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.

This study compared and analyzed the difference in performance between the existing soot probe and the improved one that was applied to the actual inspection vehicle at the actual inspection site, which has been developed under the specific conditions based on the excellent results through the performance evaluation. As the results, probe(b) involves a structure designed close to the center of the circumference of the exhaust pipe, and the suction efficiency was improved by adding a center unit. The improved probe(b) can enhance the effectiveness of the inspection when applied to total and regular tests inspections, and the possibility of contributing to the reduction of carbon dioxide emissions generated in the transportation sector has been confirmed.

In the present study, the thermal conductivity of a silicon nitride(Si3N4) thin-film is evaluated using the dualwavelength pump-probe technique. A 100-nm thick Si3N4 film is deposited on a silicon (100) wafer using the radio frequency plasma enhanced chemical vapor deposition technique and film structural characteristics are observed using the X-ray reflectivity technique. The film’s thermal conductivity is measured using a pump-probe setup powered by a femtosecond laser system of which pump-beam wavelength is frequency-doubled using a beta barium borate crystal. A multilayer transient heat conduction equation is numerically solved to quantify the film property. A finite difference method based on the Crank-Nicolson scheme is employed for the computation so that the experimental data can be curve-fitted. Results show that the thermal conductivity value of the film is lower than that of its bulk status by an order of magnitude. This investigation offers an effective way to evaluate thermophysical properties of nanoscale ceramic and dielectric materials with high temporal and spatial resolutions.

We consider a wafer lot transfer/release planning problem from semiconductor wafer fabrication facilities to probing facilities with the objective of minimizing the deviation of workload and total tardiness of customers’ orders. Due to the complexity of the considered problem, we propose a two-level hierarchical production planning method for the lot transfer problem between two parallel facilities to obtain an executable production plan and schedule. In the higher level, the solution for the reduced mathematical model with Lagrangian relaxation method can be regarded as a coarse good lot transfer/release plan with daily time bucket, and discrete-event simulation is performed to obtain detailed lot processing schedules at the machines with a priority- rule-based scheduling method and the lot transfer/release plan is evaluated in the lower level. To evaluate the performance of the suggested planning method, we provide computational tests on the problems obtained from a set of real data and additional test scenarios in which the several levels of variations are added in the customers’ demands. Results of computational tests showed that the proposed lot transfer/planning architecture generates executable plans within acceptable computational time in the real factories and the total tardiness of orders can be reduced more effectively by using more sophisticated lot transfer methods, such as considering the due date and ready times of lots associated the same order with the mathematical formulation. The proposed method may be implemented for the problem of job assignment in back-end process such as the assignment of chips to be tested from assembly facilities to final test facilities. Also, the proposed method can be improved by considering the sequence dependent setup in the probing facilities.

The necessity of large-area and high-precision measurements has increased in industry area. The high-speed/high-precision multi probe measurement system has been developed to measure the 3D shape. The three different multi probes are combined in measurement system. This system is synchronized between the probes to measure the same position in the sample. Also this paper shows the measurement results with multi probe measurement system.

자전거는 에너지 소모나 오염을 발생시키지 않는 지속가능한 녹색교통수단으로 정부는 자전거이용 활성화를 적극 추진하고 있다. 그러나 자전거 시장규모 증가와 함께 자전거 사고 또한 지속적으로 증가하고 있다. 현재 KHCM에서 제시하고 있는 자전거도로 평가는 자전거도로를 유형별로 구분하여 각기 다른 효과척도를 적용하여 자전거의 이동성과 안전성을 평가하고 있으며, 현장조사를 통한 자료수집을 통해 도로를 평가하고 있다. 본 연구에서는 공공자전거에 장착된 GPS수신기를 활용한 자전거 주행환경 평가 방안으로 수집된 자전거 주행속도자료를 이용하여 모든 자전거도로에 적용 가능한 구간단위의 자전거 주행환경을 평가하는 방법론을 제시하였다. 주행쾌적성 평가지표 개발을 위해 자전거GPS속도계를 장착한 실험자전거를 이용하여 도로선형, 신호교차로 및 과속방지턱의 유무 등 주행환경이 다른 자전거도로의 속도자료를 수집하였다. 분석시간동안 자전거 주행환경평가를 위해 자전거 주행속도 감소량을 분석하여 주행쾌적성을 0~1 사이의 값으로 계량화한 CCI(Cycling Comfortability Index)를 개발하였다. 실험구간별로 산출된 CCI를 분석한 결과, 주행쾌적성은 신호교차로와 종단구배의 영향을 많이 받는 것으로 나타났으며, 이를 바탕으로 도로환경에 따른 LOCC(Level of Cycling Comfortability)를 도출하였다. 본 연구의 결과는 GPS수신기를 장착한 공공자전거에 적용하여 자전거 교통의 모니터링 시스템으로 발전시키는데 유용한 기초자료로 활용될 것으로 기대된다.

교통사고 원인분석 및 사고예방을 위해서는 교통사고 유발요인에 대한 이해가 필요하다. 기존 연구에서는 기하구조, 운전자 특성 등의 요인을 고려하여 연구를 진행하였다. 그러나, 운전자 특성요인 분석에 사용된 자료는 검지기에서 측정된 집계된 속도로써, 속도 변화량을 이용한 사고분석연구에는 한계가 존재한다. 따라서, 본 연구에서는 차량의 속도변화 등의 수집이 용이한 센서를 이용하여 자료를 수집하였다. 가속도자료 및 기하구조 특성을 나타내는 변수를 설정하고, 사고자료와 매칭을 통해 사고개연성이 높은 잠재적 변수로의 적합성을 평가하였다. T-test, 이항 로지스틱 회귀분석을 사용했으며, T-test 결과로써 도출된 변수를 이항 로지스틱 회귀분석의 독립변수에 적용하고, 사고발생 유 무를 종속변수로 설정하였다. 분석결과, 5개의 변수가 사고발생에 영향을 주는 변수로 도출되었다. 또한, 도출된 모형은 사고발생구간의 예측에 적용할 수 있는 타당성을 확보하는 것으로 분석되었다. 본 연구에서 도출된 위험 운전행태 변수 및 모형은 프로브차량에 설치하여 활용할 수 있는 장치 등에 적용시켜 사고위험도 및 안전성 평가에 활용할 수 있을 것으로 기대된다.