Future autonomous vehicles need to recognize the ego lanes required for lane change and the side left and right lanes differently. Therefore, multi-lane recognition is needed. In this study, using the YOLO network, mainly used for object recognition, the proposed method recognizes the ego, left and right side lanes as different objects and identifies the correct lanes. As a result of the performance evaluation on the TuSimple test data, the proposed method recognized the ego lanes and the left and right side lanes differently. It showed very stable lane recognition results. And by detecting lanes that do not exist in the ground truth of TuSimple data, the proposed method is very robust in lanes detection. Nevertheless, studies related to learning data reinforcement in which lanes are located in the center or at the left and right edges of the image and accurate network learning for lanes are needed.

In this study, the multi-lane detection problem is expressed as a CNN-based regression problem, and the lane boundary coordinates are selected as outputs. In addition, we described lanes as fifth-order polynomials and distinguished the ego lane and the side lanes so that we could make the prediction lanes accurately. By eliminating the network branch arrangement and the lane boundary coordinate vector outside the image proposed by Chougule’s method, it was possible to eradicate meaningless data learning in CNN and increase the fast training and performance speed. And we confirmed that the average prediction error was small in the performance evaluation even though the proposed method compared with Chougule’s method under harsher conditions. In addition, even in a specific image with many errors, the predicted lanes did not deviate significantly, meaningful results were derived, and we confirmed robust performance.

최근에는 경량화 문제에 대한 합리적인 솔루션을 제공하고 유용한 개념설계를 제공할 수 있는 다중 재료 구조 위상최적화가 더욱 중요해지고 있다. 기존의 MMTO(Multi-Material Topology Optimization)의 경우 후보 물질의 수가 증가할수록 설계변수의 수도 증가 하고, 결과적으로 계산 시간이 크게 증가한다. 따라서 PSM(Phase Section Method)과 같은 단일 설계변수를 갖는 MMTO가 제안되었 다. 본 연구는 조성비가 면적이나 부피비를 나타내지 못하고, 설계변수가 목표치에 충분히 집중되지 않고, 특정 재료가 요구량보다 적 게 생성되는 PSM의 세 가지 주요 제한점을 고려하여 이를 개선하는데 중점을 둔다. 이러한 한계를 극복하기 위해 재정의된 조성비와 더 나은 수렴을 위한 조정된 매개변수를 제안한다. 제안된 수정 사항의 유효성을 2차원 및 3차원 수치 예제를 통해 검증한다.

이 논문은 울릉도, 독도를 포함하는 동해 울릉분지 해저 화산그룹(가칭: 울릉도-독도 화산그룹)의 분화사를 복원 하고 미래 분화 가능성을 평가하기 위해 진행중인 육상-해양 복합 다중스케일 화산연구의 개념과 지금까지의 결과를 소개하는데 목적을 둔다. 다중채널 탄성파반사자료 해석을 통해 유추되는 울릉도-독도 화산그룹의 주 활동 시기는 대략 5-2.5 Ma로, 동쪽 끝의 이사부평정해산에서 처음 시작되어 서쪽의 울릉도로 전파된 것으로 해석된다. 울릉도의 육상부 는 5단계에 걸쳐 생성되었으며 최종 단계에 접어들어 폭발성 분화를 통해 폭 약 3 km의 칼데라 지형과 그 내부에 화 산돔을 형성하였다. 울릉도의 마지막 분화 산물인 알봉 화산돔과 그 하부의 수증기마그마성 분화로부터 기원된 층단위 N-1 화산쇄설층은 기존 포놀라이트질 마그마방으로 유입된 보다 염기성의 새로운 마그마의 분화로부터 생성되었다. 층 단위 N-1과 알봉 분석이 갖는 조면안산암질 전암조성은 기존 포놀라이트질 마그마방 하부에 존재한 고철질 집적암이 새로 주입된 마그마를 오염시킨 결과로, 그 자체가 마그마의 조성을 지시하지는 않는다. 층단위 N-1과 알봉 화산돔에서 확인되는 새로운 마그마 주입의 증거들은 최근 밝혀진 울릉도의 높은 지온구배 및 분화주기와 더불어 울릉도가 여전히 작동중인 마그마 배관체계를 갖는 활화산임을 암시한다.

In this study, indoor air quality indices (IAQI-C and IAQI-E) were developed for child care centers and elderly care centers based on health effects, and compared to the air quality index (CAI) for outdoor atmosphere. In addition, composite indices that integrate individual indices for each pollutant were developed to quickly and conveniently recognize the current air quality. Among all data, 71.2% and 35.6% belonged to the grades 'Good' and 'Moderate' for child care centers and elderly care centers, respectively. This assessment is more stringent than the CAI's criteria.

We need data such as the number of lanes for lane change on the road as well as environmental and object recognition of the road for the autonomous vehicle of the future. This study proposed an algorithm that recognizes the left and right lanes and the center lane while driving differently from the black box image taken from a car. In general, deep learning does not recognize lanes individually but recognizes all lanes as only one lane. Therefore, using YOLO's object recognition function, the left and right lanes and the center lane were detected as different lanes, and a heuristic method was applied to recognize multi-lanes as more correct lanes. As a result of the performance evaluation, we confirmed that the proposed method detects the lane more accurately than Fast R-CNN and only YOLOv2.

Based on the multilingual perspective that emphasizes that non-native variants of English need to be recognized as having values equivalent to the native speakers’ English, this study aimed at discovering elementary school teachers’ perceptions and beliefs of native-speakerism, multilingualism, and monolingualism in English education, and the contradictions and questions they had as they reflected on the language ideologies. Nine elementary teachers participated in the study, and two written reflections on language ideologies and two follow-up group interviews were used as the data source. The findings were as follows. First, the teachers confirmed, consolidated, or realized how native speakerism and monolingualism were deeply rooted in English teaching and learning. However, they showed that they had agency in their own teaching, and also showed changes in their perception through reflecting on their experiences. Last, the teachers had questions and contradictions about implementing a multilingual approach in teaching English, which provides the issues and challenges to be solved for the transformation of English education in the multilingual world. This study has implications for primary English education.

본 연구는 제품과 물리적 접촉이 불가능한 온라인 환경에서 다중 감각 단서가 소비자의 촉각적 심상과 제품에 대한 태도 및 구매 의사에 미치는 영향을 알아보고자 하였다. 실험 1에서는 참가자들을 음향 없이 사진만 제시한 조건, 음향 없이 영상만 제시한 조건, 일치하는 음향과 영상을 제시한 조건, 불일치하는 음향과 영상을 제시한 조건으로 나누어 제품을 탐색하게 하였고 그 후 촉각적 심상선명도, 제품 사용의 상상용이성, 제품에 대한 태도-구매 의사를 측정하였 다. 그 결과, 모든 변수에서 불일치하는 음향과 영상이 주어진 조건이 가장 낮은 평균 점수를 보였다. 실험 2는 참가자 내 디자인으로 설계되어, 참가자들에게 동일한 제품을 실험 1과 같은 네 개의 조건에서 탐색하게 하였다. 탐색 후,촉각적 심상선명도, 제품 사용의 상상용이성, 제품에 대한 태도-구매 의사, 접촉 욕구 등을 측정하였다. 접촉 욕구를 공변인으로 하는 반복측정 공분산분석 결과, 일치하는 음향과 영상을 제시한 조건은 다른 조건과 비교했을 때 유의미 하게 심상선명도, 제품에 대한 태도, 제품 구매 의사가 높았다. 이중 매개 분석 결과, 다중 감각 단서 조건은 심상선명 도와 제품 사용의 상상용이성을 순차적으로 매개하여 제품에 대한 태도-구매 의사를 유의미하게 예측했다. 결론적으 로, 음향 및 영상 단서로 유발된 선명한 촉각적 심상은 소비자가 제품을 사용하는 상황의 상상을 쉽게 만들어 태도-구 매 의사와 같은 의사 결정에 긍정적 영향을 미칠 수 있다.

In this study, a drifting test using a experimental vessel (2,966 tons) in the northern waters of Jeju was carried out for the first time in order to obtain the fundamental data for drift. During the test, it was shown that the average leeway speed and direction by GPS position were 0.362 m/s and 155.54° respectively and the leeway rate for wind speed was 8.80%. The analysis of linear regression modes about leeway speed and direction of the experimental vessel indicated that wind or current (i.e. explanatory variable) had a greater influence upon response variable (e.g. leeway speed or direction) with the speed of the wind and current rather than their directions. On the other hand, the result of multiple regression model analysis was able to predict that the direction was negative, and it was demonstrated that predicted values of leeway speed and direction using an experimental vessel is to be more influential by current than wind while the leeway speed through variance and covariance was positive. In terms of the leeway direction of the experimental vessel, the same result of the leeway speed appeared except for a possibility of the existence of multi-collinearity. Then, it can be interpreted that the explanatory variables were less descriptive in the predicted values of the leeway direction. As a result, the prediction of leeway speed and direction can be demonstrated as following equations. However, many drift tests using actual vessels and various drifting objects will provide reasonable estimations, so that they can help search and rescue fishing gears as well.