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arXiv 2025年

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C.L. Philip The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Department of New Network Pengcheng Laboratory Shenzhen518000 China The China Tobacco Guangdong Industrial Corporation Guangzhou510385 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenMix, a data augmentation technique specifically designed for semi-supervised semantic segmentation with Vision Transformers. TokenMix aligns well with the global attention mechanism by mixing images at the token level, enhancing learning capability for contextual information among image patches. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations. Copyright © 2025, The Authors. All rights reserved.

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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Human-centric assembly in smart factories

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CIRP Annals 2025年

作者： Wang, Lihui Gao, Robert X. Krüger, Jörg Váncza, József Department of Production Engineering KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Aerospace Engineering Case Western Reserve University Cleveland United States Institute for Machine Tools and Factory Management Technical University Berlin Germany Laboratory of Engineering and Management Intelligence Institute for Computer Science and Control Hungary

Assembly in future smart factories needs to address three challenges, including human centricity, sustainability, and resilience. Conventional approaches for automation in assembly have reached a bottleneck in terms of operation automomy, leaving various tasks to continued manual labour by human operators. To ease the burden on humans both physically and intellectually, human-centric assembly enhanced by augmented robots, cognitive systems, mixed reality and collaborative intelligence, assisted by thought-driven brain robotic controls, provides a promising solution. Within the context, this keynote provides an in-depth analysis of the state of human-centric assembly and identifies potentially fruitful research directions in future smart factories. © 2025 The Author(s)

关键词： Assembly Human-centricity Robot

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Constrained Optimization with Decision-Dependent Distributions

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IEEE Transactions on automatic control 2025年

作者： Wang, Zifan Liu, Changxin Parisini, Thomas Zavlanos, Michael M. Johansson, Karl H. KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures StockholmSE-10044 Sweden East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy Duke University Department of Mechanical Engineering and Materials Science DurhamNC United States

In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distributions has assumed either the absence of constraints or fixed constraints. This work considers a more general setting where the constraints can also dynamically adjust in response to changes in the decision variables. Specifically, we consider linear constraints and analyze the effect of decision-dependent distributions in both the objective function and constraints. Firstly, we establish a sufficient condition for the existence of a constrained equilibrium point, at which the distributions remain invariant under retraining. Moreover, we propose and analyze two algorithms: repeated constrained optimization and repeated dual ascent. For each algorithm, we provide sufficient conditions for convergence to the constrained equilibrium point. Furthermore, we explore the relationship between the equilibrium point and the optimal point for the constrained decision-dependent optimization problem. Notably, our results encompass previous findings as special cases when the constraints remain fixed. To show the effectiveness of our theoretical analysis, we provide numerical experiments on both a market problem and a dynamic pricing problem for parking based on real-world data. © 1963-2012 IEEE.

关键词： Optimization algorithms

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Semantic Feature Division Multiple Access for Digital Semantic Multiple Access Channels

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Shen, Bin Ma, Shuai Chen, Rui Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University State Key Laboratory of Integrated Service Networks School of Telecommunications Engineering Shaanxi Xi'an710071 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Xidian University School of Artificial Intelligence Shaanxi Xi'an710071 China Peng cheng Laboratory Shenzhen518066 China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

To solve the multi-user semantic communication network capacity bottlenecks, this paper proposes a semantic feature domain multiple access (SFDMA) scheme. By extracting and encoding semantic features and jointly processing multi-user data, the scheme can realize the encoding and transmission of multi-user semantic information in the separated feature subspace. Based on this scheme, a semantic digital multiple access network model is proposed to perform multi-user inference tasks, and an efficient semantic encoding and decoding scheme is developed by using variational information bottleneck (VIB) theory to ensure both the orthogonality in the feature domain and the classification accuracy. Furthermore, a SFDMA model for image reconstruction is designed based on the Swin Transformer. The modified model also considers the reconstruction quality and orthogonality of semantic feature domain. In addition, we design an optimization target for multiple access to implement an adaptive power allocation method for multi-user semantic communications. Simulation results show that our proposed SFDMA scheme exhibits semantic feature domain orthogonality in different applications, and the adaptive power allocation method shows excellent performance in the classification task, and effectively guarantee the quality of service (QoS) of the semantic communications. © 2015 IEEE.

关键词： Image reconstruction

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Privacy Preservation for Cloud-Edge-Collaborative Energy Management System Using Post-Quantum Homomorphic Encryption

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IEEE Transactions on Smart Grid 2025年

作者： Jiang, Cheng Li, Xue Du, Dajun Qian, Hong Wen, Mi Wu, Lei Findeisen, Rolf Shanghai University of Electric Power Shanghai Key Laboratory of Power Station Automation Technology College of Automation Engineering Shanghai200090 China Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai200072 China Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States TU Darmstadt Control and Cyber-Physical Systems Laboratory Darmstadt64227 Germany

Cloud-based energy management systems (EMS) in smart grids face privacy challenges, as existing methods based on traditional homomorphic encryption support limited operations and are vulnerable to quantum attacks. We propose a privacy preservation method for smart grid energy management system, leveraging edge-assisted computing and ring learning with errors (RLWE) homomorphic encryption. Our approach enables both addition and multiplication operations on encrypted smart grid data while defending against quantum threats. We demonstrate the methods security under the RLWE hardness assumption and validate its effectiveness through an experimental platform simulating smart grid environments. This work addresses computational limitations in current privacy-preserving techniques, enhancing data security for evolving smart grid technologies © 2010-2012 IEEE.

关键词： Power management (telecommunication)

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AutoFE-Pointer: Auto-weighted feature extractor based on pointer network for DNA methylation prediction

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International Journal of Biological Macromolecules 2025年 311卷 143668页

作者： Feng, Xin Xin, Ruihao Wu, Jiezhang Zheng, Jiaxin Wang, Chenyan Yu, Cuinan School of Science Jilin Institute of Chemical Technology Jilin130000 China State Key Laboratory of Inorganic Synthesis and Preparative Chemistry College of Chemistry Jilin University Changchun130012 China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin130000 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Civil Aviation General Hospital Beijing100123 China School of Engineering Westlake University Zhejiang Hangzhou310030 China Hunan Bojii Life Technology Co. Ltd. Hunan Province Changsha410000 China

DNA methylation is a critical epigenetic modification that plays a central role in gene regulation, cellular differentiation, and the development of various diseases, including cancers. Aberrant methylation patterns have emerged as both biomarkers and mechanistic drivers in pathogenesis, underscoring the urgent need for precise and efficient predictive tools. Although some deep learning techniques have advanced methylation prediction, most existing models are trained independently on single-species datasets. This species-specific approach limits efficiency because each model can only handle one dataset at a time, often at the expense of predictive performance. Additionally, the state-of-the-art deep learning models tend to have enormous parameter counts and computational overhead, making them impractical for integration into local offline software applications. To overcome these challenges, we propose AutoFE-Pointer, a lightweight and novel framework that harnesses an improved softened pointer network to dynamically extract and weight features from diverse DNA sequences. AutoFE-Pointer is designed to simultaneously process 17 different benchmark datasets spanning multiple species, achieving superior performance compared to models that are trained individually on single-species data. In doing so, it not only offers state-of-the-art predictive accuracy and robust cross-species generalization but also significantly reduces computational demands, facilitating its deployment in local offline environments. This breakthrough represents a significant advancement in the field of epigenetic modeling and computational biology. © 2025

关键词： Enterprise software

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A Benchmark and Frequency Compression Method for Infrared Few-Shot Object Detection

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Ruiheng Yang, Biwen Xu, Lixin Huang, Yan Xu, Xiaofeng Zhang, Qi Jiang, Zhizhuo Liu, Yu Beijing Institute of Technology State Key Laboratory of Electromechanical Dynamic Control Beijing100081 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia Anhui Polytechnic University School of Computer and Information Wuhan241000 China Technical University of Munich Data Science in Earth Observation Munich85521 Germany Tsinghua University Shenzhen International Graduate School Shenzhen518055 China Tsinghua University Department of Electronic Engineering Beijing100084 China

Infrared few-shot object detection (IFSOD) aims to detect infrared objects with limited labeled examples. Current infrared datasets, however, suffer from limited diversity in object types and classes, hindering robust evaluation of model generalization on novel classes. To systematically assess dataset quality, we propose metrics for class diversity, instance variability, and object density. By integrating three widely used infrared datasets, we construct the first dataset specifically tailored for IFSOD, increasing instance density to 4.8 (a 1.1 improvement) and expanding the number of classes to 18 (a 5-class increase) compared to the source datasets. Furthermore, frequency analysis of spatial features reveals that sparse annotations introduce spectral bias in the frequency domain. Directly transforming spatial features to the frequency domain, however, mixes background noise with object features, causing spectral leakage and impairing the learning of discriminative features for novel classes. To address these issues, we propose the frequency compression few-shot detection (FC-fsd) method, which incorporates a frequency compression (FC) module. The FC module leverages Discrete Cosine Transform (DCT) within localized windows to reduce spectral leakage and enhance feature clarity. With minimal additional computational overhead, FC-fsd significantly outperforms state-of-the-art methods, achieving nAP50 scores of 28.57 (+13.37) and 35.63 (+2.59) in 1-shot and 2-shot settings, respectively. Our dataset is published at https://***/RuihengZhang/IFSOD-dataset. © 2025 IEEE.

关键词： Zero-shot learning

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A high-performance measurement method for artery intima-media thickness in medical ultrasound images

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Optoelectronics Letters 2010年第6期6卷 469-472页

作者：金晶王艳高欣王祁 Laboratory of Detection Technology and Automatic Equipment Department of Control Science and EngineeringHarbin Institute of Technology

A high-performance measurement method for the artery intima-media thickness(IMT) in medical ultrasound images is *** scheme mainly consists of two *** first part is the detection process,including adaptive gray stretching for image enhancing,gradient transforming to restrain the over-segmentation,and the watershed algorithm for edge *** intima can be detected automatically and *** second part,combined with regional positioning and boundary fitting stages,is used to measure the *** results show that the proposed scheme can yield a good measurement result,and the calculation time can satisfy the practical requirement.

关键词：测量方法超声图像内膜医学性能厚度中层动脉

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3D printing of self-powered triboelectric intelligent system with enhanced output performance for material identification via machine learning

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Chemical engineering Journal 2025年 515卷

作者： Huang, Bingyu Xu, Kailong Zhang, Xiaofei Zhang, Yongcheng Long, Yun-Ze Ge, Shuzhi Sam Wei, Xiangxia School of Automation Shandong Key Laboratory of Industrial Control Technology Qingdao University Qingdao266071 China College of Materials Science and Engineering Qingdao University Qingdao266071 China Collaborative Innovation Center for Nanomaterials & Devices College of Physics Qingdao University Qingdao266071 China College of Physics Qingdao University Qingdao266071 China Department of Electrical & Computer Engineering National University of Singapore 117576 Singapore

Triboelectric nanogenerators (TENGs) have attracted global attention for energy conversion from various types of mechanical energy into electricity. Traditional approaches for improving triboelectric conversion efficiency generally involved complicated preparation methods and strict structural design, which cannot fulfill the stringent requirements of material adaptability and application scenarios. In this work, a flexible TENG was developed based on zirconium-doped barium titanate (BaZrxTi1−xO3, BZT) particles in polydimethylsiloxane (PDMS) matrix. Subsequently, BZT/PDMS composites with patterned structures were fabricated by direct ink writing (DIW) based three-dimensional (3D) printing to significantly enhance the electrical performance. It was demonstrated that the TENG could deliver a maximum open-circuit voltage (VOC) of 350 V and a short-circuit current (ISC) of 4.9 µA with a maximum power density of 2.4 W m−2. Moreover, an effective utilization of the TENG was designed to detect the presence of humans, saving a huge amount of electrical energy. Finally, a TENG-based tactile sensor was integrated with machine learning to construct a material identification system, which was capable of recognizing different materials with a high accuracy of 98.54 %. The TENG-based tactile sensor with machine learning demonstrated a tremendous application in material perception, promoting the development of versatile sensors in the field of machine intelligence. © 2025 Elsevier B.V.

关键词： Liquid crystals

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