• 대한토목학회논문집
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• 제41권6호
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• pp.663-673
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• 2021

해안구조물 설계시 허용평균월파량은 설계요소중의 하나이며, 구조물의 마루높이는 구조물의 안전, 보행자의 안전 및 운영 등에 요구되는 월파량 이하가 되도록 하여야 한다. 최근 들어 보다 안전한 설계를 위해 평균월파량보다 개별 최대월파량을 기준으로 적용하자는 제안이 이루어지고 있다. 본 연구는 비쇄파조건에서 개별 최대월파량에 대한 정보를 제공하고자 하는 것이며, 경사식구조물의 기하학적 형상을 고려안 2차원 수리모형실험을 수행하였다. 또한 실험을 위해 새로운 계측방법을 고안하였다. 실험결과를 이용하여 평균월파량에 기반하여 개별 최대월파량을 산정할 수 있는 경험식을 제안하였다. 그리고 피복재 어깨폭에 따른 평균월파량의 저감효과에 대해서도 검토하였다.

• 한국경제지리학회지
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• 제25권1호
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• pp.183-201
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• 2022

주택가격을 정확히 추정하기 위한 많은 연구가 진행되어 왔다. 선행연구들은 주택의 고유 특성과 인근 지역 특성을 통제하는 계량경제모형을 활용한 분석이 많았다. 본 연구에서는 인공신경망 모형(ANN)을 활용하여 주택가격을 추정하였다. 딥러닝 기술의 장점은 변수 간의 복잡하고 비선형적인 특성을 모델링하고 데이터의 패턴을 인식할 수 있다는 것이다. 본 연구에서는 부동산 시장에서 공간적 분포도 패턴으로 인식할 수 있다는 가정하에 지리좌표를 설명변수로 ANN에 투입하였다. 선형회귀분석과 ANN 모형 간 비교 결과, 선형 모형 대비 ANN 모형의 설명력이 높았으며, 특히 ANN 모형은 지리좌표를 투입하였을 때 더 높은 정확도를 보여주었다. 또한 ANN 모형의 경우 지리좌표를 통해 모형 잔차의 공간적 자기 상관성이 크게 감소하였다는 점을 확인하였다. 이를 통해 ANN 모형의 패턴인식 능력을 활용하면 공간적 패턴을 학습시킴으로써 주택가격을 정확히 추정할 수 있음을 밝혔다.

• 한국가스학회지
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• 제25권6호
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• pp.14-21
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• 2021

-162℃ 초저온 상태의 LNG를 저장하는 저장탱크의 내조는 균열과 같은 결함에 대한 구조 건전성 평가가 필요하다. 전통적인 파괴역학 관점에서는 응력확대계수 K, J-적분 그리고 CTOD를 이용한 단일 매개변수 평가가 주로 수행되어왔다. 그러나 실제 구조에서 발생되는 균열선단은 구조물의 크기, 시편형상 그리고 인장과 굽힘과 같은 하중의 형태에 따라 구속효과의 차이로 인한 영향이 발생하게 된다. 단일 매개변수 파괴역학을 보완하기 위해 다양한 시도가 있었고, 대표적으로 Q-응력법이 있다. 본 논문에서는 비선형 탄성영역의 균열선단 응력장 평가에 적합한 J적분에 Q응력을 유도하여 2 매개변수 접근법을 사용하고자 한다. SENB 시편의 균열비 0.1~0.7 그리고 광폭시편 균열비 0.2~0.6에 시편 균열선단의 응력을 J-Q 평가법을 이용하여 구속효과를 정량적으로 평가 하였다.

• 한국주조공학회지
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• 제41권6호
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• pp.543-549
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• 2021

알루미늄 주철은 내산화성, 내황화성 및 부식성이 우수하다. Ti, Ni 합금에 비해 비전략적 원소인 Fe를 사용하는 비용이 상대적으로 저렴하여 구조재 및 스테인리스강의 대체재로 기대되고 있다. 이는 스테인리스 스틸을 사용하는 경우에 비해 약 30%의 중량 감소 효과를 가져온다. 알루미늄 합금의 경우 최근 몇 년간 주철의 합금원소로 널리 사용되고 있는 원소이다. 실온에서 연성이 부족하고 600℃를 초과하면 강도가 급격히 감소하여 실용화가 지연되었다. 실온 연성이 약한 원인은 수소에 의한 환경 취화로 알려져 있으며, 이러한 특성의 약점을 개선하기 위해 다양한 합금원소의 첨가가 시도되고 있다. 회주철의 경도와 내마모성을 높이기 위해 주로 바나듐, 크롬, 망간 등의 합금원소를 사용하고 있지만, 이러한 원소를 포함하는 완제품의 가격과 합금화의 문제는 많은 한계가 있다.

• Structural Engineering and Mechanics
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• 제83권3호
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

• 한국멀티미디어학회논문지
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• 제25권1호
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• pp.1-9
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• 2022

Ship detection in synthetic aperture radar (SAR) images is an important application in marine monitoring for the military and civilian domains. Over the past decade, object detection has achieved significant progress with the development of convolutional neural networks (CNNs) and lot of labeled databases. However, due to difficulty in collecting and labeling SAR images, it is still a challenging task to solve SAR ship detection CNNs. To overcome the problem, some methods have employed conventional data augmentation techniques such as flipping, cropping, and affine transformation, but it is insufficient to achieve robust performance to handle a wide variety of types of ships. In this paper, we present a novel and effective approach for deep SAR ship detection, that exploits label-rich Electro-Optical (EO) images. The proposed method consists of two components: a data augmentation network and a ship detection network. First, we train the data augmentation network based on conditional generative adversarial network (cGAN), which aims to generate additional SAR images from EO images. Since it is trained using unpaired EO and SAR images, we impose the cycle-consistency loss to preserve the structural information while translating the characteristics of the images. After training the data augmentation network, we leverage the augmented dataset constituted with real and translated SAR images to train the ship detection network. The experimental results include qualitative evaluation of the translated SAR images and the comparison of detection performance of the networks, trained with non-augmented and augmented dataset, which demonstrates the effectiveness of the proposed framework.

• Genomics & Informatics
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• 제19권4호
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• pp.44.1-44.9
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• 2021

Autism is a complex neurodevelopmental disorder, the prevalence of which has increased drastically in India in recent years. Neuroligin is a type I transmembrane protein that plays a crucial role in synaptogenesis. Alterations in synaptic genes are most commonly implicated in autism and other cognitive disorders. The present study investigated the neuroligin 3 gene in the Indian autistic population by sequencing and in silico pathogenicity prediction of molecular changes. In total, 108 clinically described individuals with autism were included from the North Karnataka region of India, along with 150 age-, sex-, and ethnicity-matched healthy controls. Genomic DNA was extracted from peripheral blood, and exonic regions were sequenced. The functional and structural effects of variants of the neuroligin 3 protein were predicted. One coding sequence variant (a missense variant) and four non-coding variants (two 5'-untranslated region [UTR] variants and two 3'-UTR variants) were recorded. The novel missense variant was found in 25% of the autistic population. The C/C genotype of c.551T>C was significantly more common in autistic children than in controls (p = 0.001), and a significantly increased risk of autism (24.7-fold) was associated with this genotype (p = 0.001). The missense variant showed pathogenic effects and high evolutionary conservation over the functions of the neuroligin 3 protein. In the present study, we reported a novel missense variant, V184A, which causes abnormal neuroligin 3 and was found with high frequency in the Indian autistic population. Therefore, neuroligin is a candidate gene for future molecular investigations and functional analysis in the Indian autistic population.

• Genomics & Informatics
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• 제20권1호
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• pp.11.1-11.20
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• 2022

Vibrio harveyi belongs to the Vibrio genus that causes vibriosis in marine and aquatic fish species through double-stranded DNA virus replication. In humans, around 12 Vibrio species can cause gastroenteritis (gastrointestinal illness). A large amount of virus particles can be found in the cytoplasm of infected cells, which may cause death. Despite these devastating complications, there is still no cure or vaccine for the virus. As a result, we used an immunoinformatics approach to develop a multi-epitope vaccine against most pathogenic hemolysin gene of V. harveyi. The immunodominant T- and B-cell epitopes were identified using the hemolysin protein. We developed a vaccine employing three possible epitopes: cytotoxic T-lymphocytes, helper T-lymphocytes, and linear B-lymphocyte epitopes, after thorough testing. The vaccine was developed to be antigenic, immunogenic, and non-allergenic, as well as having a better solubility. Molecular dynamics simulation revealed significant structural stiffness and binding stability. In addition, the immunological simulation generated by computer revealed that the vaccination might elicit immune reactions in the actual life after injection. Finally, using Escherichia coli K12 as a model, codon optimization yielded ideal GC content and a higher codon adaptation index value, which was then included in the cloning vector pET2+ (a). Altogether, our experiment implies that the proposed peptide vaccine might be a good option for vibriosis prophylaxis.

• 한국공간구조학회논문집
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• 제22권3호
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• pp.25-32
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• 2022

In this paper, the instability of the domed spatial truss structure using wood and the characteristics of the buckling critical load were studied. Hexagonal space truss was adopted as the model to be analyzed, and two boundary conditions were considered. In the first case, the deformation of the inclined member is only considered, and in the second case, the deformation of the horizontal member is also considered. The materials of the model adopted in this paper are steel and timbers, and the considered timbers are spruce, pine, and larch. Here, the inelastic properties of the material are not considered. The instability of the target structure was observed through non-linear incremental analysis, and the buckling critical load was calculated through the singularities and eigenvalues of the tangential stiffness matrix at each incremental step. From the analysis results, in the example of the boundary condition considering only the inclined member, the critical buckling load was lower when using timber than when using steel, and the critical buckling load was determined according to the modulus of elasticity of timber. In the case of boundary conditions considering the effect of the horizontal member, using a mixture of steel and timber case had a lower buckling critical load than the steel case. But, the result showed that it was more effective in structural stability than only timber was used.

• Structural Engineering and Mechanics
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• 제78권4호
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• pp.473-484
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• 2021

Similar to other structures, ultimate strength values showing the maximum load that the structure can resist without damaging has great importance on ships. Therefore, increasing the ultimate strength values will be an important benefit for the structure. Low carbon steels used in ships due to their low cost and good weldability. Improving the ultimate strength values without interfering with the chemical composition to prevent of the weldability properties of these steels would be very beneficial for ships. Grain refinement via severe plastic deformation (SPD) is an essential strengthening mechanism without changing the chemical composition of metallic materials. Among SPD methods, equal channel angular pressing (ECAP) is one of the most commonly used one due to its capacity for achieving bulk ultrafine-grained (UFG) materials. When the literature is examined, it is seen that there is no study about ultimate strength calculation in ships after ECAP. Therefore, the mean purpose of this study is to apply ECAP to a shipbuilding low carbon steel to be able to achieve mechanical properties and investigate the alteration of ship hull girder grillage system's ultimate strength via finite element analysis approach. A fine-grained (FG) microstructure with a mean grain size of 6 ㎛ (initial grain size was 25 ㎛) was after ECAP. This microstructural evolution brought about a considerable increase in strength values. Both yield and tensile strength values increased from 280 MPa and 425 MPa to about 420 MPa and 785 MPa, respectively. This improvement in the strength values reflected a finite element method to determine the ultimate strength of ship hull girder grillage system. As a result of calculations, it was reached significantly higher ultimate strength values (237,876 MPa) compared the non-processed situation (192,986 MPa) on ship hull girder grillage system.