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PC Cluster Based Parallel Genetic Algorithm-Tabu Search for Service Restoration of Distribution Systems (PC 클러스터 기반 병렬 유전 알고리즘-타부 탐색을 이용한 배전계통 고장 복구)

  • Mun Kyeong-Jun;Lee Hwa-Seok;Park June Ho
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.54 no.8
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    • pp.375-387
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    • 2005
  • This paper presents an application of parallel Genetic Algorithm-Tabu Search (GA-TS) algorithm to search an optimal solution of a service restoration in distribution systems. The main objective of service restoration of distribution systems is, when a fault or overload occurs, to restore as much load as possible by transferring the do-energized load in the out of service area via network reconfiguration to the appropriate adjacent feeders at minimum operational cost without violating operating constraints, which is a combinatorial optimization problem. This problem has many constraints with many local minima to solve the optimal switch position. This paper develops parallel GA-TS algorithm for service restoration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solutions of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper $10\%$ of the population to enhance the local searching capabilities. With migration operation, best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC cluster system consists of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through ethernet switch based fast ethernet. To show the validity of the proposed method, proposed algorithm has been tested with a practical distribution system in Korea. From the simulation results, we can find that the proposed algorithm is efficient for the distribution system service restoration in terms of the solution quality, speedup, efficiency and computation time.

Parallel Genetic Algorithm-Tabu Search Using PC Cluster System for Optimal Reconfiguration of Distribution Systems (배전계통 최적 재구성 문제에 PC 클러스터 시스템을 이용한 병렬 유전 알고리즘-타부 탐색법 구현)

  • Mun Kyeong-Jun;Song Myoung-Kee;Kim Hyung-Su;Kim Chul-Hong;Park June Ho;Lee Hwa-Seok
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.53 no.10
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    • pp.556-564
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    • 2004
  • This paper presents an application of parallel Genetic Algorithm-Tabu Search(GA-TS) algorithm to search an optimal solution of a reconfiguration in distribution system. The aim of the reconfiguration of distribution systems is to determine switch position to be opened for loss minimization in the radial distribution systems, which is a discrete optimization problem. This problem has many constraints and very difficult to solve the optimal switch position because it has many local minima. This paper develops parallel GA-TS algorithm for reconfiguration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solution of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper 10% of the population to enhance the local searching capabilities. With migration operation, best string of each node is transferred to the neighboring node aster predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium Ⅳ CPU and is connected with others through ethernet switch based fast ethernet. To show the usefulness of the proposed method, developed algorithm has been tested and compared on a distribution systems in the reference paper. From the simulation results, we can find that the proposed algorithm is efficient and robust for the reconfiguration of distribution system in terms of the solution qualify. speedup. efficiency and computation time.

Parallel Genetic Algorithm-Tabu Search Using PC Cluster System for Optimal Reconfiguration of Distribution Systems

  • Mun Kyeong-Jun;Lee Hwa-Seok;Park June-Ho
    • KIEE International Transactions on Power Engineering
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    • v.5A no.2
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    • pp.116-124
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    • 2005
  • This paper presents an application of the parallel Genetic Algorithm-Tabu Search (GA- TS) algorithm, and that is to search for an optimal solution of a reconfiguration in distribution systems. The aim of the reconfiguration of distribution systems is to determine the appropriate switch position to be opened for loss minimization in radial distribution systems, which is a discrete optimization problem. This problem has many constraints and it is very difficult to solve the optimal switch position because of its numerous local minima. This paper develops a parallel GA- TS algorithm for the reconfiguration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solution of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper 10$\%$ of the population to enhance the local searching capabilities. With migration operation, the best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through switch based rapid Ethernet. To demonstrate the usefulness of the proposed method, the developed algorithm was tested and is compared to a distribution system in the reference paper From the simulation results, we can find that the proposed algorithm is efficient and robust for the reconfiguration of distribution system in terms of the solution quality, speedup, efficiency, and computation time.

Binary Image Search using Hierarchical Bintree (계층적 이분트리를 활용한 이진 이미지 탐색 기법)

  • Kim, Sung Wan
    • Journal of Creative Information Culture
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    • v.6 no.1
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    • pp.41-48
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    • 2020
  • In order to represent and process spatial data, hierarchical data structures such as a quadtree or a bintree are used. Various approaches for linearly representing the bintree have been proposed. S-Tree has the advantage of compressing the storage space by expressing binary region image data as a linear binary bit stream, but the higher the resolution of the image, the longer the length of the binary bit stream, the longer the storage space and the lower the search performance. In this paper, we construct a hierarchical structure of multiple separated bintrees with a full binary tree structure and express each bintree as two linear binary bit streams to reduce the range required for image search. It improves the overall search performance by performing a simple number conversion instead of searching directly the binary bit string path. Through the performance evaluation by the worst-case space-time complexity analysis, it was analyzed that the proposed method has better search performance and space efficiency than the previous one.

Development of Workbench for Analysis and Visualization of Whole Genome Sequence (전유전체(Whole gerlome) 서열 분석과 가시화를 위한 워크벤치 개발)

  • Choe, Jeong-Hyeon;Jin, Hui-Jeong;Kim, Cheol-Min;Jang, Cheol-Hun;Jo, Hwan-Gyu
    • The KIPS Transactions:PartA
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    • v.9A no.3
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    • pp.387-398
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    • 2002
  • As whole genome sequences of many organisms have been revealed by small-scale genome projects, the intensive research on individual genes and their functions has been performed. However on-memory algorithms are inefficient to analysis of whole genome sequences, since the size of individual whole genome is from several million base pairs to hundreds billion base pairs. In order to effectively manipulate the huge sequence data, it is necessary to use the indexed data structure for external memory. In this paper, we introduce a workbench system for analysis and visualization of whole genome sequence using string B-tree that is suitable for analysis of huge data. This system consists of two parts : analysis query part and visualization part. Query system supports various transactions such as sequence search, k-occurrence, and k-mer analysis. Visualization system helps biological scientist to easily understand whole structure and specificity by many kinds of visualization such as whole genome sequence, annotation, CGR (Chaos Game Representation), k-mer, and RWP (Random Walk Plot). One can find the relations among organisms, predict the genes in a genome, and research on the function of junk DNA using our workbench.

A New merging Algorithm for Constructing suffix Trees for Integer Alphabets (정수 문자집합상의 접미사트리 구축을 위한 새로운 합병 알고리즘)

  • Kim, Dong-Kyu;Sim, Jeong-Seop;Park, Kun-Soo
    • Journal of KIISE:Computer Systems and Theory
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    • v.29 no.2
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    • pp.87-93
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    • 2002
  • A new approach of constructing a suffix tree $T_s$for the given string S is to construct recursively a suffix tree $ T_0$ for odd positions construct a suffix tree $T_e$ for even positions from $ T_o$ and then merge $ T_o$ and $T_e$ into $T_s$ To construct suffix trees for integer alphabets in linear time had been a major open problem on index data structures. Farach used this approach and gave the first linear-time algorithm for integer alphabets The hardest part of Farachs algorithm is the merging step. In this paper we present a new and simpler merging algorithm based on a coupled BFS (breadth-first search) Our merging algorithm is more intuitive than Farachs coupled DFS (depth-first search ) merging and thus it can be easily extended to other applications.

Design of Digital Circuit Structure Based on Evolutionary Algorithm Method

  • Chong, K.H.;Aris, I.B.;Bashi, S.M.;Koh, S.P.
    • Journal of Electrical Engineering and Technology
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    • v.3 no.1
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    • pp.43-51
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    • 2008
  • Evolutionary Algorithms (EAs) cover all the applications involving the use of Evolutionary Computation in electronic system design. It is largely applied to complex optimization problems. EAs introduce a new idea for automatic design of electronic systems; instead of imagine model, ions, and conventional techniques, it uses search algorithm to design a circuit. In this paper, a method for automatic optimization of the digital circuit design method has been introduced. This method is based on randomized search techniques mimicking natural genetic evolution. The proposed method is an iterative procedure that consists of a constant-size population of individuals, each one encoding a possible solution in a given problem space. The structure of the circuit is encoded into a one-dimensional genotype as represented by a finite string of bits. A number of bit strings is used to represent the wires connection between the level and 7 types of possible logic gates; XOR, XNOR, NAND, NOR, AND, OR, NOT 1, and NOT 2. The structure of gates are arranged in an $m{\times}n$ matrix form in which m is the number of input variables.

Improved First-Phoneme Searches Using an Extended Burrows-Wheeler Transform (확장된 버로우즈-휠러 변환을 이용한 개선된 한글 초성 탐색)

  • Kim, Sung-Hwan;Cho, Hwan-Gue
    • KIISE Transactions on Computing Practices
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    • v.20 no.12
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    • pp.682-687
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    • 2014
  • First phoneme queries are important functionalities that provide an improvement in the usability of interfaces that produce errors frequently due to their restricted input environment, such as in navigators and mobile devices. In this paper, we propose a time-space efficient data structure for Korean first phoneme queries that disassembles Korean strings in a phoneme-wise manner, rearranges them into circular strings, and finally, indexes them using the extended Burrows-Wheeler Transform. We also demonstrate that our proposed method can process more types of query using less space than previous methods. We also show it can improve the search time when the query length is shorter and the proportion of first phonemes is higher.

Functional annotation of uncharacterized proteins from Fusobacterium nucleatum: identification of virulence factors

  • Kanchan Rauthan;Saranya Joshi;Lokesh Kumar;Divya Goel;Sudhir Kumar
    • Genomics & Informatics
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    • v.21 no.2
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    • pp.21.1-21.14
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    • 2023
  • Fusobacterium nucleatum is a gram-negative bacteria associated with diverse infections like appendicitis and colorectal cancer. It mainly attacks the epithelial cells in the oral cavity and throat of the infected individual. It has a single circular genome of 2.7 Mb. Many proteins in F. nucleatum genome are listed as "Uncharacterized." Annotation of these proteins is crucial for obtaining new facts about the pathogen and deciphering the gene regulation, functions, and pathways along with discovery of novel target proteins. In the light of new genomic information, an armoury of bioinformatic tools were used for predicting the physicochemical parameters, domain and motif search, pattern search, and localization of the uncharacterized proteins. The programs such as receiver operating characteristics determine the efficacy of the databases that have been employed for prediction of different parameters at 83.6%. Functions were successfully assigned to 46 uncharacterized proteins which included enzymes, transporter proteins, membrane proteins, binding proteins, etc. Apart from the function prediction, the proteins were also subjected to string analysis to reveal the interacting partners. The annotated proteins were also put through homology-based structure prediction and modeling using Swiss PDB and Phyre2 servers. Two probable virulent factors were also identified which could be investigated further for potential drug-related studies. The assigning of functions to uncharacterized proteins has shown that some of these proteins are important for cell survival inside the host and can act as effective drug targets.

A Study on the Convergence of Optimal Value using Selection Method in Genetic Algorithms (유전자 알고리즘에서 선택 기법을 이용한 해의 수렴 과정에 관한 연구)

  • 김용범;김병재;박명규
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.20 no.42
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    • pp.171-179
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    • 1997
  • Genetic Algorithms face an inherent conflict between exploitation and exploration. Exploitation refers to taking advantage of information already obtained in the search. Exploration show that a pattern in bits coupled with another pattern elsewhere in the string is more effective. In this paper shows that the selection method has a major impact on the balance between exploitation and exploration. A more heavy-handed approach seeks to exploit the available information. If decisions must be made quickly, especially those in real-time trading environments, then quicker convergence through exploitation may be more desirable. Also this paper we present some theoretical and empirical the selection method in genetic algorithms for a GA-hard problem.

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