탈전후 사상의 역사성과 중층성

서동주(Seo, Dongju) 동북아시아문화학회 2020 동북아시아문화학회 국제학술대회 발표자료집 Vol.2020 No.10

Recently, discussions pointing to the “right wing of Japanese politics” have become popular. So what about people"s consciousness? It would not be enough to discuss the "right wing" of Japanese society with the case of a politician alone. This is because the Abe government that led the “right wing of Japanese politics” was also the result of the Japanese voters" choice. In fact, the results of public opinion polls show that over the past decade, Japanese people have increased their positive feelings toward “Japan”. A sociologist says that this affirmation of the present is rooted in the Japanese"s "realization of abundance" in the 1970s. According to him, he calls for a look at the “post-war” movement of contemporary Japan in a longer historical context. In the end, this discussion cannot be limited to the political changes resulting from the emergence of the second Abe regime (2012), and it must be analyzed in conjunction with the structural changes of the post-war system that began in the 1970s and continues to this day. It reminds us that it is a task.

Gate Oxide Nitrogen Profile Engineering by Re-Oxidation in NNFC 180 nm CMOS Technology

서동주(Dongju Seo),임유리(Yuri Lim),노길선(Kil Sun Roh),김영수(Young Su Kim),이동욱(Dong Wook Lee),이원철(Won-Chul Lee),황해철(Hae chul Hwang),송종현(Jong Hyun Song),설우석(Woo-Suk Sul) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6

광주지역 하수슬러지 중 PAHs 함량 조사 및 유해성 평가

서동주 ( Dongju Seo ),이우진 ( Woojin Lee ),서희정 ( Heejeong Seo ),박지영 ( Jiyoung Park ),박주현 ( Juhyun Park ),정연재 ( Yeonjae Jeong ),전홍대 ( Hongdae Jeon ),서광엽 ( Gwangyeob Seo ),정재운 ( Jaewoon Jung ),김난희 ( Nanhee K 한국폐기물자원순환학회 2024 한국폐기물자원순환학회지 Vol.41 No.3

In this study, the content of polycyclic aromatic hydrocarbons (PAHs) contained in the sewage sludge generated in Gwangju was investigated. Additionally, the possibility of using the sewage sludge as a resource rather than a waste was confirmed through hazard assessment. The total PAH concentration ranged from 0.66 to 3.73 mg/kg dry weight (d.w.), with a mean concentration of 1.63 mg/ kg d.w. Total PAH concentrations varied significantly with seasonal rainfall and water usage, and the proportion of high molecular weight (HMW)-PAHs was relatively high when affected by wastewater discharged from industrial areas. The source of PAHs was estimated according to the proportion of individual isomers, and it was found that most PAHs were caused by burning fossil fuels or biomass. The PAH concentration in sewage sludge in Gwangju was less than 6 mg/kg, which is the threshold for use on farmland in developed countries, such as countries in Europe. Therefore, the sewage sludge can be used as a resource.

머신러닝 기반 위성영상과 수질·수문·기상 인자를 활용한 낙동강의 Chlorophyll-a 농도 추정

박소련,손상훈,배재구,이도이,서동주,김진수,Soryeon Park,Sanghun Son,Jaegu Bae,Doi Lee,Dongju Seo,Jinsoo Kim 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5

Algal bloom outbreaks are frequently reported around the world, and serious water pollution problems arise every year in Korea. It is necessary to protect the aquatic ecosystem through continuous management and rapid response. Many studies using satellite images are being conducted to estimate the concentration of chlorophyll-a (Chl-a), an indicator of algal bloom occurrence. However, machine learning models have recently been used because it is difficult to accurately calculate Chl-a due to the spectral characteristics and atmospheric correction errors that change depending on the water system. It is necessary to consider the factors affecting algal bloom as well as the satellite spectral index. Therefore, this study constructed a dataset by considering water quality, hydrological and meteorological factors, and sentinel-2 images in combination. Representative ensemble models random forest and extreme gradient boosting (XGBoost) were used to predict the concentration of Chl-a in eight weirs located on the Nakdong river over the past five years. R-squared score (R²), root mean square errors (RMSE), and mean absolute errors (MAE) were used as model evaluation indicators, and it was confirmed that R² of XGBoost was 0.80, RMSE was 6.612, and MAE was 4.457. Shapley additive expansion analysis showed that water quality factors, suspended solids, biochemical oxygen demand, dissolved oxygen, and the band ratio using red edge bands were of high importance in both models. Various input data were confirmed to help improve model performance, and it seems that it can be applied to domestic and international algal bloom detection.

머신러닝 기반 MMS Point Cloud 의미론적 분할

배재구 ( Jaegu Bae ),서동주 ( Dongju Seo ),김진수 ( Jinsoo Kim ) 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.5

자율주행차에 있어 가장 중요한 요소는 차량 주변 환경과 정확한 위치를 인식하는 것이며, 이를 위해 다양한 센서와 항법 시스템 등이 활용된다. 하지만 센서와 항법 시스템의 한계와 오차로 인해 차량 주변 환경과 위치 인식에 어려움이 있다. 이러한 한계를 극복하고 안전하고 편리한 자율주행을 위해서 고정밀의 인프라 정보를 제공하는 정밀도로지도(high definition map, HD map)의 필요성은 증대되고 있다. 정밀도로지도는 모바일 매핑 시스템(mobile mapping system, MMS)을 통해 획득된 3차원 point cloud 데이터를 이용하여 작성된다. 하지만 정밀도로지도 작성에 많은 양의 점을 필요로 하고 작성 항목이 많아 수작업이 요구되어 많은 비용과 시간이 소요된다. 본 연구는 정밀도로지도의 필수 요소인 차선을 포함한 도로, 연석, 보도, 중앙분리대, 기타 6개의 클래스로MMS point cloud 데이터를 유의미한정보로 분할하여 정밀도로지도의 효율적인 작성에 목적을 둔다. 분할에는 머신러닝 모델인 random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN) 그리고 gradient boosting machine (GBM)을 사용하였고 MMS point cloud 데이터의 기하학적, 색상, 강도 특성과 차선 분할을 위해 추가한 도로 설계적 특성을 고려하여 11개의 변수를 선정하였다. 부산광역시 미남역 일대 5차선도로 130 m 구간의 MMS point cloud 데이터를 사용하였으며, 분할 결과 각 모델의 평균 F1 score는 RF 95.43%, SVM 92.1%, GBM 91.05%, KNN 82.63%로 나타났다. 가장 좋은 분할 성능을 보인 모델은 RF이며 클래스 별 F1 score는 도로, 보도, 연석, 중앙분리대, 차선에서 F1 score가 각각 99.3%, 95.5%, 94.5%, 93.5%, 90.1% 로 나타났다. RF 모델의 변수 중요도 결과는 본 연구에서 추가한 도로 설계적 특성의 변수 XY dist., Z dist. 모두 mean decrease accuracy (MDA), mean decrease gini (MDG)가 높게 나타났다. 이는 도로 설계적 특성을 고려한 변수가 차선을 포함한 여러 클래스 분할에 중요하게 작용하였음을 뜻한다. 본 연구를 통해 MMS point cloud를 머신러닝 기반으로 차선을 포함한 여러 클래스로 분할 가능성을 확인하고 정밀도로지도 작성 시 수작업으로 인한 비용과 시간 소모를 줄이는데 도움이 될 것으로 기대한다. The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

UNet기반 Sentinel-1 SAR영상을 이용한 수체탐지: 섬진강유역 대상으로

이도이 ( Doi Lee ),박소련 ( Soryeon Park ),서동주 ( Dongju Seo ),김진수 ( Jinsoo Kim ) 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.5

전 세계적인 기후변화로 재해발생빈도가 증가하고 있으며, 국내에서도 이례적인 폭우 및 장마현상이 발생되고 있다. 이러한 기상이변현상은 가뭄, 홍수 등으로 이어져 2차피해를 유발할 수 있으므로 주기적인 모니터링과 신속한 탐지가 중요하다. 수체탐지를 위하여 광학영상을 활용한 연구가 지속적으로 이루어지고 있으나, 폭우를 동반하여 발생하는 홍수를 탐지하기 위해서는 구름의 영향으로 탐지하기 어렵다는 한계를 대변하기 위해 전천후 주야에 관계없이 관측가능한 합성개구레이더(synthetic aperture radar, SAR)를 활용한 연구가 필요하다. 본 연구에서는 개방데이터로서 24시간 이내에 수집 가능한 Sentinel-1 SAR 영상을 활용하여 최근 다양한 분야에서 활용되고 있는 딥러닝 알고리즘인 UNet을 적용하였다. 선행연구에서 SAR영상과 딥러닝 알고리즘을 이용하여 수체탐지 연구가 진행되고 있지만, 국내를 대상으로 소수의 연구만이 진행되었다. 따라서 SAR 영상의 딥러닝 적용가능성을 파악해보고자 UNet과 기존의 알고리즘인 임계값(thresholding) 방법을 비교하였으며, 5가지 지수와 Sentinel-2 normalized difference water index (NDWI)로 평가하였다. Intersect of union (IoU)로 정확도를 평가해 본 결과 UNet은 0.894, 임계값 방법은 0.699로 UNet의 정확도가 높은 것을 확인할 수 있었다. 본 연구를 통해 딥러닝 기반 SAR영상의 적용가능성을 확인할 수 있었으며, 고해상도의 SAR영상과 딥러닝 알고리즘을 적용한다면, 국내를 대상으로 주기적이고 정확한 수체의 변화탐지가 가능할 것이라 기대된다. The frequency of disasters is increasing due to global climate change, and unusual heavy rains and rainy seasons are occurring in Korea. Periodic monitoring and rapid detection are important because these weather conditions can lead to drought and flooding, causing secondary damage. Although research using optical images is continuously being conducted to determine the waterbody, there is a limitation in that it is difficult to detect due to the influence of clouds in order to detect floods that accompany heavy rain. Therefore, there is a need for research using synthetic aperture radar (SAR) that can be observed regardless of day or night in all weather. In this study, using Sentinel-1 SAR images that can be collected in near-real time as open data, the UNet model among deep learning algorithms that have recently been used in various fields was applied. In previous studies, waterbody detection studies using SAR images and deep learning algorithms are being conducted, but only a small number of studies have been conducted in Korea. In this study, to determine the applicability of deep learning of SAR images, UNet and the existing algorithm thresholding method were compared, and five indices and Sentinel-2 normalized difference water index (NDWI) were evaluated. As a result of evaluating the accuracy with intersect of union (IoU), it was confirmed that UNet has high accuracy with 0.894 for UNet and 0.699 for threshold method. Through this study, the applicability of deep learning-based SAR images was confirmed, and if high-resolution SAR images and deep learning algorithms are applied, it is expected that periodic and accurate waterbody change detection will be possible in Korea.

광물탄산화 및 알칼리 용융법을 연계한 고로슬래그 기반 희토류 회수 방법

심규대(Gyudae Sim),박영준(Youngjune Park),박예찬(Yechan Park),홍수진(Sujin Hong),서동주(Dongju Seo),문석윤(Seokyoon Moon),조준희(Junhee Cho) 대한환경공학회 2021 대한환경공학회 학술발표논문집 Vol.2021 No.11

포토다이오드의 광대역 흡수를 위한 블랙 실리콘 기반의 나노 구조 개발

김채환(Chaehwan Kim),나예은(Yeeun Na),김근희,유종범(Jong-Bum You),임부택(Boo Taek Lim),강일석(Il-Suk Kang),권수현(Soo-Hyun Kwon),김수현(Su-Hyeon Kim),조의제(Eui-Je Jo),송종현(Jong Hyun Song),서동주(Dongju Seo),이승인(Seung-In Lee),이병일(B 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6

比例計數管의 제작과 充塡가스 壓力의 효과에 관한 硏究

崔勝平,李官敎,徐東珠 조선대학교 기초과학연구소 1980 自然科學硏究 Vol.3 No.1

We have studied the performance of the gas proportional counter tube using a mixture of argon and carbon cioxide (Ar-98%, CO_2-2%). In this report, by using a heating system, a gas proportional tube made of copper cylinder was covered with Cu_2O thin film. It's characteristics depending on the pressure in the counter tube are described. The shape and plateau curves resulting from the data were very similar to measurements which were reported in the past. We found that the possibility of production of the gas proportional counter tube exists in our laboratorty.