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Kibeom. Lee,Dongsuk. Kum 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10
Due to the development of advanced driver assistance systems (ADAS) and autonomous vehicle (AV) technology, various path planning algorithms are being developed. However, most path planning algorithms operate in general and safety scenarios, and in emergency scenarios, the autonomous emergency brake (AEB) and autonomous emergency steering (AES) systems are operated limitedly. In an emergency situation in which the vehicle dynamic characteristics changed, planning a safe trajectory that can be safely tracking in necessary. In this study, a safe trajectory planning method in which the vehicle does not lose stability by using combined longitudinal and lateral acceleration vectors is proposed. The combined acceleration vector is limited by friction limit. The proposed trajectory planning algorithm is evaluated by simulation in a single obstacle scenario through comparison with existing AEB and AES algorithms. The simulation results show the proposed algorithm generates only safe trajectory that guarantee the vehicle stability, excluding dangerous trajectories when the AEB and AES algorithms operated.
Direct Anlysis of Impurities in Solides with Glow Discharge Mass Spectrometry
Ki Beom Lee,Dae Won Moon,Kwang Woo Lee Korean Chemical Society 1989 Bulletin of the Korean Chemical Society Vol.10 No.6
A glow discharge mass spectrometric(GDMS) analytical method was developed for direct analysis of impurities in solids. Ions extracted from a glow discharge ion source with a sample as a cathode were analyzed by a quadrupole mass filter. Ion extractions were carried out through differentially-pumped orifices biased to positive and negative potentials. Operating parameters of the glow discharge source such as discharge current, orifice-to-cathode distance, energy analyzer setting and bias voltages have been optimized. The developed GDMS was applied to the analysis of KSS copper-base alloy standards certified by Korea Standards Research Institute(KSRI). In the analysis, the reproducibility and the detection limits were estimated to be about 2.5% RSD, and in the low ppm range, respectively.
Using Experts Among Users for Novel Movie Recommendations
Kibeom Lee,Kyogu Lee 한국정보과학회 2013 Journal of Computing Science and Engineering Vol.7 No.1
The introduction of recommender systems to existing online services is now practically inevitable, with the increasing number of items and users on online services. Popular recommender systems have successfully implemented satisfactory systems, which are usually based on collaborative filtering. However, collaborative filtering-based recommenders suffer from well-known problems, such as popularity bias, and the cold-start problem. In this paper, we propose an innovative collaborative-filtering based recommender system, which uses the concepts of Experts and Novices to create finegrained recommendations that focus on being novel, while being kept relevant. Experts and Novices are defined using pre-made clusters of similar items, and the distribution of users’ ratings among these clusters. Thus, in order to generate recommendations, the experts are found dynamically depending on the seed items of the novice. The proposed recommender system was built using the MovieLens 1 M dataset, and evaluated with novelty metrics. Results show that the proposed system outperforms matrix factorization methods according to discovery-based novelty metrics, and can be a solution to popularity bias and the cold-start problem, while still retaining collaborative filtering.