• Journal of the Korean Vacuum Society
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• v.4 no.S1
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• pp.196-201
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• 1995

새로운 증착방법인 UHV-ECRCVD를 이용하여 기판온도 $440^{\circ}C$의 저온에서 격자이온이 일어나지 않고 완벽한 정합상태를 유지하고 있는 무전위 SiGe 에피박막을 성장시켰다. 박막의 두께는 기계적 평형이론(mechanical equilibrium theory)인 Mattews-Blakeslee 임계두께를 초과하였으며, 따라서 본 연구에서 사용하는 낮은 기판온도에 의해 격자이완이 억제되고 있음을 알았다. 한편 성장시에 가해주는 GeH4의 유량이 증가함에 따라 박막내에 GeH4으로부터 생성된 무거운 ion의 기판입사량이 증가하여 격자손상(lattice damage)에 의한 결함이 증가하므로 높은 Ge 함량을 갖는 무전위 SiGe 에피박막을 얻을 수 없었다. 그러나 전체압력을 증가시켜서 에피층을 성장시키면 격자손상에 의한 결함은 생성되지 않았으며, 따라서 전체압력을 증가시키면 높은 Gegkafid을 갖는 무전위 SiGe 에피박막을 성장시킬 수 있을 것이라고 생각된다. 이것은 전체압력 증가로 인해 ECR 플라즈마 안의 전자온도가 감소하여 성장을 주도하는 활성종(reactive species)이 ion에서 radical 로 바뀌기 때문이라고 추정하였다. 본 연구에서는 박막의 Ge 함량이 증가함에 따라 에피층의 성장속도가 증가하는 현상을 관찰하였다. 따라서 ECR 플라즈마를 사용하는 본 연구에서도 표면에서의 수소탈착이 성장속도결정단계임을 알 수 있었다. 한편 인입률(incorporation ratio)은 1에 근접하였으며, 이것은 플라즈마에 의한 원료기체의 분해과정이 thermal CVD와는 달리 무차별적으므로 SiH4과 GeH4의 분해효율이 크게 다르지 않기 때문이라고 추정하였다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.7
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• pp.667-675
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• 2012

A damage estimation method for support structure of offshore wind turbine using modal parameters is presented for effective structural health monitoring. Natural frequencies and mode shapes for a support structure with monopile of an offshore wind turbine were calculated considering soil condition and added mass. A neural network was learned based on training patterns generated by the changes of natural frequency and mode shape due to various damages. Natural frequencies and mode shapes for 10 prospective damage cases were input to the trained neural network for damage estimation. The identified damage locations and severities agreed reasonably well with the accurate damages. Multi-damage cases could also be successfully estimated. Enhancement of estimation result using another parameters as input to neural network will be carried out by further study. Proposed method could be applied to other type of support structure of offshore wind turbine for structural health monitoring.

• Journal of Ocean Engineering and Technology
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• v.29 no.5
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• pp.351-358
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• 2015

This paper presents an effective damage detection method using a meta model. A meta model is an approximation model that uses the relations between the design and response variables. It eliminates the need for repetitive analyses of computationally expensive models during the optimization process. In this study, a response surface model was employed as the meta model. The surface model was estimated using the correlation of the stiffness and natural frequencies of the structures. The locations and values of the damages were identified using a meta model-based damage detection method. Two numerical examples (a cantilever beam and jacket structure) were considered to verify the performance of the proposed method. As a result, the damages to the structures were accurately detected.

• Journal of Ocean Engineering and Technology
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• v.26 no.5
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• pp.11-17
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• 2012

In this study, a damage detection method that uses sensitivity-based finite (FE) element model updating with the natural frequency and zero frequency was proposed. The stiffness matrix for a structure was modified using the sensitivity-based FE model updating method. A sensitivity analysis was used to update the FE model, and the natural frequencies and zero frequencies were considered as target parameters to supplement the information on the vibration characteristics. The locations and values of the damages were estimated from the modified stiffness matrix. Several numerical examples were considered to verify the performance of the proposed method.

• Journal of KIBIM
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• v.9 no.1
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• pp.42-51
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• 2019

Maintenance of aging structures has attracted societal attention. Maintenance of the aging structure can be efficiently performed with a digital twin. In order to maintain the structure based on the digital twin, it is required to accurately detect the damage of the structure. Meanwhile, deep learning-based damage detection approaches have shown good performance for detecting damage of structures. However, in order to develop such deep learning-based damage detection approaches, it is necessary to use a large number of data before and after damage, but there is a problem that the amount of data before and after the damage is unbalanced in reality. In order to solve this problem, this study proposed a method based on Generative adversarial network, one of Generative Model, for generating acceleration data usually used for damage detection approaches. As results, it is confirmed that the acceleration data generated by the GAN has a very similar pattern to the acceleration generated by the simulation with structural analysis software. These results show that not only the pattern of the macroscopic data but also the frequency domain of the acceleration data can be reproduced. Therefore, these findings show that the GAN model can analyze complex acceleration data on its own, and it is thought that this data can help training of the deep learning-based damage detection approaches.

• Journal of the Society of Naval Architects of Korea
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• v.31 no.3
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• pp.119-128
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• 1994

Deflection estimation at engine room upper deck panel is performed for the actual ship structure. These deflection behaviours are basically investigated from not only the data based on the full series results of nonlinear analysis using Incremental Galerkin's Method but also actual deflection data measured from damaged ship under construction in dry dock. The effects of residual stress, initial deflection and static loading are also included. The computed estimation results of upper deck plate panel including theme effects are shown that upper deck platings of new ship expected less deflection magnitude than damaged ship.

• Progress in Medical Physics
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• v.14 no.4
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• pp.259-267
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• 2003

In vivo $^1$H magnetic resonance spectroscopy (MRS) at 4.7 T was applied to investigate the cerebral metabolite changes of mice brain before and after experimental brain trauma. In vivo $^1$H MR spectra were acquired from a voxel covering right parietal cortex in normal brain, used as control subjects. After experimental brain trauma using the fluid percussion injury (FPI) method, $^1$H MR spectra were acquired from the same lesion three days after trauma. Metabolite ratios of the injured lesion were compared to those of controls. After trauma, N-acetylaspartate (NAA)/creatine (Cr) ratio, as a neuronal marker was decreased significantly versus controls, indicating neuronal loss. The ratio of NAA/Cr in traumatic brain contusion was 0.90$\pm$0.11, while that in normal control subjects was 1.13$\pm$0.12 (P=0.001). Choline (Cho)/Cr ratio had a tendency to rise in experimental brain contusion (P=0.02). Cho/Cr ratio after trauma was 0.91$\pm$0.17 while that before traumas was 0.76$\pm$0.15. Cho/Cr ratio was increased and this might indicate a inflammatory activity. However, no significant difference of [(glutamate＋glutamine) (Glx)]/Cr was established between experimental traumatic brain injury models and normal controls. Lactate (Lac)/Cr ratio was appeared as a sign of shifted posttraumatic energy metabolism and increased versus controls. These findings strongly suggest that in vivo $^1$H MRS may be a useful modality for clinical evaluation of traumatic contusion and could aid in better understanding the neuropathologic process of traumatic contusion induced by FPI. In the present study, in vivo $^1$H MRS was proved to be a useful non-invasive method for in vivo diagnosis and monitoring of posttraumatic metabolism in models of brain contusion.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.7
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• pp.87-94
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• 2019

Recently, the advance of accurate dynamic displacement measurement devices, such as GPS, computer vision, and optic laser sensor, has enhanced the structural monitoring technology. In this study, the dynamic displacement data was used to verify the applicability of the structural physical parameter estimation method through subspace system identification. The subspace system identification theory for estimating state-space model from measured data and physics-based interpretation for deriving the physical parameter of the estimated system are presented. Three-degree-freedom steel structures were fabricated for the experimental verification of the theory in this study. Laser displacement sensor and accelerometer were used to measure the displacement data of each floor and the acceleration data of the shaking table. Discrete state-space model generated from measured data was verified for precision. The discrete state-space model generated from the measured data extracted the floor stiffness of the building after accuracy verification. In addition, based on the story stiffness extracted from the state space model, five column stiffening and damage samples were set up to extract the change rate of story stiffness for each sample. As a result, in case of reinforcement and damage under the same condition, the stiffness change showed a high matching rate.

• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.4
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• pp.121-128
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• 2003

In recent years, various artificial neural network algorithms are used in the damage assessment of civil infrastructures. So far, many researchers have used the artificial neural network as a pattern classifier for the structural damage assessment but, in this paper, the neural network is used as a structural reanalysis tool not as a pattern classifier. For the model updating using the optimization algorithm, the summation of the absolute differences in the structural vibration modes between undamaged structures and damaged ones is considered as an objective function. The stiffness of structural components are treated as unknown parameters to be determined. The structural damage detection is achieved using model updating based on the optimization techniques which determine the estimated stiffness of components minimizing the objective function. For the verification of the proposed damage identification algorithm, it is numerically applied to a simply supported bridge model.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06a
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• pp.87-90
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• 1998

움직임 열화(motion blur)현상은 카메라와 피사체간의 상대적임 움직임에 발생되는 영상의 번짐 현상으로, 본 논문에서는 새롭게 제시한, 노이즈의 분산을 산출해 내기 위한 노이즈 지배영역과, 움직임 열화와 각도와 길이를 추정해내기 위한 신호 지배영역을 통하여 움직임 열화의 파라메터를 효율적으로 추정할 수 있는 방법을 제시하였다. 또한, 새롭게 제안한 가변가중치(weight)를 적용한 최소자승법(Least Man Square)은 극점 자취의 방향 추정에 있어 정밀한 측정이 가능케 한다. 열화의 방향이 얻어지면, 1차원 셉스트럼(Cepstrum)방법으로 빠르게 움직임 열화의 길이를 구할 수 있게 된다. 이러한 방법으로 얻어진 정보들을 이용하여, 실제 손상되어진 영상을 효과적으로 복원할 수 있었다.