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Data correlation analysis for optimal sensor placement using a bond energy algorithm

MEASUREMENT 2016年 91卷 509-518页

作者： Lu, Wei Wen, Runfa Teng, Jun Li, Xiaoling Li, Chao Harbin Inst Technol Shenzhen Grad Sch Dept Civil & Environm Engn Xili Univ Town HIT Campus Shenzhen 518055 Peoples R China Fujian Univ Technol Coll Civil Engn 3 Xueyuan Rd Univ Town Fuzhou 350108 Peoples R China

Optimal sensor placement is one of the crucial and fundamental factors for constructing a cost-effective structural health monitoring system and is related to the effective evaluation of the state of the structure. Structural responses are correlated to some extent, as the structural behavior is continuous. Based on the above two considerations, the question arises of how to obtain the maximum amount of information for understanding the structure using measurements from limited sensors and not be limited to direct monitoring at the placements where the limited sensors are located. Data correlation analysis for optimal sensor placement is proposed using a bond energy algorithm, in which the objectives, such as structural response evaluation covering the maximum structural responses using measurements from sensors located at the optimal placements, are taken into account. The data correlation analysis is conducted for the structural responses, and the correlation matrix is established. Furthermore, the optimal sensor placements and the correlation of the responses at element locations can be determined using the bond energy algorithm. A Schwedler single-layer spherical lattice dome-like structure, which is a common large space steel structure, is used to simulate the structural responses and verify the effectiveness of the proposed method by discussion of different scenarios of parameter selection. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Structural health monitoring Optimal sensor placement Data correlation bond energy algorithm

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Differential bond energy algorithm for optimal vertical fragmentation of distributed databases

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第1期34卷 1466-1471页

作者： Mehta, Shikha Agarwal, Parul Shrivastava, Prakhar Barlawala, Jharna Jaypee Inst Indormat Technol Dept Comp Sci & Informat Technol Noida 201301 India

Distributed database systems are gaining importance due to the production in massive amount of data. The efficacy of such systems is highly dependent upon the design of the system. To increase the effectiveness and efficiency of distributed databases, two processes are mainly employed i.e. fragmentation and allocation. Fragmentations can be vertical or horizontal. This work focuses on vertical fragmentation design methods. In this paper, a novel differential bond energy (DBE) algorithm is proposed with objective to determine optimal partition point. The performance of proposed algorithm is compared with classical bond energy algorithm (BEA) on basis of global affinity measure (GAM) value. Results are depicted in form of line graphs. The mean difference in GAM values for both algorithms are also illustrated. The experimental results portrays that DBE is suitable for vertical fragmentation of high dimensional problems as it attain high GAM value as compared to BEA on various datasets. (c) 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Distributed database systems Vertical fragmentation bond energy algorithm Differential evolution algorithm

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Enhanced bond energy algorithm for Vertical Fragmentation of IoT Data in a Fog Environment 9th

Enhanced Bond Energy Algorithm for Vertical Fragmentation of...

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9th International Conference on Big Data Analytics (BDA)

作者： Kaur, Parmeet Jaypee Inst Informat Technol Noida India

ISBN: (纸本)9783030966003;9783030965990

The vast benefits of Internet of Things (IoT) stem from the big data collected by the IoT devices. Currently, majority of this data is transferred to cloud-based servers where it is processed and analyzed for further actions. However, the transfer of the IoT big data to the cloud utilizes high network bandwidth and may also result in unacceptable response time for time-critical applications. The advent of fog computing has provided a direction for addressing these constraints of bandwidth and latency. IoT data related to time-critical applications can be stored at the fog nodes for faster processing. However, this data is generally high dimensional apart from being large sized and thus needs to be efficiently stored for scalability of related applications. Fragmentation of data has been established as a suitable technique for scaling of big data storage. This work presents an enhanced version of the existing bond energy algorithm for vertical fragmentation of IoT data to be stored and processed on fog nodes. The proposed algorithm considers the criticality, access frequency and latency requirements of applications to determine how data may be fragmented across the fog nodes. The results of the algorithm show a marked improvement in performance as compared to the scenario where data is not fragmented. The findings are applicable to both relational and non-relational databases.

关键词： Internet of Things Big data Fog Vertical fragmentation bond energy algorithm

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A Modified Ontology Based Personalized Search Engine Using bond energy algorithm

A Modified Ontology Based Personalized Search Engine Using B...

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1st International Conference on Advances in Computing and Communications

作者： Boddu, Bhaskara Rao Vatsavayi, Valli Kumari GITAM Univ GIT Dept IT Visakhapatnam 45 Andhra Pradesh India Andhra Univ Coll Engn Dept CS & SE Waltair 530003 Andhra Pradesh India

ISBN: (纸本)9783642227134

Search engines play an important function in increasing the speed of access to web information. As the volume of the content on the web is dynamically increasing, the general purpose web search engines are becoming inadequate. Since users with different backgrounds give queries in different contexts expecting different responses. The large number of irrelevant results returned by a search engine usually disappoints the user. The personalization of search engine overcomes this problem by ranking the results of web documents based on the inherent relations and closeness between the query concept and web document. In this paper we personalize the search engine using the Fuzzy Concept Network (FCN) and the bond energy algorithm (BEA). The BEA calculates the closeness called affinity. Our main idea is to employ the concept network built based on the user's profile and ranks based on the bond energy algorithm for searching and quickly retrieving of web pages. The advantage is that the most relevant search results can be retrieved. We examined our approach with data sets and prove our claims.

关键词： Personalized Search Engine Fuzzy Concept Network Profile Ranks Affinity bond energy algorithm Ontology

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Arabic Documents Clustering using bond energy algorithm and Genetic algorithm 13

Arabic Documents Clustering using Bond Energy Algorithm and ...

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13th International Conference on Information and Communication Systems (ICICS)

作者： Alazzam, Hadeel AbuAlghanam, Orieb Alsmady, Abdulsalam Alhenawi, Esra'a Al Balqa Appl Univ Dept Intelligence Syst Al Salt Jordan Alahliyya Amman Univ Networks & Informat Secur Amman Jordan Jordan Univ Sci & Technol Dept Comp Engn Irbid Jordan Univ Jordan Dept Comp Sci Amman Jordan

ISBN: (纸本)9781665480970

Text clustering is a popular data mining process used in data indexing and information retrieval. However, Existing clustering techniques suffer from several shortcomings such as sensitivity to the initial value, slow convergence, etc... In this paper, the bond energy algorithm (BEA) has been used for clustering Arabic documents. The bond energy algorithm has been modified with the help of a Genetic algorithm to enhance the accuracy of the results. The efficiency of modified BEA has been compared to BEA baseline and K-means clustering in terms of precision, recall, and F-Score. The results show that Modified BEA can be used for clustering Arabic Documents, and the efficiency of the algorithm can be enhanced in the future to give a better result.

关键词： bond energy algorithm K-means Arabic Documents Clustering

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Hierarchical simultaneous vertical fragmentation and allocation using modified bond energy algorithm in distributed databases

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Applied Computing and Informatics 2018年第2期14卷 127-133页

作者： Rahimi, Hossein Parand, Fereshteh-Azadi Riahi, Davoud Math and Computer Science Department Allameh Tabataba'i University Ahmad Ghasir St. Beheshti Av. Tehran Iran

Designing an efficient Distributed Database System (DDBS) is considered as one of the most challenging problems because of multiple interdependent factors which are affecting its performance. Allocation and fragmentation are two processes which their efficiency and correctness influence the performance of DDBS. Therefore, efficient data fragmentation and allocation of fragments across the network sites are considered as an important research area in distributed database design. This paper presents an approach which simultaneously fragments data vertically and allocates the fragments to appropriate sites across the network. bond energy algorithm (BEA) is applied with a better affinity measure that improves the generated clusters of attributes. The algorithm simultaneously generates clusters of attributes, calculates the cost of allocating each cluster to each site and allocates each cluster to the most appropriate site. Results show more efficient clustering and allocation which gives better performance. © 2015 The Authors

关键词： bond energy algorithm Clustering Data allocation and fragmentation Distributed database system

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A modified bond energy algorithm with fuzzy merging and its application to Arabic text document clustering

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 159卷 113598-113598页

作者： AlMahmoud, Rana Husni Hammo, Bassam Faris, Hossam Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan

Conventional textual documents clustering algorithms suffer from several shortcomings, such as the slow convergence of the immense high-dimensional data, the sensitivity to the initial value, and the understandability of the description of the resulted clusters. Although many clustering algorithms have been developed for English and other languages, very few have tackled the problem of clustering the under-resourced Arabic language. In this work, we propose a modified version of the bond energy algorithm (BEA) combined with a fuzzy merging technique to solve the problem of Arabic text document clustering. The proposed algorithm, Clustering Arabic Documents based on bond energy, hereafter named CADBE, attempts to identify and display natural variable clusters within huge sized data. CADBE has three steps to cluster Arabic documents: the first step instantiates a cluster affinity matrix using the BEA, the second step uses a new and novel method to partition the cluster matrix automatically into small coherent clusters, and the last step uses a fuzzy merging technique to merge similar clusters based on the associations and interrelations between the resulted clusters. Experimental results showed that the proposed algorithm effectively outperformed the conventional clustering algorithms such as Expectation-Maximizati on (EM), Single Linkage, and UPGMA in terms of clustering purity and entropy. It also outperformed kmeans, k-means++, spherical k-means, and CoclusMod in most test cases. However, there are several merits of CADBE. First, unlike the traditional clustering algorithms, it does not require to specify the number of clusters. In addition, it produces clusters with distinct boundaries, which makes its results more objective, and finally it is deterministic, such that it is insensitive to the order in which documents are presented to the algorithm. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： bond energy algorithm Arabic text document clustering Fuzzy Merging

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Rearrangement clustering: Pitfalls, remedies, and applications

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JOURNAL OF MACHINE LEARNING RESEARCH 2006年第6期7卷 919-943页

作者： Climer, Sharlee Zhang, Weixiong Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA Washington Univ Dept Genet St Louis MO 63130 USA

Given a matrix of values in which the rows correspond to objects and the columns correspond to features of the objects, rearrangement clustering is the problem of rearranging the rows of the matrix such that the sum of the similarities between adjacent rows is maximized. Referred to by various names and reinvented several times, this clustering technique has been extensively used in many fields over the last three decades. In this paper, we point out two critical pitfalls that have been previously overlooked. The first pitfall is deleterious when rearrangement clustering is applied to objects that form natural clusters. The second concerns a similarity metric that is commonly used. We present an algorithm that overcomes these pitfalls. This algorithm is based on a variation of the Traveling Salesman Problem. It offers an extra benefit as it automatically determines cluster boundaries. Using this algorithm, we optimally solve four benchmark problems and a 2,467-gene expression data clustering problem. As expected, our new algorithm identifies better clusters than those found by previous approaches in all five cases. Overall, our results demonstrate the benefits of rectifying the pitfalls and exemplify the usefulness of this clustering technique. Our code is available at our websites.

关键词： clustering visualization of patterns in data bond energy algorithm traveling salesman problem asymmetric clustering

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Text mining biomedical literature for discovering gene-to-gene relationships: A comparative study of algorithms

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2005年第1期2卷 62-76页

作者： Liu, Y Navathe, SB Civera, J Dasigi, V Ram, A Ciliax, BJ Dingledine, R Georgia Inst Technol Coll Comp Atlanta GA 30322 USA So Polytech State Univ Sch Comp & Software Engn Dept Comp Sci Marietta GA 30060 USA Emory Univ Sch Med Dept Neurol Atlanta GA 30322 USA Emory Univ Sch Med Dept Pharmacol Atlanta GA 30322 USA

Partitioning closely related genes into clusters has become an important element of practically all statistical analyses of microarray data. A number of computer algorithms have been developed for this task. Although these algorithms have demonstrated their usefulness for gene clustering, some basic problems remain. This paper describes our work on extracting functional keywords from MEDLINE for a set of genes that are isolated for further study from microarray experiments based on their differential expression patterns. The sharing of functional keywords among genes is used as a basis for clustering in a new approach called BEA-PARTITION in this paper. Functional keywords associated with genes were extracted from MEDLINE abstracts. We modified the bond energy algorithm (BEA), which is widely accepted in psychology and database design but is virtually unknown in bioinformatics, to cluster genes by functional keyword associations. The results showed that BEA-PARTITION and hierarchical clustering algorithm outperformed k-means clustering and self-organizing map by correctly assigning 25 of 26 genes in a test set of four known gene groups. To evaluate the effectiveness of BEA-PARTITION for clustering genes identified by microarray profiles, 44 yeast genes that are differentially expressed during the cell cycle and have been widely studied in the literature were used as a second test set. Using established measures of cluster quality, the results produced by BEA-PARTITION had higher purity, lower entropy, and higher mutual information than those produced by k-means and self-organizing map. Whereas BEA-PARTITION and the hierarchical clustering produced similar quality of clusters, BEA-PARTITION provides clear cluster boundaries compared to the hierarchical clustering. BEA-PARTITION is simple to implement and provides a powerful approach to clustering genes or to any clustering problem where starting matrices are available from experimental observations.

关键词： bond energy algorithm microarray MEDLINE text analysis cluster analysis gene function

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A modeling approach to logistics in concurrent engineering

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 1999年第1期115卷 59-76页

作者： Dowlatshahi, S Univ Texas Coll Business Adm Dept Informat & Decis Sci El Paso TX 79968 USA

This paper advocates logistics involvement in the early phases of product design and development in a concurrent engineering environment. A concurrent engineering environment and the benefits of such involvement are explained in detail. The paper focuses on facilitating an interface and a collaboration between the designer and the logistician. A conceptual interface for design for logistics is presented. Four areas of interface are considered: (a) logistics engineering, (b) manufacturing logistics, (c) design for packaging, and (d) design for transportability. A set of detailed design factors pertaining to each area are presented. A modeling approach, bond energy algorithm, is used to accomplish the design for logistics concerns developed throughout the paper. An example is provided to test and validate the algorithm. The results are analyzed and appropriate perspectives for managerial implication of the methodology are provided. Finally, some conclusions and assessments are presented. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： design for logistics concurrent engineering transportability manufacturing product design bond energy algorithm

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