IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics最新文献_第9页

Fast graph-based relaxed clustering for large data sets using minimal enclosing ball. 使用最小封闭球的大型数据集的快速基于图的松弛聚类。

IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics Pub Date : 2012-06-01 Epub Date: 2012-02-03 DOI: 10.1109/TSMCB.2011.2172604

Pengjiang Qian, Fu-Lai Chung, Shitong Wang, Zhaohong Deng

{"title":"Fast graph-based relaxed clustering for large data sets using minimal enclosing ball.","authors":"Pengjiang Qian, Fu-Lai Chung, Shitong Wang, Zhaohong Deng","doi":"10.1109/TSMCB.2011.2172604","DOIUrl":"https://doi.org/10.1109/TSMCB.2011.2172604","url":null,"abstract":"Although graph-based relaxed clustering (GRC) is one of the spectral clustering algorithms with straightforwardness and self-adaptability, it is sensitive to the parameters of the adopted similarity measure and also has high time complexity O(N(3)) which severely weakens its usefulness for large data sets. In order to overcome these shortcomings, after introducing certain constraints for GRC, an enhanced version of GRC [constrained GRC (CGRC)] is proposed to increase the robustness of GRC to the parameters of the adopted similarity measure, and accordingly, a novel algorithm called fast GRC (FGRC) based on CGRC is developed in this paper by using the core-set-based minimal enclosing ball approximation. A distinctive advantage of FGRC is that its asymptotic time complexity is linear with the data set size N. At the same time, FGRC also inherits the straightforwardness and self-adaptability from GRC, making the proposed FGRC a fast and effective clustering algorithm for large data sets. The advantages of FGRC are validated by various benchmarking and real data sets.","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2011.2172604","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30446469","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: 46

Symbolic dynamic filtering and language measure for behavior identification of mobile robots. 移动机器人行为识别的符号动态滤波和语言度量。

IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics Pub Date : 2012-06-01 Epub Date: 2011-11-03 DOI: 10.1109/TSMCB.2011.2172419

Goutham Mallapragada, Asok Ray, Xin Jin

引用次数: 19

A price- and-time-slot-negotiation mechanism for Cloud service reservations. 用于云服务预订的价格和时间段协商机制。

IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics Pub Date : 2012-06-01 Epub Date: 2011-12-19 DOI: 10.1109/TSMCB.2011.2174355

Seokho Son, Kwang Mong Sim

{"title":"A price- and-time-slot-negotiation mechanism for Cloud service reservations.","authors":"Seokho Son, Kwang Mong Sim","doi":"10.1109/TSMCB.2011.2174355","DOIUrl":"https://doi.org/10.1109/TSMCB.2011.2174355","url":null,"abstract":"When making reservations for Cloud services, consumers and providers need to establish service-level agreements through negotiation. Whereas it is essential for both a consumer and a provider to reach an agreement on the price of a service and when to use the service, to date, there is little or no negotiation support for both price and time-slot negotiations (PTNs) for Cloud service reservations. This paper presents a multi-issue negotiation mechanism to facilitate the following: 1) PTNs between Cloud agents and 2) tradeoff between price and time-slot utilities. Unlike many existing negotiation mechanisms in which a negotiation agent can only make one proposal at a time, agents in this work are designed to concurrently make multiple proposals in a negotiation round that generate the same aggregated utility, differing only in terms of individual price and time-slot utilities. Another novelty of this work is formulating a novel time-slot utility function that characterizes preferences for different time slots. These ideas are implemented in an agent-based Cloud testbed. Using the testbed, experiments were carried out to compare this work with related approaches. Empirical results show that PTN agents reach faster agreements and achieve higher utilities than other related approaches. A case study was carried out to demonstrate the application of the PTN mechanism for pricing Cloud resources.","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2011.2174355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30344777","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: 143

Voting among virtually generated versions of a classification problem. 对一个分类问题的虚拟生成版本进行投票。

IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics Pub Date : 2012-06-01 Epub Date: 2012-01-02 DOI: 10.1109/TSMCB.2011.2177084

Aboozar Hosseinzadeh, Ali M Reza

引用次数: 2

Automatic image annotation and retrieval using group sparsity. 使用组稀疏性的自动图像注释和检索。

IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics Pub Date : 2012-06-01 Epub Date: 2012-01-10 DOI: 10.1109/TSMCB.2011.2179533

Shaoting Zhang, Junzhou Huang, Hongsheng Li, Dimitris N Metaxas

{"title":"Automatic image annotation and retrieval using group sparsity.","authors":"Shaoting Zhang, Junzhou Huang, Hongsheng Li, Dimitris N Metaxas","doi":"10.1109/TSMCB.2011.2179533","DOIUrl":"https://doi.org/10.1109/TSMCB.2011.2179533","url":null,"abstract":"Automatically assigning relevant text keywords to images is an important problem. Many algorithms have been proposed in the past decade and achieved good performance. Efforts have focused upon model representations of keywords, whereas properties of features have not been well investigated. In most cases, a group of features is preselected, yet important feature properties are not well used to select features. In this paper, we introduce a regularization-based feature selection algorithm to leverage both the sparsity and clustering properties of features, and incorporate it into the image annotation task. Using this group-sparsity-based method, the whole group of features [e.g., red green blue (RGB) or hue, saturation, and value (HSV)] is either selected or removed. Thus, we do not need to extract this group of features when new data comes. A novel approach is also proposed to iteratively obtain similar and dissimilar pairs from both the keyword similarity and the relevance feedback. Thus, keyword similarity is modeled in the annotation framework. We also show that our framework can be employed in image retrieval tasks by selecting different image pairs. Extensive experiments are designed to compare the performance between features, feature combinations, and regularization-based feature selection methods applied on the image annotation task, which gives insight into the properties of features in the image annotation task. The experimental results demonstrate that the group-sparsity-based method is more accurate and stable than others.","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2011.2179533","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30389846","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: 118

A hierarchical gene regulatory network for adaptive multirobot pattern formation. 自适应多机器人模式形成的层次基因调控网络。

IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics Pub Date : 2012-06-01 Epub Date: 2012-01-31 DOI: 10.1109/TSMCB.2011.2178021

Yaochu Jin, Hongliang Guo, Yan Meng

{"title":"A hierarchical gene regulatory network for adaptive multirobot pattern formation.","authors":"Yaochu Jin, Hongliang Guo, Yan Meng","doi":"10.1109/TSMCB.2011.2178021","DOIUrl":"https://doi.org/10.1109/TSMCB.2011.2178021","url":null,"abstract":"Most existing multirobot systems for pattern formation rely on a predefined pattern, which is impractical for dynamic environments where the pattern to be formed should be able to change as the environment changes. In addition, adaptation to environmental changes should be realized based only on local perception of the robots. In this paper, we propose a hierarchical gene regulatory network (H-GRN) for adaptive multirobot pattern generation and formation in changing environments. The proposed model is a two-layer gene regulatory network (GRN), where the first layer is responsible for adaptive pattern generation for the given environment, while the second layer is a decentralized control mechanism that drives the robots onto the pattern generated by the first layer. An evolutionary algorithm is adopted to evolve the parameters of the GRN subnetwork in layer 1 for optimizing the generated pattern. The parameters of the GRN in layer 2 are also optimized to improve the convergence performance. Simulation results demonstrate that the H-GRN is effective in forming the desired pattern in a changing environment. Robustness of the H-GRN to robot failure is also examined. A proof-of-concept experiment using e-puck robots confirms the feasibility and effectiveness of the proposed model.","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2011.2178021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30441722","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: 67

Segmenting human from photo images based on a coarse-to-fine scheme. 基于粗到精的人像图像分割方法。

IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics Pub Date : 2012-06-01 Epub Date: 2012-01-23 DOI: 10.1109/TSMCB.2011.2182048

Huchuan Lu, Guoliang Fang, Xinqing Shao, Xuelong Li

引用次数: 32

Efficient clustering aggregation based on data fragments. 基于数据片段的高效聚类聚合。

IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics Pub Date : 2012-06-01 Epub Date: 2012-02-10 DOI: 10.1109/TSMCB.2012.2183591

Ou Wu, Weiming Hu, Stephen J Maybank, Mingliang Zhu, Bing Li

{"title":"Efficient clustering aggregation based on data fragments.","authors":"Ou Wu, Weiming Hu, Stephen J Maybank, Mingliang Zhu, Bing Li","doi":"10.1109/TSMCB.2012.2183591","DOIUrl":"https://doi.org/10.1109/TSMCB.2012.2183591","url":null,"abstract":"Clustering aggregation, known as clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a single better clustering. Existing clustering aggregation algorithms are applied directly to data points, in what is referred to as the point-based approach. The algorithms are inefficient if the number of data points is large. We define an efficient approach for clustering aggregation based on data fragments. In this fragment-based approach, a data fragment is any subset of the data that is not split by any of the clustering results. To establish the theoretical bases of the proposed approach, we prove that clustering aggregation can be performed directly on data fragments under two widely used goodness measures for clustering aggregation taken from the literature. Three new clustering aggregation algorithms are described. The experimental results obtained using several public data sets show that the new algorithms have lower computational complexity than three well-known existing point-based clustering aggregation algorithms (Agglomerative, Furthest, and LocalSearch); nevertheless, the new algorithms do not sacrifice the accuracy.","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2012.2183591","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30459270","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: 52

Learning of fuzzy cognitive maps using density estimate. 使用密度估计学习模糊认知地图。

IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics Pub Date : 2012-06-01 Epub Date: 2012-02-14 DOI: 10.1109/TSMCB.2011.2182646

Wojciech Stach, Witold Pedrycz, Lukasz A Kurgan

{"title":"Learning of fuzzy cognitive maps using density estimate.","authors":"Wojciech Stach, Witold Pedrycz, Lukasz A Kurgan","doi":"10.1109/TSMCB.2011.2182646","DOIUrl":"https://doi.org/10.1109/TSMCB.2011.2182646","url":null,"abstract":"Fuzzy cognitive maps (FCMs) are convenient and widely used architectures for modeling dynamic systems, which are characterized by a great deal of flexibility and adaptability. Several recent works in this area concern strategies for the development of FCMs. Although a few fully automated algorithms to learn these models from data have been introduced, the resulting FCMs are structurally considerably different than those developed by human experts. In particular, maps that were learned from data are much denser (with the density over 90% versus about 40% density of maps developed by humans). The sparseness of the maps is associated with their interpretability: the smaller the number of connections is, the higher is the transparency of the map. To this end, a novel learning approach, sparse real-coded genetic algorithms (SRCGAs), to learn FCMs is proposed. The method utilizes a density parameter to guide the learning toward a formation of maps of a certain predefined density. Comparative tests carried out for both synthetic and real-world data demonstrate that, given a suitable density estimate, the SRCGA method significantly outperforms other state-of-the-art learning methods. When the density estimate is unknown, the new method can be used in an automated fashion using a default value, and it is still able to produce models whose performance exceeds or is equal to the performance of the models generated by other methods.","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2011.2182646","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30469450","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: 69

Deadlock-free genetic scheduling algorithm for automated manufacturing systems based on deadlock control policy. 基于死锁控制策略的自动化制造系统无死锁遗传调度算法。

IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics Pub Date : 2012-06-01 Epub Date: 2011-11-16 DOI: 10.1109/TSMCB.2011.2170678

KeYi Xing, LiBin Han, MengChu Zhou, Feng Wang

引用次数: 127