• 정보통신설비학회논문지
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• 제10권2호
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• pp.53-61
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• 2011

This research presents an adaptive fast motion estimation (ME) computation on the stage of uneven multi-hexagon grid search (UMHGS) algorithm included in an unsymmetrical-cross multi-hexagon-grid search (UMHexagonS) in H.264 standard. The proposed adaptive method is based on statistical analysis and previously obtained motion vectors to reduce the computational complexity of ME. For this purpose, the algorithm is decomposed into three processes: skipping, terminating, and reducing search areas. Skipping and terminating are determined by the statistical analysis of the collected minimum SAD (sum of absolute difference) and the search area is constrained by the slope of previously obtained motion vectors. Simulation results show that 13%-23% of ME time can be reduced compared with UMHexagonS, while still maintaining a reasonable PSNR (peak signal-to-noise ratio) and average bitrates.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제10권1호
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• pp.57-65
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• 2004

서로 다른 형태와 크기를 가지는 탐색패턴과 움직임 벡터의 분포는 블록 정합 알고리즘에서 탐색 속도와 화질을 좌우하는 중요한 요소이다. 본 논문에서는 단위 다이아몬드패턴과 납작한 육각패턴을 이용한 새로운 고속 블록 정합 알고리즘을 제안한다. 이 알고리즘은 단위 다이아몬드패턴을 이용하여 적은 탐색점으로 움직임이 적은 벡터를 우선 찾은 다음에 움직임이 큰 벡터에 대해서는 납작한 육각패턴을 이용하여 고속으로 움직임 벡터를 찾게 하였다. 실험결과, 제안된 알고리즘은 육각패턴 탐색 알고리즘에 비하여 움직임 벡터 예측의 속도에 있어서 약 11∼51%의 높은 성능 향상을 보였으며 화질 또한 PSNR 기준으로 약 0.05∼0.74㏈의 향상을 보였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권3호
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• pp.264-269
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• 2006

This paper presents the strategy for searching a hidden object in an unknown area for using by multiple distributed autonomous robotic systems (DARS). To search the target in Markovian space, DARS should recognize th ε ir surrounding at where they are located and generate some rules to act upon by themselves. First of all, DARS obtain 6-distances from itself to environment by infrared sensor which are hexagonally allocated around itself. Second, it calculates 6-areas with those distances then take an action, i.e., turn and move toward where the widest space will be guaranteed. After the action is taken, the value of Q will be updated by relative formula at the state. We set up an experimental environment with five small mobile robots, obstacles, and a target object, and tried to research for a target object while navigating in a un known hallway where some obstacles were placed. In the end of this paper, we present the results of three algorithms - a random search, an area-based action making process to determine the next action of the robot and hexagon-based Q-learning to enhance the area-based action making process.

• ETRI Journal
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• 제34권3호
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• pp.369-378
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• 2012

In this paper, we propose new adaptive search range motion estimation methods where the search ranges are constrained by the probabilities of motion vector differences and a search point sampling technique is applied to the constrained search ranges. Our new methods are based on our previous work, in which the search ranges were analytically determined by the probabilities. Since the proposed adaptive search range motion estimation methods effectively restrict the search ranges instead of search point sampling patterns, they provide a very flexible and hardware-friendly approach in motion estimation. The proposed methods were evaluated and tested with JM16.2 of the H.264/AVC video coding standard. Experiment results exhibit that with negligible degradation in PSNR, the proposed methods considerably reduce the computational complexity in comparison with the conventional methods. In particular, the combined method provides performance similar to that of the hybrid unsymmetrical-cross multi-hexagon-grid search method and outstanding merits in hardware implementation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.220-224
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• 2008

This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. Then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning, and dodecagon-based Q-learning and parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.

• 대한임베디드공학회논문지
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• 제3권2호
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• pp.119-125
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• 2008

This paper presents the polygon-based Q-leaning and Cascade Support Vector Machine algorithm for object search with multiple robots. We organized an experimental environment with ten mobile robots, twenty five obstacles, and an object, and then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning and dodecagon-based Q-learning and Cascade SVM to enhance the fusion model with DBAM and ABAM process.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1044-1047
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• 2005

In this paper, we present the strategy of object search for distributed autonomous robotic systems (DARS). The DARS are the systems that consist of multiple autonomous robotic agents to whom required functions are distributed. For instance, the agents should recognize their surrounding at where they are located and generate some rules to act upon by themselves. In this paper, we introduce the strategy for multiple DARS robots to search a hidden object at the unknown area. First, we present an area-based action making process to determine the direction change of the robots during their maneuvers. Second, we also present Q learning adaptation to enhance the area-based action making process. Third, we introduce the coordinate system to represent a robot's current location. In the end of this paper, we show experimental results using hexagon-based Q learning to find the hidden object.

• 한국지능시스템학회논문지
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• 제18권3호
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• pp.291-296
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• 2008

본 논문에서는 군집로봇시스템에서 목표물 추적을 위하여 SVM을 이용한 12각형 기반의 Q-learning 알고리즘을 제안한다. 제안한 알고리즘의 유효성을 보이기 위해 본 논문에서는 여러 대의 로봇과 장애물 그리고 하나의 목표물로 정하고, 각각의 로봇이 숨겨진 목표물을 찾아내는 실험을 가정하여 무작위, DBAM과 AMAB의 융합 모델, 마지막으로는 본 논문에서 제안한 SVM과 12각형 기반의 Q-learning 알고리즘을 이용하여 실험을 수행하고, 이 3가지 방법을 비교하여 본 논문의 유효성을 검증하였다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2003년도 가을 학술발표논문집 Vol.30 No.2 (2)
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• pp.712-714
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• 2003

서로 다른 형태와 크기를 가지는 탐색패턴과 움직임 벡터의 분포는 블록 정합 기법에서 탐색 속도와 화질을 좌우하는 중요한 요소이다. 본 논문에서는 납작한 육각패턴을 이용한 새로운 고속 블록 정합 알고리즘을 제안한다. 이 방법은 작은 육각패턴을 이용하여 적은 탐색점으로 움직임이 적은 벡터를 우선 찾은 다음에 움직임이 큰 벡터에 대해서는 납작한 육각패턴을 이용하여 고속으로 움직임 벡터를 찾게 하였다. 실험결과, 제안된 알고리즘은 육각패턴 탐색기법에 비하여 움직임 벡터 예측의 속도에 있어서 약 11~51% 이상의 높은 성능 향상을 보였으며 화질 또한 PSNR 기준으로 약 0.05~0.74dB 의 향상을 보였다.