• The Transactions of the Korea Information Processing Society
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• v.6 no.10
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• pp.2659-2669
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• 1999

This paper a heuristic, clustering based task allocation scheme applicable to non-directed task graph on a distributed system. This scheme firstly builds a task-machine graph, and then applies a clustering process where in a pair of tasks that are connected to the highest cost edge is merged into a big one or a task is allocated to a machine. During the process, the proposed scheme figure out a machine onto which the task allocation may cause deduction of large communication overhead that has incurred between the task and tasks that are already allocated to the machine while the computation costs is slightly increased in the machine. Simulation for the various task graphs shows that the scheduling using the proposed scheme result far better than ones by using the traditional schemes. A comparison with optimal task scheduling also promises that our scheme derives optimal results more occasionally than the traditional schemes do.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.678-681
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• 2007

분산형 데스크탑 그리드 시스템에서 안정적인 연산 수행을 위한 노드 구성 기법과 동적인 환경에 적응적인 스케줄링 기법은 필수 요소이다. 그러나 기존 연구에서는 자원 제공자의 휘발성에 적응적으로 대처하지 못하는 연산 수행 모델을 사용하였기 때문에 데드라인 내에 전체 작업을 완료해야하는 응용에 대처하지 못하는 문제점이 발생한다. 이에 본 논문에서는 자원제공자의 성능과 논리적인 위치기반으로 자가 조직적 연산 오버레이 네트워크(Computation Overlay Network) 구성 기법과 자원제공자의 작업 완료 확률과 신용도 값을 이용하여 응용 QoS 보장을 위한 휴리스틱기반 복제 (Heuristic based Replication) 기법을 제안한다. 성능평가에서는 기존 스케줄링 기법과 자원제공자의 작업 완료 확률과 신용도에 따른 분포를 이용한 복제기반 스케줄링 기법을 비교평가 한다.

• The KIPS Transactions:PartB
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• v.8B no.2
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• pp.183-188
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• 2001

이 논문은 눈의 형태에 대한 정보를 이용하여 눈동자를 효과적으로 찾는 방법과 얼굴 특성을 반영하는 평가함수를 이용하여 눈동자, 입의 위치와 같은 얼굴 특징들을 인식하는 방법을 제안하였다. 색 정보를 이용하여 입술과 얼굴 영역을 추출하고 눈동자와 흰자위간의 명도 차를 이용하는 함수를 사용하여 눈동자를 인식하였다. 마지막으로 얼굴 특성을 반영하느 평가함수를 정의하고 이를 이용하여 최종적인 얼굴과 눈, 입을 인식하였다. 제안한 방법을 사용하여 여러 영상들에 대해 실험하여 좋은 결과를 얻었다.

• Journal of Korea Game Society
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• v.15 no.4
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• pp.179-188
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• 2015

The A* algorithm is a well-known pathfinding algorithm. However, if the information about obstacles is not exploited, the algorithm may collide with obstacles or lead into swamp areas unnecessarily. In this paper, we propose new heuristic functions using the information of obstacles to avoid them or swamp areas. It takes time to process the information of obstacles before starting pathfinding, but it may not cause any problems most of cases because it is not processed in real time. We showed that the proposed methods could reduce the search space effectively through experiments. Furthermore, we showed that heuristic functions using obstacle information could reduce the search space effectively without processing obstacle information at all.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1207-1210
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• 2011

본 논문에서는 PAR-트리라는 공간색인구조를 제안한다. PAR-트리는 object distribution heuristic(객체분할법)으로 absolute crowd index(절대 복잡도)를 사용하여 공간 데이터 객체를 여러 개의 데이터 공간에 균일하게 배치함으로써 질의처리 속도를 향상시킨다. PAR-트리는 MXR-트리에 비해 높은 공간활용도와 빠른 질의 반응시간을 보임으로써 다차원 데이터베이스를 위한 효율적인 색인구조로 사용이 될 것으로 기대된다.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.719-724
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• 2003

This paper proposed a heuristic to enhance the speed of kNN without loss of its accuracy. The proposed heuristic minimizes the computation of the similarity between two documents which is the dominant factor in kNN. To do this, the paper proposes a method to calculate the upper limit of the similarity and to sort the training documents. The proposed heuristic was implemented on the existing framework of the text categorization, so called, AI :: Categorizer and it was compared with the conventional kNN with the well-known data, Router-21578. The comparisons show that the proposed heuristic outperforms kNN about 30∼40% with respect to the execution time.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.55-58
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• 2006

This paper presents solving the Sudoku puzzle as a constraint satisfaction problem (CSP). After introducing the rules and characteristics of the puzzle, we formulate the puzzle as a CSP and develop various methods of solving the problem. Blind search, minimum remaining value (MRV) heuristic, and some advanced methods are investigated, and their algorithms are implemented in this undergraduate project. The performance comparisons of these methods are discussed in the paper.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.708-710
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• 2018

강화학습은 게임의 인공지능을 대체할 수 있는 수단이지만 불완전한 게임에서 학습하기 힘들다. 학습하기 복잡한 불완전안 카드게임에서 휴리스틱한 전략을 만들고 비슷한 상태끼리 묶으면서 학습의 복잡성을 낮추었다. 인공신경망 없이 Q-러닝만으로 게임을 5만판을 통해서 상태에 따른 전략을 학습하였다. 그 결과 동일한 전략만을 사용하는 대결보다 승률이 높게 나왔고, 다양한 상태에서 다른 전략을 선택하는 것을 관찰하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1114-1125
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• 2013

License Plate Detection (LPD) is a key component in automatic license plate recognition system. Despite the success of License Plate Recognition (LPR) methods in the past decades, the problem is quite a challenge due to the diversity of plate formats and multiform outdoor illumination conditions during image acquisition. This paper aims at automatical detection of car license plates via image processing techniques. In this paper, we proposed a real-time and robust method for license plate detection using Heuristic Energy Map(HEM). In the vehicle image, the region of license plate contains many components or edges. We obtain the edge energy values of an image by using the box filter and search for the license plate region with high energy values. Using this energy value information or Heuristic Energy Map(HEM), we can easily detect the license plate region from vehicle image with a very high possibilities. The proposed method consists two main steps: Region of Interest (ROI) Detection and License Plate Detection. This method has better performance in speed and accuracy than the most of existing methods used for license plate detection. The proposed method can detect a license plate within 130 milliseconds and its detection rate is 99.2% on a 3.10-GHz Intel Core i3-2100(with 4.00 GB of RAM) personal computer.

• The KIPS Transactions:PartB
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• v.18B no.2
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• pp.73-82
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• 2011

There are a lot of people trying to solve the Traveling Salesman Problem (TSP) by using the Meta Heuristic Algorithms. TSP is an NP-Hard problem, and is used in testing search algorithms and optimization algorithms. Also TSP is one of the models of social problems. Many methods are proposed like Hybrid methods and Custom-built methods in Meta Heuristic. In this paper, we propose the S-MINE Algorithm to use the MINE Algorithm introduced in 2009 on the TSP.