http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
도시철도차량 차축의 경량화를 위한 EN 규격 기반의 차축 응력 평가
한순우(SoonWoo Han),정현승(HyunSeung Jung) 한국자동차공학회 2011 한국자동차공학회 부문종합 학술대회 Vol.2011 No.5
Stress analysis of urban railway axle, especially K-EMU, is discussed in this work to design lightweight hollow axle. Railway axle is one of the heaviest parts in railway car system and weight reducing of an axle should be considered in designing lightweight railway car system. EN standard, EN 13103 is referred to calculate stress of a T car axle of K-EMU and the process of stress analysis based on the EN standard is explained. It is verified that maximum stress of K-EMU axle is far below the maximum permissible strength of railway axles. The stress of a hollow axle having similar outer dimension and material with a T car axle of K-EMU is estimated and shows satisfactory result.
박순우(Soonwoo Park),한희제(Heeje Han),김홍준(Hongjoon Kim) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.11
본 논문에서는 이중대역에서 입사하는 파에 대해 특이반사가 가능한 메타 구조체를 설계/제작하고 그 결과를 고찰하였다. 설계한 메타 구조체는 이층 평면 구조로 설계되었고, 각 층이 상위 대역 주파수 및 하위대역 주파수의 특이반사 특성을 각각 보이도록 설계하였다. 설계된 메타 구조체는 4.24 GHz와 7GHz, 두 대역에서 45°의 입사각을 가지는 파에 대해 –45°의 반사각으로 산란시키는 특성을 보였다.
한희제(Heeje Han),박순우(Soonwoo Park),김홍준(Hongjoon Kim) 대한전기학회 2021 전기학회논문지 Vol.70 No.9
By employing varactor diodes in a metasurface, the surface impedance can be changed by a bias voltage, as the resonant frequency altered. In order to convert the frequency of a reflected wave, 360° reflection phase variation is required. This study presents the related theory and demonstrates the frequency conversion through the experimental results. We achieved the reflection frequency conversion which is increased by 50 kHz more than the incident wave of 2.65 GHz, and its SFDR and conversion loss are 7.38 dBc and 5.62 dB, respectively. The proposed metasurface can contribute an efficient improvement in anti-RADAR system fields.
Impact parameter prediction of a simulated metallic loose part using convolutional neural network
Moon, Seongin,Han, Seongjin,Kang, To,Han, Soonwoo,Kim, Kyungmo,Yu, Yongkyun,Eom, Joseph Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.4
The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.