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

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

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

• Archives of Plastic Surgery
• /
• 제36권4호
• /
• pp.385-392
• /
• 2009

Purpose: In the partial thickness burn management, despite of several advantages, the use of human amniotic membrane has been limited. The authors applied dried bovine amniotic membrane(DBAM) to overcome disadvantages of amniotic membrane for partial thickness burn and compared the effectiveness with cultured allogenic keratinocytes(CAK) that have been recently used for the management of burn. Methods: 16 patients with partial thickness burn, the mean age of 38 ranging 12 to 59 years, between August 2007 and May 2008 were assigned to this study. Either DBAM or CAK was applied, and the secondary dressing was removed on the following day. To compare treatment effect, time for epithelization, Vancouver scar scale and chromameteric results were evaluated. Results: The time for epithelization of DBAM was 10.1 days, that of CAK was 9.1 days, and they were shorter than the previous 2 - 3 weeks. At the follow up Vancouver scar scale was 2.8 for DBAM and 3.0 points for CAK and showed good results. The result of chromameter showed that the $L^*$, $a^*$, and $b^*$ values of the area applied DBAM were 60.1, 13.6, and 13.3, respectively, and the values of the area applied CAK were 60.1, 12.4, and 12.4, respectively. It was found that the skin color of the healed area after burn was darker, the redness was higher, and the yellowness was lower. After dressing, significant side effects were not observed, and in the cases of applying CAK, it was inconvenient as the moving area had to be fixed. Conclusion: With CAK, DBAM has several advantages such as the shortening of the epithelization period, reduction of scar and pigmentation, and convenient application, etc. Thus it is an effective method for the partial thickness burn management.

• 한국지능시스템학회:학술대회논문집
• /
• 한국지능시스템학회 2007년도 추계학술대회 학술발표 논문집
• /
• pp.227-230
• /
• 2007

본 논문에서는 로봇의 목표물 추적을 위하여 SVM을 이용한 12각형 기반의 Q-learning 알고리즘을 제안한다. 제안한 알고리즘의 유효성을 보이기 위해 본 논문에서는 두 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.

• 한국지능시스템학회:학술대회논문집
• /
• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
• /
• pp.143-146
• /
• 2008

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