Silicon carbide (SiC) has emerged as a promising material for next-generation power semiconductor materials, due to its high thermal conductivity and high critical electric field (~3 MV/cm) with a wide bandgap of 3.3 eV. This permits SiC devices to operate at lower on-resistance and higher breakdown voltage. However, to improve device performance, advanced research is still needed to reduce point defects in the SiC epitaxial layer. This work investigated the electrical characteristics and defect properties using DLTS analysis. Four deep level defects generated by the implantation process and during epitaxial layer growth were detected. Trap parameters such as energy level, capture-cross section, trap density were obtained from an Arrhenius plot. To investigate the impact of defects on the device, a 2D TCAD simulation was conducted using the same device structure, and the extracted defect parameters were added to confirm electrical characteristics. The degradation of device performance such as an increase in on-resistance by adding trap parameters was confirmed.

Safe operation of freight vehicles is an important issue for drivers, cargo, and other road users. In particular, the center of gravity of a freight vehicle is directly related to the stability of the vehicle, and this can fluctuate in real time depending on weight changes. Every time a freight vehicle loads or unloads cargo, its center of gravity changes, and these changes greatly affect the risk of vehicle rollover. We researched a continuous center of gravity measurement system for freight vehicles for safe driving.

This study investigates the utilization of existing CCTV networks for road traffic volume measurement, a key indicator of road infrastructure utility. Traditionally, traffic studies, which are costly and time-consuming, are divided into continuous or ad-hoc surveys. By leveraging current CCTV systems, the proposed method eliminates the need for new installations, conserving resources and increasing efficiency. Preliminary results indicate that this approach offers a time and cost-effective alternative for traffic assessments, with the potential to transform traditional survey techniques.

In contemporary society, traffic concerns and road safety are of paramount importance. Overloading and improper loading significantly jeopardize road safety. Leveraging AI Hub's CCTV traffic videos, overloading hazard data, and Yeosu Sunsin Bridge HS-WIM data from Jeollanam-do, we've crafted detection models. These models facilitate the development of a vehicle detection system calibrated for specific scenarios, making the detection of overloading and loading discrepancies more accessible. Such a system promises to streamline detection operations while substituting manual efforts with AI, thereby economizing on both time and resources.

The lane departure warning device can not detect the lane to be driven in the future by sensing the departure of the lane passing by during driving and warning the driver. Considering the safe operation of the truck, it is also expected that the departure of the future lanes according to the dynamic weight and speed of the current truck should be predicted. This study attempted to predict whether or not to deviate from the lanes of curved roads to be driven in the future according to the current dynamic driving weight and speed in consideration of the safe driving of trucks.

Recently, traffic accidents have continued to occur due to the failure to secure a safe distance for trucks. Unlike passenger cars, freight cars have a large fluctuation in the weight of the vehicle's shaft depending on the load, and the fatality of accidents and the possibility of accidents are high. In this study, a braking distance prediction model according to the driving speed and loading weight of a three-axis truck was implemented to prevent a forward collision accident. Learning data was generated based on simulation, and a prediction model based on machine learning was implemented to finally verify accuracy. The extra trees algorithm was selected based on the most frequently used R2 Score among regression analyses, and the accuracy of the braking distance prediction model was 98.065% through 10 random scenarios.

Foreign materials with a variety of types and sizes are found in food; thus, extraordinary efforts and various analytical methods are required to identify the types of foreign materials and to find out accurate causes of how they unintentionally enter food. In this study, human, cow, pig, mouse, duck, goose, dog, and cat were chosen as various types of animal hairs because they can be frequently incorporated into food during its production or consumption step. We morphologically analyzed them using stereoscopic, optical, SUMP method, and scanning electron microscopes, showing differences in each type. In addition, X-ray fluorescence spectrometer (XRF) was used to analysis chemical compositions (11Na~92U, Mass%) of samples. As a result, we observed that mammalian hairs were mainly composed of sulfur. Organic compounds of samples were further analyzed by fourier transform infrared spectroscopy (FT-IR) that can compare spectra of given materials; however, this method did not show significant differences in each sample. In this study, we suggest a rapid method for the identification of the causes and types of foreign materials in food.

This study evaluated the mechanical joining characteristics of substrate Al7075 using the filler metals of ER5356 and Al7075 to secure the joining integrity of the specimens by GTAW. The results of radiographic test show that the welded specimens meet the first level standard of KS D 2042. Besides, welding defects were not occurred. The tensile strengths of the specimens using the filler metals of Al7075 and ER5356 had 240MPa and 252MPa, respectively. The yield strengths were 132MPa and 120MPa, respectively. In case of using the filler metal of Al7075, However, in case of using the filler metal of ER5356, Two filler metals of Al7075 and ER5356 were similar to each other in tensile and yield strength.

유러피언(Eu) 착물을 이용하여 산화 그래핀 시트와 비공유 결합방법을 이용하여 제조하였으며, 산화 그래핀(GOS)뿐만 아니라 혼합된 각각의 물질의 특성을 유러피언(Eu) 착물의 흡착을 확인하였다. 또한, 하이브리드 산화 그래핀(GOS)-유러피언(Eu) 착물의 최종생성물은 생물학적 labeling과 anti-counterfeiting 등 여러 실용적인 분야에 적용 가능한 밝은 적색의 발광을 방출하는 물질이다.