• The Research Journal of the Costume Culture
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• v.29 no.1
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• pp.28-47
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• 2021

The purpose of this study is to provide basic data to assist students and industrial designers in fashion by examining the trend of designing women's wear with the SCAMPER method. In the research, the seven types of SCAMPER methods for fashion design were classified based on the previous studies. From 2010 S/S to 2020 S/S, data from 5,149 photographs were collected through overlapping checks and classified by SCAMPER method type. Data analysis was performed using SPSS 25.0 for frequency analysis. As a result, in the SCAMPER "combine" method, more than two items were combined, or structural details were combined with items. In the most applied "adapt" method was involved imitating similar images, or natural forms, or other objects. The "modify" method was applied by changing the shape of some details in basic fashion items. The "magnify" method was applied by enlarging, elongating, or elevating some details of fashion items. The "minify" method was applied by minimizing, shortening, or lowering some details of basic fashion. The method of "put to other use" was expressed throughout the clothing by using non-fabric or trimmings such as metal, beads, and strings. The "rearrange" method was applied by repositioning the top and bottom, front and back, or outside and inside in fashion items and in details. The "reverse" method was applied by reversing the style of fashion, mix-and-match fabric, or switching the gender of the fashion items.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.498-510
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• 2021

This study aims to analyze the influence relationship between the level of fringe benefits and relational contract among social workers in the non-profit organization. Under this relationship, we explored the dual mediating effect of perceived superior's support and perceived organizational support. A total of 371 self-administered questionnaires were distributed to social workers in Gwangju, South Korea, and 271 questionnaires (73.0%) were used for study analysis after excluding incomplete and unreliable responses. This study performed structural equation modeling, the results are as follows. Perceived superior's support and perceived organizational support both have a positive (+) influence on relational contract. Furthermore, perceived superior's support and perceived organizational support were found to mediate effect between fringe benefits and relational contract. Therefore, these results contribute to provide academic meaning that the range of future studies by identifying the dual mediated effect of the fringe benefits and social worker 's relational contract.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.663-673
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• 2021

Normally, allowable mean overtopping discharge is used as a design parameter for coastal structures. The crest elevation of a structure must ensure wave overtopping discharge within acceptable limits for structural safety and the safety of pedestrians, vehicles, operations, and so on. Some researchers have alternatively proposed using the maximum individual wave overtopping volumes as design criteria during a design storm, since these can provide a better design measure than the mean overtopping rate. This study contributes to the knowledge on maximum individual overtopping volumes in Rayleigh-distributed wave conditions. Two-dimensional physical model tests on typical rubble mound structure geometries were performed, and the new measurement method for individual overtopping was adopted. An empirical formula was proposed to predict the maximum individual overtopping volumes based on the mean overtopping rate, and the reduction effects by the armor crest width on the mean wave overtopping discharge were assessed.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.183-201
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• 2022

Estimating the implicit value of housing assets is a very important task for participants in the housing market. Until now, such estimations were usually carried out using multiple regression analysis based on the inherent characteristics of the estate. However, in this paper, we examine the estimation capabilities of the Artificial Neural Network(ANN) and its 'Deep Learning' faculty. To make use of the strength of the neural network model, which allows the recognition of patterns in data by modeling non-linear and complex relationships between variables, this study utilizes geographic coordinates (i.e. longitudinal/latitudinal points) as the locational factor of housing prices. Specifically, we built a dataset including structural and spatiotemporal factors based on the hedonic price model and compared the estimation performance of the models with and without geographic coordinate variables. The results show that high estimation performance can be achieved in ANN by explaining the spatial effect on housing prices through the geographic location.

• Journal of the Korean Institute of Gas
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• v.25 no.6
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• pp.14-21
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• 2021

Inner shell material of LNG storage tanks that store ultra-low temperature LNG at -162℃ requires structural integrity assessment of a crack-like defect. From the viewpoint of conventional fracture mechanics, the assessment has mainly performed by single parameter using stress intensity factor K, J-integral and CTOD. However, the stresses in a material of crack tip are not unique caused by constraint loss due to size and geometry of the structure. Various attempts have been made to complement a single parameter fracture mechanics, typically with Q-stress. In this paper, we have performed a two-parameter approach by deriving the Q-stress coupling with J-integral suitable for the evaluation of the crack tip stress field in the non-linear elastic region. A quantitative evaluation of the constraint effect has performed by using the J-Q approach. It was evaluated that the SENB type specimen had a crack ratio of 0.1 to 0.7 and the wide type specimen had a crack ratio of 0.2 to 0.6.

• Journal of Korea Foundry Society
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• v.41 no.6
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• pp.543-549
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• 2021

Aluminum cast iron has excellent oxidation resistance and good resistance to sulfide and corrosion. Compared to Ti and Ni alloys, it is expected to be a substitute material for structural materials and stainless steels because it is relatively inexpensive to use Fe, which is a non-strategic element. This results in a weight reduction effect of about 30% as compared to the use of stainless steel. With regard to aluminum as an alloying material, it is an element that has been widely used for the alloying of cast iron in recent years. Practical use has been delayed owing to the resulting lack of ductility at room temperature and the sharp decrease in the strength above 600℃ of this alloy, however. The cause of the weak room temperature ductility is known to be environmental embrittlement by hydrogen, and the addition of various alloying elements has been attempted in order to mitigate these shortcomings. Although alloying elements such as vanadium, chromium, and manganese are mainly used to increase the hardness and wear resistance of gray cast iron, the price of finished products containing these elements and the problems associated with alloys with this material impose many limitations.

• Structural Engineering and Mechanics
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• v.83 no.3
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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.

• Journal of Korea Multimedia Society
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• v.25 no.1
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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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• v.19 no.4
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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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• v.20 no.1
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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.