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Concept-Level Semantic Transfer and Context-Level Distribution Modeling for Few-Shot Segmentation

IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Luo, Yuxuan Chen, Jinpeng Cong, Runmin Ip, Horace Ho Shing Kwong, Sam City University of Hong Kong Department of Computer Science Hong Kong Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Lingnan University Hong Kong

Few-shot segmentation (FSS) methods aim to segment objects using only a few pixel-level annotated samples. Current approaches either derive a generalized class representation from support samples to guide the segmentation of query samples, which often discards crucial spatial contextual information, or rely heavily on spatial affinity between support and query samples, without adequately summarizing and utilizing the core information of the target class. Consequently, the former struggles with fine detail accuracy, while the latter tends to produce errors in overall localization. To address these issues, we propose a novel FSS framework, CCFormer, which balances the transmission of core semantic concepts with the modeling of spatial context, improving both macro and micro-level segmentation accuracy. Our approach introduces three key modules: (1) the Concept Perception Generation (CPG) module, which leverages pre-trained category perception capabilities to capture high-quality core representations of the target class;(2) the Concept-Feature Integration (CFI) module, which injects the core class information into both support and query features during feature extraction;and (3) the Contextual Distribution Mining (CDM) module, which utilizes a Brownian Distance Covariance matrix to model the spatial-channel distribution between support and query samples, preserving the fine-grained integrity of the target. Experimental results on the PASCAL-5i and COCO-20i datasets demonstrate that CCFormer achieves state-of-the-art performance, with visualizations further validating its effectiveness. Our code is available at ***/lourise/ccformer. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Energy-carbon pricing-guided collaborative optimization for local integrated energy communities with multi-vector electrification consumers

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Applied Energy 2025年 392卷

作者： Xi, Yufei Zuo, Lujie Chen, Meng Zhang, Jiansheng Cheng, Lin Lestas, Ioannis State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China Department of Energy and Power Engineering Tsinghua University Beijing100084 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom

The integration of multi-energy services and CO2 reduction measures is crucial for advancing the transition to low-carbon energy. This study proposes a novel Integrated Energy Community (IEC) framework that synergizes Electric Vehicles (EVs), Power-to-Chemical (P2C), and Power-to-Heat (P2H) electrification consumers to optimize renewable energy utilization and carbon management. The framework incorporates Electrochemical CO2 Reduction (ECO2R) and Carbon Capture and Storage (CCS) technologies, enabling carbon trading and chemical product commercialization. Meanwhile, an energy-carbon pricing-guided collaborative optimization model is developed, utilizing Nash bargaining game theory to coordinate electricity, gas, and heat operations under limited communication. Case studies validate the model's effectiveness, with numerical results demonstrating its superior performance in operational efficiency, renewable energy utilization, and carbon reduction compared to conventional community systems. © 2025 The Author(s)

关键词： Energy utilization

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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Improved Insulation Properties of Polypropylene With Highly Orientated Lamellar Induced by NonIsothermal Shear Stress

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IET Nanodielectrics 2025年第1期8卷

作者： Huang, Lei Song, Yan-Hui Yin, Li-Juan Zhong, Shao-Long Dang, Zhi-Min Department of Electrical Engineering State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China ZhongYuan Electric Laboratory Xuchang China

Polypropylene plays an irreplaceable role in electrical insulation and electrostatic capacitors and has been extensively studied with its microstructure and insulation properties optimised for high-power density miniaturised devices. The extrinsic modifiers or industrial upgrading have not yet achieved a remarkable breakthrough in either concise theory or comprehensive performance. Here, we develop a nonisothermal shear process on melt to regulate the nucleation density and the lamellar orientation along the surface and reveal pore defects in disorder boundaries between spherulites as the microscopic nature of the property bottleneck. The high-rated shear process induces a crystallographic surface texture of (1 1 0) planes from which the elimination of micro-defect benefits. Our high-rated shear strategy achieves an excellent dielectric strength of 573.7 MV/m with an increase of 33% and an improved insulation resistance by 130% at 100 MV/m with the minimised absorption of injected current. © 2025 The Author(s). IET Nanodielectrics published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Crystal orientation

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Relationship Between the Number of Agents and Sparse Observability Index

IEEE Open Journal of Control Systems

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IEEE Open Journal of control systems 2025年 4卷 144-155页

作者： Shinohara, T. Namerikawa, T. KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm100 44 Sweden Keio University Department of System Design Engineering 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan

The state estimation problem in the presence of malicious sensor attacks is commonly referred to as a secure state estimation problem. Central to addressing this problem is the concept of the sparse observability index, defined as the largest integer δ for which the system remains observable after the removal of any δ sensors. This index plays a critical role in quantifying the resilience of the system, as a higher δ enables unique state reconstruction despite the presence of more compromised sensors. In this study, for undirected multi-agent systems consisting of n agents, we analyze the relationship between the number of agents n and the sparse observability index δ for effective secure state estimation. In particular, we consider four typical graph structures: path, cycle, complete, and complete bipartite graphs. Our analysis reveals that δ does not increase monotonically with n, and that resilience is intricately tied to the underlying network structure. Notably, we demonstrate that the system exhibits enhanced resilience when the number of agents n is a prime number, although the specifics of this relationship vary depending on the graph topology. © 2022 IEEE.

关键词： Multi agent systems

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UW-DNeRF: Deformable Soft Tissue Reconstruction with Uncertainty-Guided Depth Supervision and Local Information Integration

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Shan, Jiwei Zhang, Zixin Li, Hao Hsieh, Cheng-Tai Li, Yirui Wu, Wenhua Wang, Hesheng School of Electronic Information and Electrical Engineering of Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University School of Mechanical Engineering Shanghai200240 China Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China

Reconstructing deformable soft tissues from endoscopic videos is a critical yet challenging task. Leveraging depth priors, deformable implicit neural representations have seen significant advancements in this field. However, depth priors from pre-Trained depth estimation models are often coarse, and inaccurate depth supervision can severely impair the performance of these neural networks. Moreover, existing methods overlook local similarities in input sequences, which restricts their effectiveness in capturing local details and tissue deformations. In this paper, we introduce UW-DNeRF, a novel approach utilizing neural radiance fields for high-quality reconstruction of deformable tissues. We propose an uncertainty-guided depth supervision strategy to mitigate the impact of inaccurate depth information. This strategy relaxes hard depth constraints and unlocks the potential of implicit neural representations. In addition, we design a local window-based information sharing scheme. This scheme employs local window and keyframe deformation networks to construct deformations with local awareness and enhances the model's ability to capture fine details. We demonstrate the superiority of our method over state-of-The-Art approaches on synthetic and in-vivo endoscopic datasets. Code is available at: https://***/IRMVLab/UW-DNeRF. © 2025 IEEE.

关键词： Endoscopy

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Sharing energy in wider area: A two-layer energy sharing scheme for massive prosumers

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Applied Energy 2025年 392卷

作者： Su, Yifan Yang, Peng Kang, Kai Wang, Zhaojian Qi, Ning Liu, Tonghua Liu, Feng State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing University Chongqing400044 China School of Electronics and Information Xi'an Polytechnic University Xi'an710048 China State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of Earth and Environmental Engineering Columbia University New YorkNY10027 United States

The popularization of distributed energy resources transforms end-users from consumers into prosumers. Inspired by the sharing economy principle, energy sharing markets for prosumers are proposed to facilitate the utilization of renewable energy. This paper proposes a novel two-layer energy sharing market for massive prosumers, which can promote social efficiency by wider-area sharing. In this market, there is an upper-layer energy sharing market (U-ESM) in the distribution system and numerous lower-layer energy sharing markets (L-ESMs) in local communities. Prosumers in the same community share energy with each other in the L-ESM, which can be uncleared. The energy surplus and shortage of L-ESMs are cleared in the U-ESM. Thanks to the wider-area two-layer structure, the market outcome is near-social-optimal in large-scale systems. However, the proposed market forms a complex mathematical program with equilibrium constraints (MPEC). To solve the problem, we propose an efficient and hierarchically distributed bidding algorithm. The proposed two-layer market and bidding algorithm are verified on the IEEE 123-bus system with 11,250 prosumers, which demonstrates the practicality and efficiency for large-scale markets. © 2025 Elsevier Ltd

关键词： Game theory

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A Quasi-Parallel Resonant Switched Capacitor DC-DC Converter with Wide Input Voltage Range Regulation and Enhanced Dynamic Performances

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Qi, Jingjing Wu, Xuezhi Sun, Kai Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Beijing Jiaotong University National Active Distribution Network Technology Research Center Beijing100044 China

Wide-range and high-efficiency DC-DC converters are in high demand for various applications, such as distributed systems, electric vehicles, and DC buildings. Resonant switched capacitor converters (ReSCC) can achieve soft switching, have small passive device capacity, and exhibit low voltage stress, but the voltage ratio is difficult to be regulated continuously. In this paper, a quasi-parallel resonant switched capacitor DC-DC converter is proposed to satisfy the design requirements, which incorporates a ReSCC with soft-switching and variable voltage ratio, as well as a Buck converter. In addition, a simplified optimal trajectory controller for ReSCC is investigated, which can be combined with PI controller of Buck converter to enhance the load transient of the proposed converter. Moreover, the input voltage feedforward control is implemented with the optimized feedforward coefficient to suppress the input voltage disturbance. The proposed converter can achieve high efficiency and rapid dynamic response over a wide conversion range. The correctness of the theoretical analyses is verified by a 36V∼60V-5V/20A prototype. © 2013 IEEE.

关键词： Capacitor bank

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Energy-carbon comprehensive efficiency evaluation of hydrogen metallurgy system considering low-temperature waste heat recovery

arXiv

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arXiv 2025年

作者： Ji, Qiang Cheng, Lin Liang, Zeng Zhuang, Yingrui Shi, Fashun Zhang, Jianliang Li, Kejiang State Key Laboratory of Power Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China School of Metallurgical and Ecological Engineering University of Science and Technology Beijing Beijing100083 China

To address the lack of energy-carbon efficiency evaluation and the underutilization of low-temperature waste heat in traditional direct reduction iron (DRI) production, this paper proposes a novel zero-carbon hydrogen metallurgy system that integrates the recovery and utilization of low-temperature and high-temperature waste heat, internal energy, and cold energy during hydrogen production, storage, reaction and circulation. Firstly, the detailed mathematical models are developed to describe energy and exergy characteristics of the operational components in the proposed zero-carbon hydrogen metallurgy system. Additionally, energy efficiency, exergy efficiency, and energy-carbon efficiency indices are introduced from a full life-cycle perspective of energy flow, avoiding the overlaps in energy inputs and outputs. Subsequently, the efficiency metrics of the proposed zero-carbon hydrogen metallurgy system are then compared with those of traditional DRI production systems with H2/CO ratios of 6:4 and 8:2. The comparative results demonstrate the superiority and advancement of the proposed zero-carbon hydrogen metallurgy system. Finally, sensitivity analysis reveals that the overall electricity energy generated by incorporating the ORC and expander equipments exceeds the heat energy recovered from the furnace top gas, highlighting the energy potential of waste energy utilization. Copyright © 2025, The Authors. All rights reserved.

关键词： Low temperature production

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High-accuracy disposable micro-optical anti-counterfeiting labels based on single-molecule quantum coherence

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Optics Express 2025年第9期33卷 19771-19787页

作者： Han, Shuangping Song, Kai Zan, Pengyu Yu, Changzhi Li, Ao Zhou, Haitao Qin, Chengbing Xiao, Liantuan College of Physics and Optoelectronics Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Key Laboratory of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of Technology Taiyuan030024 China Department of Nuclear Medicine The First Hospital of Shanxi Medical University Collaborative Innovation Center of Molecular Imaging Precision Medical Shanxi Medical University Taiyuan030001 China State Key Laboratory of Quantum Optics and Quantum Optics Devices Institute of Laser Spectroscopy Shanxi University Shanxi Taiyuan030006 China

In this work, we introduce an innovative approach to single-molecule quantum coherence (SMQC)-based disposable micro-optical anti-counterfeiting labels. This method facilitates the editing and reading of anti-counterfeiting with single molecules used as the anti-counterfeiting information labels. The label is meticulously crafted through inkjet printing technology, while its authentication is achieved via frequency domain imaging. Through a validation process including experimental demonstration, numerical simulation, and neural network analysis, we demonstrate the feasibility of this approach, further validate the integrity of the miniature anti-counterfeiting information storage, and verify the signal extraction accuracy with the recognition accuracy of the labels is consistently above 99.995%. The combination of SMQC-based disposable micro-optical anti-counterfeiting technology is expected to enable more precise preparation of single-molecule-array chips, thus providing a crucial foundation for the advancement of high-tech and intelligent manufacturing industries. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Frequency domain analysis

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