인종위기의 리듬: 아이스 큐브의 갱스터 랩 <Black Korea>

이찬행 이주사학회 2023 Homo Migrans Vol.29 No.-

본 논문은 로스앤젤레스에서 활동한 갱스터 래퍼 아이스 큐브가 1991년 10월에 발표한 곡 <Black Korea>에 의해 로스앤젤레스의 한흑관계가 악화 되었던 과정을 검토하고자 한다. 이와 동시에 본 논문은 아이스 큐브의 노래에 분노한 한인들의 집단적 대응에 대해서도 살펴볼 것이다. 음악적으로 보면, <Black Korea>가 실려 있는 아이스 큐브의 앨범 <<Death Certificate>>는 그가 동료 래퍼들보다 높은 수준의 랩 실력을 갖췄다는 사실을 보여주었다. 하지만 <Black Korea>의 가사는 노골적으로 한인 이민자 상인들을 위협하는 내용으로 채워졌다. 짧고 논란이 많았던 이 노래에 대응하기 위해 로스앤젤레스 한인 커뮤니티는 정치적 힘을 발휘하고자 했다. 또한 이러한 대립과는 별개로 흑인과 한인 사이의 관계를 개선하기 위한 노력도 진행되었다. 그러나 두순자 사건 재판은 두 커뮤니티 사이에 화해의 다리를 놓으려는 모든 노력을 무너뜨렸다. 그 결과 1991년 겨울부터 이듬해 초봄까지 로스앤젤레스에서 한인을 대상으로 한 폭력 및 혐오 관련 사건은 급격히 증가하고 말았다. This essay examines the processes through which the release of “Black Korea” in October, 1991 by Los Angeles-based gangster rapper Ice Cube exacerbated black-Korean relations in Los Angeles. It also presents an overview of collective actions of Korean Americans against the song. Musically speaking, Ice Cube’s album Death Certificate which included a track titled “Black Korea” showed that he had reached a higher level of rapping skill than his peers. However, the lyrics of “Black Korea” explicitly threatened Korean immigrant merchants. To respond to this short and highly controversial song, Korean American community in Los Angeles sought to exert political muscle. Apart from such confrontations, there were also efforts to improve the relations between blacks and Koreans. But, the case of Soon Ja Du undermined every effort to construct a bridge between the two communities. As a result, the winter of 1991-1992 witnessed a sharp increase in violent, hate-related incidents against Korean immigrants in Los Angeles.

노면결빙 사고에 영향을 미치는 기상인자의 통계적 분석

김효원,최문규,김세호,김병준,백승범,정진훈 한국도로학회 2023 한국도로학회논문집 Vol.25 No.6

PURPOSES : The purpose of this study is to statistically analyze the meteorological factors that contribute to the formation of road surface icing based on actual cases of icing accidents and provide directions for improving icing evaluation criteria. METHODS : In this study, we collected cases of domestic road icing accidents by searching news articles with the keyword ‘icing collision accidents’. Subsequently, we determined the latitude, longitude, and altitude of accident locations using satellite map service. We applied the Inverse Distance Weighting (IDW) method and temperature lapse rate to estimate meteorological data at each location. Finally, statistical analysis was conducted for temperature, humidity, and precipitation occurrence using probability density functions. RESULTS : As a result, road icing accident data points with identifiable location coordinates were collected. Among these, temperature, humidity, and precipitation occurrence from Automated Weather Stations (AWS) data were selected for analysis. During the process of correcting meteorological factors using the Inverse Distance Weighting (IDW) method, the optimal Weighting Exponent (p) that minimizes the error was determined and applied. The results showed that accidents occurring in the morning indicated the highest accident occurrence rate. The average temperature at the time of the accidents was -1.4°C, with a humidity level of 85.1%. Precipitation was observed at the time of the accident in 19 cases. CONCLUSIONS : Icing on pavement can occur not only under extreme weather conditions but also under typical meteorological conditions. Typically, icing can occur when the relative humidity is above 70%. Accordingly, for future improvements in the evaluation criteria for icing-prone areas by the Ministry of Land, Infrastructure and Transport, it is possible to incorporate the temperature and humidity ranges that generally lead to icing, taking into account climate characteristics.

도로 위의 블랙아이스 사례 분석을 통한 발생 가능 기상 환경 연구

김완희,강서영,박소연,임상욱 한국도로학회 2022 한국도로학회논문집 Vol.24 No.1

of actual and suspicious black-ice cases that occurred during the last 10 years in the Republic of Korea. METHODS : Based on literature review, meteorological observation data associated with black-ice formation are selected: wind speed, air temperature (T), dew point temperature (Td), and relative humidity, to set minimum or maximum threshold values based on the normal distribution of each variable. In addition, weights are assigned based on the relationship among the variables to calculate the probability of occurrence. RESULTS : The threshold values are calculated using the average and standard deviation, resulting in 7.65 °C, 56.63%, 2.99 ms-1 for T-Td, relative humidity, and wind speed, respectively. Whereas the threshold value of T-Td and wind speed is set to the maximum threshold, that of the relative humidity is set to the minimum threshold value. These threshold values are applied to the diagnosis algorithm of black-ice formation, including a 1-h accumulated precipitation. CONCLUSIONS : The algorithm is expected to be utilized as a research methodology for diagnosing suspected cases of black ice.

열화상카메라를 이용한 블랙아이스 특성 연구

김승준 ( Seung-jun Kim ),윤원섭 ( Won-sub Yoon ),김연규 ( Yeon-kyu Kim ) 한국산업융합학회 2021 한국산업융합학회 논문집 Vol.24 No.6

In this study, a study was conducted to develop a system for predicting/responding to black ice occurring on roads in winter. Tests conditions were studied by making models of cement concrete pavement and asphalt concrete pavement. In order to freeze water on the manufactured model package, an tests was conducted at a temperature below zero using a freezer, and the freezing process was photographed using a thermal imaging camera. Black ice is generated when water is present on the road surface and the temperature is below freezing or the road surface temperature is below the dew point temperature. Under sub-zero conditions, the pavement, water, and ice were classified with a thermal imaging camera. As a result of the tests, it was possible to distinguish with a thermal imaging camera at a temperature below freezing in the same freezer due to the difference in the emissivity of the packaging, water, and ice. In the process of changing from water to ice during the tests, it was analyzed that ice and water were clearly distinguished by the thermal imaging camera due to the difference in emissivity and reflectance, so black ice could be predicted using the thermal imaging camera.

Research on Deep learning based black ice and pothole detection in edge-devices

Dongsu Lee,Seungmin Oh,Jihoon Lee,Yeonggwang Kim,Sangjoon Lee 한국디지털콘텐츠학회 2020 The Journal of Contents Computing Vol.2 No.2

Research on the detection of dangerous areas such as potholes and black ice, which can cause traffic accidents on roads and bridges, helps maintain faster and smoother traffic flow and is essentially essential in protecting the lives and property of drivers and pedestrians. In this dissertation, we propose a deep learning model that can detect dangerous regions such as potholes and black ice. To learn and evaluate the proposed deep learning model, we collect 806 images for pothole detection, 1,000 images for black ice detection. Subjectively/objectively evaluating the performance of the proposed deep learning model, we derive 95% accuracy for pothole detection and 79% accuracy for black ice detection. This study was limited to not being able to practice in real-time road driving situations, and the next study is to implement real-time output of the results with respect to the hazardous areas detected by the camera. This will also be used for research related to smart factories, smart cities and eco-friendly cars that require hard real-time such as traffic control systems, airport control systems, and satellite launch control systems in the future.

블랙아이스로 인한 포장체의 미끄럼 저항 특성

이관호,손민수,김성겸,최성진,박도원 한국방재학회 2024 한국방재학회논문집 Vol.24 No.2

우리나라는 4계절 기후를 가지고 있어서 계절별 도로 포장체의 노면 조건의 변화가 심하고 이로 인한 교통사고가 많이 발생한다. 2015년부터 5년간 도로의 결빙과 서리로 인한 교통사고 건수는 5200여 건으로 보고되고 있고, 치사율은 일반 교통사고에 비해 9배 높은 것으로 나타났다. 본 연구에서는 블랙아이스로 인한 교통사고 저감 및 관리용 지표를 만들기 위한 첫 단계로 국내 아스팔트 포장의 미끄럼 저항 특성을 분석하였다. 실험은 ASTM 및 KS 표준에 제시된 실내실험용 BPT 장비를 이용하여 미끄럼 저항값(BPN)을 측정하였다. 도로포장 표면 상태를 건조, 습윤, 동결(블랙아이스) 등으로 구분하여 평가하였다. 측정된 미끄럼 저항값은 건조, 습윤, 동결 순으로 작게 나타났다. 습윤상태의 경우 밀입도 아스팔트 포장과 배수성 포장의 장단점이 명확하게 비교되었다. 또한, 동결상태에서 평가한 결과 도로포장의 종류에 큰 차이를 보이지 않았고, 결빙으로 인한 도로 포장체의 골재 노출과 노출된 골재의 표면 거칠기가 미끄럼 저항에 거의 영향을 주지 않는 것으로 평가되었다. Korea experiences a four-season climate, leading to significant fluctuations in seasonal road surface conditions and contributing to numerous traffic accidents. Over the period of five years since 2016, approximately 4,800 traffic accidents have been attributed to icy and frosty road conditions, with a fatality rate 1.5 times higher than that of general traffic accidents. This study aims to analyze the skid resistance properties of domestic asphalt pavements as an initial step toward establishing indicators to mitigate and manage traffic accidents caused by black ice. In this study, the BPN was measured using BPT equipment specified in the ASTM and KS standards. The road pavement surface conditions were assessed under three categories: dry, wet, and frozen (black ice). The recorded BPN values decreased in the following order: dry, wet, and frozen conditions. Under wet conditions, a comparative analysis was performed between dense asphalt pavements and drainable pavements. Furthermore, the evaluation under frozen conditions revealed no significant disparity between the types of road pavement. Notably, the exposure of the road pavement aggregate owing to freezing and the resultant surface roughness had a minimal impact on the skid resistance.

CCTV 동영상과 IR 센서 기반 강설 및 결빙 상태 감지 기술

김준철,김병석,박민철,오한진,박준용,이기세,마경훈 한국도로학회 2022 한국도로학회논문집 Vol.24 No.1

PURPOSES : Road surface conditions are vital to traffic safety, management, and operation. To ensure traffic operation and safety during periods of snow and ice during the winter, each local government allocates considerable resources for monitoring that rely on field-oriented manual work. Therefore, a smart monitoring and management system for autonomous snow removal that can rapidly respond to unexpected abrupt heavy snow and black ice in winter must be developed. This study addresses a smart technology for automatically monitoring and detecting road surface conditions in an experimental environment using convolutional neural networks based on a CCTV camera and infrared (IR) sensor data. METHODS : The proposed approach comprises three steps: obtaining CCTV videos and IR sensor data, processing the dataset acquired to apply deep learning based on convolutional neural networks, and training the learning model and validating it. The first step involves a large dataset comprising 12,626 images extracted from the acquired CCTV videos and the synchronized surface temperature data from the IR sensor. In the second step, image frames are extracted from the videos, and only foreground target images are extracted during preprocessing. Hence, only the area (each image measuring 500 × 500) of the asphalt road surface corresponding to the road surface is applied to construct an ideal dataset. In addition, the IR thermometer sensor data stored in the logger are used to calculate the road surface temperatures corresponding to the image acquisition time. The images are classified into three categories, i.e., normal, snow, and black-ice, to construct a training dataset. Under normal conditions, the images include dry and wet road conditions. In the final step, the learning process is conducted using the acquired dataset for deep learning and verification. The dataset contains 10,100 (80%) data points for deep learning and 2,526 (20%) points for verification. RESULTS : To evaluate the proposed approach, the loss, accuracy, and confusion matrix of the addressed model are calculated. The model loss refers to the loss caused by the estimated error of the model, where 0.0479 and 0.0401 are indicated in the learning and verification stages, respectively. Meanwhile, the accuracies are 97.82% and 98.00%, respectively. Based on various tests that involve adjusting the learning parameters, an optimized model is derived by generalizing the characteristics of the input image, and errors such as overfitting are resolved. This experiment shows that this approach can be used for snow and black-ice detections on roads. CONCLUSIONS : The approach introduced herein is feasible in road environments, such as actual tunnel entrances. It does not necessitate expensive imported equipment, as general CCTV cameras can be applied to general roads, and low-cost IR temperature sensors can be used to provide efficiency and high accuracy in road sections such as national roads and highways. It is envisaged that the developed system will be applied to in situ conditions on roads.

MWCNT를 혼입한 발열체의 크기에 따른 발열 효율 분석

박소현(Park, Sohyeon),안승주(An, Sungju),이희영(Lee, Heeyoung),김동휘(Kim, Donghwi),정원석(Chung, Wonseok) 한국방재학회 2021 한국방재학회논문집 Vol.21 No.5

도로살얼음은 겨울철 급격한 기온 변화에 따라 도로 표면에 발생하는 도로 결빙 현상이다. 이러한 도로살얼음은 차량 통행에 위협이 되며 사고 발생위험을 증가시킨다. 기존에는 노면 온도가 2 ℃ 이하일 경우 주로 제설제를 살포하였으나, 제설제는 도로의 노후화를 촉진시킨다. 따라서 본 연구는 Multi-Walled Carbon Nanotube (MWCNT)가 혼입된 발열체를 활용하여 문제를 해결하고자 하며, 발열체의 크기에 따른 발열 효율을 분석하고자 한다. 발열체는 크기가 50 × 50 × 50 mm3인 큐브형과 100 × 300 × 60 mm3인 큐보이드형으로 구분하였다. 매개변수는 발열체의 크기, 양생일, 공급 전압으로 선정하였다. 큐브형의 최대 발열량은 75.7 ℃이고 큐보이드형은 큐브형의 78%까지 최대 발열량이 발생하였다. 열화상 이미지를 분석한 결과, 큐보이드형은 큐브형보다 열 분산성이 더 우수함을 확인하였다. 따라서 큐보이드형의 발열 효율은 우수한 것으로 분석되며, 큐보이드형 발열체는 도로살얼음으로 인한 피해를 감소시키는데 효과적일 것으로 판단된다. Black ice is a road-freezing phenomenon that occurs on the surfaces of roads and is caused by sub-zero temperatures. Black ice is dangerous to vehicular traffic because it is difficult for a driver to detect its presence on roads. Further, it causes vehicles to lose traction on roads, thus causing accidents. Therefore, this study aims to solve this problem by utilizing a heating module with multi-walled carbon nanotubes (MWCNTs) and analyzing the heating efficiency according to the size effect of the heating module. The heating modules were divided into cubes (50 × 50 × 50 mm3) and cuboids (100 × 300 × 60 mm3). The parameters considered were the size of the heating module, number of curing days, and supply voltage. The maximum temperature change of the cubes was 75.7 ℃, and the maximum temperature change of the cuboids was 78% of the cube. The thermal images demonstrated that the cuboids exhibited better thermal dispersibility than that exhibited by the cubes. Therefore, the heating efficiency of the cuboids was inferred to be excellent. Thus, the cuboid heating module can be used to reduce the risk of accidents occurring caused by black ice.

블랙아이스 경고 네비게이션 제안

박지성(Ji-Seong Park),장민석(Minseok Jang),배석찬(Seok-chan Bae),이연식(Yonsik Lee) 한국정보통신학회 2022 한국정보통신학회 종합학술대회 논문집 Vol.26 No.2

기존 내비게이션 어플은 주로 과속단속을 알려주는 기능이 주기능이며, 블랙아이스 위치를 경고해주지는 않는다. 만약 내비게이션 어플이 이 기능을 수행한다면 교통사고 저감에 큰 효과를 발휘할 것이다. 따라서 본 논문에서는 블랙아이스를 탐지하는 방법과 이를 경고해주는 내비게이션 어플을 제안하고자 한다. The existing navigation applications mainly have a function that informs speed control, and it does not warn the location of black ice. If the navigation application performs this function, it will have a great effect on reducing traffic accidents. Therefore, in this paper, we propose a method for detecting black ice and a navigation application that warns it.

도로 기상자료를 이용한 앙상블 모형의 노면온도예측 비교연구

권태용,윤형채,최용호,윤상후 한국데이터정보과학회 2023 한국데이터정보과학회지 Vol.34 No.5

도로살얼음은 눈 또는 비가 낮은 대기온도로 인하여 도로 노면이 얇게 어는 현상으로 겨울철 도로에서 발생하는 대부분의 대형 사고는 도로살얼음과 관련이 있다. 도로 살얼음으로 발생하는 피해를 줄이기 위해서 도로 노면온도의 예측이 필요하다. 본 연구에서는 이동식 도로 기상자료와 고정식 도로 기상자료를 이용하여 도로 노면온도를 예측하기위한 기계학습 모형을 세웠다. 고려된 기계학습 모형은 랜덤포레스트 (random forest), gradient boosting, XGboost이다. 기계학습 모형 간 예측성능 평가는 평균제곱근오차 (root means squared error), 평균절대오차 (mean absolute error), 평균오차 (mean error), 상관계수 (correlation coefficient)를 이용하였다. 연구자료는 2020년 1월 5일, 1월 8일, 1월 13일, 1월 21일, 2월 5일로 총 5일의 이동식 도로 기상자료와 2017년 11월 5일부터 2020년 12월 31일까지 관측된 고정식 도로 기상자료를 사용하였다. 이동식 도로 기상자료는 품질관리 알고리즘을 적용하여 일관적인 자료를 활용하였다. 연구결과 이동식 노면온도 예측모형의 이동식 검증자료 예측과 고정식 노면온도 예측모형의 고정식 검증자료 예측은 랜덤포레스트가 가장 우수하였다. 또한, 교차예측에서는 XGboost가 우수한 예측성능을 보였다. Black ice is a phenomenon that occurs when the road surface freezes thinly due to snow or rain and low atmospheric temperature. It is related to most large-scale accidents on roads in winter and causes significant damage. Therefore, predicting the road surface temperature is necessary to prevent or mitigate the impact of black ice. This study aimed to establish a machine learning model for predicting road surface temperature using moving and fixed road weather data. The machine learning models compared in this study were random forest, gradient boosting, and XGboost. The predictive performance of each model was evaluated using root mean squared error, mean absolute error, mean error, and correlation coefficient as metrics. The study data consisted of moving road meteorological data for five days (January 5, January 8, January 13, January 21, and February 5, 2020) and fixed road meteorological data observed from November 5, 2017, to December 31, 2020. A quality control algorithm was applied to the moving road meteorological data to ensure data consistency. The results showed that random forest performed the best in predicting the moving test data for the moving road surface temperature prediction model and the fixed test data for the fixed road surface temperature prediction model. However, XGboost exhibited superior predictive performance in cross-validation.