• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.94-96
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• 2004

에이전트의 '물체 따라가기'는 전통적으로 자동운전이나 가이드 등의 다양한 서비스를 제공할 수 있는 기본적인 기능이다. 여러 가지 물체가 있는 환경에서 '물체 따라가기'를 하기 위해서는 목적하는 대상이 어디에 있는지 찾을 수 있어야 하며, 실제 환경에는 사람이나 차와 같이 움직이는 물체들이 존재하기 때문에 다른 물체들을 피할 수 있어야 한다. 그런데 에이전트의 최적화된 피하기 행동은 장애물의 모양과 크기에 따라 다르게 생성될 수 있다. 본 논문에서는 다양한 모양과 크기의 장애물이 있는 환경에서 최적의 피하기 행동을 생성하면서 물체를 추적하기 위해 반응형 에이전트의 행동선택을 강화학습 한다. 여기에서 정확하게 상태를 인식하기 위하여 상태를 추론하고 목표물과 일정거리를 유지하기 위해 베이지안 추론을 이용한다 베이지안 추론은 센서정보를 이용해 확률 테이블을 생성하고 가장 유력한 상황을 추론하는데 적합한 방법이고, 강화학습은 실시간으로 장애물 종류에 따른 상태에서 최적화된 행동을 생성하도록 평가함수를 제공하기 때문에 베이지안 추론과 강화학습의 결합모델로 장애물에 따른 최적의 피하기 행동을 생성할 수 있다. Webot을 이용한 시뮬레이션을 통하여 다양한 물체가 존재하는 환경에서 목적하는 대상을 따라가면서 이종의 움직이는 장애물을 최적화된 방법으로 피할 수 있음을 확인하였다.

• 대한방사선방어학회:학술대회논문집
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• 2009.04a
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• pp.264-265
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• 2009

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.139-147
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• 2011

There is a growing tendency toward a vehicle demand and a utilization of traffic information systems. Due to various kinds of traffic information systems and increasing of communication data, the traffic information service requires a very high IT infrastructure. A cloud computing environment is an essential approach for reducing a IT infrastructure cost. And the traffic information service needs a provisioning scheduling method for managing a resource. So we propose a provisioning scheduling with conditional probability inference (PSCPI) for the traffic information service on cloud environment. PSCPI uses a naive bayse inference technique based on a status of a virtual machine. And PSCPI allocates a job to the virtual machines on the basis of an availability of each virtual machine. Naive bayse based PSCPI provides a high throughput and an high availability of virtual machines for real-time traffic information services.

• Journal of The Korean Association For Science Education
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• v.19 no.1
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• pp.117-127
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• 1999

The present study investigated Korean and the US college students' scientific reasoning skills involving hypothesis-testing skills and tested the hypothesis that hypothesis-testing skills are more advanced ones than other scientific reasoning skills investigated in this study. Seven hundred and seventy-four(774) Korean and five hundred and sixty-eight(568) the US students were sampled in university level. The Test of Scientific Reasoning was used as a scientific reasoning test. The test is consisted of two conservational reasoning, two proportional reasoning, one pendulum, two probability reasoning, two controlling variable, one correlational reasoning, and two hypothesis-testing reasoning tasks. Korean students showed a significant higher score in proportional and probability reasoning tasks than the US students. However, the Korean showed a significant lower score in conservation and correlation reasoning tasks than their American counterparts. Further, Korean and the US college students showed a notably poor performance in hypothesis-testing skills comparing with other scientific reasoning skills, which supported the hypothesis that hypothesis-testing skills are more advanced ones than other scientific reasoning skills. In addition, the Korean showed a severe deficiency in candle-burning task which required the skill that students have to design a scientific test-procedure to test theoretical hypotheses. This study also discussed on the educational implications of the results of the present study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.8
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• pp.1386-1392
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• 2006

This study is a numerical representative modelling analysis for applying the process that unravels networks between cells in genetics to WWW of informatics. Using the probabilistic graphical model, the insight from the data describing biological networks is used for making a probabilistic function. Rather than a complex network of cells, we reconstruct a simple lower-stage model and show a genetic representation level from the genetic based network logic. We made probabilistic graphical models from genetic data and extends them to genetic representation data in the method of network modelling in informatics.

• KSBB Journal
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• v.21 no.4
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• pp.231-235
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• 2006

This study is a numerical representative modeling analysis for the application of the process that unravels networks between cells in genetics to web of informatics. Using the probabilistic graphical model, the insight from the data describing biological networks is used for making a probabilistic function. Rather than a complex network of cells, we reconstruct a simple lower-stage model and show a genetic representation level from the genetic based network logic. We made probabilistic graphical models from genetic data and extends them to genetic representation data in the method of network modeling in informatics

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.32 no.7
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• pp.15-20
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• 2014

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1017-1028
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• 2011

When it is not easy to decide the credit scoring for some loan applicants, credit evaluation is postponded and reserve to ask a specialist for further evaluation of undecided applicants. This undecided inference is one of problems that happen to most statistical models including the biostatistics and sportal statistics as well as credit evaluation area. In this work, the undecided inference is regarded as a missing data mechanism under the assumption of MNAR, and use the bivariate probit model which is one of sample selection models. Two undecided inference methods are proposed: one is to make use of characteristic variables to represent the state for decided applicants, and the other is that more accurate and additional informations are collected and apply these new variables. With an illustrated example, misclassification error rates for undecided and overall applicants are obtainded and compared according to various characteristic variables, undecided intervals, and thresholds. It is found that misclassification error rates could be reduced when the undecided interval is increased and more accurate information is put to model, since more accurate situation of decided applications are reflected in the bivariate probit model.

• Communications of Mathematical Education
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• v.18 no.1 s.18
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• pp.89-98
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• 2004

한국과 미국(North Carolina주)의 확률과 통계 교육 내용을 고찰한 결과 한국과 미국(North Carolina주)은 내용적인 면에서 많은 차이를 보였다. 한국의 경우, 9-가 단계와 10-가 단계, 선택과목 중 수학 I, 실용수학, 이산수학 과목에 제시되어 있는 확률과 통계 영역은 심화선택과목인 확률과 통계 과목의 내용을 축소하여 재구성한 내용을 제시하고 있다. 미국(North Carolina주)은 한국과는 달리, Introductory Mathematics, Algebra(I, II), Technical Mathematics(1, 2) Advanced Mathematics, Advanced Placement Calculus, Discrete Mathematics, Integrated Mathematics(1, 2, 3), Geometry 과목에서 확률과 통계 영역은 각 과목과 연관성 있는 내용으로 구성되어 있다. 한국의 심화 선택과목인 확률과 통계 과목과 미국(North Carolina주)의 AP통계(Advanced Placement Statistics)를 비교한 결과, 전체적으로, 자료의 정리, 확률변수와 확률분포 영역에서 한국과 미국(North Carolina주)은 거의 유사성을 보이고 있지만, 통계적 추론에서는 미국(North Carolina주)이 한국에 비하여 강화되어 있음을 알 수 있다.

• The Korean Journal of Applied Statistics
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• v.1 no.2
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• pp.61-65
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• 1987

Randomization is one of the principles that should be adopted in comparative experiments. Randomization is well known as a useful tool for averaging out the effects of external factors. It also validates statistical inference based on mathematical model. This teaching meterial is designed for the purpose of illustrating the role of randomization.