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An Improvement Video Search Method for VP-Tree by using a Trigonometric Inequality
Lee, Samuel Sangkon,Shishibori, Masami,Han, Chia Y. Korea Information Processing Society 2013 Journal of information processing systems Vol.9 No.2
This paper presents an approach for improving the use of VP-tree in video indexing and searching. A vantage-point tree or VP-tree is one of the metric space-based indexing methods used in multimedia database searches and data retrieval. Instead of relying on the Euclidean distance as a measure of search space, the proposed approach focuses on the trigonometric inequality for compressing the search range, which thus, improves the search performance. A test result of using 10,000 video files shows that this method reduced the search time by 5-12%, as compared to the existing method that uses the AESA algorithm.
On the size of the F-test for the one-way random model with heterogeneous error variances
Lee, Juneyoung,Khuri, André,I.,Kim, Kee Whan,Lee, Sangkon Taylor Francis 2007 JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION Vol.77 No.6
<P> Traditional analysis of variance tests are based on the assumption of homogeneous error variances, which often fails in real experimental situations. Violation of this assumption affects not only the power of the standard F-test, but also its size. When a design is unbalanced, the effect of unequal error variances is even more complex. In this paper, we study the effect of heterogeneous error variances on the size of the F-test concerning the among-group variance component in an unbalanced random one-way model. We also provide a method for computing the true critical value of the F-test for a given level of significance.</P>
Association between Serotonin 2A Receptor Gene Polymorphism and Posttraumatic Stress Disorder
HeonJeong Lee,SangKon Kwak,JongWoo Paik,RheeHun Kang,MinSoo Lee 대한신경정신의학회 2007 PSYCHIATRY INVESTIGATION Vol.4 No.2
Objective-The present study examined the possible association between the serotonin 2A receptor (5-HTR2A)-1438 A/G polymorphism and post-traumatic stress disorder (PTSD). Methods-The genotype, allele and allele carrier frequencies of the 5-HTR2A gene polymorphism were analyzed in 107 PTSD patients and 161 unrelated healthy controls using a case-control design. Results-While there was no difference in the genotype and allele distribution of the 5-HTR2A gene polymorphism between the PTSD patients and normal controls, there was a marginal difference in the allele carrier frequency between the two groups (χ2=2.82, df=1, p=0.093), that is the GG genotype frequency tended to be higher in the PTSD samples. When the analyses were conducted separately by gender, the frequency of the GG genotype was significantly higher in the female PTSD patients than in the female normal controls (χ2=4.38, df=1, p=0.036; OR=2.21, 95% confidence interval: 1.04-4.71). Conclusion-These findings suggest that the 5-HTR2A GG genotype is one of the possible genetic factors for susceptibility to PTSD, especially in the female population. Further investigations are required into the influence of gene polymorphisms on the biological mechanisms of PTSD.
소프트웨어공학적 방법론을 고려한 소설 네트워크 서비스의 그룹 관리용 어플리케이션의 설계 및 구현
이상곤(Lee, Sangkon) 한국정보전자통신기술학회 2013 한국정보전자통신기술학회논문지 Vol.6 No.2
본 논문에서는 그룹 전용으로 사용하기 위한 소셜 네트워크 서비스(SNS)를 제안한다. 본 논문의 시스템 개발을 위해 그룹 서비스, 채팅, 위치 추적, 전송 기능, 그림 그리기와 편집 기능, 사진첩 관리 기능, 파일 전송 기능 등을 구현하고 전체 모듈을 결합하여 안드로이드용 그룹 관리 어플리케이션 시스템을 설계하였다. This paper is to present a new social network system that is convenient to group message system. For this system, we implement a group messaging system, chatting, position monitoring, file transfer, picture drawing and/or editing, photo management system, and we combine them as one group manager application based on smart system.
운전자의 주의분산 연구동향 및 딥러닝 기반 동작 분류 모델
한상곤(Sangkon Han),최정인(Jung-In Choi) 한국컴퓨터정보학회 2021 韓國컴퓨터情報學會論文誌 Vol.26 No.11
본 논문에서는 운전자의 주의산만을 유발하는 운전자, 탑승자의 동작을 분석하고 핸드폰과 관련된 운전자의 행동 10가지를 인식하였다. 먼저 주의산만을 유발하는 동작을 환경 및 요인으로 분류하고 관련 최근 논문을 분석하였다. 분석된 논문을 기반으로 주의산만을 유발하는 주요 원인인 핸드폰과 관련된 10가지 운전자의 행동을 인식하였다. 약 10만 개의 이미지 데이터를 기반으로 실험을 진행하였다. SURF를 통해 특징을 추출하고 3가지 모델(CNN, ResNet-101, 개선된 ResNet-101)로 실험하였다. 개선된 ResNet-101 모델은 CNN보다 학습 오류와 검증 오류가 8.2배, 44.6배가량 줄어들었으며 평균적인 정밀도와 f1-score는 0.98로 높은 수준을 유지하였다. 또한 CAM(class activation maps)을 활용하여 딥러닝 모델이 운전자의 주의 분산 행동을 판단할 때, 핸드폰 객체와 위치를 결정적 원인으로 활용했는지 검토하였다. In this paper, we analyzed driver"s and passenger"s motions that cause driver"s distraction, and recognized 10 driver"s behaviors related to mobile phones. First, distraction-inducing behaviors were classified into environments and factors, and related recent papers were analyzed. Based on the analyzed papers, 10 driver"s behaviors related to cell phones, which are the main causes of distraction, were recognized. The experiment was conducted based on about 100,000 image data. Features were extracted through SURF and tested with three models (CNN, ResNet-101, and improved ResNet-101). The improved ResNet-101 model reduced training and validation errors by 8.2 times and 44.6 times compared to CNN, and the average precision and f1-score were maintained at a high level of 0.98. In addition, using CAM (class activation maps), it was reviewed whether the deep learning model used the cell phone object and location as the decisive cause when judging the driver"s distraction behavior.