به نام خدا
Title: Visualizing Uncertainty in Multi-resolution Volumetric Data Using Marching Cubes
Authors: J Ma,D Murphy,M Hayes,G Provan
Abstract: Data sets acquired from complex scientific simulation, high precision engineering experiment and high-speed computer network have been exponentially increased, and visualization and analysis of such large-scale of data sets have been identified as a significant challenge to the visualization com-munity. Over the past years many scientists have made at-tempt to address this problem by proposing various data reduction techniques. Consequently the size of data can be reduced and issues associated to the visualization can be improved (e.g. real-time interaction and visual overload).However, during the process of data reduction, the information of original data sets was approximated and potential errors were introduced. It leads to a new problem with regard to the integrity of the data and might mislead users for incorrect decision making. Therefore in this paper we aim to solve the problem by introducing three novel uncertainty visualization methods, which depict both the multi-resolution(MR) approximations of the original data set and the errors associated with each of its low resolution representations. As a result we faithfully represent the MR data sets and allow users to make suitable decisions from the visual output. We applied our techniques on a data set from medical domain to demonstrate their effectiveness and usability.
Publish Year: 2012
Published in: AVI - ACM
Number of Pages: 8
موضوع: مصورسازی داده ها (Data Visualization)
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به نام خدا
Title: An artificial bee colony algorithm for the maximally diverse grouping problem
Authors: Francisco J Rodriguez , M Lozano a , C GarcaMartnez b , Jonathan D GonzlezBarrera
Abstract: In this paper, an artificial bee colony algorithm is proposed to solve the maximally diverse grouping problem. This complex optimization problem consists of forming maximally diverse groups with restricted sizes from a given set of elements. The artificial bee colony algorithm is a new swarm intelligence technique based on the intelligent foraging behavior of honeybees. The behavior of this algorithm is determined by two search strategies: an initialization scheme employed to construct initial solutions and a method for generating neighboring solutions. More specifically, the proposed approach employs a greedy constructive method to accomplish the initialization task and also employs different neighborhood operators inspired by the iterated greedy algorithm. In addition, it incorporates an improvement procedure to enhance the intensification capability. Through an analysis of the experimental results, the highly effective performance of the proposed algorithm is shown in comparison to the current state-of-the-art algorithms which address the problem.
Publish Year: 2013
Published in: Information Sciences - Science Direct
Number of Pages: 14
موضوع: الگوریتم زنبور عسل (Bee Colony Algorithm)
لینک مقاله در سایت Science Direct
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به نام خدا
Title: Three-stage hybrid-flowshop model for cross-docking
Authors: Adrien Bellanger , Said Hanafl , Christophe Wilbaut
Abstract: This paper deals with the optimization of a cross-docking system. It is modeled as a three-stage hybrid flowshop, in which shipments and orders are represented as batches. The flrst stage corresponds to the receiving docks, the second stage corresponds to the sorting stations, and the third stage corresponds to the shipping docks. The objective of the problem is to flnd a schedule that minimizes the completion time of the latest batch. In order to obtain good quality feasible solutions, we have developed several heuristic schemes depending on the main stage considered, and several rules to order the batches in this stage. Then, we propose a branch-and-bound algorithm that takes into account the decomposition of the problem into three stages. To evaluate the heuristics and to reduce the tree size during the branch-and-bound computation, we also propose lower bounds. Finally, the computational experi- ments are presented to demonstrate the efflciency of our heuristics. The results show that the exact approach can solve instances containing up to 9 10 batches in each stage (i.e., up to 100 jobs). In addition, our heuristics were evaluated over instances with up to 3000 jobs, and they can provide good quality feasible solutions in a few seconds (i.e., less than 2 s per heuristic).
Publish Year: 2013
Published in: Computers & Operations Research - Science Direct
Number of Pages: 10
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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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