2013 IEEE International Conference on Granular Computing (GrC)最新文献_第6页

An introduction to the variable neighborhood and the related adaptive determination algorithm 介绍了变量邻域及相关的自适应确定算法

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740430

Fang Wang, Wei Pan, Lifeng Wu, Yong Guan

引用次数: 0

The analysis and prediction of stock price 股票价格的分析与预测

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740438

Tao Xing, Yuan Sun, Qian Wang, Guo Yu

引用次数: 21

Combining SURF with MSER for image matching 结合SURF和MSER进行图像匹配

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740423

Lei Tao, Xiaojun Jing, Songlin Sun, Hai Huang, Na Chen, Yueming Lu

引用次数: 5

Towards sharing disks between host OS and virtual machine with volume snapshot technology 利用卷快照技术实现主机操作系统与虚拟机之间的磁盘共享

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740434

Yan Wen, Jinjing Zhao, Hua Chen, Minhuan Huang

引用次数: 0

MC framework: High-performance distributed framework for standalone data analysis packages over Hadoop-based cloud MC框架:用于基于hadoop的云上的独立数据分析包的高性能分布式框架

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 1900-01-01 DOI: 10.1109/GrC.2013.6740375

Chao-Chun Chen, Nguyen Huu Tinh Giang, Tzu-Chao Lin, Min-Hsiung Hung

{"title":"MC framework: High-performance distributed framework for standalone data analysis packages over Hadoop-based cloud","authors":"Chao-Chun Chen, Nguyen Huu Tinh Giang, Tzu-Chao Lin, Min-Hsiung Hung","doi":"10.1109/GrC.2013.6740375","DOIUrl":"https://doi.org/10.1109/GrC.2013.6740375","url":null,"abstract":"The Hadoop MapReduce is the programming model of designing the scalable distributed computing applications, that provides developers can attain automatic parallelization. However, most complex manufacturing systems are arduous and restrictive to migrate to private clouds, due to the platform incompatible and tremendous complexity of system reconstruction. For increasing the efficiency of manufacturing systems with minimum efforts on modifying source codes, a high-performance framework is designed in this paper, called Multi-users-based Cloud-Adaptor Framework (MC-Framework), which provides the simple interface to users for fairly executing requested tasks worked with traditional standalone data analysis packages in MapReduce-based private cloud environments. Moreover, this framework focuses on multiuser workloads, but the default Hadoop scheduling scheme, i.e., FIFO, would increase delay under multiuser scenarios. Hence, a new scheduling mechanism, called Job-Sharing Scheduling, is designed to explore and fairly share the jobs to machines in the private cloud. Then, we prototype an experimental virtual-metrology module of a manufacturing system as a case study to verify and analysis the proposed MC-Framework. The results of our experiments indicate that our proposed framework enormously improved the time performance compared with the original package.","PeriodicalId":415445,"journal":{"name":"2013 IEEE International Conference on Granular Computing (GrC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130093575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A fuzzy data mining approach for remote sensing image recommendation 遥感图像推荐的模糊数据挖掘方法

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 1900-01-01 DOI: 10.1109/GrC.2013.6740410

E. H. Lu, Jung-Hong Hong, Z. Su, Chun-Hao Chen

{"title":"A fuzzy data mining approach for remote sensing image recommendation","authors":"E. H. Lu, Jung-Hong Hong, Z. Su, Chun-Hao Chen","doi":"10.1109/GrC.2013.6740410","DOIUrl":"https://doi.org/10.1109/GrC.2013.6740410","url":null,"abstract":"Nowadays research on Remote Sensing Images (RS-Images) ranking and recommendation for meeting the user-specific Area-Of-Interest (AOI) has received a log of attentions due to a wide range of potential applications. In this paper, we propose a novel approach named Fuzzy rs-Image Recommender (FIR) to rank and recommend relevant RS-Images according to the queried AOI. In FIR, we first propose two features named Available Space (AS) and Image Extension (IE) as two indicators to represent the relationships between AOI and RS-Image. Then, we mine the fuzzy association rules between the proposed indicators and user rating score. Finally, we propose two fuzzy inference strategies named FIR with Weightarea (FIR_area) and FIR with Weightall(FIR_all) to rank and recommend the relevant RS-Images to users. To our best knowledge, this is the first work on RS-Image recommendation that considers the issues of feature extraction and fuzzy rule mining, simultaneously. Through comprehensive experimental evaluations, the results show that the proposed FIR approach outperforms the state-of-the-art approach Hausdorff in terms of Normalized Discounted Cumulative Gain (NDCG).","PeriodicalId":415445,"journal":{"name":"2013 IEEE International Conference on Granular Computing (GrC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114958085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0