• 한국인터넷방송통신학회논문지
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• 제24권1호
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• pp.83-88
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• 2024

본 연구에서는 Railroad surface 데이터를 활용하여 Semi-Supervised learning방식으로 railroad surface의 defect를 검출해내는 방안을 제안한다. Resnet50에 ImageNet으로 pretrained된 모델을 이용한다. Label이 없는 데이터에서 무작위로 데이터를 선정, 선정한 데이터에 label을 부여한 뒤 이 데이터로 모델을 학습시킨다. 학습된 모델을 이용하여 나머지 데이터의 결과값을 예측한 후, 그 예측값이 일정한 threshold보다 큰 것을 골라내고, threshold보다 큰 값들을 값이 큰 순서대로 정렬하여, 일정한 크기만큼 training data에 추가한다. 이 때, 각 class에 속할 확률이 높은 쪽으로 pseudo-labeling을 수행한다. 초기에 label이 부여된 데이터 개수에 따른 전체적인 class 분류 성능을 확인하는 실험 또한 진행하였고, 전체 training data대비 10% 미만의 labeled data로 최대 98%의 정확도를 얻는 성능을 보였다.

• 한국측량학회지
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• 제29권4호
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• pp.359-365
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• 2011

본 연구는 스캔라인 기반의 2차원 이웃 관계를 활용하여 레이블링 알고리즘과 윈도우 기반의 알고리즘을 함께 적용함으로써 항공레이저측량 자료의 지면점과 비지면점을 효과적으로 분리하는 것을 목적으로 한다. 이를 위하여 스캔라인 구조를 바탕으로 최소의 탐색을 통해 점들의 인접 관계를 구축하고, 구축된 인접관계를 기반으로 연결성분 레이블링 알고리즘을 적용하여 항공레이저측량 자료를 지면점과 비지면점으로 분리하였다. 그리고, 모폴로지 필터링을 통해 작은 개체를 추가로 제거하고 거리반비례 추정을 통해 고립 지면점을 복원하여 정확도를 향상시켰다. 다양한 특성을 나타내는 지역에 적용하고 평가한 결과 대부분의 점들이 올바르게 분리 되었고 약97%의 전체 정확도를 도출하였으며, 인접관계 구축 및 데이터 처리 시간이 TIN 또는 격자 구조 자료 구축시간에 비하여 적게 소요되었다.

• 동아시아식생활학회지
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• 제25권6호
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• pp.942-951
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• 2015

This study compared nutrition knowledge, dietary behaviors, and checking behaviors of food and nutrition labels between Korean and Chinese university students to obtain useful data for development of an education program for healthy dietary life. The data were collected by a self-administered questionnaire in Korea and China. Frequencies, t tests, ${\chi}^2$ tests, Cronbach's ${\alpha}$, and Pearson's correlation coefficient analysis were conducted by SPSS Win. V.21.0. The levels of nutrition knowledge and dietary behaviors were not high. Korean students showed higher percentage of correct answers about nutrition knowledge and levels of dietary behaviors than Chinese students. The means of degree of checking contents of food labels were 3.46 points for Korean students and 3.11 for Chinese students. Both groups of students showed the highest degree of checking milk and dairy products. The degree of understanding nutritive component labeling of Chinese students was higher than that of Korean students. Both groups of students showed higher than normal levels of confidence about nutritive component labeling and necessity of education on food and nutrition labels. The most preferred method of education on food and nutrition labels was broadcast media for both groups of students. In addition, there were significant correlations among nutrition knowledge, dietary behaviors, checking degree of food labels, checking degree of nutritive component labeling, and experience of nutrition education.

• Archives of Plastic Surgery
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• 제32권4호
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• pp.533-538
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• 2005

Due to its safety and softness, autologous fat transplantation has been commonly performed for soft tissue correction. However, the injected fat is absorbed resulting in the reduction of volume of the graft by 40-60% within a few months. Thus, there was an attempt to use adipocytes differentiated from preadipocytes in vitro for transplantation. Differentiated adipocytes were biocompatible and matured with gradual volume increase at transplantation site in clinical study(unpublished data). In addition, they did not induce immune rejection in response to nonself lymphocytes in a mixed lymphocyte reaction(MLR)(unpublished data). The purpose of this study is to differentiate mouse preadipocytes following labeling into adipocytes to establish an animal model for allogenic transplantation. Preadipocytes isolated from inguinal and retroperitoneal fat pad of C57BL/6 mice were proliferated with growth medium by passage 3 and differentiated into adipocytes with different culture conditions after labeled with BrdU. At most suitable conditions, above 90% of preadipocytes were differentiated and BrdU labeling did not affect differentiation rate and function of differentiated adipocytes. These results demonstrate that BrdU-labeled adipocytes resulting from this in vitro differentiation protocol are useful for allogenic transplantation study.

• 대한가정학회지
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• 제38권12호
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• pp.29-45
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• 2000

The purposes of this study were to determine what factors influence risk perceptions of females and males for milk produced using food-related biotechnology, to test whether risk perceptions or other factors influence self-protection actions and to estimate milk demand response in light of self-protection actions and other economic and demographic factors. The expected utility model was applied to explain the way consumers would take self-protection actions regarding risk perceptions and to drive milk demand. Telephone interviews were conducted and the data were collected from households(females=1,029, males=437) nationwide in the U.S. And the data were analyzed by Heckman two-step method using the software package LIMDEP. Risk perceptions were found to be influenced not by demographic factors but by outrage factors as well as attitudinal factors in both females and males, although some factors were different. In addition, risk perceptions and labeling availability were found to significantly influence self-protection actions in both groups. Furthermore, as an important concern in this study, self-protection action was found to significantly influence milk demand in only male group, implying a consistent behavior of males. Also milk price and household size were found to significantly influence milk demand in both groups. In fact, the results did demonstrate that labeling availability significantly influenced self-protection actions. That is, in markets where labeled laternatives were present, concerned consumers were more likely to self protect by substituting to these products. A policy implication of this result is that labeling food products produced using biotechnology enhances consumer choice. Hence, consumer could express a more accurate demand response and reduce the perceived food safety risk. Furthermore, education for females might be necessary to have a consistent behavior because self-protection action did not significantly influence female's milk demand, though they have greater risk perceptions than males have.

• 정보처리학회논문지A
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• 제9A권4호
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• pp.525-534
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• 2002

병렬프로그램에서 경합 조건은 비결정적인 수행 결과를 초래하므로 디버깅을 위해 반드시 탐지되어야 한다. 이러한 경합을 수행 중에 탐지하는 기존의 기법들은 병행성 정보 생성 시에 공유 자료구조를 사용하여 심각한 병목현상을 발생시키거나, 병행성 정보 비교 시에 내포병렬성의 정도에 의존하는 비효율적인 시간 복잡도를 가진다. 본 논문에서는 개별 자료구조를 사용함으로써 병목현상을 제거하여 병행성 정보를 확장적으로 생성하며, 생성된 병행성 정보의 비교 시간을 상수적인 복잡도로 개선한 새로운 레이블링 기법을 제안한다. 그러므로 제안된 레이블링 기법의 확장성 및 효율성은 공유메모리와 메시지전달 프로그램뿐만 아니라 이를 혼합하여 사용하는 병렬프로그램에서도 효율적인 수행중 경합탐지를 가능하게 한다.

• 대한건축학회논문집:계획계
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• 제35권12호
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• pp.43-53
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• 2019

The construction industry has made great strides in the past decades by utilizing computer programs including CAD. However, compared to other manufacturing sectors, labor productivity is low due to the high proportion of workers' knowledge-based task in addition to simple repetitive task. Therefore, the knowledge-based task efficiency of workers should be improved by recognizing the visual information of computers. A computer needs a lot of training data, such as the ImageNet project, to recognize visual information. This study, aim at proposing building facade datasets that is efficiently constructed by quickly collecting building facade data through portal site road view and automatically labeling using deep learning as part of construction of image dataset for visual recognition construction by the computer. As a method proposed in this study, we constructed a dataset for a part of Dongseong-ro, Daegu Metropolitan City and analyzed the utility and reliability of the dataset. Through this, it was confirmed that the computer could extract the significant facade information of the portal site road view by recognizing the visual information of the building facade image. Additionally, In contribution to verifying the feasibility of building construction image datasets. this study suggests the possibility of securing quantitative and qualitative facade design knowledge by extracting the facade design knowledge from any facade all over the world.

• 대한영양사협회학술지
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• 제30권3호
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• pp.161-180
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• 2024

Social and environmental changes, such as the rise of single-person households and advances in the food industry, have led to the replacement of home-cooked meals with home meal replacements products (HMRs). This study compared the nutrient content of a total of 1,680 HMRs and 158 restaurant foods by collecting data on the nutrient content of comparable food types from the Food composition data for restaurant foods published by the Ministry of Food and Drug Safety (MFDS) and evaluating the calorie and nutrient content of HMRs based on nutrition labeling through market research from May 2022 to May 2024, focusing on rice, porridge, noodles, soup/stew. The nutritional content and price of the HMRs varied widely, even for similar foods, depending on the detailed food type. Therefore, it is necessary to make an appropriate choice based on nutrition labeling according to the purpose of consumption. The HMRs had a lower calorie and nutrient content due to the smaller serving size when compared with restaurant foods. However, when the same weights were compared, the sugar and sodium content in the rice and soup/stew were higher in the HMRs than in the restaurant foods. In addition, due to the wide variety of HMRs available, many HMRs that can replace restaurant foods are being produced. However, even for the same type of food, the serving sizes of the HMRs and the restaurant foods were widely different, suggesting the need for a study to examine the appropriateness of the serving sizes of HMRs and restaurant foods.

• Journal of Platform Technology
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• 제11권3호
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• pp.56-67
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• 2023

최근 데이터 기반 산업계의 오랜 숙원이었던 개인정보 비식별화가 2020년 8월 데이터3법[1]이 개정되어 명시화 되었다. 4차 산업시대의 원유[2]라 불리는 데이터를 산업 분야에서 활성화할 수 있는 기틀이 되었다. 하지만, 일각에서는 비식별개인정보(personally non-identifiable information)가 정보주체의 기본권 침해를 우려하고 있는 실정이다[3]. 이에 개인정보 비식별화 자동화 도구인 Batch De-Identification Tool을 개발 연구를 수행하였다. 본 연구에서는 첫 번째로, 학습용 데이터 구축을 위해 사람 얼굴(눈, 코, 입) 및 다양한 해상도의 자동차 번호판 등을 라벨링하는 이미지 라벨링 도구를 개발하였다. 두 번째로, 객체 인식 모델을 학습하여 객체 인식 모듈을 실행함으로써 개인정보 비식별화를 수행할 수 있도록 하였다. 본 연구의 결과로 개발된 개인정보 비식별화 자동화 도구는 온라인 서비스를 통해 개인정보 침해 요소를 사전에 제거할 수 있는 가능성을 보여주었다. 이러한 결과는 데이터 기반 산업계에서 개인정보 보호와 활용의 균형을 유지하면서도 데이터의 가치를 극대화할 수 있는 가능성을 제시하고 있다

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2023년도 춘계학술대회
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• pp.204-205
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• 2023

본 연구는 준지도학습(SSL)을 기반한 소형 어선의 충돌 경보 송출 예측 모델에 관한 연구이다. 지도학습(SL) 방법은 레이블링된 다수의 데이터가 필요하지만 레이블링 과정에서 많은 자원과 시간이 소요된다. 본 연구는 '지능형 해상교통정보 서비스'와 연계한 데이터 파이프 라인을 통해 수집된 서비스 데이터와 실해역 시험에서 수집한 데이터를 사용하였다. 실제 사용자 만족도 기반으로 레이블이 결정된 실해역 시험 데이터만 아니라 레이블이 결정되지 않은 서비스 데이터를 함께 학습시킨 결과, 모델 정확도가 향상되었다.