• 대한장애인치과학회지
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• 제9권1호
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• pp.56-65
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• 2013

I participated in Academic Exchange Program(Action plan II) between KADH(Korean Association for Disability and Oral Health) and JSDH(Japanses Society for Disability and Oral Health) for 2 months from 3rd July 2012 to 2nd september 2012 in the Department of Hygiene and Oral Health, School of Dentistry, Showa University at Tokyo, Japan. I have observed their operation process and learned what dysphagia is and how it is consulted and taken care of as a therapy for patients with eating and swallowing disorders for two months in The department of special needs dentistry at Showa University Dental Hospital, Jonan Branch of Tokyo Metropolitan Kita Medical Rehabilitation Center for the Disabled, Smile Nakano Center, Tokyo metropolitan center for persons with disabilities in Lidabashi for one week, Eating and swallowing functional therapy workshop for disabled children, Tokyo metropolitan Tobu medical center for Persons with Developmental/Multiple Disabilities located in Minamisunamitchi for one week and on The 17-18th JSDR(Japanese Society of Dysphagia rehabilitation) in Sapporo. Through Action Plan II program, I learned how precious eating, drinking and swallowing with ease are and observed how they do and what they do as a dentist or a dental hygienist in Japan for dysphagia patients. Therefore, I want to present the dental approaches of children with dysphagia in Japan, based on my experience for two months.

• 한국디지털정책학회:학술대회논문집
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• 한국디지털정책학회 2005년도 춘계학술대회
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• pp.505-516
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• 2005

According to Mead's (1934) symbolic interaction theory, social roles are learned through interaction and experiences in everyday life. Over time, these rules of behavior become internalized and serve to structure one's actions accordingly. Ultimately, these rules provide us with a powerful means of controlling our actions, and in time, they define our identity. Transforming the socialization process is one's conscious interpretation of stimuli through the use of symbols. Furthermore, society's perceptual processes can be shaped by the symbols we learn. The meaning of symbols can be learned from a variety of social influences, one of which may be mediated messages and advertising. This paper attempts to establish a link between media exposure and one's perception of social reality regarding character judgments made of unknown others based on the target's product or brand usage. Using magazine advertisements for fictitious products, the experiment herein seeks to establish two fundamental goals: 1) to determine if perceptions can be manipulated via association with companion symbolic elements: and 2) to detect whether television exposure is a moderating factor. Respondents were asked their perceptions of both product quality and of product users.

• 한국컴퓨터그래픽스학회논문지
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• 제23권1호
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• pp.57-65
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• 2017

본 논문에서는 포인트 클라우드로 구성된 모델 내의 오류를 줄이고, 기하학적 형태를 복원하기 위한 사전 학습 방법을 제시한다. 이를 위해, 대상 모델과 유사한 형태 특징을 갖는 모델로부터 3차원 특징 정보를 추출하여 사전을 구성하고, 이를 통해 기하 복원을 수행한다. 본 연구에서 제시한 방법은 다음과 같이 세 단계로 구성된다. 첫째, 유사 모델로부터 기하 패치를 구성하는 단계, 둘째, 획득한 패치의 3차원 형태 특징을 학습하는 단계, 셋째, 학습된 사전을 이용하여 기하를 복원하는 단계이며, 최종적으로 원본 모델과 복원 결과의 오차를 계산하며, 복원 결과의 정확도를 확인한다.

• 로봇학회논문지
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• 제12권2호
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• pp.239-247
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• 2017

The purpose of this study is to develop a motion generation technique based on a double inverted pendulum model (DIPM) that learns and reproduces humanoid robot (or virtual human) motions while keeping its balance in a pattern similar to a human. DIPM consists of a cart and two inverted pendulums, connected in a serial. Although the structure resembles human upper- and lower-body, the balancing motion in DIPM is different from the motion that human does. To do this, we use the motion capture data to obtain the reference motion to keep the balance in the existence of external force. By an optimization technique minimizing the difference between the motion of DIPM and the reference motion, control parameters of the proposed method were learned in advance. The learned control parameters are re-used for the control signal of DIPM as input of linear quadratic regulator that generates a similar motion pattern as the reference. In order to verify this, we use virtual human experiments were conducted to generate the motion that naturally balanced.

• 대한기계학회논문집A
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• 제25권3호
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• pp.353-361
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• 2001

Recently the boundary element method has been developed swiftly. The boundary element method is an efficient and accurate means for analysis of two dimensional elastic crack problems. This paper is concerned with the evaluation and the prediction of the stress intensity factor(SIF) for the crack emanating from the circular hole using boundary element method-neural network. The SIF of the crack emanating from the hole was calculated by using boundary element method. Neural network is used to evaluate and to predict SIF from the results of boundary element method. The organized neural network system (structure of four processing element) was learned with the accuracy 99%. The learned neural network system could be evaluated and predicted with the accuracy of 83.3% and 71.4% (in cases of SIF and virtual SIF). Thus the proposed boundary element method-neural network is very useful to estimate the SIF.

• 한국컴퓨터정보학회논문지
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• 제22권12호
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• pp.49-54
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• 2017

Pathfinding for pedestrians provided by various navigation programs is based on a shortest path search algorithm. There is no big difference in their guide results, which makes the path quality more important. Multiple criteria should be included in the search cost to calculate the path quality, which is called a multi-criteria pathfinding. In this paper we propose a user adaptive pathfinding algorithm in which the cost function for a multi-criteria pathfinding is defined as a weighted sum of multiple criteria and the weights are learned automatically by Perceptron learning. Weight learning is implemented in two ways: short-term weight learning that reflects weight changes in real time as the user moves and long-term weight learning that updates the weights by the average value of the entire path after completing the movement. We use the weight update method with momentum for long-term weight learning, so that learning speed is improved and the learned weight can be stabilized. The proposed method is implemented as an app and is applied to various movement situations. The results show that customized pathfinding based on user preference can be obtained.

• 로봇학회논문지
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• 제11권3호
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• pp.115-126
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• 2016

This paper suggests the method of the spherical signature description of 3D point clouds taken from the laser range scanner on the ground vehicle. Based on the spherical signature description of each point, the extractor of significant environmental features is learned by the Deep Belief Nets for the urban structure classification. Arbitrary point among the 3D point cloud can represents its signature in its sky surface by using several neighborhood points. The unit spherical surface centered on that point can be considered to accumulate the evidence of each angular tessellation. According to a kind of point area such as wall, ground, tree, car, and so on, the results of spherical signature description look so different each other. These data can be applied into the Deep Belief Nets, which is one of the Deep Neural Networks, for learning the environmental feature extractor. With this learned feature extractor, 3D points can be classified due to its urban structures well. Experimental results prove that the proposed method based on the spherical signature description and the Deep Belief Nets is suitable for the mobile robots in terms of the classification accuracy.

• 품질경영학회지
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• 제47권3호
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• pp.467-478
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• 2019

Purpose: The purpose of this paper is to improve quality for water infiltration to FCISA during military aircraft operation. Methods: A series of troubleshooting studies were conducted to identify the root cause of the water infiltration and reproduce the defects through various simulation tests. And design improvement measures were derived, and countermeasures were taken to prevent recurrence of moisture inflow defects. Conclusion: FCISA operates a very important role in the operation of military aircraft, and defects due to water infiltration are very fatal to flight safety. In this study, the root cause was identified and the design improvement to prevent recurrence was carried out through the failure investigation performed in this study, and the FCISA was improved so that the flight safety was not affected. The results of this study will be valuable back data that can be reflected in the design process through Lessons-Learned in the design phase of the aircraft that will be developed in the future.

• 한국컴퓨터정보학회논문지
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• 제26권2호
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• pp.19-25
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• 2021

본 논문에서는 비전 기술과 딥러닝 기반의 얼굴인식을 통해 실종자를 식별하는 방법을 제안하였다. 모바일 디바이스에서 전송된 원본 이미지에 대해 얼굴인식에 적합하도록 이미지를 전처리한 후, 얼굴인식의 정확도 향상을 위한 이미지 데이터 증식과 CNN 기반 얼굴학습 및 검증을 통해 실종자를 인식하였다. 본 논문의 구현 결과를 이용하여 가상의 실종자 이미지를 식별한 결과, 원본 데이터와 블러 처리한 데이터를 함께 학습한 모델의 성능이 가장 우수하게 나왔다. 또한 사전학습된 가중치를 사용한 학습 모델은 사용하지 않은 모델보다 높은 성능을 보였지만, 편향과 분산이 높게 나오는 한계를 확인할 수 있었다.

• 한국컴퓨터그래픽스학회논문지
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• 제27권5호
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• pp.73-79
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• 2021

최근 컴퓨터 애니메이션 분야에서는 기존의 유한상태기계나 그래프 기반의 방식들에서 벗어나 딥러닝을 이용한 동작 생성 방식이 많이 연구되고있다. 동작 학습에 요구되는 네트워크의 표현력은 학습해야하는 동작의 단순한 길이보다는 그 안에 포함된 동작의 다양성에 더 큰 영향을 받는다. 본 연구는 이처럼 학습해야하는 동작의 종류가 다양한 경우에 효율적인 네트워크 구조를 찾는것을 목표로 한다. 기본적인 fully-connected 구조, 여러개의 fully-connected 레이어를 병렬적으로 사용하는 mixture of experts구조, seq2seq처리에 널리 사용되는 순환신경망(RNN), 그리고 최근 시퀀스 형태의 데이터 처리를 위해 자연어 처리 분야에서 사용되고있는 transformer구조의 네트워크들을 각각 학습하고 비교한다.