• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.401-409
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• 2023

This study compared and analyzed the performance of a distributed area search algorithm and a distributed probability search algorithm based on information exchange between ships. The distributed algorithm is a method that can search for an optimal avoidance route based on information exchange between ships. In the distributed area search algorithm, only a ship with the maximum cost reduction among neighboring ships has priority, so the next expected location can be changed. The distributed stochastic search algorithm allows a non-optimal value to be searched with a certain probability so that a new value can be searched. A circular-based ship collision avoidance model was used for the ship-to-ship collision avoidance experiment. The experimental method simulated the distributed area search algorithm and the distributed stochastic search algorithm while increasing the number of ships from 2 to 50 that were the same distance from the center of the circle. The calculation time required for each algorithm, sailing distance, and number of message exchanges were compared and analyzed. As a result of the experiment, the DSSA(Distributed Stochastic Search Algorithm) recorded a 25%calculation time, 88% navigation distance, and 84% of number of message exchange rate compared to DLSA.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.164-170
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• 2004

This paper proposes a Distributed Autonomous Robotic System(AIS) based on Artificial Immune System(AIS) and Distributed Genetic Algorithm(DGA). The behaviors of robots in the system are divided into global behaviors and local behaviors. The global behaviors are actions to search tasks in environment. These actions are composed of two types: dispersion and aggregation. AIS decides one among above two actions, which robot should select and act on in the global. The local behaviors are actions to execute searched tasks. The robots learn the cooperative actions in these behaviors by the DGA in the local. The proposed system is more adaptive than the existing system at the viewpoint that the robots learn and adapt the changing of tasks.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.313-315
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• 2000

클러스터링에 있어서 K-means와 FCM(Fuzzy C-means)와 같은 기존의 알고리즘들은 지역적 최소 해에 수렴될 문제와 사전에 클러스터 개수를 결정해야 하는 문제점을 가지고 있다. 본 논문에서는 병렬 탐색을 통해 최적 해를 찾는 진화 알고리즘을 사용하여 지역적 최소 해에 수렴되는 문제점을 개선하였으며, 클러스터의 특성을 표준편차 벡터를 계산하여 중심으로부터 포함된 데이터가 얼마나 분포되어 있는지 알 수 있는 분산도와 임의의 데이터와 모든 중심들간의 거리의 비율로서 얻어지는 소속정도를 고려하여 클러스터간의 간격을 알 수 있는 분리도를 정의함으로써 자동으로 클러스터 개수를 결정할 수 있게 하였다. 실험데이터와 가우시안 분포에 의해 생성된 다차원 실험데이터를 사용하여 제안한 알고리즘이 이러한 문제점들을 해결하고 있음을 보인다.

• Journal of Advanced Navigation Technology
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• v.21 no.5
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• pp.443-449
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• 2017

Swarm of low-cost UAVs for search mission has benefit in the sense of rapid search compared to use of single high-end UAV. As the number of UAVs forming swarm increases, not only the time for the mission planning increases, but also the system to operate UAVs has excessive burden. This paper addresses a decentralized area search algorithm adequate for multiple UAVs which takes advantages of flexibility, robustness, and simplicity. To down the cost, it is assumed that each UAV has limited ability: close-communication, basic calculation, and limited memory. In close-communication, heath conditions and search information are shared. And collision avoidance and consensus of next search direction are then done. To increase weight on un-searched area and to provide overlapped search, the score function is introduced. Performance and operational characteristics of the proposed search algorithm and mission planning logic are verified via numerical simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.699-704
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• 2004

This paper proposes a Distributed Autonomous Robotic System(DARS) based on an Artificial Immune System(AIS) and a Classifier System(CS). The behaviors of robots in the system are divided into global behaviors and local behaviors. The global behaviors are actions to search tasks in environment. These actions are composed of two types: aggregation and dispersion. AIS decides one among these two actions, which robot should select and act on in the global. The local behaviors are actions to execute searched tasks. The robots learn the cooperative actions in these behaviors by the CS in the local. The proposed system is more adaptive than the existing system at the viewpoint that the robots learn and adapt the changing of tasks.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.5
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• pp.457-467
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• 2000

본 논문에서는 분산 메모리 구조를 갖는 병렬 컴퓨터 상에서 방대한 크기를 갖는 볼륨 데이터의 효과적인 가시화를 위한 병렬 광선 투사법을 제안한다. 데이터의 압축을 기반으로 하는 본 기법은 다른 프로세서의 메모리로부터 데이터를 읽기보다는 자신의 지역 메모리에 존재하는 압축된 데이터를 빠르게 복원함으로써 병렬 렌더링 성능을 향상시키는 것을 목표로 한다. 본 기법은 객체-순서와 영상-순서 탐색 알고리즘 모두의 정점을 이용하여 성능을 향상시켰다. 즉, 블록 단위의 최대-최소 팔진트리의 탐색과 각 픽셀의 불투명도 값을 동적으로 유지하는 실시간 사진트리를 응용함으로써 객체-공간과 영상-공간 각각의 응집성을 이용하였다. 본 논문에서 제안하는 압축 기반 병렬 볼륨 렌더링 방법은 렌더링 수행 중 발생하는 프로세서간의 통신을 최소화하도록 구현되었는데, 이러한 특징은 프로세서 사이의 상당히 높은 데이터 통신 비용을 감수하여야 하는 PC 및 워크스테이션의 클러스터와 같은 더욱 실용적인 분산 환경에서 매우 유용하다. 본 논문에서는 Cray T3E 병렬 컴퓨터 상에서 Visible Man 데이터를 이용하여 실험을 수행하였다.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.45-51
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• 2011

This paper proposes a method to accelerate the evolution speed of individuals through hybrid reproduction of monogenesis and gamogenesis. Monogenesis as a reproduction method that bacteria or monad without sexual distinction divide into two individuals has an advantage for local search and gamogenesis as a reproduction method that individuals with sexual distinction mate and breed the offsprings has an advantages for keeping the diversity of individuals. These properties can be properly used for improvement of evolution speed of individuals in genetic algorithms. In this paper, we made relatively good individuals among selected parents to do monogenesis for local search and forced relatively bad individuals among selected parents to do gamogenesis for global search by increasing the diversity of chromosomes. The mutation probability for monogenesis was set to a lower value than that of original genetic algorithm for local search and the mutation probability for gamogenesis was set to a higher value than that of original genetic algorithm for global search. Experimental results with four function optimization problems showed that the performances of three functions were very good, but the performances of fourth function with distributed global optima were not good. This was because distributed global optima prevented individuals from steady evolution.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2000-2007
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• 1999

Recently, DDS (Distributed Database System) has been often implemented on VAN (Value Added Network) as we know the amazing expansion of information network. DDS can yield significant cost and response time advantages over centrailzed systems for geographically distributed organizations. However, inappropriate design can result in high cost and poor response time. In a DDS design, the main problem is 1) how to select proper computer, and 2) how to allocate data fragment into proper nodes. This paper addresses DDS design problem of selecting the proper class of computers and the allocating data files on VAN. Also, the formulated model includes tow objectives, the operating and investment cost. GA (Genetic Algorithm) is developed to solve this mathematical formulation. A numerical experiment shows that the proposed method arrives at a good solution.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.2797-2806
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• 2000

Recently, DDSs(Distributed Database Systems) have been implemented on V AN(V alue Added Network) as we know the amazing expansion of information network. DDS can yield significant cost and response time advantages over centralized systems for geographically distributed organizations. However, inappropriate design can result in high cost and poor response time to maintain the database at each site. In a DDS design, the main problem is how to select proper computer and how to allocate data fragment into a proper site. In this paper, we address DDS design problem of selecting the proper class of computers and the allocating data files on VAN. Also, the formulated model includes two objectives, the waited response time and the investment cost to include their relationship. Specially, the formulation of waited response time is based on M/M/1 queueing system to evaluate more precisely. GA(Genetic Algorithm), a kind of heuristic search method, is developed to search an optimal solution in the proposed design model and we show the simulation result to examine the algorithm performance.

• The Journal of the Acoustical Society of Korea
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• v.34 no.4
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• pp.303-309
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• 2015

Speaker diarization is the task of determining the speakers for unlabeled data, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has been widely used in the field of speaker diarization for its simplicity and computational efficiency. One challenging issue, however, is that if different clusters in non-spatial dataset are adjacent to each other, over-clustering may occur which subsequently degrades the performance of DBSCAN. In this paper, we identify the drawbacks of DBSCAN and propose a new density clustering algorithm based on local distribution property around object. Variable density criterions for local density and spreadness of object are used for effective data clustering. We compare the proposed algorithm to DBSCAN in terms of clustering accuracy. Experimental results confirm that the proposed algorithm exhibits higher accuracy than DBSCAN without over-clustering and confirm that the new approach based on local density and object spreadness is efficient.