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2023 International Conference on Optics and Machine Vision, ICOMV 2023

作者： Qiu, Zixia Ma, Junchao Zhang, Haitang Sibahee, Mustafa A.Al. Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China College of Applied Technology Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61001 Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518063 China Faculty of Biological & Physical Sciences Tom Mboya University Homabay40300 Kenya

ISBN: (纸本)9781510664777

Compared with rasterization rendering, ray tracing rendering can improve the image’s visual effect and make the image look more realistic. Real-time ray tracing requires very high computing power of graphics processing unit (GPU). When the number of GPUs is limited and the performance of a single GPU cannot be fully utilized, high rendering latency will occur. In this paper, we propose Multi-Queue Concurrent Pipeline Rendering (MQCPR), a novel ray-tracing parallel rendering scheme based on GPU multi-queue. This scheme divides the image area into multiple parts and uses multi-queue of GPU to enable the computation and transmission tasks in the rendering process to be executed simultaneously, which can maximize the performance of a single GPU and improve the graphics rendering speed. MQCPR may keep the GPU busy to make full use of the GPU resources. Experiments illustrate that in the case of a single GPU, compared with the single queue serial rendering scheme, the number of frames per second (FPS) is increased by 1.5 times after using MQCPR. © 2023 SPIE. All rights reserved.

关键词： Graphics processing unit

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Research on Intelligent Frequency Control of Permanent Magnet Synchronous Motor

Research on Intelligent Frequency Control of Permanent Magne...

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2020 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2020

作者： Chang, Kuo-Chi Hu, Zhongxin Chu, Kai-Chun Chang, Fu-Hsiang Wang, Hsiao-Chuan Hsu, Tsui-Lien Lin, Yuh-Chung Zhou, Yuwen Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Business Management Fujian University of Technology Fuzhou China Department of Business Administration North Borneo University College Sabah Malaysia Fujian University of Technology Department of Business Management Fuzhou China Shih-Hsin University Department of Tourism Taipei Taiwan Institute of Environmental Engineering National Taiwan University Taipei Taiwan Institute of Construction Engineering and Management National Central University Taoyuan Taiwan

ISBN: (纸本)9781728191461

The development of PMSM is inseparable from the continuous improvement of the performance-to-price ratio of permanent magnet materials and the development of power electronic equipment. This is exactly the study of PMSM. Entered a new stage, PMSM plays an important role in military, industry and daily life. Dating vector control theory to the field of PMSM speed regulation is also a major breakthrough. Researchers combined the mathematical model of PMSM with the vector control model. It is based on this that the PMSM vector control double closed-loop model was established. Simulate in MATLAB software. There are current module, speed module and PMSM main module in the simulation diagram, and then analyze the simulation results. © 2020 IEEE.

关键词： MATLAB

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LECF: recommendation via learnable edge collaborative filtering

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Science China(Information Sciences) 2022年第1期65卷 116-130页

作者： Shitao XIAO Yingxia SHAO Yawen LI Hongzhi YIN Yanyan SHEN Bin CUI School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications School of Information Technology and Electrical Engineering The University of Queensland Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Computer Science and Technology Peking University National Engineering Laboratory for Big Data Analysis and Applications Peking University

The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering(CF) algorithms compute the likelihood by utilizing simple relationships between objects, e.g., user-item, item-item, or user-user. They always rely on a single type of object-object relationship, ignoring other useful relationship information in data. In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering(LECF). LECF predicts the existence probability of an edge based on the connections among edges and is able to capture the complex relationship in data. Specifically, we first adopt the concept of line graph where each node represents an interaction edge; then calculate a weighted sum of similarity between the query edge and the observed edges(i.e., historical interactions) that are selected from the neighborhood of query edge in the line graph for a recommendation. In addition, we design an efficient propagation algorithm to speed up the training and inference of LECF. Extensive experiments on four public datasets demonstrate LECF can achieve better performance than the state-of-the-art methods.

关键词： collaborative filtering recommender system implicit feedback edge-edge similarity line graph

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Source-free domain adaptation via avatar prototype generation and adaptation

arXiv

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arXiv 2021年

作者： Qiu, Zhen Zhang, Yifan Lin, Hongbin Niu, Shuaicheng Liu, Yanxia Du, Qing Tan, Mingkui School of Software Engineering South China University of Technology School of Computing National University of Singapore Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education Pazhou Laboratory

We study a practical domain adaptation task, called source-free unsupervised domain adaptation (UDA) problem, in which we cannot access source domain data due to data privacy issues but only a pre-trained source model and unlabeled target data are available. This task, however, is very difficult due to one key challenge: the lack of source data and target domain labels makes model adaptation very challenging. To address this, we propose to mine the hidden knowledge in the source model and exploit it to generate source avatar prototypes (i.e., representative features for each source class) as well as target pseudo labels for domain alignment. To this end, we propose a Contrastive Prototype Generation and Adaptation (CPGA) method. Specifically, CPGA consists of two stages: (1) prototype generation: by exploring the classification boundary information of the source model, we train a prototype generator to generate avatar prototypes via contrastive learning. (2) prototype adaptation: based on the generated source prototypes and target pseudo labels, we develop a new robust contrastive prototype adaptation strategy to align each pseudo-labeled target data to the corresponding source prototypes. Extensive experiments on three UDA benchmark datasets demonstrate the effectiveness and superiority of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： data privacy

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Study on Structural Stability of Black Phosphorus/Bismuth Oxysulfide/Tungsten Trioxide Heterojunction Based on XRD Measurement 5

Study on Structural Stability of Black Phosphorus/Bismuth Ox...

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2022 5th International Conference on Power Electronics and Control engineering, ICPECE 2022

作者： He, Xinyi Xiong, Yi Zhao, Meng Zeng, Wei Industry-Education-Research Institute of Advanced Materials and Technology for Integrated Circuits National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information Engineering Anhui University Anhui Hefei230601 China School of Mathematical and Physical Sciences Department of Microelectronics National Local Joint Engineering Laboratory for Advanced Textile Processing and Clean Production Wuhan Textile University Hubei Wuhan430073 China

X-ray diffraction is employed to characterize the black phosphorus/bismuth oxysulfide/tungsten trioxide heterojunction composites to explore the alterations in crystallite size and micro-strain. The sample is immersed in sodium sulfite and tested each week. The Scherrer formula and Williamson-Hall plots are used to explore the effect of black phosphorus on improving the stability of the bismuth oxysulfide/tungsten trioxide heterojunction. The results show that the structure of black phosphorus/bismuth oxysulfide/tungsten trioxide heterojunction has changed as follows. The value of crystallite size of tungsten trioxide decreases from 13.82 nm to 9.23 nm, then to 7.45 nm per week, while the value of compressive micro-strain of WO3 increases from -2.02×10-3 to -7.58×10-3, then to -8.93×10-3. It illustrates that the lattice constant of WO3 is compressed due to the lattice mismatch. © Published under licence by IOP Publishing Ltd.

关键词： Tungsten compounds

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Finedge: A Dynamic Cost-Efficient Edge Resource Management Platform for NFV Network

Finedge: A Dynamic Cost-Efficient Edge Resource Management P...

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International Workshop on Quality of Service

作者： Miao Li Qixia Zhang Fangming Liu National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

With the evolution of network function virtualization (NFV) and edge computing, software-based network functions (NFs) can be deployed on closer-to-end-user edge servers to support a broad range of new services with high bandwidth and low latency. However, due to the resource limitation, strict QoS requirements and real-time flow fluctuations in edge network, existing cloud-based resource management strategy in NFV platforms is inefficient to be applied to the edge. Thus, we propose Finedge, a dynamic, fine-grained and cost-efficient edge resource management platform for NFV network. First, we conduct empirical experiments to find out the effect of NFs' resource allocation and their flow-level characteristics on performance. Then, by jointly considering these factors and QoS requirements (e.g., latency and packet loss rate), Finedge can automatically assign the most suitable CPU core and tune the most cost-efficient CPU quota to each NF. Finedge is also implemented with some key strategies including real-time flow monitoring, elastic resource scaling up and down, and also flexible NF migration among cores. Through extensive evaluations, we validate that Finedge can efficiently handle heterogeneous flows with the lowest CPU quota and the highest SLA satisfaction rate as compared with the default OS scheduler and other state-of-the-art resource management schemes.

关键词： Resource management Noise measurement Packet loss Servers Throughput Quality of service

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datasets of Visualization for Machine Learning

arXiv

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arXiv 2024年

作者： Liu, Can Jiang, Ruike Tan, Shaocong Yu, Jiacheng Yang, Chaofan Shao, Hanning Yuan, Xiaoru Key Laboratory of Machine Perception Ministry of Education School of Intelligence Science and Technology Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China

datasets of visualization play a crucial role in automating data-driven visualization pipelines, serving as the foundation for supervised model training and algorithm benchmarking. In this paper, we survey the literature on visualization datasets and provide a comprehensive overview of existing visualization datasets, including their data types, formats, supported tasks, and openness. We propose a what-why-how model for visualization datasets, considering the content of the dataset (what), the supported tasks (why), and the dataset construction process (how). This model provides a clear understanding of the diversity and complexity of visualization datasets. Additionally, we highlight the challenges faced by existing visualization datasets, including the lack of standardization in data types and formats and the limited availability of large-scale datasets. To address these challenges, we suggest future research directions. © 2024, CC BY.

关键词： Machine learning

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Associations of Serum 25-Hydroxyvitamin D Concentrations with Risks of Mortality and Cardiovascular Disease among Individuals with Psoriasis

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Journal of the American Academy of Dermatology 2025年

作者： Kaiqing Lin Ya Miao Lu Gan Bin Zhao Fang Fang Ru Wang Xiang Chen Jiaqi Huang National Clinical Research Center for Metabolic Diseases Metabolic Syndrome Research Center Key Laboratory of Diabetes Immunology Ministry of Education and Department of Metabolism and Endocrinology The Second Xiangya Hospital of Central South University Changsha Hunan 410011 China； Xiangya School of Public Health Central South University Changsha China； CSU-Sinocare Research Center for Nutrition and Metabolic Health Changsha China； Furong Laboratory Changsha China Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden School of Exercise and Health Shanghai University of Sport Shanghai China Department of Dermatology Hunan Engineering Research Center of Skin Health and Disease Hunan Key Laboratory of Skin Cancer and Psoriasis Xiangya Clinical Research Center for Cancer Immunotherapy Xiangya Hospital Central South University Changsha Hunan China； National Clinical Research Center for Geriatric Disorders Xiangya Hospital Central South University Changsha Hunan China； National Engineering Laboratory of Medical Big Data Application Technology (Central South University) Changsha China； Furong Laboratory Changsha China

Background Evidence remains scarce regarding the relationship between serum 25-hydroxyvitamin D (25(OH)D) and the risk of mortality and cardiovascular disease (CVD) in psoriasis. Objective To explore the potential protective effect of serum 25(OH)D on the risk of mortality and CVD among patients with psoriasis. Methods This cohort study included 8,947 psoriasis patients and 178,937 controls identified at baseline in the UK Biobank cohort. Serum 25(OH)D levels were prospectively collected at the time of recruitment. Multivariable-adjusted Cox proportional hazards regression models were used to evaluate the associations between levels of serum 25(OH)D and risk of overall mortality and CVD incidence. Results Compared to the lowest level of serum 25(OH)D (<25 nmol/L), hazard ratios were 0.54 and 0.78 for overall mortality and incident CVD for the highest level of serum 25(OH)D (≥50 nmol/L) among psoriasis patients, respectively (all P<0.05). Psoriasis patients with serum 25(OH)D ≥50 nmol/L appeared to have no excess risk of mortality and CVD incidence, compared with matched controls. Limitations Information on psoriasis subtypes was not available. Conclusion A higher level of serum 25(OH)D was associated with a reduced risk of overall mortality and CVD in individuals with psoriasis.

关键词： cardiovascular disease cohort study epidemiology mortality psoriasis vitamin D

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Heat to Power: Thermal Energy Harvesting and Recycling for Warm Water-Cooled datacenters

Heat to Power: Thermal Energy Harvesting and Recycling for W...

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Annual International Symposium on Computer Architecture, ISCA

作者： Xinhui Zhu Weixiang Jiang Fangming Liu Qixia Zhang Li Pan Qiong Chen Ziyang Jia National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (数字)9781728146614

ISBN: (纸本)9781728146621

Warm water cooling has been regarded as a promising method to improve the energy efficiency of water-cooled datacenters. In warm water-cooling systems, hot spots occur as a common problem where the hybrid cooling architecture integrating thermoelectric coolers (TECs) emerges as a new remedy. Equipped with this architecture, the inlet water temperature can be raised higher, which provides more opportunities for heat recycling. However, currently, the heat absorbed from the server components is ejected directly into the water without being recycled, which leads to energy wasting. In order to further improve the energy efficiency, we propose Heat to Power (H2P), an economical and energy-recycling warm water cooling architecture, where thermoelectric generators (TEGs) harvest thermal energy from the “used” warm water and generate electricity for reusing in datacenters. Specifically, we propose some efficient optimization methods, including an economical water circulation design, fine-grained adjustments of the cooling setting and dynamic workload scheduling for increasing the power generated by TEGs. We evaluate H2P based on a real hardware prototype and cluster traces from Google and Alibaba. Experiment results show that TEGs equipped with our optimization methods can averagely generate 4.349 W, 4.203 W, and 3.979 W (4.177 W averagely) electricity on one CPU under the drastic, irregular and common workload traces, respectively. The power reusing efficiency (PRE) can reach 12.8%~16.2% (14.23% averagely) and the total cost of ownership (TCO) of datacenters can be reduced by up to 0.57%.

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Fluorescence imaging assisted precise assessment of the depth of myometrial invasion in endometrial cancer lesions

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Clinical and translational medicine 2025年第5期15卷 e70309页

作者： Qiaojun Qu Huilong Nie Shuang Hou Xiaoyong Guo Feng Wang Hua Yang Shangqiu Chen Panxia Deng Zhenhua Hu Jie Tian Department of Radiology First Hospital of Shanxi Medical University Taiyuan China. CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China. Department of Gynecology The Fifth Affiliated Hospital of Sun Yat-sen University Zhuhai China. Key Laboratory of Carcinogenesis and Translational Research Department of Gastrointestinal Cancer Center Ward I Peking University Cancer Hospital & Institute Beijing China. School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China. National Key Laboratory of Kidney Diseases Beijing China. Key Laboratory of Big Data-Based Precision Medicine of Ministry of Industry and Information Technology School of Engineering Medicine Beihang University Beijing China. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an China.

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