• Journal of Information Processing Systems
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• 제8권4호
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• pp.685-692
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• 2012

B frame bi-directional predictions and the DIRECT mode coding of the H.264 video compression standard necessitate a complex mode decision process, resulting in a long computation time. To make H.264 feasible, this paper proposes an image backtrack-based fast (IBFD) algorithm and evaluates the performances of two promising fast algorithms (i.e., AFDM and IBFD). Evaluation results show that an image backtrack-based fast (IBFD) algorithm can determine DIRECT mode macroblocks with 13% higher accuracy, as compared with the AFDM. Furthermore, IBFD is shown to reduce the motion estimation time of B frames by up to 23% with a negligible quality degradation.

• 한국태양에너지학회 논문집
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• 제34권4호
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• pp.23-29
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• 2014

Recently, in power system studies, Multiple Energy Carriers (MECs) such as Energy Hub has been broadly utilized in power system planners and operators. Particularly, Energy Hub performs one of the most important role as the intermediate in implementing the MECs. However, it still needs to be put under examination in both modeling and operating concerns. For instance, a probabilistic optimization model is treated by a robust global optimization technique such as multi-agent genetic algorithm (MAGA) which can support the online economic dispatch of MECs. MAGA also reduces the inevitable uncertainty caused by the integration of selected input energy carriers. However, MAGA only considers current state of the integration of selected input energy carriers in conjunctive with the condition of smart grid environments for decision making in Energy Hub. Thus, in this paper, we propose an immune algorithm based Multiple Energy Carriers System which can adopt the learning process in order to make a self decision making in Energy Hub. In particular, the proposed immune algorithm considers the previous state, the current state, and the future state of the selected input energy carriers in order to predict the next decision making of Energy Hub based on the probabilistic optimization model. The below figure shows the proposed immune algorithm based Multiple Energy Carriers System. Finally, we will compare the online economic dispatch of MECs of two algorithms such as MAGA and immune algorithm based MECs by using Real Time Digital Simulator (RTDS).

• 한국방재학회 논문집
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• 제11권3호
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• pp.49-56
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• 2011

본 연구는 위험물 수송의 최적경로를 결정하는 방법론을 제안하였다. 위험물 차량의 최적경로를 결정할 때에는 위험도 최소화를 목적으로 하는 공공의 입장과 통행시간 최소화를 목적으로 하는 민간기업의 입장이 서로 상충한다. 본 연구에서는 이러한 다기준 의사결정(Multi-criteria decision making)문제 중 하나인 위험물 수송용 최적경로를 탐색하는 방법론으로 Efficient Vector Labeling(이하 EVL) 알고리즘을 제시하였다. EVL 알고리즘은 위험도와 통행시간을 동시에 고려하여 복수의 Pareto optimal 경로(또는 비지배경로)를 탐색하게 한다. 본 연구는 또한 탐색된 비지배경로간의 중복도를 제어할 수 있도록 설계하였다. 개발된 Efficient Vector Labeling 알고리즘을 Test bed network에 적용하여 기존의 경로탐색 방법론과 비교하였다. 적용 결과 새로운 알고리즘이 기존의 알고리즘보다 합리적인 대안경로를 탐색할 수 있는 것으로 분석되었다.

• 한국통신학회논문지
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• 제18권12호
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• pp.1797-1805
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• 1993

이 논문에서는 DS/SS(Direct Sequence Spread/Spectrum) 시스템에서 초기동기(aquisition)를 위해, 정합 필터의 출력중 부정합된 최대 상관값을 임계값(threshold)으로 결정하는 TDMMC(Threshold Decision from the Maximum Mismatching Correlation value) 알고리듬을 제안한다. PN코드는 주기성과 정합됐을 때의 상관값이 정합되지 않을 때의 상관값보다 크다는 성질을 가지고 있다. 기존의 방식은 위의 2가지 성질 중에서 후자에 기반을 두고 있는 반면, TDMMC는 위의 성질 둘 모두에 기반을 두고 있다. TDMMC의 궁극적인 목적은 수신기에서 신호 검파확률를 1에 가깝게 하는데 있다(Pd$\longrightarrow$1). TDMMC는 임계값 블록(threshold block)을 이용하여 S/N이 변함에 따라 적절한 임계값을 설정할 수 있다. 시뮬레이션 결과, 비동기식 DS/SS-CSK 시스템에 적용된 TDMMC는 기존의 방식에 비해 성능이 향상됨을 보여준다.

• 전기학회논문지
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• 제57권6호
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• pp.1058-1062
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• 2008

In clinical decision support system(CDSS), unlike rule-based expert method, appropriate data-driven machine learning method can easily provide the information of individual feature(clinical test) for disease classification. However, currently developed methods focus on the improvement of the classification accuracy for diagnosis. With the analysis of feature importance in classification, one may infer the novel clinical test sets which highly differentiate the specific diseases or disease states. In this background, we introduce a novel CDSS that integrate a classifier and feature selection module together. Random forest algorithm is applied for the classifier and the feature importance measure. The system selects the significant clinical tests discriminating the diseases by examining the classification error during backward elimination of the features. The superior performance of random forest algorithm in clinical classification was assessed against artificial neural network and decision tree algorithm by using breast cancer, diabetes and heart disease data in UCI Machine Learning Repository. The test with the same data sets shows that the proposed system can successfully select the significant clinical test set for each disease.

• 대한전자공학회논문지SP
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• 제46권2호
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• pp.48-56
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• 2009

우리는 H.264/AVC의 비디오 부호화에서 조기 인트라 모드 생략을 결정하기 위한 알고리즘을 제안한다. 새롭게 추가된 다양한 예측 방법들에 의한 매크로블록 부호화 방법은 압축의 효율의 증가를 가져오지만, 모든 부호화 가능한 모드에 대해 율-왜곡 함수를 계산하여 가장 효율이 좋은 모드를 선택하기 때문에 상당한 계산량을 요구한다. 이 논문에서는 인터 프레임에 대한 부호화 시간을 감소시키기 위해서, 적응적인 움직임 벡터 맵(AMVM)을 이용한 모드 결정 방법을 H.264/AVC 비디오 부호화기에서 제안한다. 제안한 알고리즘은 PSNR과 Bit rate 그리고 부호화 처리시간에 대해서 일반적으로 좋은 성능을 가진다.

• Journal of Forest and Environmental Science
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• 제30권1호
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• pp.45-49
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• 2014

This study was tried to find out the applicability of decision support system for forest land use conversion, which developed based on algorithm for forest land-use conversion. Decision support system developed by Ministry of Safety Administration is free from the existing licensed laws omission. And it made the input requirements for each value of the final result so that you can determine whether the permit was available by the laws and regulations related to the algorithm for forest land use conversion. Also, in order to do field surveys, equal sampling interval method is used to extract samples for the operability by comparing and analyzing the actual area. As a result, 88 areas of total 100 areas are able to get permission by the decision support system for forest land use conversion, and it means if there is enough data with sufficient research, it can make the availability permits easily.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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• pp.2330-2332
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• 1998

This paper suggests a method to recognize the various defect patterns of cold mill strip using binary decision tree automatically constructed by genetic algorithm. The genetic algorithm and K-means algorithm were used to select a subset of the suitable features at each node in binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes by a linear decision boundary. This process was repeated at each node until all the patterns are classified into individual classes. The final recognizer is accomplished by neural network learning of a set of standard patterns at each node. Binary decision tree classifier was applied to the recognition of the defect patterns of cold mill strip and the experimental results were given to demonstrate the usefulness of the proposed scheme.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1999년도 추계학술대회 논문집
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• pp.63-69
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• 1999

The research trend of the simulation optimization has been focused on exploring continuous decision variables. Yet, the research in discrete decision variable area has not been fully studied. A new research trend for optimizing discrete decision variables ha just appeared recently. This study, therefore, deals with a discrete simulation method to get the system evaluation criteria required for designing a complex probabilistic discrete event system and to search the effective and reliable alternatives to satisfy the objective values of the given system through a on-line, single run with the short time period. Finding the alternative, we construct an algorithm which changes values of decision variables and a design alternative by using the stopping algorithm which ends the simulation in a steady state of system. To avoid the loss of data while analyzing the acquired design alternative in the steady state, we provide background for estimation of an auto-regressive model and mean and confidence interval for evaluating correctly the objective function obtained by small amount of output data through simulation with the short time period. In numerical experiment we applied the proposed algorithm to (s, S) inventory system problem with varying Δt value. In case of the (s, S) inventory system, we obtained good design alternative when Δt value is larger than 100.

• 한국통신학회논문지
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• 제33권12C호
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• pp.1039-1045
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• 2008

H.264/AVC 비디오 부호화 표준은 높은 부호화 효율을 보여주지만, 그에 따른 부호기의 복잡도가 증가하는 단점이 있다. 특히 비트율-왜곡 최적화 기법을 이용하여 인트라 모드를 결정하는 방법은 부호기의 복잡도를 증가시키는 가장 큰 원인이다. 따라서 부호화 효율을 유지하면서 부호기의 복잡도를 감소시키는 여러 가지 방법이 제안되고 있다. 본 논문에서는 MPEG-2에서 H.264로의 변환 과정에서 $4{\times}4$ 정수 변환 계수를 이용하여 효율적으로 인트라 예측 모드를 결정하는 방법을 제안한다. 제안하는 알고리듬을 적용한 모의실험 결과는 영상의 부호화 성능을 유지하면서 복잡도 및 부호화 수행 시간을 감소시킬 수 있었다.