به نام خدا
Title: Thermal energy storage using thermo-chemical heat pump
Authors: MA Hamdan a, , SD Rossides b , R Haj Khalil
Abstract: A theoretical study was performed to investigate the potential of storing thermal energy using a heat pump which is a thermo-chemical storage system consisting of water as sorbet, and sodium chloride as the sorbent. The effect of different parameters namely; the amount of vaporized water from the evaporator, the system initial temperature and the type of salt on the increase in temperature of the salt was investigated and hence on the performance of the thermo chemical heat pump. It was found that the performance of the heat pump improves with the initial system temperature, with the amount of water vaporized and with the water remaining in the system. Finally it was also found that lithium chloride salt has higher effect on the performance of the heat pump that of sodium chloride.
Publish Year: 2013
Published in: Energy Conversion and Management - Science Direct
موضوع: مهندسی انرژی
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به نام خدا
Title: Clustering local frequency items in multiple databases
Authors: Animesh Adhikari
Abstract: Frequent items could be considered as a basic type of patterns in a database. In the context of multiple data sources, most of the global patterns are based on local frequency items. A multi-branch company transacting from different branches often needs to extract global patterns from data distributed over the branches. Global decisions could be taken effectively using such patterns. Thus, it is important to cluster local frequency items in multiple databases. An overview of the existing measures of association is presented here. For the purpose of selecting the suitable technique of mining multiple databases, we have surveyed the existing multi-database mining techniques. A study on the related clustering techniques is also covered here. The notion of high frequency item sets is introduced here, and an algorithm for synthesizing supports of such item sets is designed. The existing clustering technique might cluster local frequency items at a low level, since it estimates association among items in an item set with a low accuracy, and thus a new algorithm for clustering local frequency items is proposed. Due to the suitability of measure of association A 2, association among items in a high frequency item set is synthesized based on it. The soundness of the clustering technique has been shown. Numerous experiments are conducted using ve datasets, and the results on different aspects of the proposed problem are presented in the experimental section. The effectiveness of the proposed clustering technique is more visible in dense databases.
Publish Year: 2013
Published in: Information Sciences - Science Direct
موضوع: داده کاوی (Data Mining) - خوشه بندی (Clustering)
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به نام خدا
Title: Menger’s theorem for fuzzy graphs
Authors: Sunil Mathew, MS Sunitha
Abstract: The concept of the strongest path plays a crucial role in fuzzy graph theory. In classical graph theory, all paths in a graph are strongest, with a strength value of one. In this article, we introduce Menger’s theorem for fuzzy graphs and discuss the concepts of strength- reducing sets and t-connected fuzzy graphs. We also characterize t-connected and t-arc connected fuzzy graphs.
Publish Year: 2013
Published in: Information Sciences - Science Direct
موضوع: نظریه گراف (Graph Theory)– منطق فازی (Fuzzy Logic)
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به نام خدا
Title: A survey of skyline processing in highly distributed environments
Authors: Katja Hose Akrivi Vlachou
Abstract: During the last decades, data management and storage have become increasingly distributed. Advanced query operators, such as skyline queries, are necessary in order to help users to handle the huge amount of available data by identifying a set of interesting data objects. Skyline query processing in highly distributed environments poses inherent challenges and demands and requires non-traditional techniques due to the distribution of content and the lack of global knowledge. This paper surveys this interesting and still evolving research area, so that readers can easily obtain an overview of the state-of-the-art. We outline the objectives and the main principles that any distributed skyline approach have to fulfill, leading to useful guidelines for developing algorithms for distributed skyline processing. We review in detail existing approaches that are applicable for highly distributed environments, clarify the assumptions of each approach, and provide a comparative performance analysis. Moreover, we study the skyline variants each approach supports. Our analysis leads to taxonomy of existing approaches. Finally, we present interesting research topics on distributed skyline computation that have not yet been explored.
Publish Year: 2012
Published in: The VLDB Journal – Springer
موضوع: پردازش توزیع شده (Distributed Processing)
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به نام خدا
Title: Efficient stochastic algorithms for document clustering
Authors: Rana Forsati, Mehrdad Mahdav, Mehrnoush Shamsfard, Mohammad Reza Meybodi
Abstract: Clustering has become an increasingly important and highly complicated research area for targeting useful and relevant information in modern application domains such as the World Wide Web. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm may generate a local optimal clustering. In this paper, we present novel document clustering algorithms based on the Harmony Search (HS) optimization method. By modeling clustering as an optimization problem, we first propose a pure HS based clustering algorithm that finds near-optimal clusters within a reasonable time. Then, harmony clustering is integrated with the K-means algorithm in three ways to achieve better clustering by combining the explorative power of HS with the refining power of the K-means. Contrary to the localized searching property of K-means algorithm, the proposed algorithms perform a globalized search in the entire solution space. Addition- ally, the proposed algorithms improve K-means by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, therefore, making it more stable. The behavior of the proposed algorithm is theoretically analyzed by modeling its population variance as a Markov chain. We also conduct an empirical study to determine the impacts of various parameters on the quality of clusters and convergence behavior of the algorithms. In the experiments, we apply the proposed algorithms along with K-means and a Genetic Algorithm (GA) based clustering algorithm on five different document data- sets. Experimental results reveal that the proposed algorithms can find better clusters and the quality of clusters is comparable based on F-measure, Entropy, Purity, and Average Distance of Documents to the Cluster Centroid (ADDC).
Publish Year: 2013
Published in: Information Sciences - Science Direct
موضوع: الگوریتمهای تکاملی (Evolutionary Algorithms)- (Stochastic Algorithms)
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