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Jo, Kichun,Jo, Yongwoo,Suhr, Jae Kyu,Jung, Ho Gi,Sunwoo, Myoungho IEEE 2015 IEEE transactions on intelligent transportation sy Vol.16 No.6
<P>This paper presents a Monte Carlo localization algorithm for an autonomous car based on an integration of multiple sensors data. The sensor system is composed of onboard motion sensors, a low-cost GPS receiver, a precise digital map, and multiple cameras. Data from the onboard motion sensors, such as yaw rate and wheel speeds, are used to predict the vehicle motion, and the GPS receiver is applied to establish the validation boundary of the ego–vehicle position. The digital map contains location information at the centimeter level about road surface markers (RSMs), such as lane markers, stop lines, and traffic sign markers. The multiple images from the front and rear mono-cameras and the around-view monitoring system are used to detect the RSM features. The localization algorithm updates the measurements by matching the RSM features from the cameras to the digital map based on a particle filter. Because the particle filter updates the measurements based on a probabilistic sensor model, the exact probabilistic modeling of sensor noise is a key factor to enhance the localization performance. To design the probabilistic noise model of the RSM features more explicitly, we analyze the results of the RSM feature detection for various real driving conditions. The proposed localization algorithm is verified and evaluated through experiments under various test scenarios and configurations. From the experimental results, we conclude that the presented localization algorithm based on the probabilistic noise model of RSM features provides sufficient accuracy and reliability for autonomous driving system applications.</P>
Yongwoo Jo,Mina K Kim 한국식품영양과학회 2021 한국식품영양과학회 학술대회발표집 Vol.2021 No.10
Doenjang is Korean traditional soybean fermented food. The characteristics of traditional doenjang differed depending on the region, which is due to the difference in the dominant microbial species involved in natural fermentation process. The objective of this study was to determine the region-specific characteristics of traditional doenjang produced in Chungcheongnam-do region using physiochemical and descriptive sensory analysis. A total of 16 doenjang were selected and physiochemical properties were analyzed following standard method of analysis. Descriptive sensory analysis using a highly trained panel (n=6) was conducted using a 15-point Universal scale in Spectrum<SUP>TM</SUP> method. Differences in physiochemical properties were observed (p<0.05). 27 sensory lexicons describing the aroma characteristics of traditional doenjang were defined. Overall, the characteristics of traditional doenjang such as alcohol, meju, soy sauce, as well as chocolate aromatics appeared in all samples in differing degrees. Finding from this study can provide the region-specific aroma characteristics as well as physiochemical characteristics of traditional doenjang produced in Chungcheongman-do.