http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Nicholas Roy (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
-
-
Understanding the Effects of Surface Finish on Diffusion Bonding of AZ31 Alloys
Matthew Criado,Abhijit Roy,John Ohodnicki,Nicholas Tondravi,Hannah Fischer,Alejandro Almarza,Howard A. Kuhn,Prashant N. Kumta 대한용접접합학회 2023 대한용접·접합학회지 Vol.41 No.5
Methods to optimize the diffusion bonding (DB) process while also creating lap joints were investigated using AZ31, the commercial Mg alloy, focusing primarily on studying the effects of varying conventional process parameters and more importantly, exploring the influence of surface roughness on creating an acceptable strong bond. The results indicate that when contact is made between surface areas of roughness (Ra > 0.2 ㎛) diffusion is facilitated at reduced process parameters of time and temperature. Furthermore, this early bond initiation combined with the optimized DB process parameters results in a stronger bond with strengths increasing by ~ 150% in comparison to the DB samples created with a smooth surface finish. Additionally, less than 20% total overall distortion is observed while preserving a uniform microstructure of consistent grain sizes in the material over the entire joining region compared with the parent material.
-
Park, Soo-Ho,Choi, Han-Lim,Roy, Nicholas,How, Jonathan P. The Korean Society for Aeronautical and Space Scie 2010 International Journal of Aeronautical and Space Sc Vol.11 No.4
This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.
-
Sooho Park,Han-Lim Choi,Nicholas Roy,Jonathan P. How 한국항공우주학회 2010 International Journal of Aeronautical and Space Sc Vol.11 No.4
This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.
-
Ge Yang,Genda Gu,Aleksey E. Bolotnikov,Yonggang Cui,Giuseppe S. Camarda,Anwar Hossain,Utpal N. Roy,Nicholas Kivi,Tiansheng Liu,Ralph B. James 대한금속·재료학회 2015 ELECTRONIC MATERIALS LETTERS Vol.11 No.3
Cadmium manganese telluride (CdMnTe or CMT), a compound semiconductor, is considered a promising material for the fabrication of high-performance room-temperature x-ray and gamma-ray detectors. The presence of material defects, e.g., high density of Te inclusions, has been a long-standing issue in CMT crystals grown by various Bridgman methods, since these defects degrade the device performance via charge-trapping. To address this issue, we employed the modified floating-zone method (MFZ) to grow CMT crystals and obtained as-grown crystals free of Te inclusions. This represents a new and distinct feature, absence of Te inclusions, compared to CMT crystals grown by Bridgman methods. White-beam x-ray diffraction topography (WBXDT) measurements demonstrated the existence of a high stress field within the MFZ-grown CMT crystals, which originates from the steep temperature gradient near the growth interface. Furthermore, we achieved a resistivity of 109 Ωcm for the MFZ-grown CMT crystals. The low-temperature photoluminescence (PL) measurements show that the intensity of the dislocation-related Y band is much higher than that of the principal exciton peaks, (D0,X) and (A0,X), confirming that the crystalline quality is affected by the high stress field. A long-term in-situ or post-growth thermal annealing will help to release such stress to improve the crystalline quality.
KCI
DBpia연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
