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International Conference on Image, Vision and Computing (ICIVC)

作者： Zhe Luo You Yu Daijun Zhang Shijie Feng Huapeng Yu Yongxin Chang Wei Shen School of Electrical Engineering and Information Southwest Petroleum University Chengdu China College of Environment and Ecology Chongqing University Big Data Application Center of Ecology and Environment of Chongqing Chongqing China College of Environment and Ecology Chongqing University State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing University Chongqing China College of Information Science and Engineering Chengdu University Chengdu China Chongqing Zhixing Shulian Intelligent Technology Co. Ltd Chongqing China

ISBN: (数字)9781728166612

ISBN: (纸本)9781728112350

Conditional Random Field is a discriminative model for time series data. In this paper, we propose an improved CRF and apply it to the task of air quality inference. Different from the classical CRF, our linear chain CRF is a supervised learning based on the deep convolution neural network, which has a strong learning ability and fast processing speed for the engineering big data. Specifically, we model the state feature function and the state transition feature function with deep convolutional neural network. The parameter space can store more feature expressions learned from a large number of data. For the state transition feature function of linear conditional random field, we add the influence of input sequence on this function. Through the modelling and learning both vertex features and edge features from data, we obtain a more powerful and more efficient CRF. Experiments on natural language and air quality data show our CRF can achieve higher accuracy.

关键词： Air quality Convolution Neural networks Indexes Electronic mail data models Biological system modeling

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KE-GAN: knowledge Embedded Generative Adversarial Networks for Semi-Supervised Scene Parsing

KE-GAN: Knowledge Embedded Generative Adversarial Networks f...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Mengshi Qi Yunhong Wang Jie Qin Annan Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Inception Institute of Artificial Intelligence UAE

In recent years, scene parsing has captured increasing attention in computer vision. Previous works have demonstrated promising performance in this task. However, they mainly utilize holistic features, whilst neglecting the rich semantic knowledge and inter-object relationships in the scene. In addition, these methods usually require a large number of pixel-level annotations, which is too expensive in practice. In this paper, we propose a novel knowledge Embedded Generative Adversarial Networks, dubbed as KE-GAN, to tackle the challenging problem in a semi-supervised fashion. KE-GAN captures semantic consistencies of different categories by devising a knowledge Graph from the large-scale text corpus. In addition to readily-available unlabeled data, we generate synthetic images to unveil rich structural information underlying the images. Moreover, a pyramid architecture is incorporated into the discriminator to acquire multi-scale contextual information for better parsing results. Extensive experimental results on four standard benchmarks demonstrate that KE-GAN is capable of improving semantic consistencies and learning better representations for scene parsing, resulting in the state-of-the-art performance.

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Residual Networks for Light Field Image Super-Resolution

Residual Networks for Light Field Image Super-Resolution

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Shuo Zhang Youfang Lin Hao Sheng Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.

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Dynamic Harmonic Modeling and Characteristics Analysis of Photovoltaic Converter 3

Dynamic Harmonic Modeling and Characteristics Analysis of Ph...

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3rd IEEE Conference on Energy Internet and Energy System Integration, EI2 2019

作者： Wang, DIng Shen, Yangwu Mao, Xun Pi, Ao Zeng, Bing Zhang, Chengye State Grid Hunan Electric Power Company Limited Research Institute Power Grid Technology Center Changsha China State Grid Anhui Electric Power Co. Ltd. Research Institute Power Grid Technology Center Hefei China XEMC Wind Power Co. Ltd. Big Data Center State Key Laboratory of Offshore Wind Power Technology and Testing Changsha China Changsha University of Science and Technology School of Electrical and Information Engineering Changsha China

ISBN: (纸本)9781728131375

Photovoltaic converters whose dynamic characteristics determine the behavior of grid-connected device are important equipment for new energy generation. Therefore, to analyze the harmonic characteristics generated by grid-connected photovoltaic devices, it is necessary to firstly study the mechanism of harmonic generation, then build the dynamic harmonic model of photovoltaic power generation unit, and discuss the influencing factors. In this paper, based on the deep comprehension of harmonic characteristics, a Norton equivalent circuit that described the harmonic characteristics of grid-connected converter of photovoltaic power generation unit is established. Besides, the effects of the power output of photovoltaic power generation unit and the filter structure on converter's harmonic output are investigated. The accuracy of the proposed model is verified by comparing the results between the theoretical calculation and simulation. © 2019 IEEE.

关键词： Equivalent circuits

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Energy Saving Strategy of Power System Cluster Based on Container Virtualization

Energy Saving Strategy of Power System Cluster Based on Cont...

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IEEE Asia Power and Energy engineering Conference (APEEC)

作者： Ran Zheng Hao Wang Hai Jin Dechao Xu Yong Chen Xiaomeng Li Yufei Rao Zhenan Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research Institute Haidian District Beijing China State Grid Henan Electric Power Company Electric Power Research Institute Zhengzhou China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

With the continuous development of power grids, the scale of supercomputing clusters has also gradually increased to carry a large number of power system simulation calculations, and the problem of high energy consumption has appeared. To solve this problem, we propose a container virtualization-based supercomputing cluster for power system. We analyze the impact of containers on power simulation calculations and compare the energy consumption effects of various container scheduling and migration algorithms on clusters. Experiments show that compared to virtual machines with hypervisor, which consumes massive resources and reduces performances by 28.4%, the performance degradation of container on power simulation calculation is 1.3%, which can be ignored. The energy consumption of load-concentration or resource-and-load-balance container scheduling algorithms is up to 4.0% lower and at least 2.2% lower than other algorithms. In container migration, the method combining autoregressive model with most-correlation and resource-andload-balance algorithms is better than other methods, which not only minimizes energy consumption, but also has lowest number of migrations and SLA violations. Experiments verify the feasibility and advantages of container migration in power system computing clusters.

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Protecting white-box cryptographic implementations with obfuscated round boundaries

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Science China(Information Sciences) 2018年第3期61卷 237-239页

作者： Tao XU Chuankun WU Feng LIU Ruoxin ZHAO State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences New United Group Co. Ltd. School of Mathematics Shandong University School of Big Data and Computer Science Guizhou Normal University

Dear editor,White-box cryptography aims to give a secure software implementation of the cryptographic algorithm running in an untrusted environment which is owned and controlled by an *** major goal is to protect the confidentiality of the secret *** the increasing demands for softwareonly applications,white-box cryptography has received a lot of attention from *** now,all

关键词： Protecting white-box cryptographic implementations with obfuscated round boundaries WBC Figure XOR

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Significant greening of China's mangrove forests during the last three decades

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Ecological Indicators 2025年 176卷

作者： Wang, Xinzhi Zhang, Rong Jia, Mingming Wang, Zongming Zhao, Chuanpeng Guo, Xianxian Li, Huiying Xue, Zhaohui Zhou, Chao Xiao, Xiangming State Key Laboratory of Black Soils Conservation and Utilization Northeast Institute of Geography and Agroecology Chinese Academy of Sciences Changchun130102 China National Marine Environmental Monitoring Center Ministry of Ecology Environment Dalian116023 China School of Environmental and Municipal Engineering Qingdao University of Technology Qingdao266520 China College of Geography and Remote Sensing Hohai University Nanjing211100 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China Department of Microbiology and Plant Biology Center for Earth Observation and Modeling University of Oklahoma NormanOK United States College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China

In recent decades, China's mangroves have experienced severe degradation, followed by significant initiatives aimed at conservation and recovery. These initiatives have brought about significant changes in the extent of mangrove areas and their health conditions. Previous research has primarily concentrated on the dynamics of mangrove areas. Few studies have addressed the health condition, limited by lower spatial resolution, short time spans, and saturation effects of traditional vegetation indices. To overcome these challenges, we leveraged 37,144 Landsat scenes from 1993 to 2022 to generate kernel normalized difference vegetation index (kNDVI) images, monitoring spatial and temporal progression of mangrove greenness by kNDVI dynamics. Additionally, we analyzed the driving forces of kNDVI changes and environmental factors. The results showed that: (1) China's mangroves’ kNDVI values were increasing obviously during the last three decades, which indicated significant greening trends;(2) mangroves with greening tendencies were predominantly found in densely distributed regions, whereas degradation occurred in dispersed and isolated mangroves;(3) in general, mangrove greenness was positively correlated with sea surface salinity (r = 0.540, p © 2025 The Authors

关键词： Mineral resources

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Power Characterization of Memory Intensive Applications: Analysis and Implications 1st

Power Characterization of Memory Intensive Applications: Ana...

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Qiu, Yeliang Jiang, Congfeng Fan, Tiantian Wang, Yumei Zhang, Liangbin Wan, Jian Shi, Weisong Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou310037 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310037 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science Wayne State University DetroitMI48202 United States

ISBN: (纸本)9783030328122

DRAM is a significant source of server power consumption especially when the server runs memory intensive applications. Current power aware scheduling assumes that DRAM is as energy proportional as other components. However, the non-energy proportionality of DRAM significantly affects the power and energy consumption of the whole server system when running memory intensive applications. Thus good knowledge of server power characterization under memory intensive workloads can help better workload placement with power reduction. In this paper, we investigate the power characteristics of memory intensive applications on real rack servers of different generations. Through comprehensive analysis we find that (1) Server power consumption changes with workload intensity and concurrent execution threads. However, fully utilized memory systems are not the most energy efficient. (2) Powered memory modules of installed memory capacity, i.e. the memory capacity per processor core has significant impact on the application’s performance and server power consumption even if the memory system is not fully utilized. (3) Memory utilization is not always a good indicator for server power consumption when it is running memory intensive applications. Our experiments show that hardware configuration, workload types, as well as concurrently running threads have significant impact on a server’s energy efficiency when running memory intensive applications. Our findings presented in this paper provide useful insights and guidance to system designers, as well as data center operators for energy efficiency aware job scheduling and power reductions. © 2019, Springer Nature Switzerland AG.

关键词： Energy efficiency

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Scalability Evaluation of big data Processing Services in Clouds 1st

Scalability Evaluation of Big Data Processing Services in Cl...

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Zhou, Xin Jiang, Congfeng Qiu, Yeliang Fan, Tiantian Wang, Yumei Zhang, Liangbin Wan, Jian Shi, Weisong Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou310037 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310037 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science Wayne State University DetroitMI48202 United States

ISBN: (纸本)9783030328122

Currently, many cloud providers deploy their big data processing systems as cloud services, which helps users conveniently manage and process their data in clouds. Among different service providers’ big data processing services, how to evaluate and compare their scalability is an interesting and challenging work. Most traditional benchmark tools focus on performance evaluation of big data processing systems, such as aggregated throughput and IOPS, but fail to conduct a quantitative analysis of their scalability. In this paper, we propose a measurement methodology to quantify the scalability of big data processing services, which makes the cloud services scalability comparable. We conduct a group of comparative experiments on AliCloud E-MapReduce and Baidu MRS, and collect their respective scalability characteristics under Hadoop and Spark workloads. The scalability characteristics observed in our work could help cloud users choose the best cloud service platform to set up an optimized big data processing system to achieve their specific goals more successfully. © 2019, Springer Nature Switzerland AG.

关键词： big data

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CEPV: A Tree Structure Information Extraction and Visualization Tool for big knowledge Graph

CEPV: A Tree Structure Information Extraction and Visualizat...

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IEEE International Conference on big knowledge (ICBK)

作者： Shaojing Sheng Peng Zhou Xindong Wu Key Laboratory of Knowledge Engineering with Big Data Heifei Unversity of Technology Ministry of Education Heifei China School of Computer Science and Information Engineering Heifei University of Technology Heifei China School of Computer Science and Technology Anhui University Heifei China Mininglamp Academy of Sciences Mininglamp Technology Beijing China Institute of Big Konowledge Science Heifei University of Technology Heifei China

A large amount of data with rich semantic and structural information has been accumulated in many real-world applications. In order to effectively describe the concepts and connections in these data sets, knowledge graph was proposed as a tool to handle it. The genealogy is a typical tree structure data and can be stored in the knowledge graph. However, due to the complexity and increasing volume of the data, how to efficiently extract and visualize the customized information from the big knowledge graph is hence a challenge and worthy of in-depth study. Motivated by this, we propose a novel user-specified information extraction and visualization tool, named CEPV (the Customized information Extracting, Processing and Visualization tool), for converting the big graph structure data into a specified tree structure display. The main steps of CEPV are as follows: firstly, according to the requirements of users, extracting the specified data from the massive, complex, heterogeneous data as fewer times as possible, which can reduce the frequency of database access and improve the overall efficiency of the algorithm. Secondly, the fault tolerance mechanism and attribute judgment rules are executed to ensure the correctness during the data processing. Finally, the processed data with a complex relationship is presented to the user in multiple visualization models. The high availability and effectiveness of our proposed tool is verified on a big knowledge graph dataset.

关键词： data visualization data mining Tools databases data processing Fault tolerance Fault tolerant systems

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