• The Journal of the Korea institute of electronic communication sciences
• /
• v.10 no.11
• /
• pp.1239-1244
• /
• 2015

In this paper, a new method for finding optimal and short span in Convolutional Self-Doubly Orthogonal(CDO) codes are proposed. This new algorithm based on Parallel Implicitly-Exhaustive search, where we applied dynamic search space reduction methods in order to reduce computational time for finding Optimal Span for R=1/2 rate CDO codes. The simulation results shows that speedup and error correction performance of the new algorithm is better.

• Journal of Korea Multimedia Society
• /
• v.7 no.1
• /
• pp.73-79
• /
• 2004

In this paper, we propose an efficient soft decision decoding algorithm for linear block codes. A conventional soft decision decoder have to invoke a hard decision decoder several times to estimate its soft decision values. However, in this method, we may not have candidate codewords, thus it is very difficult to produce soft decision values. We solve this problem by introducing an efficient algorithm to search candidate codewords. By using this, we can highly reduce the cases we cannot find candidate codewords. We estimate the performance of the proposed algorithm by using the computer simulations. The simulation is performed for binary (63, 36) BCH code in fading channel.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.1
• /
• pp.7-13
• /
• 2008

This paper suggests a Path-planning algorithm for mobile agents. While there are a lot of studies on the path-planning for mobile agents, mathematical modeling of complex environment which constrained by spatio-temporally is very difficult and it is impossible to obtain the optimal solutions. In this paper, an optimal path-planning algorithm based on the graphic technique is presented. The working environment is divided into two areas, the one is free movable area and the other is not permissible area in which there exist obstacles and spatio-temporally constrained, and an optimal solution is obtained by using a new algorithm which is based on the well known ACO algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2016.06a
• /
• pp.215-219
• /
• 2016

본 논문에서는 인간의 시각적 특성을 반영한 Just Noticeable Difference (JND) 모델을 사용한 움직임 예측을 통한 프레임율 향상 기법을 제안한다. 기존의 다중 프레임 기반 움직임 예측을 통한 프레임율 향상 기법은 움직임 벡터의 정확성을 높이기 위해 다중 프레임을 사용하며, 전체 영역에 대해 같은 블록 크기와 탐색 영역으로 움직임 예측을 수행함으로써 불필요한 계산량이 많아지고 움직임 벡터의 부정확한 예측이 수행된다는 단점이 있다. 제안하는 알고리듬은 인간의 시각적 특성을 고려한 Free Energy-based Just Noticeable Difference (FEJND) 모델을 사용하여 이전 프레임과 현재 프레임만을 사용하여 영역 특성에 따른 적응적 움직임 예측을 수행하여 정확성을 높인다. 실험 결과에 따라 제안하는 알고리듬의 성능이 향상되었음을 알 수 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2014.06a
• /
• pp.264-267
• /
• 2014

본 논문에서는 High Efficiency Video Coding (HEVC)의 확장 버전에 적용된 바 있는 화면 내 블록 복사 (Intra Block Copy; IntraBC) 기술에 대해 개선 알고리듬을 제안하고 있다. IntraBC 는 화면 내 부호화 진행 시, 예측 유닛 (prediction unit; PU)단위로 현재 부호화하고 있는 코딩 트리 유닛 (coding tree unit; CTU)의 왼쪽에 위치하는 CTU 를 탐색하는 기법이다. 이때, 왼쪽 CTU 에서 가장 비슷한 블록을 찾아 그 움직임 벡터 (motion vector)를 저장 및 전송하게 된다. 제안하는 알고리듬은 움직임 벡터를 찾기 전, 왼쪽의 복원된 CTU 에 대해 특정 필터를 적용하여 블록킹 열화 (blocking artifact)를 완화시키고, 더욱 정확하고 섬세한 움직임 벡터를 찾도록 하였고, 이는 자연영상보다 스크린 콘텐츠에 더욱 적합함을 확인할 수 있었다.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.26 no.6
• /
• pp.1008-1015
• /
• 2002

This paper proposes a combinatorial method to compute the global and local solutions of optimization problem. The present hybrid algorithm is the synthesis of a genetic algorithm and a local concentrate search algorithm (simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm, but also gives a more accurate solution. In addition, this algorithm can find both the global and local optimum solutions. An optimization result is presented to demonstrate that the proposed approach successfully focuses on the advantages of global and local searches. Three numerical examples are also presented in this paper to compare with conventional methods.

• Proceedings of the KSME Conference
• /
• 2001.11a
• /
• pp.698-702
• /
• 2001

This paper presents a combinatorial method to compute the solutions of optimization problem. The present hybrid algorithm is the synthesis of an artificial life algorithm and the random tabu search method. The hybrid algorithm is not only faster than the conventional artificial life algorithm, but also gives a more accurate solution. In addition, this algorithm can find all global optimum solutions. And the enhanced artificial life algorithm is applied to optimum design of high-speed, short journal bearings and the usefuless is verified through this example.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.5 s.176
• /
• pp.1193-1202
• /
• 2000

The paper describes the use of genetic algorithms (GA's) to the minimum weight design of stiffened composite panels for buckling constraints. The proposed design problem is characterized by mixture of continuous and discrete design variables corresponding to panel elements and stacking sequence of laminates, respectively. Design space is multimodal and non-convex, thereby introducing the need for global search strategies. Advanced strategies in GA's such as directed crossover, multistage search and separated crossover are adopted to improve search ability and to save computational resource requirements. The paper explores the effectiveness of genetic algorithms and their advanced strategies in designing stiffened composite panels under various uniaxial compressive load conditions and the linrlit on stacking sequence of laminates.

• Proceedings of the KSME Conference
• /
• 2000.04a
• /
• pp.797-802
• /
• 2000

Flow shop scheduling problem involves processing several jobs on common facilities where a setup time Is incurred whenever there is a switch of jobs. Practical aspect of scheduling focuses on finding a near-optimum solution within a feasible time rather than striving for a global optimum. In this paper, a hybrid meta-heuristic method called tabu-genetic algorithm(TGA) is suggested, which combines the genetic algorithm(GA) with tabu list. The experiment shows that the proposed TGA can reach the optimum solution with higher probability than GA or SA(Simulated Annealing) in less time than TS(Tabu Search). It also shows that consideration of setup time becomes more important as the ratio of setup time to processing time increases.

• The KIPS Transactions:PartC
• /
• v.10C no.1
• /
• pp.27-32
• /
• 2003

An efficient soft-decision decoding algorithm for binary block codes it proposed. The proposed soft-decision decoding algorithm is implemented by a series of hard-decision decoding process. By the hard-decision decoding result, the candidate codewords are efficiently searched for A new decoding method, which prevents the missing of the candidate codeword, is proposed. Also, the method fir reducing complexity is developed. This method removes the practical complexity increase caused by the improved algorithm. There facts are confirmed by the simulation results for binary (63, 36) BCH code.