In this paper, nonlinear finite element analysis was conducted based on the experimental results on buckling restrained brace. The reliability of the analytical model was verified by comparing the results of experimental studies with hysteresis loop, bi-linear curve, cumulative energy dissipation capacity, and equivalent viscous damping. A valid finite element model has been secured and will be used as basic data for finite element analysis of buckling restrained braces in the future.

This paper aims to develop numerical models for seismically-deficient reinforced concrete columns retrofitted using a fiber-reinforced polymer jacketing system under blast loading scenarios. To accomplish the research goal, a coupling model reproducing blast loads was developed and implemented to the column model. The column model was validated with a past experimental study, and the blast responses were compared to the numerical responses produced by past researchers. The validated modeling method was implemented to the non-retrofitted and retrofitted column models to estimate the effectiveness of the retrofit system. Based on the numerical responses, the retrofit system can significantly reduce the peak dynamic responses under a given blast loading scenario.

Ambient Air Vaporizer (AAV) is an essential facility in the process of generating natural gas that uses air in the atmosphere as a medium for heat exchange to vaporize liquid natural gas into gas-state gas. AAV is more economical and eco-friendly in that it uses less energy compared to the previously used Submerged vaporizer (SMV) and Open-rack vaporizer (ORV). However, AAV is not often applied to actual processes because it is heavily affected by external environments such as atmospheric temperature and humidity. With insufficient operational experience and facility operations that rely on the intuition of the operator, the actual operation of AAV is very inefficient. To address these challenges, this paper proposes an artificial intelligence-based model that can intelligent AAV operations based on operational big data. The proposed artificial intelligence model is used deep neural networks, and the superiority of the artificial intelligence model is verified through multiple regression analysis and comparison. In this paper, the proposed model simulates based on data collected from real-world processes and compared to existing data, showing a 48.8% decrease in power usage compared to previous data. The techniques proposed in this paper can be used to improve the energy efficiency of the current natural gas generation process, and can be applied to other processes in the future.

자기공명영상은 고해상도의 연부조직에 대한 영상정보를 제공하며, 뇌종양 등 연부조직 진단에 활용된다. 본 연구는 합성곱신경망 인공지능을 통해 뇌종양 자기공명영상 분류성능을 확인해 보고자 한다. 4개 종류로 구분된 3264 장의 MRI 데이터 세트(data set)를 이용하였으며, 인공지능 학습을 위해 훈련용 데이터와 시험용 데이터를 9 : 1, 훈련용 데이터의 10%를 검증용 데이터로 구분하였다. 합성곱신경망은 기본 CNN과 VGG16으로 구성하였으며, 학습 평가는 정확도와 손실율로 확인하였으며, 생성된 모델을 통해 분류성능 정확도를 확인하였다. 실험 결과 과적합은 없었으며, 분류성능은 기본 CNN과 VGG16 각각 67%와 80%의 분류성능을 보였다. 도출된 뇌종양 자기공명영상 분류 결과를 통해 자기공명영상과 인공지능 접목에 관한 기초 자료로 사용될 수 있을 것이라 사료된다.

최근 소프트웨어 유통망(ESD)를 통해 유통되는 게임이 늘어남에 따라 사용자들이 원하는 게임을 찾는데 어려움을 겪고 있다. 이에 따라 사용자들이 원하는 게임을 찾기 쉽게 태그를 생성하는 모델의 필요성이 대두 되고 있다. 본 논문에서는 태그를 생성하는 모델을 BERT를 통해 설계하였다. 태그 100개 중 가장 적합한 태그를 4개 추출하기 위해 입력된 문장에 대해 각 태그별로 이진분류를 수행하고 이진분류 당시의 Softmax 값이 가장 컸던 태그 4개를 선택했다. 또한, 모델의 정확도를 위해서 약 33억 개의 다국어 단어로 학습한 pre-trained Multilingual BERT 모델과 약 5천만 개의 한국어 단어로 학습한 KoBERT 모델을 가져와 한국어 데이터로 학습(finetuning) 시켜 사용하였다. 실험에서 BERT 모델은 KoBERT 모델보다 F- 점수에서 9.19 % 더 나은 성능을 보입니다. 이는 언어 학습 데이터 세트의 크기가 특정 언어인 한국어 특성보다 더 중요하다는 것을 나타낸다.

KAERI has planned to carry out a series of dynamic tests using a shaking table and time-history analyses for a channel-type concrete shear wall to investigate its seismic performance because of the recently frequent occurrence of earthquakes in the south-eastern parts of Korea. The overall size of a test specimen is ×× 2500 mm×3500 mm×4500 mm, and it consists of three stories having slabs and walls with thicknesses of 140 mm and 150 mm, respectively. The system identification, FE model updating, and time-history analysis results for a test shear wall are presented herein. By applying the advanced system identification, so-called pLSCF, the improved modal parameters are extracted in the lower modes. Using three FE in-house packages, such as FEMtools, Ruaumoko, and VecTor4, the eigenanalyses are made for an initial FE model, resulting in consistency in eigenvalues. However, they exhibit relatively stiffer behavior, as much as 30 to 50% compared with those extracted from the test in the 1st and 2nd modes. The FE model updating is carried out to consider the 6-dofs spring stiffnesses at the wall base as major parameters by adopting a Bayesian type automatic updating algorithm to minimize the residuals in modal parameters. The updating results indicate that the highest sensitivity is apparent in the vertical translational springs at few locations ranging from 300 to 500% in variation. However, their changes seem to have no physical meaning because of the numerical values. Finally, using the updated FE model, the time-history responses are predicted by Ruaumoko at each floor where accelerometers are located. The accelerograms between test and analysis show an acceptable match in terms of maximum and minimum values. However, the magnitudes and patterns of floor response spectra seem somewhat different because of the slightly different input accelerograms and damping ratios involved.

Deep learning, which has recently shown excellent performance, has a problem that the amount of computation and required memory are large. Model compression is very useful because it saves memory and reduces storage size while maintaining model performance. Model compression methods reduce the number of edges by pruning weights that are deemed unnecessary in the calculation. Existing weight pruning methods using ADMM construct an optimization problem by a layer-by-layer addition of pre-defined removal-ratio constraints. Decomposing into two subproblems through the ADMM process, one can solve them through gradient descent and projection. However, the layer-by-layer removal ratios must be structurally specified, causing a sharp increase in training time due to a large number of parameters, and hardly feasible to use for large models that actually require weight pruning. Our proposed method performs weight pruning, producing similar performance, by setting a global removal ratio for the entire model without prior knowledge of structural characteristics in order to solve the shortcomings of the existing ADMM weight-pruning methods. To effectively avoid performance degradation, the method removes a relatively small number of previous layers in charge of feature extraction. Experiments show high-quality performance, not necessarily setting layer-by-layer removal ratios. Additionally, experiments increasing layers yield an insight for feature extraction in pruned layers. The experiment of the proposed method to the LeNet-5 model using MNIST data results in a higher compression ratio of 99.3% outperforming those of other existing algorithms. We also demonstrate the effectiveness of the proposed method in YOLOv4, an object detection model requiring substantial computation.

A60 급 갑판 관통 관은 선박과 해양플랜트에서 화재사고가 발생할 경우 화염의 확산을 방지하고 인명을 보호하기 위해 수평구조에 설치되는 방화장치이다. 본 연구에서는 다양한 대리모델과 다중 섬유전자 알고리즘을 이용하여 A60 급 갑판 관통 관의 방화설계에 대한 이산변수 근사최적화를 수행하였다. A60 급 갑판 관통 관의 방화설계는 과도 열전달해석을 통해 평가하였다. 근사최적화에서 관통 관의 길이, 지름, 재질, 그리고 단열재의 밀도는 이산설계변수로 적용하였고, 제한조건은 온도, 생산성 및 가격을 고려하였다. 대리모델 기반의 근사최적설계 문제는 제한조건을 만족하면서 A60 급 갑판 관통 관의 중량을 최소화할 수 있는 이산설계변수를 결정하도록 정식화 하였다. 반응표면모델, 크리깅, 그리고 방사기저함수 신경망과 같은 다양한 대리모델이 근사최적화에 사용되었다. 근사최적화의 정확도를 검토하기 위해 최적해의 결과는 실제 계산 결과와 비교하였다. 근사최적화에 사용된 대리모델 중 방사기저함수 신경망 모델이 A60 급 갑판 관통 관의 방화설계에 대해 가장 정확한 최적설계 결과를 나타내었다.

In this study, a welding heat source model was presented and verified during fiber laser welding. The multi-layered heat source model is a model that can cover most of existing studies and can be defined with a simple formula. It consists of a total of 12 parameters, and an optimization algorithm was used to find them. As optimization algorithms, adaptive simulated annealing, multi island genetic algorithm, and Hooke-Jeeves technique were applied for comparative analysis. The parameters were found by comparing the temperature distribution when the STS304L was bead on plate welding and the temperature distribution derived through finite element analysis, and all three models were able to derive a model with similar trends. However, there was a deviation between parameters, which was attributed to the many variables. It is expected that a more clear welding heat source model can be derived in subsequent studies by giving a guide to the relational expression and range between variables and increasing the temperature measurement point, which is the target value.

This study observed the anti-inflammatory effect of the polysaccharide derived from the mycelium of Tremella fuciformis in mice with colitis induced with dextran sulfate sodium (DSS). The experimental groups were normal, DSS, DSS-TFL50, DSS-TFH100, and suflasalazine. Body weights, colon lengths, and organ weights were measured, and the plasma level of pro-inflammatory cytokine and mRNA and protein expression in colon tissue were analyzed. Body weight loss, a symptom of DSS-induced colitis, was suppressed by DSS-TF and the speed of weight recovery proceeded rapidly. In addition, DSS-TF showed a significant inhibitory effect on the decrease of colon length typically caused by colon damage. TNF-α, IL-6 and IL-1β cytokine levels in plasma were reduced in DSS-TF and positive control groups. TNF-α, COX-2 and IL-1β mRNA expression in colon tissue were inhibited in DSS-TF and positive control, and it was significantly different from that of the DSS group. The protein expression of inflammation-related genes (IL-6, TNF-α and COX-2) in the colon tissue was significantly increased by DSS compared to that of the normal group, but by DSS-TFL50, DSS-TFH100 and sulfasalarin decreased. In conclusion, the polysaccharide derived from the mycelium of Tremella fuciformis showed the anti-inflammatory effect on DSS-induced colitis in mice.

Welding is the most widely used technology for manufacturing in the automobile, and shipbuilding industries. Fiber laser welding is rapidly introduced into the field to minimize welding distortion and fast welding speed. Although it is advantageous to use finite element analysis to predict welding distortion and find optimized welding conditions, there are various heat source model for fiber laser welding. In this study, a welding heat source was proposed using a multi-layered heat source model that encompasses most of the existing various welding heat source models: conical shape, curved model, exponential model, conical-cylindrical model, and conical-conical model. A case study was performed through finite element analysis using the radius of each layer and the ratio of heat energy of the layer as variables, and the variables were found by comparing them with the actual experimental results. For case study, by applying Adaptive simulated annealing, one of the global optimization algorithms, we were able to find the heat source model more efficiently.

다중 하중 과도응답해석은 시간에 따른 작용 하중에 대한 과도응답을 확인하므로 정교한 시스템 모델링 및 조밀한 시간 간격을 가 질수록 해당 시스템에 대한 동특성은 정확하게 나타내지만 이에 따른 계산 시간은 크게 증가하게 된다. 크리로프 부공간 기반 모델차 수축소법은 기계 시스템이 가지는 동적 특성과 거의 동일한 결과를 나타내면서 계산 시간을 줄일 수 있기 때문에 효율적인 과도응답 해석 방법이다. 본 연구에서는 다중 하중 및 이동 하중을 가지는 수치 예제를 통하여 크리로프 부공간 모델차수축소법 기반 과도응답 해석을 수행하고, 이를 통해 초기 시스템 및 축소차수 모델의 정확성 및 효율성을 비교하였다. 또한, 시스템 행렬 추출, 크리로프 부공 간의 기저 벡터로 구성되는 변환행렬 생성 및 축소차수모델 생성 그리고 이를 바탕으로 과도응답해석을 하는 절차를 수립하여 상용 유한요소 프로그램인 ANSYS Workbench ACT를 통해 과도응답해석 과정 자동화를 구현하여 그 효용성과 효율성을 보였다.

본 연구에서는 섬유보강콘크리트(SFRC) 구조물의 수치해석을 위한 K&C모델의 보정기법을 소개하였다. SFRC 1축 및 3축 압축강도 실험결과를 기반으로 보정을 수행하였으며, 단일요소 해석결과를 실험결과와 비교함으로써 보정 기법의 검증을 수행하였다. 또 한, 변형률 속도의 영향을 반형하기 위해 동적증가계수(DIF)를 고려하여 SFRC 구조물의 발사체 관통해석을 수행함으로써 보정기법의 적용 가능성을 확인하였다.