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Fe-10Mn-3.5Si 합금의 초소성에 미치는 반복 냉연 및 소둔의 영향
정현빈 ( Hyun-bin Jeong ),최석원 ( Seok-won Choi ),이영국 ( Young-kook Lee ) 한국열처리공학회 2022 熱處理工學會誌 Vol.35 No.4
It is known that superplastic materials with ultrafine grains have high elongation mainly due to grain boundary sliding. Therefore, in the present study we examined the influence of grain refinement, caused by a repetitive cold rolling and annealing process, on both superplastic elongation and superplastic deformation mechanism. The cold rolling and annealing process was repetitively applied up to 4 times using Fe-10Mn-3.5Si alloy. High-temperature tensile tests were conducted at 763 K with an initial strain rate of 1 × 10<sup>-3</sup> s<sup>-1</sup> using the specimens. The superplastic elongation increased with the number of the repetitive cold rolling and annealing process; in particular, the 4 cycled specimen exhibited the highest elongation of 372%. The primary deformation mechanism of all specimens was grain boundary sliding between recrystallized α-ferrite and reverted γ-austenite grains. The main reason for the increase in elongation with the number of the repetitive cold rolling and annealing process was the increase in fractions of fine recrystallized α-ferrite and reverted γ-austenite grains, which undergo grain boundary sliding. (Received July 13, 2022; Revised July 22, 2022; Accepted July 25, 2022)
운전자의 음성, 영상 및 생체 신호와 차량 상태 정보를 이용한 차량내 기기 조작 의도 파악에 관한 연구
이명구(Myoung Gu Lee),정현빈(Hyeon Bin Jeong),양지현(Ji Hyun Yang),이상헌(Sang Hun Lee) (사)한국CDE학회 2014 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2014 No.8
Recently, to reduce driver"s workload, multimodal user interfaces including voice and gesture recognition functions have been introduced to intelligent vehicles. However, the non-contact input methods are vulnerable to environmental conditions such as noise and light. To enhance their accuracy, in this paper, we proposed a complementary intelligent method to infer driver’s intention for the operation of in-vehicle equipment. To this end, first, on controlling in-vehicle equipment, the driver’s voice, video and physiological signals and the vehicle state information were collected through various sensors and ports of a driving simulator. Next, a set of selected machine learning algorithms including decision tree, Bayesian network, support vector machine, and multilayer perceptron approaches were trained using the collected data. Finally, the most efficient algorithm were selected by comparing their accuracy and performance.
지능형 칵핏 모듈의 통합 인간-차량 인터랙션 매니저 알고리즘 개발을 위한 기초 연구
류동운(Dong Woon Ryu),최선우(Seon Woo Choi),김형준(Hyung Jun Kim),정현빈(Hyeon Bin Jeong),이상헌(Sang Hun Lee),양지현(Ji Hyun Yang) (사)한국CDE학회 2014 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2014 No.2
This paper presents basic research results for development of the integrated HVI Manager algorithm needed for intelligent cockpit module. With the development of the current sensor and vehicle technologies, we expect to reduce the rate of traffic accidents, however increased complexity of vehicles could easily become the contributing factor of traffic accidents. Driver monitoring systems, such as driver state, intention, tendency, can help to minimize the above problem. This paper presents literature survey results for HVI Manager framework.