中国科学院计算技术研究所机构知识库

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发表日期：2019

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	Chaotic particle swarm optimization with sigmoid-based acceleration coefficients for numerical function optimization 期刊论文 SWARM AND EVOLUTIONARY COMPUTATION, 2019, 卷号: 51, 页码: 16 作者:  Tian, Dongping;  Zhao, Xiaofei;  Shi, Zhongzhi 收藏  |  浏览/下载：47/0  |  提交时间：2020/12/10 Particle swarm optimization  Acceleration coefficients  Logistic map  Swarm diversity  Inertial weight  Premature convergence
	HybridTune: Spatio-Temporal Performance Data Correlation for Performance Diagnosis of Big Data Systems 期刊论文 JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2019, 卷号: 34, 期号: 6, 页码: 1167-1184 作者:  Ren, Rui;  Cheng, Jiechao;  He, Xi-Wen;  Wang, Lei;  Zhan, Jian-Feng;  Gao, Wan-Ling;  Luo, Chun-Jie 收藏  |  浏览/下载：45/0  |  提交时间：2020/12/10 Big Data system  spatio-temporal correlation  rule-based diagnosis  machine learning
	LPM: A Systematic Methodology for Concurrent Data Access Pattern Optimization from a Matching Perspective 期刊论文 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2019, 卷号: 30, 期号: 11, 页码: 2478-2493 作者:  Liu, Yuhang;  Sun, Xian-He 收藏  |  浏览/下载：44/0  |  提交时间：2020/12/10 Concurrent computing  Optimization  Delays  Program processors  Hardware  Systematics  Analytical models  Memory wall  memory stall time  efficiency  performance optimization  layered performance matching (LPM)  memory concurrency
	On the performance and convergence of distributed stream processing via approximate fault tolerance 期刊论文 VLDB JOURNAL, 2019, 卷号: 28, 期号: 5, 页码: 821-846 作者:  Cheng, Zhinan;  Huang, Qun;  Lee, Patrick P. C. 收藏  |  浏览/下载：249/0  |  提交时间：2019/12/10 Distributed stream processing  Approximate fault tolerance  Online learning
	The Extension of Semantic Formalization of Service Workflow Specification Language 期刊论文 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2019, 卷号: 15, 期号: 2, 页码: 741-754 作者:  Viriyasitavat, Wattana;  Xu, Li Da;  Bi, Zhuming 收藏  |  浏览/下载：79/0  |  提交时间：2019/04/03 Distributed intelligence  proof  quality of service  semantic formulization  service composition  service-oriented computing  service selection  service workflow  specification languages  system complexity
	Model Aggregation Method for Data Parallelism in Distributed Real-Time Machine Learning of Smart Sensing Equipment 期刊论文 IEEE ACCESS, 2019, 卷号: 7, 页码: 172065-172073 作者:  Fan, Yuchen;  Zhang, Jilin;  Zhao, Nailiang;  Ren, Yongjian;  Wan, Jian;  Zhou, Li;  Shen, Zhongyu;  Wang, Jue;  Zhang, Juncong;  Wei, Zhenguo 收藏  |  浏览/下载：47/0  |  提交时间：2020/12/10 Distributed machine learning  stochastic gradient descent  model aggregation method  smart sensing equipment