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T. S. 엘리엇의 번트 노턴 읽기: 아우구스티누스의 고백록 의 관점에서
이철희(CheolHee Lee) 한국영미어문학회 2016 영미어문학 Vol.- No.121
As widely known, Augustine’s ideas of time affected Eliot profoundly, leading the latter to classify time into three different categories. In short, the concept of Augustine’s time is that of extension where time never ends in the past, present nor future but interlocks them. Furthermore, Eliot also suggests that these three tenses are always present and contained within each other. Therefore, both Augustine and Eliot emphasize the present. The present has a continuity without breaks in it. Time’s significance which Augustine and Eliot define is drawn in a similar way as a continuum.
이철희 ( Cheolhee Lee ),구태회 ( Taehoe Koo ),박남욱 ( Namwook Park ),임낙훈 ( Nakhoon Lim ) 한국인터넷정보학회 2024 인터넷정보학회논문지 Vol.25 No.2
This paper was studied abouta technology for detecting damage to temporary works equipment used in construction sites with explainable artificial intelligence (XAI). Temporary works equipment is mostly composed of steel or aluminum, and it is reused several times due to the characters of the materials in temporary works equipment. However, it sometimes causes accidents at construction sites by using low or decreased quality of temporary works equipment because the regulation and restriction of reuse in them is not strict. Currently, safety rules such as related government laws, standards, and regulations for quality control of temporary works equipment have not been established. Additionally, the inspection results were often different according to the inspector’s level of training. To overcome these limitations, a method based with AI and image processing technology was developed. In addition, it was devised by applying explainableartificial intelligence (XAI) technology so that the inspector makes more exact decision with resultsin damage detect with image analysis by the XAI which is a developed AI model for analysis of temporary works equipment. In the experiments, temporary works equipment was photographed with a 4k-quality camera, and the learned artificial intelligence model was trained with 610 labelingdata, and the accuracy was tested by analyzing the image recording data of temporary works equipment. As a result, the accuracy of damage detect by the XAI was 95.0% for the training dataset, 92.0% for the validation dataset, and 90.0% for the test dataset. This was shown aboutthe reliability of the performance of the developed artificial intelligence. It was verified for usability of explainable artificial intelligence to detect damage in temporary works equipment by the experiments. However, to improve the level of commercial software, the XAI need to be trained more by real data set and the ability to detect damage has to be kept or increased when the real data set is applied..
엘리엇의 『네 사중주』를 통한 “집착, 초탈, 그리고 무관심” 읽기
이철희(Cheolhee Lee) 한국영미어문학회 2017 영미어문학 Vol.- No.125
Eliot divides human attitudes into three characteristics “indifference,” “detachment” and “attachment.” Among them, Eliot regards detachment as the most important thing. Detachment means abandoning our selfish mind. By doing so we could live a better life, allowing us to liberate from time and history. And Eliot says that indifference is between the live and the dead nettle, so it does not affect anything at all. And attachment is characterized by selfish love and men’s curiosity about past and future. Eliot draws these characteristics in his Four Quartets in detail.
XAI 기반의 공공시설물 건전도 안전검사 평가시스템 연구
박예슬(Yesul Park),경선재(Seonjae Kyeong),김민준(Minjun Kim),오찬미(Chanmi Oh),이재성(Jeasung Lee),이재환(Jaehwan Lee),이현승(Hyunseung Lee),이철희(Cheolhee Lee),문현준(Hyeonjoon Moon) 한국방송·미디어공학회 2020 한국방송공학회 학술발표대회 논문집 Vol.2020 No.7
공공시설에 대한 안전점검은 공공시설의 노후화에 따라 정기적인 검사의 필요성이 요구되고 있다. 기존의 안전점검 방식은 대부분 육안으로 점검하는 것에 의존하는데 이는 점검자의 숙련도에 따라 결과의 품질이 달라지게 된다. 본 논문에서는 XAI 기반의 공공시설물 건전도 안전검사 평가시스템을 제안하며, 이는 점검자의 숙련도와 무관하게 항상 같은 결과를 도출해 내며 XAI 를 통해 사용자에게 안전점검에 대한 결과를 제시해준다. 공공시설물 중 터널 시설물의 안전검사 평가시스템을 기반으로 하는 연구를 진행하였으며 이는 수정없이 교량 시설물 등 다른 공공시설물에 적용이 가능하다. 본 논문은 5 가지로 구분된다. 1) 터널 이미지와 균열에 마스크를 적용한 이미지 두 가지의 데이터 셋을 448x448 로 생성한다. 2) UNet 과 Resnet152 의 두 모델을 적용한 혼합 모델을 이용하여 생성한 데이터 셋을 훈련시킨다. 3) 훈련된 혼합 모델에서 생성된 분할 이미지에 대해 노이즈 제거 과정을 진행한다. 4) 노이즈 제거가 끝난 이미지에 스켈레톤화(Skeletonization)를 적용시켜 균열 이미지의 뼈대를 구한다. 뼈대 이미지 기반으로 균열의 길이, 두께, 위치등의 정보를 얻는다. 5) XAI 부분에서는 뼈대 이미지의 정보를 토대로 균열의 위치, 두께, 길이 등에 대해 계산을 진행한 후 사용자에게 제시해준다.