Science Nanjing University of Posts and Telecommunications Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal University Tongda College of Nanjing University of Posts and Telecommunications Henan Key Laboratory of Network Cryptography Technology School of Computer Science and Technology Xidian University State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Faculty of Software Fujian Normal University

Ciphertext-Policy Attribute-Based Encryption(CP-ABE) enables fine-grained access control on ciphertexts, making it a promising approach for managing data stored in the cloud-enabled Internet of Things. But existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute content. Additionally, resource-constrained IoT devices, especially those adopting wireless communication, frequently encounter affordability issues regarding decryption costs. In this paper, we propose an efficient and fine-grained access control scheme with fully hidden policies(named FHAC). FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them. A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access policy. Dictionary attacks are thwarted by providing unauthorized users with invalid values. The heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud servers. Additionally, users can verify the correctness of multiple outsourced decryption results simultaneously. Security analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.

关键词： Access control Policy hiding Verifiable outsourced computation Cloud IoT

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Ensemble Knowledge Distillation for Federated Semi-Supervised Image Classification

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Tsinghua Science and technology 2025年第1期30卷 112-123页

作者： Ertong Shang Hui Liu Jingyang Zhang Runqi Zhao Junzhao Du School of Computer Science and Technology Xidian UniversityXi’an 710126China Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of EducationXi’an 710126China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi’an 710126China

Federated learning is an emerging privacy-preserving distributed learning paradigm,in which many clients collaboratively train a shared global model under the orchestration of a remote *** current works on federated learning have focused on fully supervised learning settings,assuming that all the data are annotated with ground-truth ***,this work considers a more realistic and challenging setting,Federated Semi-Supervised Learning(FSSL),where clients have a large amount of unlabeled data and only the server hosts a small number of labeled *** to reasonably utilize the server-side labeled data and the client-side unlabeled data is the core challenge in this *** this paper,we propose a new FSSL algorithm for image classification based on consistency regularization and ensemble knowledge distillation,called *** algorithm uses the global model as the teacher in consistency regularization methods to enhance both the accuracy and stability of client-side unsupervised learning on unlabeled ***,we introduce an additional ensemble knowledge distillation loss to mitigate model overfitting during server-side retraining on labeled *** experiments on several image classification datasets show that our EKDFSSL outperforms current baseline methods.

关键词： federated learning semi-supervised learning federated semi-supervised learning knowledge distillation

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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

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Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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Machine Learning-Based Methods for Materials Inverse Design: A Review

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computers, Materials & Continua 2025年第2期82卷 1463-1492页

作者： Yingli Liu Yuting Cui Haihe Zhou Sheng Lei Haibin Yuan Tao Shen Jiancheng Yin Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650093China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China Yunnan Tin Company Limited Tin Industry Branch Gejiu661000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China

Finding materials with specific properties is a hot topic in materials *** materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high *** the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new *** through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired *** paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse ***,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application ***,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are *** authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.

关键词： Materials inverse design machine learning target properties deep learning new materials discovery

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DMPNet: A lightweight remote sensing forest wildfire detection network based on multi-scale heterogeneous attention mechanism and dynamic scaling fusion strategy

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Digital Signal Processing: A Review Journal 2025年 164卷

作者： Long, Yingping Ding, Hongwei Zhu, Yuanjing Yang, Zhijun Li, Bo College of Information Technology Yunnan University Chenggong District Yunnan Province Kunming China College of Science and Technology Dianchi College of Yunnan University Xishan District Yunnan Province Kunming China The Key Laboratory of Internet of Things Technology and Application in Yunnan Province China

Unmanned Aerial Vehicle (UAV)-based remote sensing technology has emerged as a critical tool for forest fire detection. However, existing methods face significant challenges in simultaneously achieving high detection accuracy and real-time performance, particularly in scenarios characterized by multi-scale variations, non-stationary behaviors, and complex occlusions. To address these issues, we propose DMPNet, a lightweight neural network specifically designed for forest fire detection. Unlike conventional approaches that sacrifice accuracy for parameter reduction, DMPNet employs an optimized feature extraction architecture (PCSPNet), which not only preserves robust feature representation but also reduces the number of parameters by 16.67% (approximately 500K) compared to YOLOv8n. Furthermore, the dynamic fusion scaling strategy (DSFNet) is integrated into the network's neck to dynamically adjust the size and weight of feature maps, overcoming the limitations of static fusion. Additionally, DMPNet incorporates a multi-scale heterogeneous attention mechanism (MSHA), which effectively addresses occlusion issues through multi-scale contextual reasoning and cross-scale feature interactions. Experimental results demonstrate that DMPNet achieves an inference speed of 1.2 ms, with mAP50 reaching 86.8% and mAP@50-95 at 59%. When compared to YOLOv10n under similar parameter constraints, DMPNet improves mAP50 by 4.2% and surpasses MobileNet, GhostNet, and EfficientNet by over 20% in accuracy. By balancing model compactness, precision, and efficiency, DMPNet provides a robust solution for real-time forest fire monitoring. © 2025 Elsevier Inc.

关键词： Unmanned aerial vehicles (UAV)

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A Novel Two-Stream Algorithm for Spatio-Temporal Fusion in Remote Sensing

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IEEE Geoscience and Remote Sensing Letters 2025年

作者： Wang, Shiying Zou, Xuechao Wang, Wentao Liu, Yao Zhang, Shun Cao, Tengfei Tao, Pin Qinghai University School of Computer Technology and Application Xining China Qinghai University Intelligent Computing and Application Laboratory of Qinghai Province China School of Computer Science & Technology Beijing China Tsinghua University Department of Computer Science and Technology Beijing China Ministry of Education Key Laboratory of Pervasive Computing China

The spatio-temporal fusion technology can effectively solve the problem of missing time series data. However, existing algorithms often struggle to accurately capture surface feature changes and fine details. To overcome these limitations, this paper introduces FemSTF, a flexible two-stream algorithm for remote sensing spatio-temporal fusion, which consists of three main stages: image sharpening, image fusion, and edge optimization. In the first stage, techniques such as interpolation, classification, and blending are employed to address missing pixel values caused by minimal input data. In the second stage, a two-stream strategy is introduced to enhance fusion accuracy, enabling the algorithm to capture regional detail changes with high sensitivity to surface variations. The third stage focuses on edge optimization, generating high-resolution predictive images under minimal input conditions (three images) while retaining rich texture and edge details. Notably, FemSTF achieves a performance comparable to that of methods requiring five input images. Experimental results demonstrate that FemSTF outperforms mainstream algorithms across multiple datasets. Ablation experiments confirm the effectiveness of the two-stream strategy, highlighting its role in enhancing remote sensing data processing accuracy. This study offers an efficient solution for spatio-temporal fusion, demonstrating great potential in the field. © 2004-2012 IEEE.

关键词： Spatio-temporal data

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A Reinforcement Learning-Based Image Mapping-Topology Optimization for Antenna Design

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Dou, Jiangling Gong, Hao Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming Yunnan650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College China

A Reinforcement Learning (RL)-based Image Mapping Topology (IMT) optimization approach is proposed. To enable RL to optimize antenna topology and increase the efficiency of the optimization process. Two crucial components, Stratified Sampling Balance (SSB) and IMT are introduced. SSB is utilized to balance different types of data samples. IMT is utilized to facilitate the interaction between RL and simulation software, it also pretrains the actor network within the RL algorithm. This methodology allows the RL agent to acquire knowledge from the interaction of the antenna topology with the environment. Subsequently, the agent outputs antenna topology with the maximum reward signal. Compared with other machine learning-based optimization methods, the proposed method significantly alleviates the issue of learning inefficiency and reduces the exploration time for feasible solutions. To validate the effectiveness of the optimization method, a microstrip antenna is optimized. The measurement results show that the impedance bandwidth is doubled while maintaining the realized gain with fewer simulation samples and less optimization time. © 2025 IEEE.

关键词： Reinforcement learning

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Predicting on the Extreme Precipitation in East and Southeast Asia during Summer 2022 from the Antecedent Soil Temperature Anomaly over the Tibetan Plateau

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Journal of Climate 2025年第9期38卷 2109-2128页

作者： Kong, Xianghui Wang, Aihui Xue, Yongkang Wei, Nan Zhang, He Hu, Qin He, Juanxiong Bi, Xunqiang Chen, Yue Nansen-Zhu International Research Centre Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing China Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information Technology Sichuan Province Chengdu China University of California Los AngelesCA United States School of Atmospheric Sciences Sun Yat-sen University Zhuhai China International Center for Climate and Environment Sciences Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China School of Atmospheric Sciences Chengdu University of Information Technology Sichuan Province Chengdu China Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China National Super Computer Center in Tianjin Tianjin China State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China

It has demonstrated that May land surface temperature (LST) and subsurface temperature (SUBT) anomalies on the Tibetan Plateau (TP) can serve as a predictor of June precipitation in downstream *** summer 2022, there was heavy precipitation (severe drought) in June (August) over southern China, coinciding with high May (July) LST/SUBT anomalies in *** the framework of the Impact of Initialized Land Temperature and Snowpack on Subseasonal to Seasonal Prediction (LS4P), this study explores the causality between 2022 May (July) TP LST/SUBT and June (August) precipitation using an ensemble of land–atmosphere coupled model *** show that model prediction with imposed strongly positive May TP LST/SUBT (i.e., a triple) anomaly could produce 67% (46%) of June precipitation anomalies in South (northeast) China, while sea surface temperature (SST) effects account for 5% (51%).The atmosphere condensation heating and surface sensible heating due to TP LST/SUBT anomaly induce anomalous South Asian high, which moves eastward along with the Rossby ***, the above process enhances upward motion, convective instability, and water vapor transport, eventually be favorable for rainfall formation in South China and northeast *** Southeast Asia, it makes a downward movement, decrease in atmospheric water vapor, and unfavorable to the occurrence of ***, July TP LST/SUBT contributes to the prediction skill for August record-breaking drought in southern *** to SST effects, it is found that TP LST/SUBT effects significantly enhance the prediction skill for downstream region, suggesting that the TP land contributes an important source for subseasonal climate prediction. © 2025 American Meteorological Society.

关键词： Rivers

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