자기공명(magnetic resonance, MR)영상에서 주로 발생하는 Rician 노이즈는 영상의 화질을 저하하는 주요 요소 중의 하나이다. 본 연구에서는 노이즈 제거에 효율적이라고 잘 알려진 총변이(total variation, TV) 알고리즘을 모 델링하여 Rician 노이즈 레벨에 따른 파라미터를 최적화하고자 한다. 시스템은 8채널 기반의 3.0 T 장치를 활용하였 고 물 팬텀 영상을 획득하여 각각 Rician 노이즈를 0.05, 0.10, 0.15, 그리고 0.20 값을 부가하였다. TV 알고리즘 은 Rudin-Osher-Fatemi 모델을 기반으로 모델링하였고 최적화를 수행하기 위하여 반복수 파라미터를 조정하여 획득된 영상에 적용하였다. 결과적으로 Rician 노이즈 레벨을 0.05, 0.10, 0.15, 그리고 0.20을 사용하였을 때 각 각 30, 40, 80, 그리고 120 반복수를 기반으로 한 TV 노이즈 알고리즘에서 가장 우수한 신호 대 잡음비(signal to noise ratio, SNR)와 대조도 대 잡음비(contrast to noise ratio, CNR) 결괏값이 도출되었다. 또한, 최적화된 반복수를 적용한 TV 알고리즘을 사용한 MR 영상에서 기존의 위너 및 중간값 필터를 사용하였을 때 비하여 SNR과 CNR 모두 우수한 값을 획득할 수 있었다. 특히 기본적으로 획득된 MR 영상보다 최적화된 TV 알고리즘을 적용한 영상의 평균 SNR과 CNR은 각각 3.11 및 3.31배 향상됨이 증명되었다. 결론적으로, 노이즈 제거 효율이 우수한 TV 알고리즘의 최적화된 파라미터를 활용한다면 MR 영상에서의 활용 가능성이 클 것으로 기대한다.

압축센스(Compressed SENSE) 기법은 검사 시간을 획기적으로 단축할 수 있으나, 시간 단축을 위한 기법적용 시 가속계수를 증가시키면 인공물의 발생이 영상에서 증가하는 문제점이 있다. 이에 인공물이 발생하지 않으면서 검사 시간을 최대한 단축할 수 있는 최적의 압축센스 가속계수를 제시하고자 하였다. 연구 방법은 인공물이 발생하지 않는 가속계수 1.0을 기준으로 0.5 간격씩 5.0까지 무릎관절 자기공명영상의 팬텀 실험과 임상실험 영상을 획득한 후, 방사선사 10명이 5점 척도로 영상을 평가하여 유의한 차이가 있는지 판단하였다. 연구 결과 T1 강조영상과 T2 강조 영상 모두 팬텀 실험은 가속계수 2.0 이하로 하였을 때 임상실험은 3.0 이하로 하였을 때 기준이 되는 1.0 영상과 차이가 없었다. 결론적으로 무릎관절 자기공명영상 검사 시 인공물이 발생하지 않으며 검사 시간을 최대로 단축할 수 있는 최적의 압축센스 가속계수는 팬텀 실험의 경우 2.0, 임상실험의 경우 3.0이 적정하리라 판단된다.

The multi-layered heat source model is a model that can cover most of existing studies and can be defined with a simple formula. Based on the methodology performed in previous studies, the welding heat source was found through experiments and FEM under the welding power conditions of three cases and the parameters of the welding heat source were analyzed according to the welding power. In this study, parameters of fiber laser welding heat source according to welding power were searched through optimization algorithm and finite element analysis, and the correlation was analyzed. It was confirmed that the concentration of the welding heat source in the 1st layer was high regardless of the welding power, and it was confirmed that the concentration of the welding heat source in the 5th layer (last layer) increased as the welding power increased. This reflects the shape of the weld bead that appears during actual fiber laser welding, and it was confirmed that this study represents the actual phenomenon.

The estimation of heat source model is very important for heat transfer analysis with finite element method. Part I of this study used adaptive simulated annealing which is one of the global optimization algorithm for anticipating the parameters of the Goldak model. Although the analysis with 3D model which depicted the real situation produced the correct answer, that took too much time with moving heat source model based on Fortran and Abaqus. This research suggests the procedure which can reduce time with maintaining quality of analysis. The lead time with 2D model is reduced by 90% comparing that of 3D model, the temperature distribution is similar to each other. That is based on the saturation of heat transfer among the direction of heat source movement. Adaptive simulated annealing with 2D model can be used to estimate more proper heat source model and which could enhance to reduce the resources and time for experiments.

Anticipation of welding deformation with finite element method is a very interested topic in the industries, adequate heat source model is essential for concluding reasonable results. This study is related to estimate the parameters of Goldak heat source model, and global optimization algorithm is applied to this research. The heat affected zone (HAZ) boundary line of bead on plate (BOP) welding is used as the target, parameters of heat sources are used as the variables. Adaptive simulated annealing is applied and the optimal result is obtained out of 1,000 candidates. The convergence of finite element method and the global optimization is meaningful for estimation of welding deformation, which could enhance to reduce the resources and time for experiments.

The object of this study is concerned about the optimal design of the supporting frame in a twist screen. Twist screen is widely used for grading and screening materials in the fields such as coal-selecting, mining-selecting, building materials, electrical power and chemical industry, etc. It is composed of base, supporting frame, vibrator, screen, spring, trough, clamp and etc. Especially, supporting frame in twist screen are stressed by high load, mostly it is shock-type loads. Nevertheless, design of base is dependent on experience, and it will cause reliability reduction and cost increase. The supporting plate is composed by the upper plate, the bracket and etc. This research purpose is to propose and verify a method to achieve higher product quality, lower costs, and far less physical testing. In this study, design parameters used for optimization are the upper plate thickness(8㎜, 10㎜, 12㎜) and the bracket thickness(8㎜, 10㎜, 12㎜) in the supporting frame. The result showed that base weight reduced to maximum 25.8% when compared with the current twist screen.

An algorithm to determine the optimum nominal value of geometrical and material parameters in divice modelling is proposed. The algorithm uses the yield and variance prediction formula and Monte-Carlo analysis. The performance specification of the noise figure must also be satisfied. In this paper, the total number of considered devices is 1000, and each parameter of geometrical and material parameters is generated randomly within the limits of ±3% of nominal value, and the distribution of 1000 geometrical and material parameters is gaussing distribution.