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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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FCaS: Fine-grained Cardiac Image Synthesis based on 3D Template Conditional Diffusion Model

arXiv

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arXiv 2025年

作者： Xia, Jiahao Hu, Yutao Qi, Yaolei Li, Zhenliang Shao, Wenqi He, Junjun Fu, Ying Zhang, Longjiang Yang, Guanyu Ministry of Education China Jiangsu Province Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing China Univ Rennes CHU Rennes Inserm LTSI– UMR 1099 RennesF-35000 France Shanghai AI Laboratory China School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Radiology Jinling Hospital Affiliated Hospital of Medical School Nanjing University Nanjing China

Solving medical imaging data scarcity through semantic image generation has attracted significant attention in recent years. However, existing methods primarily focus on generating whole-organ or large-tissue structures, showing limited effectiveness for organs with fine-grained structure. Due to stringent topological consistency, fragile coronary features, and complex 3D morphological heterogeneity in cardiac imaging, accurately reconstructing fine-grained anatomical details of the heart remains a great challenge. To address this problem, in this paper, we propose the Fine-grained Cardiac image Synthesis(FCaS) framework, established on 3D template conditional diffusion model. FCaS achieves precise cardiac structure generation using Template-guided Conditional Diffusion Model (TCDM) through bidirectional mechanisms, which provides the fine-grained topological structure information of target image through the guidance of template. Meanwhile, we design a deformable Mask Generation Module (MGM) to mitigate the scarcity of high-quality and diverse reference mask in the generation process. Furthermore, to alleviate the confusion caused by imprecise synthetic images, we propose a Confidence-aware Adaptive Learning (CAL) strategy to facilitate the pre-training of downstream segmentation tasks. Specifically, we introduce the Skip-Sampling Variance (SSV) estimation to obtain confidence maps, which are subsequently employed to rectify the pre-training on downstream tasks. Experimental results demonstrate that images generated from FCaS achieves state-of-the-art performance in topological consistency and visual quality, which significantly facilitates the downstream tasks as well. Code will be released in the future. © 2025, CC0.

关键词： Image segmentation

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Saliency-Based Fidelity Adaptation Preprocessing for Video Coding

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Journal of computer science & technology 2011年第1期26卷 195-202页

作者：卢少平张松海 Department of Computer Science and Technology Tsinghua University Beijing Engineering Research Center for Intelligent Processing of Visual Media and Content Security

In this paper, we present a video coding scheme which applies the technique of visual saliency computation to adjust image fidelity before compression. To extract visually salient features, we construct a spatio-temporal saliency map by analyzing the video using a combined bottom-up and top-down visual saliency model. We then use an extended bilateral filter, in which the local intensity and spatial scales are adjusted according to visual saliency, to adaptively alter the image fidelity. Our implementation is based on the H.264 video encoder JM12.0. Besides evaluating our scheme with the H.264 reference software, we also compare it to a more traditional foreground-background segmentation-based method and a foveation-based approach which employs Gaussian blurring. Our results show that the proposed algorithm can improve the compression ratio significantly while effectively preserving perceptual visual quality.

关键词： visual saliency bilateral filter fidelity adjustment region-of-interest encoder

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Generalized Tensor-Aided Channel Estimation for Hardware Impaired Device Identification

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IEEE Transactions on Vehicular technology 2025年

作者： Wu, Qi Sui, Zeping Ngo, Hien Quoc Wan, Qun Matthaiou, Michail School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China National Key Laboratory on Blind Signal Processing Chengdu610041 China Queen’s University Belfast BelfastBT3 9DT United Kingdom School of Computer Science and Electronics Engineering University of Essex ColchesterCO4 3SQ United Kingdom Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of

In this paper, we investigate the joint generalized channel estimation and device identification problem in Internet of Things (IoT) networks under multipath propagation. To fully utilize the received signal, we decompose the generalized channel into three components: transmitter hardware characteristics, path gains, and angles of arrival. By modeling the received signals as parallel factor (PARAFAC) tensors, we develop alternating least squares (ALS)-based algorithms to simultaneously estimate the generalized channels and identify the transmitters. Simulation results show that the proposed scheme outperforms both Khatri-Rao Factorization (KRF) and the conventional least squares (LS) method in terms of channel estimation accuracy and achieves performance close to the derived Cramér–Rao lower bound. © 2025 IEEE. All rights reserved,

关键词： Tensors

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Examining the Fourier Spectrum of Speech Signal from a Time-Frequency Perspective for Automatic Depression Level Prediction

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IEEE Transactions on Affective Computing 2025年

作者： Niu, Mingyue Tao, Jianhua He, Yongjun Zhang, Shiqing Li, Ming YanShan University School of Information Science and Engineering Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China Tsinghua University Department of Automation Bejing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Taizhou University Institute of Intelligent Information Processing Taizhou318000 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

Currently, many studies use Fourier amplitude spectra of speech signals to predict depression levels. However, those works often treat Fourier amplitude spectra as images or sequences to capture depression cues using convolutional neural networks or multilayer perceptrons. Therefore, they ignore the complex element composition and time-frequency attributes of Fourier spectra, which is not conducive to capturing the differences among individuals with different depression levels. For this reason, we construct a Time-Frequency Self-Embedding (TFSE) module, which not only stores the correlation relationship among real (imaginary) parts of Fourier spectra of different subjects from the time-frequency perspective, but also maintain the physical properties of data through the weight embedding process. Besides, Global Average Pooling (GAP) or linear layers are difficult to balance both temporal and frequency dimensions in the vectorization process. Therefore, we construct a Time-Frequency Tensor Vectorization (TFTV) module, which summarizes each channel along time and frequency dimensions, and then generates the vectorization result by integrating various channels. In this way, we combine TFSE and TFTV modules to form our SpectrumFormer model for predicting depression levels. Evaluation indicators on AVEC 2013 and AVEC 2014 depression databases imply the progressiveness of our model. © 2010-2012 IEEE.

关键词： Depression level prediction Real and imaginary parts Tensor vectorization Time-frequency attributes

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A uniform solution to the independent set problem through tissue P systems with cell separation

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Frontiers of computer science 2012年第4期6卷 477-488页

作者： Xingyi ZHANG Xiangxiang ZENG Bin LUO Zheng ZHANG Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230039China Department of Computer Science Xiamen UniversityXiamen 361005China Key Laboratory of Image Processing and Intelligent Control Department of Control Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China

Membrane computing is an emergent branch of natural computing, which is inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, and other higher order structures. Tissue P systems are a class of the most investigated computing mod- els in the framework of membrane computing, especially in the aspect of efficiency. To generate an exponential resource in a polynomial time, cell separation is incorporated into such systems, thus obtaining so called tissue P systems with cell separation. In this work, we exploit the computational efficiency of this model and construct a uniform family of such tissue P systems for solving the independent set problem, a well-known NP-complete problem, by which an efficient so- lution can be obtained in polynomial time.

关键词： membrane computing tissue P system cell sep- aration independent set problem

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A novel unsupervised method for new word extraction

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science China(Information sciences) 2016年第9期59卷 11-21页

作者： Lili MEI Heyan HUANG Xiaochi WEI Xianling MAO Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Department of Computer Science and TechnologyBeijing Institute of Technology

New words could benefit many NLP tasks such as sentence chunking and sentiment analysis. However, automatic new word extraction is a challenging task because new words usually have no fixed language pattern, and even appear with the new meanings of existing words. To tackle these problems, this paper proposes a novel method to extract new words. It not only considers domain specificity, but also combines with multiple statistical language knowledge. First, we perform a filtering algorithm to obtain a candidate list of new words. Then, we employ the statistical language knowledge to extract the top ranked new words. Experimental results show that our proposed method is able to extract a large number of new words both in Chinese and English corpus, and notably outperforms the state-of-the-art methods. Moreover, we also demonstrate our method increases the accuracy of Chinese word segmentation by 10% on corpus containing new words.

关键词： new word extraction word segmentation domain specificity statistical language knowledge domain word extraction

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Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study

arXiv

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arXiv 2025年

作者： Liu, Tianhao Liu, Jiqiang Zhang, Tao Wang, Jian Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100045 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Ensuring end-to-end cross-layer communication security in military networks by selecting covert schemes between nodes is a key solution for military communication security. With the development of communication technology, covert communication has expanded from the physical layer to the network and application layers, utilizing methods such as artificial noise, private networks, and semantic coding to transmit secret messages. However, as adversaries continuously eavesdrop on specific communication channels, the accumulation of sufficient data may reveal underlying patterns that influence concealment, and establishing a cross-layer covert communication mechanism emerges as an effective strategy to mitigate these regulatory challenges. In this article, we first survey the communication security solution based on covert communication, specifically targeting three typical scenarios: device-to-device, private network communication, and public network communication, and analyze their application scopes. Furthermore, we propose an end-to-end cross-layer covert communication scheme driven by Generative Artificial Intelligence (GenAI), highlighting challenges and their solutions. Additionally, a case study is conducted using diffusion reinforcement learning to sovle cloud edge internet of things cross-layer secure communication. © 2025, CC BY.

关键词： Generative adversarial networks

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Lightweight Vision Model-based Multi-user Semantic Communication Systems

arXiv

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arXiv 2025年

作者： Jiang, Feibo Tu, Siwei Dong, Li Wang, Kezhi Yang, Kun Liu, Ruiqi Pan, Cunhua Wang, Jiangzhou Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China School of Information Science and Engineering Hunan Normal University Changsha410081 China School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science Brunel University London United Kingdom School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China Wireless and Computing Research Institute ZTE Corporation Beijing100029 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

Semantic Communication (SemCom) is a promising new paradigm for next-generation communication systems, emphasizing the transmission of core information, particularly in environments characterized by uncertainty, noise, and bandwidth constraints. However, existing image SemCom systems face several challenges, such as inefficient knowledge base construction, insufficient semantic encoding, and lack of multi-user semantic sharing. To address these issues, we propose a Lightweight Vision Model-based Multi-user Semantic Communication System (LVM-MSC). First, we construct a Lightweight Knowledge Base (LKB) based on the fast Segment Anything Model (SAM). LKB incorporates the extensive image knowledge of the SAM model while significantly reducing the number of parameters through its convolutional architecture. Next, we design an Efficient Semantic Codec (ESC) based on the Masked AutoEncoder (MAE) architecture. ESC enhances semantic compression at both the pixel and semantic levels and implements lightweight semantic decoding tailored for user devices. Furthermore, we propose a Multi-user Semantic Sharing (MSS) transmission for the multi-user SemCom. By calculating the similarity of semantic information among different users in the sharing semantic space, we unify the transmissions of similar semantic information through broadcasting, further improving the transmission efficiency. Finally, simulation results demonstrate the feasibility and effectiveness of the proposed LVM-MSC system. Copyright © 2025, The Authors. All rights reserved.

关键词： Latent semantic analysis

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Betweenness-based algorithm for a partition scale-free graph

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Chinese Physics B 2011年第11期20卷 556-564页

作者：张百达吴俊杰唐玉华周静 National Laboratory for Parallel and Distributed Processing School of ComputersNational University of Defense Technology Department of Computer Science and Technology School of ComputersNational University of Defense Technology

Many real-world networks are found to be scale-free. However, graph partition technology, as a technology capable of parallel computing, performs poorly when scale-free graphs are provided. The reason for this is that traditional partitioning algorithms are designed for random networks and regular networks, rather than for scale-free networks. Multilevel graph-partitioning algorithms are currently considered to be the state of the art and are used extensively. In this paper, we analyse the reasons why traditional multilevel graph-partitioning algorithms perform poorly and present a new multilevel graph-partitioning paradigm, top down partitioning, which derives its name from the comparison with the traditional bottom-up partitioning. A new multilevel partitioning algorithm, named betweenness-based partitioning algorithm, is also presented as an implementation of top-down partitioning paradigm. An experimental evaluation of seven different real-world scale-free networks shows that the betweenness-based partitioning algorithm significantly outperforms the existing state-of-the-art approaches.

关键词： graph partitioning betweenness-based partitioning algorithm scale free network

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