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2nd International Conference on Advanced Electrical engineering, ICAEE 2022

作者： Boudiaf, A. Bouazabia, S. Harid, N. Houari Boumediène University of Science and Technology Electric and Industrial Systems Laboratory Algiers Algeria Khalifa University of Science and Technology Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates

ISBN: (数字)9781665417419

ISBN: (纸本)9781665417419

The presence of a conductive contaminant within the insulation of a high voltage cable is at the origin of a conduction current likely to heat the insulation and lead to its *** the gas cavity (insulating defect), a very small conductive contaminant generates a very high electrical electric field. These fields are harmful and favor the creation of electrical trees. © 2022 IEEE.

关键词： Deterioration

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EvoPath: Evolutionary Meta-path Discovery with Large Language Models for Complex Heterogeneous Information Networks

arXiv

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arXiv 2025年

作者： Liu, Shixuan Cheng, Haoxiang Wang, Yunfei He, Yue Fan, Changjun Liu, Zhong Laboratory for Big Data and Decision College of Systems Engineering National University of Defense Technology Hunan Changsha China National Key Laboratory of Information Systems Engineering College of Systems Engineering National University of Defense Technology Hunan Changsha China Department of Computer Science and Technology Tsinghua University Beijing China

Heterogeneous Information Networks (HINs) encapsulate diverse entity and relation types, with meta-paths providing essential meta-level semantics for knowledge reasoning, although their utility is constrained by discovery challenges. While Large Language Models (LLMs) offer new prospects for meta-path discovery due to their extensive knowledge encoding and efficiency, their adaptation faces challenges such as corpora bias, lexical discrepancies, and hallucination. This paper pioneers the mitigation of these challenges by presenting EvoPath, an innovative framework that leverages LLMs to efficiently identify high-quality meta-paths. EvoPath is carefully designed, with each component aimed at addressing issues that could lead to potential knowledge conflicts. With a minimal subset of HIN facts, EvoPath iteratively generates and evolves meta-paths by dynamically replaying meta-paths in the buffer with prioritization based on their scores. Comprehensive experiments on three large, complex HINs with hundreds of relations demonstrate that our framework, EvoPath, enables LLMs to generate high-quality meta-paths through effective prompting, confirming its superior performance in HIN reasoning tasks. Further ablation studies validate the effectiveness of each module within the framework. © 2025, CC BY-NC-ND.

关键词： Semantics

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A Conceptual Deep Learning Model for Real-Time Routin 16

A Conceptual Deep Learning Model for Real-Time Routin

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16th IEEE International Conference on Signal-Image Technology and Internet-Based systems, SITIS 2022

作者： Ikidid, Abdelouafi Fazziki, Abdelaziz El Sadgal, Mohammed Ghazouani, Mohamed El Ichahane, My Youssef Cadi Ayyad University Computer Systems Engineering Laboratory Computer Science Dept Marrakesh Morocco Chouaib Doukkali University Polydisciplinary Faculty of Sidi Bennour El Jadida Morocco Chouaib Doukkali University Information Technologiy Laboratory El Jadida Morocco

ISBN: (纸本)9781665464956

Urban congestion has been a known problem since the first urban revolution throughout the world. Today's major metropolises are synonymous with traffic congestion and complicated urban circulation. This paper introduces a traffic management system based on agent technology and deep learning model. It acts on the phase layouts constituted of sequences and length to reduce average delay time, maximize intersection throughput, and suggest a routing planning scheme to avoid congested intersection. In the optimization model, lights of each intersection are controlled by a group of agents. The system uptake the ability of the agent to communicate, coordinate and collaborate, to decompose the routing process into decentralized architecture. The proposed model was instantiated in the ANYLOGIC simulator. © 2022 IEEE.

关键词： Traffic congestion

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COD-SAM: Camouflage object detection using SAM

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Pattern Recognition 2025年 168卷

作者： Gao, Dongyang Zhou, Yichao Yan, Hui Chen, Chen Hu, Xiyuan School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210014 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100038 China College of Computer Science Beijing University of Technology Beijing100124 China

The recently introduced Segment Anything Model(SAM) signifies a major advancement in segmentation, offering robust zero-shot generalization and interactive prompts that herald a new paradigm for segmentation models. Despite training on 1.1 billion masks, SAM's predictive performance is limited in tasks with complex structures or backgrounds. We introduce COD-SAM, which retains the original prompt design and zeroshot generalization capabilities of SAM, while incorporating the minimal number of additional trainable parameters to enhance its performance in downstream tasks such as camouflaged object detection. First, we freeze SAM's encoder and introduce a integrated learnable module, named "Fuse Anything as Prompts"(FAPs) comprising: (1) COD-Adapter, a global gradient weak prompt embedding to enhance adaptability for downstream tasks;(2) Corner Prompts, a strong prompt embedding for edge corners. For SAM's decoder, we introduce COD-Head, a learnable feature refinement module aimed at enhancing the quality of predicted masks. Subsequently, we introduce a task-adaptive loss function named sIoU loss. Comprehensive experiments not only verify the effectiveness of our method's in camouflage object detection but also assess its applicability to other downstream tasks. With less than a 0.1% increase in parameters, our approach achieves a notable 5.6% improvement in performance, demonstrating its superior efficacy. © 2025 Elsevier Ltd

关键词： Image segmentation

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Three-Step Carrier Frequency Offset Estimation Algorithm for Burst-Mode Communications*

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Chinese Journal of Electronics 2023年第1期19卷 165-169页

作者： Wu Luo An Liu Qinglin Liang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronic Engineering and Computer Science Peking University Beijing China

A data-aided frequency offset estimation algorithm suited for burst-mode communications is described and analyzed. The proposed algorithm is based on a Pilot symbol assisted modulation (PSAM) frame structure, which consists of regularly spaced pilot symbols and a group of consecutive pilot symbols embedded in. Firstly, an initial estimate based on consecutive pilot symbols is generated for reducing the phase ambiguity. Secondly, based on both spaced and consecutive pilot symbols, fine preliminary estimates are generated to improve estimation accuracy. Lastly, the final estimate is obtained by combining the fine preliminary estimates with the initial. This three-step algorithm is shown to provide high estimation accuracy close to those of FFT-based algorithm at low Signal-to-noise ratio (SNR). In addition, it is suitable for high carrier frequency offset cases.

关键词： Frequency modulation Simulation Symbols Estimation Frequency estimation Signal to noise ratio

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Boosting Document-Level Relation Extraction by Mining and Injecting Logical Rules

Boosting Document-Level Relation Extraction by Mining and In...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Fan, Shengda Mo, Shasha Niu, Jianwei School of Cyber Science and Technology Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

Document-level relation extraction (DocRE) aims at extracting relations of all entity pairs in a document. A key challenge to DocRE lies in the complex interdependency between the relations of entity pairs. Unlike most prior efforts focusing on implicitly powerful representations, the recently proposed LogiRE (Ru et al., 2021) explicitly captures the interdependency by learning logical rules. However, LogiRE requires extra parameterized modules to reason merely after training backbones, and this disjointed optimization of backbones and extra modules may lead to sub-optimal results. In this paper, we propose MILR, a logic enhanced framework that boosts DocRE by Mining and Injecting Logical Rules. MILR first mines logical rules from annotations based on frequencies. Then in training, consistency regularization is leveraged as an auxiliary loss to penalize instances that violate mined rules. Finally, MILR infers from a global perspective based on integer programming. Compared with LogiRE, MILR does not introduce extra parameters and injects logical rules during both training and inference. Extensive experiments on two benchmarks demonstrate that MILR not only improves the relation extraction performance (1.1%-3.8% F1) but also makes predictions more logically consistent (over 4.5% Logic). More importantly, MILR also consistently outperforms LogiRE on both counts. Code is available at https://***/XingYing-stack/MILR. © 2022 Association for Computational Linguistics.

关键词： Integer programming

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Facilitating Continuous Facial Aging through Latent Age Attribute Modulation

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Hu, Xiyuan Qu, Jinglei Chen, Chen Beijing University of Technology School of Computer Science Beijing100124 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China

In recent years, facial aging has attracted significant research interest due to its broad applications and potential benefits. While Generative Adversarial Networks (GANs) have achieved notable progress in synthesizing realistic facial images, many GAN-based facial aging methods struggle to accurately capture the continuous progression of age-related changes over time. In this paper, we propose an innovative framework featuring the Latent Age Attribute Module (LAAM), which maps age attributes to a structured latent space that facilitates efficient sampling for precisely age attribute modeling. We further introduce the Age-AdaIN Fusion Module (AFM), which seamlessly integrates age features from LAAM with facial content features, enabling the generation of images that exhibit smooth, continuous age transitions. This framework excels in capturing fine-grained aging details, particularly for elderly individuals. Quantitative and qualitative evaluations on benchmark datasets demonstrate the effectiveness of our approach in generating realistic age-progressed facial images, with a notable improvement in elderly aging accuracy and detail. © 2025 IEEE.

关键词： Generative adversarial networks

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Decentralized and Uncoordinated Learning of Stable Matchings: A Game-Theoretic Approach

arXiv

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arXiv 2024年

作者： Etesami, S. Rasoul Srikant, R. Department of Industrial and Systems Engineering Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering and Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States

We consider the problem of learning stable matchings with unknown preferences in a decentralized and uncoordinated manner, where "decentralized" means that players make decisions individually without the influence of a central platform, and "uncoordinated" means that players do not need to synchronize their decisions using pre-specified rules. First, we provide a game formulation for this problem with known preferences, where the set of pure Nash equilibria (NE) coincides with the set of stable matchings, and mixed NE can be rounded to a stable matching. Then, we show that for hierarchical markets, applying the exponential weight (EXP) learning algorithm to the stable matching game achieves logarithmic regret in a fully decentralized and uncoordinated fashion. Moreover, we show that EXP converges locally and exponentially fast to a stable matching in general markets. We also introduce another decentralized and uncoordinated learning algorithm that globally converges to a stable matching with arbitrarily high probability. Finally, we provide stronger feedback conditions under which it is possible to drive the market faster toward an approximate stable matching. Our proposed game-theoretic framework bridges the discrete problem of learning stable matchings with the problem of learning NE in continuous-action games. Copyright © 2024, The Authors. All rights reserved.

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Impact of Motorcycle Placement on Travel Time and Traffic Dynamics at Urban Intersections

Impact of Motorcycle Placement on Travel Time and Traffic Dy...

引用

Innovative Research in Applied science, engineering and Technology (IRASET),Conference on

作者： Ayoub Charef Zahi Jarir Mohamed Quafafou LAMIGEP Research Laboratory EMSI Moroccan School of Engineering Sciences Marrakech Morocco Laboratory of Computer Systems Engineering (LISI) Cadi Ayyad University Marrakech Marrakech Morocco Department of Computer Science University of Aix-Marseille Marseille France

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

This paper delves into a comprehensive exploration of the impact of motorcycle positioning on travel times at signalized intersections in urban settings. Leveraging a synergistic approach that integrates data analysis and VISSIM simulation, the study focuses on three key intersections in Marrakech, dissecting traffic dynamics during distinct time intervals. The analysis reveals consistent trends, showcasing that Scenario 2, where motorcycles are separated to the right of cars, consistently yields lower travel times compared to Scenario 1, where motorcycles navigate in front of cars. VISSIM simulations visually underscore these findings, emphasizing the efficiency gains achieved through the introduction of dedicated motorcycle lanes. The discussion articulates the significance of these results, offering insights into the potential benefits of segregated motorcycle traffic for urban traffic planning. This research contributes valuable perspectives and actionable considerations for optimizing intersection efficiency and reducing travel times in the context of urban mobility.

关键词： Data analysis Correlation Navigation Simulation Urban planning Motorcycles Market research

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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