2011 11th International Conference on Hybrid Intelligent Systems (HIS)最新文献_第2页

Trends and challenges in dynamic optimization problems 动态优化问题的发展趋势与挑战

2011 11th International Conference on Hybrid Intelligent Systems (HIS) Pub Date : 2011-12-01 DOI: 10.1109/HIS.2011.6122119

Ramoza Ahsan, W. Shahzad, A. R. Baig

引用次数: 3

Automated system for drilling operations classification using statistical features 使用统计特征的钻井作业自动分类系统

2011 11th International Conference on Hybrid Intelligent Systems (HIS) Pub Date : 2011-12-01 DOI: 10.1109/HIS.2011.6122104

B. Esmael, A. Arnaout, R. Fruhwirth, G. Thonhauser

{"title":"Automated system for drilling operations classification using statistical features","authors":"B. Esmael, A. Arnaout, R. Fruhwirth, G. Thonhauser","doi":"10.1109/HIS.2011.6122104","DOIUrl":"https://doi.org/10.1109/HIS.2011.6122104","url":null,"abstract":"Operations classification is one of the most needed tasks in the oil & gas industry. It provides the engineers with detailed information about what is happening on the rig site. In this paper we propose an approach to classify drilling operations automatically using machine learning techniques. This approach takes as input the sensors data in a specific time range, and predicts the drilling operation. Our approach is simple but effective, where for each sensor data (channel) a list of statistical features will be extracted, then features selection algorithms will be used to select the most informative features, and finally, a classifier will be trained based on these features. In this paper many feature weighting and selection algorithms were tested to find which statistical measures clearly distinguish between many different rig operations. In addition, many classification techniques were employed to find the best one in terms of accuracy and speed. Experimental evaluation with real data, from four different drilling scenarios, shows that our approach has the ability to extract and select the best features and build accurate classifiers. The performance of the classifiers was evaluated by using the cross-validation method.","PeriodicalId":418397,"journal":{"name":"2011 11th International Conference on Hybrid Intelligent Systems (HIS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126753698","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: 7

An effective adaptive technique for impulse noise detection and reduction in digital images 一种有效的数字图像脉冲噪声检测与降噪的自适应技术

2011 11th International Conference on Hybrid Intelligent Systems (HIS) Pub Date : 2011-12-01 DOI: 10.1109/HIS.2011.6122108

M. Javidi, Soheila Khatami, Roghiyeh Hoseinpour Fard, M. Jampour

引用次数: 5

Negotiating decision makers' reference points for group preference-based Evolutionary Multi-objective Optimization 基于群体偏好的进化多目标优化协商决策者参考点

2011 11th International Conference on Hybrid Intelligent Systems (HIS) Pub Date : 2011-12-01 DOI: 10.1109/HIS.2011.6122135

Slim Bechikh, L. B. Said, K. Ghédira

引用次数: 31

Hybrid local search algorithms on Graph Coloring Problem 图着色问题的混合局部搜索算法

2011 11th International Conference on Hybrid Intelligent Systems (HIS) Pub Date : 2011-12-01 DOI: 10.1109/HIS.2011.6122150

Cagri Yesil, Buse Yilmaz, E. E. Korkmaz

引用次数: 5

A fuzzy approach for mining general temporal association rules in a publication database 发布数据库中通用时态关联规则的模糊挖掘方法

2011 11th International Conference on Hybrid Intelligent Systems (HIS) Pub Date : 2011-12-01 DOI: 10.1109/HIS.2011.6122175

Guo-Cheng Lan, Chun-Hao Chen, T. Hong, Shih-Bin Lin

引用次数: 9

Design of fractional order PID controller using Sobol Mutated Artificial Bee Colony alogrithm 基于Sobol变异人工蜂群算法的分数阶PID控制器设计

2011 11th International Conference on Hybrid Intelligent Systems (HIS) Pub Date : 2011-12-01 DOI: 10.1109/HIS.2011.6122096

A. Rajasekhar, A. Abraham, M. Pant

引用次数: 7

Connection manager using FAHP of MCDM techniques in heterogeneous network 异构网络中使用FAHP的MCDM连接管理器

2011 11th International Conference on Hybrid Intelligent Systems (HIS) Pub Date : 2011-12-01 DOI: 10.1109/HIS.2011.6122072

Chih-Wei Hsu, Jian-Pan Li, Sheng-Tzong Cheng

{"title":"Connection manager using FAHP of MCDM techniques in heterogeneous network","authors":"Chih-Wei Hsu, Jian-Pan Li, Sheng-Tzong Cheng","doi":"10.1109/HIS.2011.6122072","DOIUrl":"https://doi.org/10.1109/HIS.2011.6122072","url":null,"abstract":"Connection Manager Intelligence Agent's (CMIA) an attractive feature is use the network traffic and behavior with multi-attributes to find the best network devices and connection devices meet user habits that it possible to access various network contents everywhere at any time. In this agent, information process is thoroughly integrated into embedded system for connection manager of MeeGo or Android. This paper proposes a new and generic algorithm for connection manager in the heterogeneous wired or wireless environments. The proposed algorithm uses a Fuzzy Analytic Hierarchy Process theory (FAHP) of Multi-Criteria Decision Making (MCDM) system to achieve scalable, adaptable, and flexible solution. This study implemented an interactive interface through of this function acquisition user behavior and to scholars of the machine used a boosting iterations for User-Case, the algorithm maintains a set of weights as a distribution classes table of case as in the parameter learning by user-case and its quite possible that the Expectation-Maximization (EM) of Maximum Probability Model (MPM) by classification from user behavior. User interaction showed that the agent satisfactorily matched user intent.","PeriodicalId":418397,"journal":{"name":"2011 11th International Conference on Hybrid Intelligent Systems (HIS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123520169","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

Automatic threshold tracking of sensor data using Expectation Maximization algorithm 基于期望最大化算法的传感器数据自动阈值跟踪

2011 11th International Conference on Hybrid Intelligent Systems (HIS) Pub Date : 2011-12-01 DOI: 10.1109/HIS.2011.6122164

A. Arnaout, B. Esmael, R. Fruhwirth, G. Thonhauser

{"title":"Automatic threshold tracking of sensor data using Expectation Maximization algorithm","authors":"A. Arnaout, B. Esmael, R. Fruhwirth, G. Thonhauser","doi":"10.1109/HIS.2011.6122164","DOIUrl":"https://doi.org/10.1109/HIS.2011.6122164","url":null,"abstract":"In this paper we present a novel method for automatic threshold handling and tracking of sensor data at drilling rigs. A hybrid system for automated drilling operation classification is extended by the Expectation Maximization algorithm in combination with the Bayes' theorem to find automatically threshold values required by a rule based system used in an automated drilling operations classification system. The streaming data from the rig site is gathered and analyzed, the main clusters in the sensor data are identified and monitored as in a real life case. The first part of the suggested method is based on the Expectation Maximization algorithm which is used to decompose Gaussian mixture models in the sensor data set. Bayes' theorem is used as a subsequent part to calculate optimal threshold values. The threshold values calculation concept is heavily depending on the likelihood probabilities of each data cluster. The work in this paper not only suggests a solution and analytical method for tracking this kind of thresholds in the sensor data but also verifies how to compute such reliable thresholds in real-time.","PeriodicalId":418397,"journal":{"name":"2011 11th International Conference on Hybrid Intelligent Systems (HIS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129685304","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: 4

Mining collaboration through textual semantic interpretation 通过文本语义解释挖掘协作

2011 11th International Conference on Hybrid Intelligent Systems (HIS) Pub Date : 2011-12-01 DOI: 10.1109/HIS.2011.6122198

S. Karmakar, Ying Zhu

引用次数: 3