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GAHE: Geometry-Aware Embedding for Hyper-Relational Knowledge Graph Representation

ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Zongsheng Cao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security SKLOIS) Institute of Information Engineering Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Security Department of Alibaba Group China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University China

Knowledge graphs have proven highly effective for learning representations of entities and relations, with hyper-relational knowledge graphs (HKGs) gaining increased attention due to their enhanced representation capabilities. Each fact in an HKG consists of a main triple supplemented by attribute-value qualifiers that provide additional contextual information. Due to the complexity of hyper-relations, HKGs typically contain complex geometric structures, such as hierarchical, ring, and chain structures, often mixed together. However, previous work mainly embeds HKGs into Euclidean space, limiting their ability to capture these complex geometric structures simultaneously. To address this challenge, we propose a novel model called Geometry Aware Hyper-relational Embedding (GAHE). Specifically, GAHE adopts a multi-curvature geometry-aware approach by modeling HKGs in Euclidean space (zero curvature), hyperbolic space (negative curvature), and hyperspherical space (positive curvature) in a unified framework. In this way, it can integrate space-invariant and space-specific features to accurately capture the diverse structures in HKGs. In addition, GAHE introduces a module termed hyper-relational subspace learning, which allocates multiple sub-relations for each hyper-relation. It enables the exploitation of abundant latent semantic interactions and facilitates the exploration of fine-grained semantics between attribute-value pairs and hyper-relations across multiple subspaces. Furthermore, we provide theoretical guarantees that GAHE is fully expressive and capable of modeling a wide range of semantic patterns for hyper-relations. Empirical evaluations demonstrate that GAHE achieves state-of-the-art results on both hyper-relational and binary-relational benchmarks.

关键词： Knowledge Graph Hyper-relational Learning Multicurvature Spaces Geometry Aware

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Design of intelligent Robot Platform based on Multi-sensor Fusion

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Ya Dajin Lei Xiaochun Li Yue Chen Junyan Huang Rongcun Lan Lin School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

The mobile robot adapts to the more complicated indoor and outdoor environments, and can expand its scope of application. In order to reduce the influence of the cumulative error caused by navigation in complex environments, the indoor mobile robot that combines Inertial Measurement Unit (IMU) and encoder fusion is designed and implemented. In view of the limitations of the traditional single lidar scheme, a Multi-sensor Fusion scheme is proposed to achieve indoor map construction, path planning, multi-point navigation and other functions, and a MSIF KartoSLAM (Multi-sensor Information Fusion) algorithm is proposed, which combines the KartoSLAM algorithm and Multi-sensor information to achieve map construction in complex environments. Through comprehensive testing in the indoor environment, the results show that the Multi-sensor Fusion scheme is superior to the traditional single lidar scheme, and can achieve higher accuracy in mapping and navigation. At the same time, the robot platform can also be combined with the Internet of Things technology and integrated into intelligent housing system.

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Design of People Flow Monitoring System in Public Place based on MD-MCNN

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Xiong Tianmin Lei Xiaochun Zheng Xingwen Chen Junyan Feng Yizhou School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

Due to the limitation of hardware resources, the traditional people flow monitoring system based on computer vision in public places can't meet different crowd-scale scenarios. Therefore, a people flow monitoring system based on MD-MCNN algorithm is designed, which is an application system combining the improved SSD object detection algorithm MNSSD and MCNN density map regression algorithm. In the initial stage, the system uses MNSSD for accurate detection and counting. If the people flow gradually reaches a certain threshold, the system automatically uses MCNN to estimate people flow until the people flow falls below the threshold. Through the experimental verification, the system can realize the people flow statistics of low-density and high-density people in different scenarios, and can be applied on the existing embedded platform. This scheme can be extended to smart cities, smart scenic spots, smart transportation and other fields.

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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ON THE OPPORTUNITIES OF GREEN computing: A SURVEY

arXiv

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arXiv 2023年

作者： Zhou, You Lin, Xiujing Zhang, Xiang Wang, Maolin Jiang, Gangwei Lu, Huakang Wu, Yupeng Zhang, Kai Yang, Zhe Wang, Kehang Sui, Yongduo Jia, Fengwei Tang, Zuoli Zhao, Yao Zhang, Hongxuan Yang, Tiannuo Chen, Weibo Mao, Yunong Li, Yi Bao, De Li, Yu Liao, Hongrui Liu, Ting Liu, Jingwen Guo, Jinchi Zhao, Xiangyu Wei, Ying Qian, Hong Liu, Qi Wang, Xiang Chan, Wai Kin Li, Chenliang Li, Yusen Yang, Shiyu Yan, Jining Mou, Chao Han, Shuai Jin, Wuxia Zhang, Guannan Zeng, Xiaodong Tsinghua University China Shanghai Jiaotong University China University of Science and Technology of China China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Huazhong University of Science and Technology China Shanghai Institute of AI for Education School of Computer Science and Technology East China Normal University China Xi’an Jiaotong University China College of Computer Science Nankai University China School of Computer Science China University of Geosciences China School of Information Science and Technology Beijing Forestry University China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration China Guangzhou University China Nanyang Technological University Singapore City University of Hong Kong Hong Kong Wuhan University China State Key Laboratory for Novel Software Technology Nanjing University China Ant Group China

Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades, and is widely used in many areas including computing vision, natural language processing, time-series analysis, speech synthesis, etc. During the age of deep learning, especially with the arise of Large Language Models, a large majority of researchers’ attention is paid on pursuing new state-of-the-art (SOTA) results, resulting in ever increasing of model size and computational complexity. The needs for high computing power brings higher carbon emission and undermines research fairness by preventing small or medium-sized research institutions and companies with limited funding in participating in research. To tackle the challenges of computing resources and environmental impact of AI, Green computing has become a hot research topic. In this survey, we give a systematic overview of the technologies used in Green computing. We propose the framework of Green computing and devide it into four key components: (1) Measures of Greenness, (2) Energy-Efficient AI, (3) Energy-Efficient computing Systems and (4) AI Use Cases for Sustainability. For each components, we discuss the research progress made and the commonly used techniques to optimize the AI efficiency. We conclude that this new research direction has the potential to address the conflicts between resource constraints and AI development. We encourage more researchers to put attention on this direction and make AI more environmental friendly. Copyright © 2023, The Authors. All rights reserved.

关键词： Carbon footprint

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基于子带瞬时能量谱的铝电解槽电压槽况敏感频域分段方法

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Engineering 2021年第9期 190-212页

作者：曾朝晖桂卫华陈晓方谢永芳张红亮孙玉波 School of Automation Central South University Key Laboratory of Intelligent Computing & Information Processing Ministry of EducationXiangtan University School of Metallurgy and Environment Central South University

槽电压是广泛使用且可在线测量的铝电解槽信号，多种电解槽槽况分析和控制用参数由槽电压计算得到。槽电压频域分段是设计获取这些参数的数字滤波器通带的依据。在对槽电压定性分析的基础上，本文提出子带瞬时能量谱（sub-band Instanta... 详细信息

槽电压是广泛使用且可在线测量的铝电解槽信号，多种电解槽槽况分析和控制用参数由槽电压计算得到。槽电压频域分段是设计获取这些参数的数字滤波器通带的依据。在对槽电压定性分析的基础上，本文提出子带瞬时能量谱（sub-band Instantaneous energy spectrum, SIEP），并用其对多种槽况下槽电压的频域特性进行定量表示，最终给出了槽电压槽况敏感频域分段方法。该频域分段方法将槽电压有效频段划分为低频信号区[0, 0.001] Hz和低频噪声区[0.001, 0.050] Hz；将低频噪声区再细分为[0.001, 0.010] Hz的铝液异常波动频段和[0.01, 0.05] Hz的次低频噪声频段。与基于经验模态分解的瞬时能量谱比较，SIEP能更精细地表示槽电压有效频段内任意频段的能量随时间变化规律。该槽电压频域分段方法对槽况更敏感，可更细致地获取在线槽况信息，为工业电解槽槽况监测和控制决策提供更可靠、准确的在线依据。

关键词：子带瞬时能量谱槽况敏感频段频域分段铝液异常波动铝电解

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Corrigendum to “Fast and scalable lock methods for video coding on many-core architecture” [J. Vis. Commun. Image R. 25(7) (2014) 1758–1762]

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Journal of Visual Communication and Image Representation 2015年 27卷 57-57页

作者： Weizhi Xu Hui Yu Dianjie Lu Fenglong Song Da Wang Xiaochun Ye Songwei Pei Dongrui Fan Hongtao Xie Institute of Microelectronics Tsinghua University Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Information Science and Engineering Shandong Normal University Jinan China Department of Computer Science and Technology Beijing University of Chemical Technology Beijing China Institute of Information Engineering Chinese Academy of Sciences National Engineering Laboratory for Information Security Technologies Beijing China

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Corrigendum to “Fast and scalable lock methods for video coding on many-core architecture” [J. Vis. Commun. 25 (7) (2014) 1758–1762]

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Journal of Visual Communication and Image Representation 2015年 26卷 350-350页

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Experimental Combined Grouping Analysis Approach for Robust Battery pack design for Electric Vehicles with Higher Performance

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IOP Conference Series: Earth and Environmental Science 2019年第1期268卷

作者： Liu Yun Li Shui Liang Gao Zhun Fan C. Ruhatiya Chin-Tsan Wang Akhil Garg Intelligent Manufacturing Key Laboratory of Ministry of Education Shantou University Guangdong China State Key Lab of Digital Manufacturing Equipment & Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shantou 515063 China School of Engineering Sciences Mahindra École Centrale Hyderabad Telangana 500043 India Department of Mechanical and Electro-Mechanical Engineering National I Lan University ILan Taiwan

Battery consistency is an important factor for battery pack performance. Excellent battery consistency can make battery packs more energy efficient and electric vehicles can have longer mileage and higher safety. Thus, in this study a comprehensive intelligent clustering methodology for the design of Li-ion battery pack on the basis of uniformity and equalization criteria of the cell was proposed. Firstly, multiple parameters (capacity, voltage, temperature and resistance) test of single cell performance was performed. Secondly, a clustering method combine with self-organizing map neural network (SOM) was proposed. Furthermore, a validation experiment (pack level) was carried out to verify the accuracy of proposed clustering algorithm. It can be concluded that the battery pack formed from SOM sorting results perform better than the battery pack having random cells combination as well as the pack originally purchased from the manufacturer.

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical Engineering 2022年 808 LNEE卷 v页

作者： Liu, Qi Liu, Xiaodong Chen, Bo Zhang, Yiming Peng, Jiansheng School of Computer and Software Nanjing University of Information Science and Technology Nanjing Jiangsu China School of Computing Edinburgh Napier University Edinburgh United Kingdom State Key Laboratory of Radar Signal Processing Xidian University Xi’an Shaanxi China School of Civil Engineering and Transportation Hebei University of Technology Tianjin Tianjin China Hechi Universtiy Guangxi Hechi China

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