PURPOSES : This study develops a model that can estimate travel speed of each movement flow using deep-learning-based probe vehicles at urban intersections. METHODS : Current technologies cannot determine average travel speeds for all vehicles passing through a specific real-world area under obseravation. A virtual simulation environment was established to collect information on all vehicles. A model estimate turning speeds was developed by deep learning using probe vehicles sampled during information processing time. The speed estimation model was divided into straight and left-turn models, developed as fully-offset, non-offset, and integrated models. RESULTS : For fully-offset models, speed estimation for both straight and left-turn models achieved MAPE within 10%. For non-offset models, straight models using data drawn from four or more probe vehicles achieved a MAPE of less than 15%. The MAPE for left turns was approximately 20%. CONCLUSIONS : Using probe-vehicle data(PVD), a deep learning model was developed to estimate speeds each movement flow. This, confirmed the viability of real-time signal control information processing using a small number of probe vehicles.

PURPOSES : Basic research to calculate the appropriate gap acceptance for autonomous vehicles at merging section. Research on whether users prefer short or long gap acceptance. METHODS : Using a driving simulator, experience autonomous driving with different gap acceptance in different weather condition, and analyze which gap acceptance is preferred using survey and biometric data. RESULTS : Regardless of the weather condition, long gap acceptance was preferred, and difference was especially clear in rainy or foggy situation. CONCLUSIONS : It was analyzed that users prefer long gap acceptance over short gap acceptance, and that they feel less frustrated due to long gap acceptance when weather condition is poor.

PURPOSES : Evaluation of the effectiveness of changing the form of yellow carpet installation as a way to reduce child pedestrian traffic accidents. METHODS : Through expert opinion, two improvement plans for yellow carpet installation (oblique type, extended type) were derived. The improvement paln was built in virtual reality, and a virtual driving experiment was performed using a driving simulator and eye-trakcing device. The improvement effects of the two alternatives were evaluated by analyzing eye-tracking data and driving behavior. RESULTS : In the case of the oblique type, it was analyzed that it was effective in improving the total gaze time and first gaze position compared to the normal type. In the case of the extended type, it was analyzed that the workload during operation can be reduced. However, neither of them had a significant effect on driving behavior. CONCLUSIONS : Although the change in the yellow carpet installation type did not affect the driver's driving behavior, it had advantages in terms of visual behavior and workload while driving, so it can be considered as an alternative among measures to improve traffic accidents involving children and pedestrians.

본 논문에서는 LS-DYNA를 활용한 원자력발전소 설치 로드블록 차량 시뮬레이션 방법을 소개한다. 차량 강습 위협이 원자력 발전 소의 설계기준위협으로 포함된 이후로 차량 강습을 대비하기 위한 차량 방벽(Anti-ram barrier)의 성능 평가 소요가 커지고 있다. 차량 방벽은 일반적으로 충돌 실험을 통하여 성능을 인증 받는다. 하지만 국내에서는 차량 방벽에 대한 성능 시험 시설이 마련되어 있지 않 아, 시뮬레이션을 통한 차량 방벽 성능 검증이 필요하다. LS-DYNA는 충돌 시뮬레이션에 특화되어 있으며, NCAC를 비롯한 여러 기 관에서 충돌 시험과의 타당성 검증을 완료한 수치 모델을 배포하고 있다. 본 논문에서는 로드블록의 가장 핵심적인 차량 차단막 모듈 의 FE 모델을 구축하여 충돌 시뮬레이션을 수행하였다. 계산된 결과는 NCHRP 179의 차량 안전 시설 충돌 시뮬레이션 검증 기준을 준용하여 검증하였다. 그 결과 모래시계 에너지(hourglass energy)가 총 에너지의 5%를 넘지 않고 내부 에너지의 10%를 넘지 않는 것 을 확인하였으며, added mass가 1% 미만으로 기준인 10%를 넘지 않는 것을 확인하였다. 향후 FE 모델을 활용하여 물리적 방벽의 성 능을 평가하여 데이터 베이스를 구축할 예정이다.

PURPOSES : This study uses deep learning image classification models and vehicle-mounted cameras to detect types of pavement distress — such as potholes, spalling, punch-outs, and patching damage — which require urgent maintenance. METHODS : For the automatic detection of pavement distress, the optimal mount location on a vehicle for a regular action camera was first determined. Using the orthogonal projection of obliquely captured surface images, morphological operations, and multi-blob image processing, candidate distressed pavement images were extracted from road surface images of a 16,036 km in-lane distance. Next, the distressed pavement images classified by experts were trained and tested for evaluation by three deep learning convolutional neural network (CNN) models: GoogLeNet, AlexNet, and VGGNet. The CNN models were image classification tools used to identify and extract the combined features of the target images via deep layers. Here, a data augmentation technique was applied to produce big distress data for training. Third, the dimensions of the detected distressed pavement patches were computed to estimate the quantity of repair materials needed. RESULTS : It was found that installing cameras 1.8 m above the ground on the exterior rear of the vehicle could provide clear pavement surface images with a resolution of 1 cm per pixel. The sensitivity analysis results of the trained GoogLeNet, AlexNet, and VGGNet models were 93 %, 86 %, and 72 %, respectively, compared to 62.7 % for the dimensional computation. Following readjustment of the image categories in the GoogLeNet model, distress detection sensitivity increased to 94.6 %. CONCLUSIONS : These findings support urgent maintenance by sending the detected distressed pavement images with the dimensions of the distressed patches and GPS coordinates to local maintenance offices in real-time.

After Dantzig and Rasmer introduced Vehicle Routing Problem in 1959, this field has been studied with numerous approaches so far. Classical Vehicle Routing Problem can be described as a problem of multiple number of homogeneous vehicles sharing a same starting node and having their own routes to meet the needs of demand nodes. After satisfying all the needs, they go back to the starting node. In order to apply the real world problem, this problem had been developed with additional constraints and pick up & delivery model is one of them. To enhance the effectiveness of pick up & delivery, hub became a popular concept, which often helps reducing the overall cost and improving the quality of service. Lots of studies have suggested heuristic methods to realize this problem because it often becomes a NP-hard problem. However, because of this characteristic, there are not many studies solving this problem optimally. If the problem can be solved in polynomial time, optimal solution is the best option. Therefore, this study proposes a new mathematical model to solve this problem optimally, verified by a real world problem. The main improvements of this study compared to real world case are firstly, make drivers visit every nodes once except hub, secondly, make drivers visit every nodes at the right time, and thirdly, make drivers start and end their journey at their own homes.

Using computational fluid dynamics (CFD), this study simulated the air supply and exhaust conditions inside KTXSancheon train cabin to analyze the airflow, velocity, temperature, and residence time distributions. Based on the analyzed airflow in the cabin, the trajectory properties of droplets with various diameters exhaled from a passenger in a specific seat were analyzed. In the train cabin, forced airflow was formed by the operation of an air conditioning unit, while air stagnation occurred through spinning vortices at the front and rear where there were no floor outlets. Droplet particles ≤36 μm in diameter were dispersed throughout the cabin following the airflow generated by the air conditioning unit. The degree of dispersion differed according to the passenger seat location. In addition, the expelled droplets were mostly deposited on the surfaces of passenger bodies, seats, and floor. The ratio of deposited droplets to suspended droplets was increased with increasing droplet size. Further, the CFD study allowed the prediction of the possibility of exposure to exhaled droplets by estimating the dispersion and deposition properties of droplets released from a passenger in a specific seat. This study can be utilized to adjust the operation of air conditioning units and encourage the installation of air-purifying units to minimize secondary infections.

PURPOSES: A hard shoulder lane (HSL) is a method of solving severe traffic congestion on an expressway. Recently, it has been applied to several expressways in Korea, and there have been numerous positive effects, which include increasing the road capacity and reducing traffic congestion. However, these effects have been limited due to tunnels which created a‘ bottle-neck’effect in HSL application for longer road segments. In the tunnel sections, an HSL is generally not operational owing to problems such as narrow roads and the risk of accidents. If an HSL can be extended to tunnel segments, great positive effects can be achieved. Therefore, this study was conducted to evaluate driver behavior and stability to investigate the risk of HSL in tunnels. METHODS: The Driving Simulator experiments were conducted using some scenarios for the Geumnam and Seojong tunnels on the Seoul- Chuncheon Highway. Based on data from the experiments such as running speed, lateral replacement, and steering handling, the running stabilities of participants were analyzed. In addition, traffic flow data from VDS(Vehicle Detection System) were analyzed as before-after studies. RESULTS: Although there were some differences in driving behaviors, most participants showed safe driving behavior at a speed of less than 50 km/h. CONCLUSIONS : Based on driving behaviors and traffic flow analyses, it is concluded that HSL in tunnels can be an alternative to increase efficiency based on safe driving environments for speeds of below 50 km/h.

There is a need to reduce fuel consumption in order to reduce GHG emissions from the transport sector, which accounts for large volumes. In this study, the fuel consumption rate can be compared with the method using the CAN communication data in the engine controller through the OBD II interface and the direct measurement method using the fuel flow meter. For this purpose, we measured the fuel consumption rate in the engine controller and the fuel flow meter with the chassis dynamometer, and confirmed the reliable data of the fuel flow meter. As a result, the fuel consumption rate in the engine controller and the fuel consumption rate in the fuel flow meter were directly measured. After that, the running test was carried out using the chassis dynamometer and the reliability of the fuel consumption rate using the flow meter was confirmed.

In this paper, we compared and analyzed the performance of the GPS L1/L2 signal with GPS L1 using the navigation system. Generally the GPS receiver calculates the position of the satellite by using the ephemeris data recevied from GSP Satellites, and also calculates the position of its own using the pseudorange. This paper provides an overview of the device which can receive an GPS L1/L2, and compares the GPS L1 with the GPS L2 signal power using the simulator. It was confirmed that the GPS L1/L2 receiver and the GPS L1 receiver met the requirements of standards and it is similar to the navigation performance of the GPS L1 Receiver with the GPS L1/L2 receiver. It was also confirmed that the GPS L2 band can not correct ionospheric error in the navigation system of the combat vehicle.

본 연구는 전기자동차 충전시스템에서 전력변환장치의 경량화를 위한 최적화 분석프로세스에 대한 내용을 서술하였다. 최적화 설계는 재료 물성치에 대한 설계민감도와 수학적 최적화를 결합하여 주어진 재료량 제한조건 하에 최적의 재료분포를 찾는 설계기법으로 위상의 고정화, 자유도가 묶이는 문제 등을 해결할 수 있는 위상 최적화방법을 사용하였으며, 위상 최적화 방법 중 비교적 수식화가 간단하고 수렴성이 좋은 SIMP법(solid isotropic material with penalization)에 의해 경량화 설계를 수행하였다. 경량화 설계는 3단계의 절차로 구성하였으며, 첫 번째 단계로 전력변환장치의 기본 설계에 대한 유한요소모델을 구성하고, 하중에 대한 정적해석을 수행하였다. 두 번째 단계로 정적해석 결과에 대해 등방성 재료의 강성계수를 적용한 밀도법을 이용하여 위상 최적화를 수행하여 경량화를 위한 최적 형상을 도출하였다. 세 번째 단계로 최적 형상에 대해 차량 탑재 부품의 충격시험기준에 만족하는 반정현파 펄스형태 충격하중을 인가하여 충격해석을 수행하였다. 위상 최적화단계에서 사용 환경조건으로 설계영역 정의는 마운팅 브래킷 영역으로 제한하였으며, 마운팅 브래킷의 설계 최적화를 통해 최종적으로 기본설계대비 20%이상의 경량화가 가능한 설계기술을 확보하였다.

The wavy fins have been widely used on the compact heat exchangers in aero system, automotive, air-conditioner and cooler system. The Special Purpose Vehicle has many oil used system and it need cooling system by air in form of fin-flat tube heat exchanger. The objective of this work is to evaluate the performance of wavy fin by Computational Fluid Dynamics(CFD) analysis. 3 modified models were suggested to change protrude direction or to remove blocked surface bot and top of corrugated fin. The base model shows the lowest performance in pressure drop, and modified model 3 shows the highest performance in heat transfer rate. But, modified model 2 has the highest value in the Area goodness factor results, and modified model 3 has the highest value in the Volume goodness factor results.

본 논문에서는 차량용 반도체가 제품 출하 후 사용 환경에 따라 발생되는 불량률을 데이터 마이닝 기법을 이용하여 분석하였다. 20세기 이후 가장 보편적인 이동 수단인 자동차는 전자 컨트롤 장치와 자동차용 반도체의 사용량이 급격히 증가하면서 매우 빠른 속도로 진화하고 있다. 자동차용 반도체는 차량용 전자 컨트롤 장치 중 핵심 부품으로 소비자들에게 안정성, 연료 사용의 효율성, 운전의 안정감을 제공하기 위해 사용되고 있다. 자동차용 반도체는 가솔린엔진, 디젤 엔진, 전기 모터를 컨트롤하는 기술, 헤드업 디스플레이, 차선 유지 시스템 등 많은 부분에 적용되고 있다. 이와 같이 반도체는 자동차를 구성하는 거의 모든 전자 컨트롤 장치에 적용되고 있으며 기계적인 장치를 단순히 조합한 이상의 효과를 만들어 내고 있다. 자동차용 반도체는 10년 이상의 자동차 사용 기간을 고려하여 높은 신뢰성, 내구성, 장기공급 등의 특성을 요구하고 있다. 자동차용 반도체의 신뢰성은 자동차의 안전성과 직접적으로 연결되기 때문이다. 반도체업계에서는 JEDEC과 AEC 등의 산업 표준 규격을 이용하여 자동차용 반도체의 신뢰성을 평가하고 있다. 또한 자동차 산업에서 표준으로 제시한 신뢰성 실험 방법과 그 결과를 이용하여 개발 초기 단계 및 제품 양산 초기 단계에서 제품의 수명을 예측 하고 있다. 하지만 고객의 다양한 사용 조건 및 사용 시간 등 여러 변수들에 의해 발생되는 불량률을 예측하는 데는 한계가 있다. 이러한 한계점을 극복하기 위하여 학계와 산업계에서 많은 연구가 있어왔다. 그 중 데이터 마이닝 기법을 이용한 연구가 다수의 반도체 분야에서 진행되고 있지만, 아직 자동 차용 반도체에 대한 적용 및 연구는 미비한 상태이다. 이러한 관점에서 본 연구는 데이터 마이닝 기법을 이용하여 반도체 조립(Assembly) 과 패키지 테스트(Package test) 공정 중 발생 된 데이터들간의 연관성을 규명하고, 고객 불량 데이터를 이용하여 잠재 불량률 예측에 적합한 데이터 마이닝 기법을 검증하였다.

가축질병 바이러스는 매우 빠르게 전파되는 특징을 가지고 있으며 질병에 걸린 동물의 분비물에 접촉하거 나, 사람이나 차량에 의한 바이러스의 이동, 또는 공기를 통하여 전파가 이루어진다. 본 연구에서는 가축 및 분뇨 운반차량이 질병을 전파시키는 요인이 될 수 있다고 판단하고 전염병 발생 시 질병의 이동 경로를 파악 하고 효율적인 방역범위를 설정하는데 기초자료로 활용 하고자 한다. 축산차량의 이동경로를 분석하기 위해 ArcGIS(Ver. 10.3, ESRI, USA)의 Spatial Statistics tool에서 Measuring geographic distribution방법을 활용하여 Directional distribution을 추출하였다. 결과 값으로 위도(Latitude), 경도(Longitude), Rotation, XstdDist, YstdDist을 얻었다. 그 결과 특정한 목적을 가지고 출발지가 일정 지역에 귀속되어 있는 가축 및 분뇨 차량의 경우 출발지점, 도착지점, 목적지가 크게 바뀌지 않을 경우, 이용하는 도로는 한정되어 있었다. 30대의 차량 중 대다수의 차량이 일정한 패턴을 가지고 평상 시 이용하는 경로만 이용하여 분뇨를 운반하는 것을 확인 할 수 있었다. 본 연구를 통해 전염병 발생 시 질병의 이동 경로를 파악하고 효율적인 방역범위를 설정하여 투입 인원과 장비를 줄이면서 현재보다 효율적인 방역 범위를 설정이 가능할 것으로 판단되었다.