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2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Huang, Yu Wang, Ru Wei, Zhuqin Wang, Guoxin School of Machanical Engineering Beijing Institute of Technology Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing China

ISBN: (纸本)9798350323153

Enterprises currently face the challenge of reducing production cycles and costs and utilizing existing cases for making changes and iterations has emerged as a viable solution. However, the acquisition and modification of historical cases present their challenges. To address this, the present paper proposes an intelligent design method based on reinforcement learning that aims to meet the demand for efficient and high-quality design solutions in the field of engineering design. This method comprises four key steps: case characterization, matching, retrieval, and selection. By employing case characterization and matching, users can acquire sets of similar cases that align closely with their specific requirements. Building upon this foundation incorporates a combination of reinforcement learning and weight order cross-reconstruction to generate more proposals. Subsequently, the multi-attribute decision-making method is utilized to select the extended set of design schemes. The effectiveness of the proposed method is demonstrated through its successful application to a radar design case. © 2023 IEEE.

关键词： case reconstruction case-based reasoning modular design rapid design reinforcement learning

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A Dental Caries Segmentation Method Based on Dental Bitewings 2

A Dental Caries Segmentation Method Based on Dental Bitewing...

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2022 2nd International Conference on Health big data and Smart Sports, HBDSS 2022

作者： Han, Zhiyuan Yang, Jingyu Wang, Chen Du, Chenxi School of Computer Science and Engineering Beijing Technology and Business University Beijing China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing China

ISBN: (纸本)9783800760428

Computer Aided Diagnosis (CAD) has become a hot research field in oral clinic. Due to the similar contrast between caries and periodontal tissues, especially the proximal caries, it is difficult for general CAD methods to accurately detect the location of caries. Aiming at this problem, an image semantic segmentation algorithm based on U-Net and attention mechanism is proposed. First, the dental bitewings dataset was constructed. Second, the CBAM module is integrated into the U-Net network for focusing on caries-affected areas. Finally, the semantic segmentation accuracy is further improved by multi-scale feature fusion. The experimental results show that the model proposed in this paper has significant improvements in multiple performances compared with algorithms such as PSP-Net. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Computer aided diagnosis

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Helmet Detection Algorithm of Electric Bicycle Riders Based on YOLOv5 with CBAM Attention Mechanism Integration 8th

Helmet Detection Algorithm of Electric Bicycle Riders Based ...

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8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

作者： Fu, Si-Yue Wei, Dong Zhou, Liu-Ying School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing Municipal Science and Technology Commission Beijing100044 China

ISBN: (纸本)9789819975921

Object detection algorithms can assist in detecting the helmet-wearing status of electric bicycle riders, thereby saving regulatory manpower costs. However, there is currently a lack of standardized and publicly available datasets. Additionally, the basic YOLOv5s object detection algorithm, due to its limited feature extraction capabilities, may lead to numerous instances of both false negative and false positive. To enhance the model’s focus on critical information within the feature maps, this paper introduces the CBAM attention mechanism module into the Backbone section of YOLOv5s. This module sequentially infers attention maps from the input feature maps along both channel and spatial dimensions independently, and then multiplies these attention maps with the input feature maps to achieve adaptive feature optimization. This paper have established self-built dataset for experimental research, and the results indicate that compared to the original YOLOv5s model, the proposed method has improved the model’s overall mAP score by 1.89%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Attribute Reduction of Object Granules of Concept Lattice Based on Three-Way Decision

Attribute Reduction of Object Granules of Concept Lattice Ba...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Li, Tong-Jun Zhang, Run-Ze School of Information Engineering Zhejiang Ocean University Zhoushan Zhejiang316022 China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province China

ISBN: (纸本)9798350303780

Formal concept analysis is a key tool in data analysis and knowledge representation and a specific method for granular computing. Object granules constitute the basic granular knowledge in a concept lattice. Three-way decision is a reasonable approach to extracting decision rules. This study employs the principle of three-way decision to divide all the object granules of a formal context into three parts: positive, negative and boundary regions, then two groups of certainty decision rules are extracted from the positive region and the negative region, and one group of possibility decision rules is induced from the boundary region. The notion of attribute reduction keeping the performance of the decision rules unchanged are proposed. An approach for computing attribute reducts is put forward by utilizing the discernibility matrix and discernibility function. Some examples are taken to explain or show the relevant conclusions and prove the feasibility of the reduction method. © 2023 IEEE.

关键词： Formal concept analysis

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Graph Prompt Clustering

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5794-5805页

作者： Chen, Man-Sheng Lai, Pei-Yuan Liao, De-Zhang Wang, Chang-Dong Lai, Jian-Huang Sun Yat-sen University School of Computer Science and Engineering Guangzhou510275 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530219 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Beijing100816 China South China Technology Commercialization Center Guangzhou510006 China Guangdong University of Technology School of Information Engineering Guangzhou510006 China Guangdong Key Laboratory of Information Security Technology Guangzhou510033 China Ministry of Education Beijing100044 China

Due to the wide existence of unlabeled graph-structured data (e.g., molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups. However, the existing methods habitually focus on learning the graphs embeddings with different graph reguralizations, and seldom refer to the obvious differences in data distributions of distinct graph-level datasets. How to characteristically consider multiple graph-level datasets in a general well-designed model without prior knowledge is still challenging. In view of this, we propose a novel Graph Prompt Clustering (GPC) method. Within this model, there are two main modules, i.e., graph model pretraining as well as prompt and finetuning. In the graph model pretraining module, the graph model is pretrained by a selected source graph-level dataset with mutual information maximization and self-supervised clustering regularization. In the prompt and finetuning module, the network parameters of the pretrained graph model are frozen, and a groups of learnable prompt vectors assigned to each graph-level representation are trained for adapting different target graph-level datasets with various data distributions. Experimental results across six benchmark datasets demonstrate the impressive generalization capability and effectiveness of GPC compared with the state-of-the-art methods. © 1979-2012 IEEE.

关键词： Graph embeddings

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Unveiling the planar deformation mechanisms for improved formability in pre-twinned AZ31 Mg alloy sheet at warm temperature

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Journal of Magnesium and Alloys 2023年第12期11卷 4659-4678页

作者： Xiaohuan Pan Lifei Wang Pengbin Lu Hua Zhang Guangsheng Huang Liuwei Zheng Bin Xing Weili Cheng Hongxia Wang Wei Liang Kwang Seon Shin Shanxi Key Laboratory of Advanced Magnesium-Based Materials College of Materials Science and EngineeringTaiyuan University of TechnologyTaiyuan 030024China National Engineering Laboratory for Industrial Big-data Application Technology Chongqing Innovation Center of Industrial Big-Data Co.Ltd.Chongqing 400707China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Institute for Advanced Studies in Precision Materials Yantai UniversityYantai 264005China National Engineering Research Center for Magnesium Alloys College of Materials Science&EngineeringChongqing UniversityChongqing 400044China Magnesium Technology Innovation Center School of Materials Science and EngineeringSeoul National University1 Gwanak-roGwanak-guSeoul 08826South Korea

To investigate the role of pre-twins in Mg alloy sheets during warm planar deformation,the stretch forming is conducted at 200℃.Results suggest the formability of the pre-twinned AZ31 Mg alloy sheet is enhanced to 11.30 *** mechanisms for the improved formability and the deformation behaviors during the planar stretch forming are systematically investigated based on the planar stress *** Schmid factor for deformation mechanisms are calculated,the results reveal that planar stress states extremely affect the Schmid factor for{10-12}*** detwinning is activated and the prismatic slip is enhanced in the pre-twinned sheet,especially under the planar extension stress state in the outer ***,the thickness-direction strain is accommodated *** dynamic recrystallization(DRX)type is continuous DRX(CDRX)regardless of the planar stress ***,the CDRX degree is greater under the planar extension stress *** twin lattices deviate from the perfect{10-12}twinning relation due to the planar compression stress state and the *** basal texture is weakened when the planar stress state tends to change the texture components.

关键词： AZ31 Mg alloy sheet Planar stretch forming Planar stress states Continuous dynamic recrystallization {10-12}tensile twinning

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Vision Transformer Based Hash Coding for Efficient Image and Audio Retrieval with Global and Local Equilibrium Distance Constraints

Vision Transformer Based Hash Coding for Efficient Image and...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Liu, Ye Pan, Yan Yin, Jian School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Lizhi Inc. Artificial Intelligence and Big Data Department Guangzhou Beijing China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (纸本)9781665410205

With the application of efficient retrieval in information systems and retrieval augmented generation with vector database for large language models, hash coding algorithms have made progress in recent years. The rise of transformer technology in the field of deep learning has brought the possibility to further improve the effect of hash coding algorithms. We introduce the vision transformer framework to both images and audios, and propose a novel approach for the tasks of multi-label image retrieval and audio event retrieval. In the proposed hash coding model, global and local equilibrium distance constraints are integrated, so that the hash codes for images can be better obtained through the global hash centers and local similar samples. In order to realize end-to-end training and hash code generation for audios, we adopt the adapter of mel spectrogram, thus the proposed approach can be simply converted and applied to audio hash coding. Comparative experiments verify that better results can be achieved on multiple image and audio datasets. © 2024 IEEE.

关键词： Image coding

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Multi-Label Learning with Label-Specific Feature Selection via Local Positive and Negative Correlation 5

Multi-Label Learning with Label-Specific Feature Selection v...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Xia, Shuxin School of Information Engineering Zhe Jiang Ocean University Zhoushan316022 China Zhejiang Province School of Information Engineering Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application Zhoushan316022 China

ISBN: (纸本)9798350385557

High dimension data increasingly emerged in many application areas. Feature selection methods are widely studied to solve the issues, playing a crucial role in eliminating redundant or irrelevant feature. Previous feature selection algorithms seldom consider the fact that each label might be determined by its specific features, which has its own characteristics. Meanwhile, they often neglect mutual exclusion among labels, named 'negative label correlations' and assume labels share global correlation in exploring label correlation. To address the above problem, we propose a novel algorithm called 'Multi-label learning with label-specific feature selection via local positive and negative correlation' (LLCPN-MIFS). Multiple datasets manifest that the effectiveness of the proposed method. © 2024 IEEE.

关键词： Feature Selection

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Power Optimization for Low Transmission Delay in Software Defined data Center Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5716-5730页

作者： Zhao, Yong Cao, Jiannong Wang, Xingwei Li, Fuliang He, Qiang Liu, Xiaojie University of Electronic Science and Technology of China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Laboratory of Intelligent Collaborative Computing Chengdu611731 China Hong Kong Polytechnic University Department of Computing Hung Hom Hong Kong Northeastern University College of Computer Science and Engineering Shenyang110819 China Northeastern University College of Medicine and Biological Information Engineering Shenyang110819 China Pengcheng Laboratory Shenzhen518066 China

Software-Defined data Center Networks (SDDCNs) utilizes Software Defined Networking (SDN) as a network architecture to achieve highly flexible, programmable, and automated management of data Center Networks (DCNs). The high energy consumption of DCNs remains a persistent and significant challenge. Thus, the energy saving is crucial and imperative for DCNs. Current energy-efficient solutions primarily rely on flow consolidation and dynamic device sleeping techniques to reduce energy consumption. However, these approaches often yield long Flow Completion Time (FCT), potentially resulting in violations of service-level agreements, particularly for delay-sensitive applications. In this paper, we formulate the problem of minimizing the power consumption in SDDCNs as key objective while ensuring timely FCT for delay-sensitive applications. To solve this problem, we first introduce the Active Network Generation (ANG) approach, which generates a minimal active subnet with the least number of active devices while meeting the current traffic demand. Subsequently, we propose two algorithms based on the type of applications: the Delay-Tolerant Flow Route (DTFR) algorithm for delay-tolerant applications and the Delay-Sensitive Flow Route (DSFR) algorithm for delay-sensitive applications. Simulation results demonstrate that our propose solution achieves an energy-saving rate of up to 67.77% and significantly reduces FCT compared to benchmark solutions. © 2002-2012 IEEE.

关键词： Energy efficiency

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CHDbase:A Comprehensive knowledgebase for Congenital Heart Disease-related Genes and Clinical Manifestations

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Genomics, Proteomics & Bioinformatics 2023年第1期21卷 216-227页

作者： Wei-Zhen Zhou Wenke Li Huayan Shen Ruby W.Wang Wen Chen Yujing Zhang Qingyi Zeng Hao Wang Meng Yuan Ziyi Zeng Jinhui Cui Chuan-Yun Li Fred Y.Ye Zhou Zhou State Key Laboratory of Cardiovascular Disease Beijing Key Laboratory for Molecular Diagnostics of Cardiovascular DiseasesCenter of Laboratory MedicineFuwai HospitalNational Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100037China International Joint Informatics Laboratory&Jiangsu Key Laboratory of Data Engineering and Knowledge Service School of Information ManagementNanjing UniversityNanjing 210023China Institute of Molecular Medicine Peking UniversityBeijing 100871China

Congenital heart disease(CHD)is one of the most common causes of major birth defects,with a prevalence of 1%.Although an increasing number of studies have reported the etiology of CHD,the findings scattered throughout the literature are difficult to retrieve and utilize in research and clinical *** therefore developed CHDbase,an evidence-based knowledgebase of CHD-related genes and clinical manifestations manually curated from 1114 publications,linking 1124 susceptibility genes and 3591 variations to more than 300 CHD types and related *** such as the information of each publication and the selected population and samples,the strategy of studies,and the major findings of studies were integrated with each item of the research *** also integrated functional annotations through parsing50 databases/tools to facilitate the interpretation of these genes and variations in disease *** further prioritized the significance of these CHD-related genes with a gene interaction network approach and extracted a core CHD sub-network with 163 *** clear genetic landscape of CHD enables the phenotype classification based on the shared genetic ***,CHDbase provides a comprehensive and freely available resource to study CHD susceptibilities,supporting a wide range of users in the scientific and medical *** is accessible at http://***.

关键词： Congenital heart disease Congenital heart defect database Genetics Classification

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