• Wind and Structures
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• v.31 no.3
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• pp.269-285
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• 2020

The statistical characteristics of typhoon wind speed records tend to have a considerable time-varying trend; thus, the stationary wind model may not be appropriate to estimate the wind characteristics of typhoon events. Several nonstationary wind speed models have been proposed by pioneers to characterize wind characteristics more accurately, but comparative studies on the applicability of the different wind models are still lacking. In this study, three landfall typhoons, Ampil, Jongdari, and Rumbia, recorded by ultrasonic anemometers atop the Shanghai World Financial Center (SWFC), are used for the comparative analysis of stationary and nonstationary wind characteristics. The time-varying mean is extracted with the discrete wavelet transform (DWT) method, and the time-varying standard deviation is calculated by the autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model. After extracting the time-varying trend, the longitudinal wind characteristics, e.g., the probability distribution, power spectral density (PSD), turbulence integral scale, turbulence intensity, gust factor, and peak factor, are comparatively analyzed based on the stationary wind speed model, time-varying mean wind speed model and time-varying standard deviation wind speed model. The comparative analysis of the different wind models emphasizes the significance of the nonstationary considerations in typhoon events. The time-varying standard deviation model can better identify the similarities among the different typhoons and appropriately describe the nonstationary wind characteristics of the typhoons.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.241-246
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• 2005

Doam watershed is located at alpine areas in the Kangwon province. The annual average precipitation, including snow accumulation during the winter, at the Doam watershed is significantly higher than other areas. Thus, pollutant laden runoff and sediment discharge from the alpine agricultural fields are causing water quality degradation at the Doam watershed. To estimate soil erosion from the agricultural fields, the Universal Soil Loss Equation (USLE) has been widely used because of its simplicity to use. The USLE rainfall erosivity (R) factor is responsible for impacts of rainfall on soil erosion. Thus, use of constant R factor for the Doam watershed cannot reflect variations in precipitation patterns, consequently soil erosion estimation. In the early spring at the Doam watershed, the stream flow increases because of snow melt, which results in erosion of loosened soil experiencing freezing and thaw during the winter. However, the USLE model cannot consider the impacts on soil erosion of freezing and thaw of the soil. Also, it cannot simulate temporal changes in USLE input parameters. Thus, the Soil and Water Assessment Tool (SWAT) model was investigated for its applicability to estimate soil erosion at the Doam watershed, instead of the widely used USLE model. The SWAT hydrology and erosion/sediment components were validated after calibration of the hydrologic component. The $R^2$ and Nash-Sutcliffe coefficient values are higher enough, thus it was found the SWAT model can be efficiently used to simulate hydrology and sediment yield at the Doam watershed. The effects of snow melt on SWAT estimated stream flow and sediment were investigated using long-term precipitation and temperature data at the Doam watershed. It was found significant amount of flow and sediment in the spring are contributed by melting snow accumulated during the winter. Thus, it is recommend that the SWAT model capable of simulating snow melt and long-term weather data needs to be used in estimating soil erosion at alpine agricultural land instead of the USLE model for successful soil erosion management at the Doam watershed.

• The Journal of the Acoustical Society of Korea
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• v.33 no.3
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• pp.216-224
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• 2014

In this paper, we propose a new method to drastically reduce computational complexity in Gaussian Mixture Model (GMM)-based Speaker Identification (SI). Generally, GMM-based SI systems have very high computational complexity proportional to the length of the test utterance, the number of enrolled speakers, and the GMM size. These make the SI systems difficult to be used in various real applications in spite of their broad applicability. Thus, a trade-off between computational complexity and identification accuracy is considered as a primary issue for practical applications. In order to reduce computational complexity sharply with a little loss of accuracy, we introduce a method based on the Universal Background Model (UBM) clustering approach and then we show that it can be used successfully in real-time applications. In experiments with the proposed algorithm, we obtained a speed-up factor of 6 with a negligible loss of accuracy.

• Human Ecology Research
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• v.58 no.4
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• pp.539-560
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• 2020

This study presents work-family multiple-role planning by female university students as a new approach to worklife balance. Accordingly, this study examines university years as a key time frame during which students establish their career paths. This study integrates the social cognitive career theory and the planned behavior theory to design and evaluate a model that explains the work-family multiple-role planning process; in addition, it develops an optimal model to predict the intentions of female university students in work-family multiple-role planning. This study has conducted a structural survey with 500 female university students. After inspecting the data, the responses of 435 participants were used in the data analysis (SEM) with SPSS 21.0 and AMOS 21.0. The findings include the following. First, suitability of predictive model presents a satisfying fit. The major factors in this study's model (parental support, subjective norms, attitudes toward multiple-role planning, career decision self-efficacy, and outcome expectations) are verified as direct and indirect predictors of the work-family multiple-role planning intent of female university students. Second, the strongest predictive factor for the work-family multiple-role planning intent is the social environment factor (subjective norms), indicating that the influence of social pressure on intent is relatively large. The predictive model formulated under this study's integrated theoretical framework supplements existing research that focused on attitudes toward multiple-role planning as well as provides a more profound theoretical foundation on which work-family multiple-role planning behaviors can be better understood.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1383-1392
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• 2019

Adversarial attack, which geneartes adversarial data to make target model misclassify the input data, is able to confuse real life applications of classification models and cause severe damage to the classification system. An Black-box adversarial attack learns a substitute model, which have similar decision boundary to the target model, and then generates adversarial data with the substitute model. Jacobian-based data augmentation is used to synthesize the training data to learn substitutes, but has a drawback that the data synthesized by the augmentation get distorted more and more as the training loop proceeds. We suggest data augmentation with 'decay factor' to alleviate this problem. The result shows that attack success rate of our method is higher(around 8.5%) than the existing method.

• Journal of the Society of Disaster Information
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• v.13 no.4
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• pp.570-576
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• 2017

Trucks take up more portions than cars on highways. Due to this, road use relatively diminish and it serves locally as a threatening factor to nearby drivers. Baggage car accident has distinct characteristics so that it needs the application of different analysis opposed to ordinary accidents. Accident prediction model, one of accident analyses, is used to predict the numbers of accident in certain parts, establish traffic plans as well as accident prevention methods, and diagnose the danger of roads. Thus, this study aims to apply the accident rate of baggage car on highways and calculate the correction factor to be put in the accident prediction models. Accident data based on highway was collected and traffic amounts and accident documents between 2014 and 2016 were utilized. The author developed an accident prediction model based on numbers of annual accidents and set mean annual and daily traffic amounts. This study intends to identify the practical accident prediction model on highway and present an appropriate solution by comparing the prediction model in accords with the accident rate between baggage cars.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.1035-1046
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• 2015

Variance components models incorporate various random factors in the form of linear models. There are two experimental Layouts for the classification of factors under variance components models: nested classification and crossed classification. We consider two-way variance components models and investigate the effect of experimental Layout on the performance of model selection criteria AIC and BIC. The effect of experimental Layout is studied through a simulation study with various combinations of parameters in a systematic fashion. The simulation study shows differences in performance of model selection methods between the two classification. There is a particular tendency to prefer the smaller model than the true model when the variance component of a nested factor becomes relatively larger than a nesting factor that is persistent even when the sample size is not small.

• Fashion & Textile Research Journal
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• v.21 no.3
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• pp.288-299
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• 2019

This study examined trends in model perceptions in the Asian fashion industry through a survey on the current status of using models, model attributes, and image recognition for companies and brands participating in the Seoul Fashion Week and Hong Kong Fashion Week. The results of the study are as follows. First, an examination of the races of models used for public relations by clothing and accessory companies indicated that the use of Asian and black models was lower than white models. Second, intimacy, reliability, similarity, and professionalism were derived as attributes for a public relation model. Among these factors, only 'intimacy' showed a difference between the countries. Third, Seoul Fashion Week participants gave the highest marks for the strong individuality of the models used for their brands; however, participants in the Hong Kong Fashion Week most appreciated suitability with products and professional appearance. Fourth, the different trends of model image recognition were shown through various analysis results by country or race, in which Seoul Fashion Week participants highly perceived the global and luxurious image of white models, and were generally highly satisfied with the models. In terms of the Hong Kong Fashion Week, Asian models tended to be perceived as a more casual image, and the participants held contributions to brand recognition as the most significant factor when using Asian models.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.3
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• pp.31-38
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• 2019

In this paper, we design a green supply chain (gSC) network model. For constructing the gSC network model, environmental and economic factors are taken into consideration in it. Environmental factor is to minimize the $CO_2$ emission amount emitted when transporting products or materials between each stage. For economic factor, the total cost which is composed of total transportation cost, total handling cost and total fixed cost is minimized. To minimize the environmental and economic factors simultaneously, a mathematical formulation is proposed and it is implemented in a genetic algorithm (GA) approach. In numerical experiment, some scales of the gSC network model is presented and its performance is analyzed using the GA approach. Finally, the efficiencies of the gSC network model and the GA approach are proved.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.2
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• pp.20-41
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• 2023

Recently, urban shrinkage due to low birth rate and aging population and the decline of local cities are causing a new urban problem of empty houses. This study examines the distribution of vacant homes using spatial panel data collected from 2015 to 2019 at local administraitve districts and estimates the factors of vacant house occurrence using a spatial panel model considering spatio-temporal dependency. As a result, the spatio-temporal dependence of vacant houses was identified and it was estimated using spatial panel model not OLS model. Based on the spatial panel model, it was found that the most influential factor in the occurrence of vacant houses was the housing-related factor. This result shows that policy considerations for housing supply are necessary for the management of vacant housing as well as population movement and poor infrastructure.