• 산업경영시스템학회지
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• 제32권2호
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• pp.85-93
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• 2009

GOMS model is a cognitive modeling method of human performance based on Goal, Operators, Methods, Selection rules. GOMS model was originally designed for desktop environment so that it is difficult for GOMS model to be implemented into the mobile environment. In addition, GOMS model would be inaccurate because the original GOMS model was based on serial processing, excluding one of most important human information processing characteristics, parallel processing. Therefore this study was designed to propose a modified GOMS model including mobile computing and parallel processing. In order to encompass mobile environment, an operator of 'look for' was divided into 'visual move to' and 'recognize' whereas 'point to' and 'click' were combined into 'tab.' The results showed that newly introduced operators were necessary to estimate more accurate mobile computing behaviors. In conclusion, modified-GOMS model could predict human performance more accurately than the original GOMS model in the mobile computing environment.

• 대한인간공학회지
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• 제5권2호
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• pp.11-15
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• 1986

In this paper, we consider the validity of a human probabilistic learning model applied to the perdiction of errors associated with the absolute identification of tones. It is shown that the probabilistic learning model describes the human error process adequately. The model parameters are estimated by two methods which are the method of maximum likelihood, and the method of mement. The MLE version of the model has the better predictive power but the ME version is more readily obtainable and may be more practical.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2009년도 9th International Meeting on Information Display
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• pp.1154-1157
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• 2009

This template provides you with an example of the The CIE Color Appearance Model (CIECAM02) is now widely used for various digital imaging systems including digital displays. The CIECAM02 were intended to be an empirical model, however, some aspects of the model are closely related to the human color vision mechanism. This paper will discuss the relationship between human color vision and color imaging.

• 한국안전학회지
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• 제9권4호
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• pp.125-131
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• 1994

This paper proposes a dynamic recursive model with the effect analysis of machine state considering human factor(human skillfulness) In a single lot man-machine production system. This model obtained using Kalman Filtering Algorithm Is based on input state, output state, machine state. For sensitivity analysis, this model constructed is examined according to the impact of human skillfulness with computer simulation. The model studied in this paper has a great advance from the point of view a combination of three factors( human engineering, dynamic control theory, quality control ) and can also be extended in several applications.

• 한국안전학회지
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• 제20권1호
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• pp.153-157
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• 2005

The injury accidents in fishing vessels account for $67.2\%$ of all marine injury casualties$(1997\~2001)$ and is on an increasing trend every year. Also, it is remarkable for the injury accidents to be basically caused by human errors. This study aims to investigate the human error of injury accidents in fishing vessels and presents the injury preventing program in them. Human errors were analysed by the methods such as SHELL & Reason Hybrid Model, GEMS Model adopted by International Maritime Organization(IMO). Based on the analysis, the following propositions were made to reduce the fishing vessels accidents by human errors : improvement of hazard awareness and quality of personnel, establishment of safety management system, and enforcement of vessels inspection.

• Journal of Information Processing Systems
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• 제14권1호
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• pp.150-161
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• 2018

We propose a novel deep neural network model for detecting human activities in untrimmed videos. The process of human activity detection in a video involves two steps: a step to extract features that are effective in recognizing human activities in a long untrimmed video, followed by a step to detect human activities from those extracted features. To extract the rich features from video segments that could express unique patterns for each activity, we employ two different convolutional neural network models, C3D and I-ResNet. For detecting human activities from the sequence of extracted feature vectors, we use BLSTM, a bi-directional recurrent neural network model. By conducting experiments with ActivityNet 200, a large-scale benchmark dataset, we show the high performance of the proposed DeepAct model.

• 한국안전학회지
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• 제21권3호
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• pp.137-142
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• 2006

Quantification of error possibility, in an HRA process, should be performed so that the result of the qualitative analysis can be utilized in other areas in conjunction with overall safety estimation results. And also, the quantification is an essential process to analyze the error possibility in detail and to obtain countermeasures for the errors through screening procedures. In previous studies for the quantification of error possibility, nominal values were assigned by the experts' judgements and utilized as corresponding probabilities. The values assigned by experts' experiences and judgements, however, require verifications on their reliability. In this study, the validity of new error possibility values in new MCR design was verified by using the Onisawa's model which utilizes fuzzy linguistic values to estimate human error probabilities. With the model of error probabilities are represented as analyst's estimations and natural language expression instead of numerical values. As results, the experts' estimation values about error probabilities are well agreed to the existing error probability estimation model. Thus, it was concluded that the occurrence probabilities of errors derived from the human error analysis process can be assessed by nominal values suggested in the previous studies. It is also expected that our analysis method can supplement the conventional HRA method because the nominal values are based on the consideration of various influencing factors such as PSFs.

• 의학교육논단
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• 제9권2호
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• pp.57-66
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• 2007

Purpose How can be a great doctor with excellence and ethics? In this study, I wanted to find out the characteristics of human environment to make a great doctor. Methods: First, I researched factors and construct of the human environment. So I conceived a model for analyzing human environment with two construction model : Howard Gardner's System Model and Bron-fenbrenner's ecological systems model. Second, I analyzed the life of the Oliver R. Evison M.D. and Ki Ryu Jang M.D. Oliver R. Evison was the pioneer of medicine of Korea and establisher of the Severance Hospital and medical college. Dr KiRyu Jang, who was called 'Schweitzer of Korea', was a good doctor of the poor and weak patients in Korea. Third, I tried to find out a new human environment model to make a great doctor. Results One model for analyzing human environment was made of relationship based on emotion. relationship teaching knowledge and skill, and relationship communicating on value. In the light of analyzing of two great doctors. Oliver R. Evison M.D. and KiRyu Jang M.D, I found out special interrelationship, Hardie, Allen, Severance for Evison, Kyosin Kim, Kyucheol Choi etc. for Ki Ryu Jang These special people were religious actors or social thinkers. Conclusions: To be a great doctor to excel and innovate medical field, medical students should have the chance to meet with people based on religious, ethical and social action, discuss on value across social fields, and can construct the idea to make and realize higher value of medical action. In sum, another important human environment for medical students would be a person who could be communicate with true value.

• 한국정보과학회논문지:소프트웨어및응용
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• 제36권12호
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• pp.996-1008
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• 2009

본 논문은 뼈대-구조(skeleton) 형태의 Active Shape Models을 이용한 사람의 자세 정합에 대한 새로운 접근 방법을 제안한다. 제안된 방법은 모델 생성과 정합 과정에서의 빠른 수행 시간을 위해 기존 윤곽 형태(silhouette)의 모델이 아닌 뼈대-구조 형태의 모델을 적용하였다. 기존 Active Shape Models을 뼈대-구조 형태로 사람 자세 정합에 적용했을 경우 자세를 결정짓는 팔과 다리의 부정확한 정합은 사람 몸의 다양한 색상 정보와 전후(fore-rear direction)만을 고려한 특징점(landmark)의 방향정보로 인해 발생되며, 이러한 문제점은 입력 영상의 차영상 정보와 사람의 자세를 결정짓는 팔과 다리의 중요 특징점에 방향정보를 추가하여 해결하였다. 사람의 뼈대-구조 모델을 생성하기 위해 600개의 이미지를 사용 하였으며, 생성된 형태 모델은 사람의 자세에 정합될 수 있는 17개의 특징점을 포함한다. 정합 과정에서 최대 30번 이하의 반복 과정을 수행 하며, 최대 수행 시간은 0.03초로 빠른 수행 시간의 결과를 얻었다.

• 방송공학회논문지
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• 제27권2호
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• pp.174-184
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• 2022

3차원 휴먼 자세 추정 모델은 다시점 모델과 단시점 모델로 분류될 수 있다. 일반적으로 다시점 모델은 단시점 모델에 비하여 뛰어난 자세 추정 성능을 보인다. 단시점 모델의 경우 3차원 자세 추정 성능의 향상은 많은 양의 학습 데이터를 필요로 한다. 하지만 3차원 자세에 대한 참값을 획득하는 것은 쉬운 일이 아니다. 이러한 문제를 다루기 위해, 우리는 다시점 모델로부터 다시점 휴먼 자세 데이터에 대한 의사 참값을 생성하고, 이를 단시점 모델의 학습에 활용하는 방법을 제안한다. 또한, 우리는 각각의 다시점 영상으로부터 추정된 자세의 일관성을 고려하는 다시점 일관성 손실함수를 제안하여, 이것이 단시점 모델의 효과적인 학습에 도움을 준다는 것을 보인다. Human3.6M과 MPI-INF-3DHP 데이터셋을 사용한 실험은 제안하는 방법이 3차원 휴먼 자세 추정을 위한 단시점 모델의 학습에 효과적임을 보여준다.