2011 IEEE International Conference on Cloud Computing and Intelligence Systems最新文献_第3页

An extended soft set model: Type-2 fuzzy soft sets 一个扩展的软集模型:二类模糊软集

2011 IEEE International Conference on Cloud Computing and Intelligence Systems Pub Date : 2011-10-13 DOI: 10.1109/CCIS.2011.6045046

Xi'ao Ma, Guoyin Wang

引用次数: 3

A non-standard-substance pesticide residue qualitative analysis method based on SVM 基于支持向量机的非标准物质农药残留定性分析方法

2011 IEEE International Conference on Cloud Computing and Intelligence Systems Pub Date : 2011-10-13 DOI: 10.1109/CCIS.2011.6045038

Yi Chen, Rui Wang, Hongqian Chen

{"title":"A non-standard-substance pesticide residue qualitative analysis method based on SVM","authors":"Yi Chen, Rui Wang, Hongqian Chen","doi":"10.1109/CCIS.2011.6045038","DOIUrl":"https://doi.org/10.1109/CCIS.2011.6045038","url":null,"abstract":"Currently, most pesticide residue analysis methods are based on mass spectrometry instruments, such as Gas Chromatography-Mass Spectrometry (GC-MS) analyzers. However, using these methods, user has to send the chemical standard-substances (pure pesticides) and the samples needed checking simultaneously in order to detect the pesticide residues in foods and agricultural products. Support Vector Machines (SVM) is a statistical learning method based on small-sample set. It has the advantages in solving the small-sample set, nonlinear and high dimension problems and is widely used in classification. A non-standard-substance pesticide residue qualitative analysis method (NSS-QAM) is presented in this paper. NSS-QAM transforms qualitative analysis problem into a problem of classification of pesticide residues based on SVM and existing official standards for pesticide residue detection and experiment results. NSS-QAM is divided into two steps. The first one is to let SVM learning according to characteristic parameters of pesticide residue from existing standards and experiment results and get classification model. This step is executed by offline and applies one-versus-one multi-class classification SVM. The second step is to use the model to classify detecting data from GC-MS analyzer and implement qualitative analysis for pesticide residue without using chemical standard-substance. NSS-QAM has been experimented with 1500 samples from 50 pesticides. The classification results demonstrate that NSS-QAM is an effective qualitative analyzing method.","PeriodicalId":128504,"journal":{"name":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","volume":"274 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116251081","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

Implementation of method object in active object-based storage systems 基于活动对象存储系统中方法对象的实现

2011 IEEE International Conference on Cloud Computing and Intelligence Systems Pub Date : 2011-10-13 DOI: 10.1109/CCIS.2011.6045061

Zhipeng Tan, Yanli Yuan, D. Feng, Tian Zhan, X. Tu

{"title":"Implementation of method object in active object-based storage systems","authors":"Zhipeng Tan, Yanli Yuan, D. Feng, Tian Zhan, X. Tu","doi":"10.1109/CCIS.2011.6045061","DOIUrl":"https://doi.org/10.1109/CCIS.2011.6045061","url":null,"abstract":"The concept of active storage was investigated through adding computational capabilities to I/O contexts, in which increasing data-locality in parallel or placing post-/preprocessing functionality for storage self-maintainability. The advantages of this scheme are tow fold, because it is able to leverage the processing power of the storage nodes and considerably reduce the network traffic at the same time. The emergence of object interface provides a good opportunity for the development of active storage to fight back with highly expressive interface semantics, and the functionality and scalability of active storage in the object storage system, The active storage that we proposed is targeting object-based storage system upon the building block of extended international standard T10 OSD (Object-based Storage Device) command set. Different with the existing work, our work focuses on researching and developing two new kinds of method object to realize active storage, in which user method object is used to manipulate the common user data object, triggered to perform by the user's read and write requests, and the system method object is used in accordance with the policy object and status of the system to implement self-management of the system respectively. Then these active objects are put together to integrated into proposed active storage framework.","PeriodicalId":128504,"journal":{"name":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","volume":"11 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123663868","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

Control algorithm based on shape recognition 基于形状识别的控制算法

2011 IEEE International Conference on Cloud Computing and Intelligence Systems Pub Date : 2011-10-13 DOI: 10.1109/CCIS.2011.6045131

Jian Lan, Shuiqing Yu, Liangliang Xu, Xuejuan Chen

引用次数: 0

User interest modeling by labeled LDA with topic features 利用带主题特征的标签LDA对用户兴趣进行建模

2011 IEEE International Conference on Cloud Computing and Intelligence Systems Pub Date : 2011-10-13 DOI: 10.1109/CCIS.2011.6045022

Wenfeng Li, Xiaojie Wang, Rile Hu, Jilei Tian

引用次数: 4

An integrated optimization model for the location of distribution centers with multiple practical constraints 考虑多种实际约束的配送中心选址集成优化模型

2011 IEEE International Conference on Cloud Computing and Intelligence Systems Pub Date : 2011-10-13 DOI: 10.1109/CCIS.2011.6045112

Yundong Gu, Yajing An, Yanchao Chen, Jin Xu

引用次数: 1

Research on dynamic reconfiguration technology of cloud computing virtual services 云计算虚拟服务动态重构技术研究

2011 IEEE International Conference on Cloud Computing and Intelligence Systems Pub Date : 2011-10-13 DOI: 10.1109/CCIS.2011.6045088

Shuai Wang, Fang Du, Xinming Li, Yi Li, Xingye Han

引用次数: 5

Ensuring data storage security through a novel third party auditor scheme in cloud computing 通过云计算中新颖的第三方审计方案确保数据存储安全

2011 IEEE International Conference on Cloud Computing and Intelligence Systems Pub Date : 2011-10-13 DOI: 10.1109/CCIS.2011.6045072

Shuai Han, Jianchuan Xing

引用次数: 75

Dirichlet Process Mixture Models for lexical category acquisition 词汇范畴习得的Dirichlet过程混合模型

2011 IEEE International Conference on Cloud Computing and Intelligence Systems Pub Date : 2011-10-13 DOI: 10.1109/CCIS.2011.6045045

Bichuan Zhang, Xiaojie Wang, Guannan Fang

引用次数: 0

A comparative review of job scheduling for MapReduce MapReduce作业调度的比较综述

2011 IEEE International Conference on Cloud Computing and Intelligence Systems Pub Date : 2011-10-13 DOI: 10.1109/CCIS.2011.6045089

Dongjin Yoo, K. Sim

{"title":"A comparative review of job scheduling for MapReduce","authors":"Dongjin Yoo, K. Sim","doi":"10.1109/CCIS.2011.6045089","DOIUrl":"https://doi.org/10.1109/CCIS.2011.6045089","url":null,"abstract":"MapReduce is an emerging paradigm for data intensive processing with support of cloud computing technology. MapReduce provides convenient programming interfaces to distribute data intensive works in a cluster environment. The strengths of MapReduce are fault tolerance, an easy programming structure and high scalability. A variety of applications have adopted MapReduce including scientific analysis, web data processing and high performance computing. Data Intensive computing systems, such as Hadoop and Dryad, should provide an efficient scheduling mechanism for enhanced utilization in a shared cluster environment. The problems of scheduling map-reduce jobs are mostly caused by locality and synchronization overhead. Also, there is a need to schedule multiple jobs in a shared cluster with fairness constraints. By introducing the scheduling problems with regards to locality, synchronization and fairness constraints, this paper reviews a collection of scheduling methods for handling these issues in MapReduce. In addition, this paper compares different scheduling methods evaluating their features, strengths and weaknesses. For resolving synchronization overhead, two categories of studies; asynchronous processing and speculative execution are discussed. For fairness constraints with locality improvement, delay scheduling in Hadoop and Quincy scheduler in Dryad are discussed.","PeriodicalId":128504,"journal":{"name":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121957015","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: 63