• Atmosphere
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• v.32 no.2
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• pp.149-162
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• 2022

This study investigated the prediction skill of the Asian dust seasonal forecasting model (GloSea5-ADAM) on the Asian dust and meteorological variables related to the dust generation for the period of 1991~2016. Additionally, we evaluated the prediction skill of those variables depending on the combination of the initial dates in the sub-seasonal scale for the dust source region affecting South Korea. The Asian dust and meteorological variables (10 m wind speed, 1.5 m relative humidity, and 1.5 m air temperature) from GloSea5-ADAM were compared to that from Synoptic observation and European Centre for medium range weather forecasts reanalysis v5, respectively, based on Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC) as evaluation criteria. In general, the Asian dust and meteorological variables in the source region showed high ACC in the prediction scale within one month. For all variables, the use of the initial dates closest to the prediction month led to the best performances based on MBE, RMSE, and ACC, and the performances could be improved by adjusting the number of ensembles considering the combination of the initial date. ACC was as high as 0.4 in Spring when using the closest two initial dates. In particular, the GloSea5-ADAM shows the best performance of Asian dust generation with an ACC of 0.60 in the occurrence frequency of Asian dust in March when using the closest initial dates for initial conditions.

• Steel and Composite Structures
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• v.45 no.2
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• pp.205-218
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• 2022

Proper calculation of splitting tensile strength (STS) of concrete has been a crucial task, due to the wide use of concrete in the construction sector. Following many recent studies that have proposed various predictive models for this aim, this study suggests and tests the functionality of three hybrid models in predicting the STS from the characteristics of the mixture components including cement compressive strength, cement tensile strength, curing age, the maximum size of the crushed stone, stone powder content, sand fine modulus, water to binder ratio, and the ratio of sand. A multi-layer perceptron (MLP) neural network incorporates invasive weed optimization (IWO), cuttlefish optimization algorithm (CFOA), and electrostatic discharge algorithm (ESDA) which are among the newest optimization techniques. A dataset from the earlier literature is used for exploring and extrapolating the STS behavior. The results acquired from several accuracy criteria demonstrated a nice learning capability for all three hybrid models viz. IWO-MLP, CFOA-MLP, and ESDA-MLP. Also in the prediction phase, the prediction products were in a promising agreement (above 88%) with experimental results. However, a comparative look revealed the ESDA-MLP as the most accurate predictor. Considering mean absolute percentage error (MAPE) index, the error of ESDA-MLP was 9.05%, while the corresponding value for IWO-MLP and CFOA-MLP was 9.17 and 13.97%, respectively. Since the combination of MLP and ESDA can be an effective tool for optimizing the concrete mixture toward a desirable STS, the last part of this study is dedicated to extracting a predictive formula from this model.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Journal of the Korean Society of Costume
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• v.56 no.1 s.100
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• pp.13-31
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• 2006

This study will explore Rococo chinoiserie not only as a prominent style of the decorative arts in general, but also as an important factor that influenced $18^{th}$ century fashions in dress. Two premises support the conclusion of this study. One is that the chinoiserie is truly a hybrid, a totally new style resulting from the mixture of various traditional elements from the East and the West, with little regard for the authentic nature of the original styles. The other is that the geographical scope for defining the chinoiserie influence in the Rococo fashion can be expanded beyond its lexical meaning; the style eventually encompassed visual cues from various Eastern cultures including China, India and Turkey. Regardless of the specific origins, the oriental influences for Rococo fashion can be categorized into two types. The first type is a complete appropriation of structural elements of Eastern clothing, such as pagoda hats, pagoda sleeves, turbans decorated with plumes or fur-trimmed open robes and then combining them with Western dress. These exotic and fancy dress ensembles were worn as masquerades, theatrical costumes or portraits. One extraordinary example is the banyan, a man's dressing gown, which also had a place in everyday life, not just as special costume. Although the banyan became more tailored as time passed, the traditional shape of this Eastern garment was accepted unaltered in the beginning of the $18^{th}$ century. The second type of influence shows in the use of eastern textiles, especially silks, which were made into women's dress. It did not matter to the fashionable lady if her dress was made of the silk produced in China or a European copy of the Chinese original, as long as it satisfied her taste. It is difficult to detect the signs of exotic style from a glance in this type of chinoiserie dresses since it was more ambiguous and conservative adaptation of the oriental influence in Rococo dress styles than the first type. In this study, various oriental influences appearing in $18^{th}$ century Rococo fashions can be defined as part of the chinoiserie style based upon the suggested premises. No matter what the origin of these oriental fashions was, this hybrid of the East and West made one of great impacts on the most frivolous and splendid period of western fashion history.

• Fashion & Textile Research Journal
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• v.17 no.4
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• pp.657-665
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• 2015

This study evaluated the thermoregulatory properties of functional thermal underwear ('heating underwear') in markets using a thermal manikin and human wear trials. One ordinary thermal underwear (ORD) and two functional thermal underwear (HEAT1 and HEAT2; manufactured goods, HEAT1: moisture absorbing heat release mechanism, HEAT2: heat storage, release mechanism) were chosen. Thermo-physiological and subjective responses were evaluated at an air temperature of $5.0{\pm}0.5^{\circ}C$ and air humidity of $30{\pm}5%RH$ with five male subjects ($21.6{\pm}1.3yr$ in age, $178.0{\pm}5.9cm$ in height, $68.2{\pm}5.9kg$ in body mass). Experimental conditions consisted of four ensembles that included winter clothes (Control: no underwear, ORD, HEAT1, HEAT2). Water-vapor resistance was greater in fabric of HEAT1 than others. The results were: 1) Total thermal insulation (IT) using a thermal manikin were not greater for HEAT1 (0.860clo) and HEAT 2 (0.873clo) than for ORD (0.886clo). 2) There were no significant differences in rectal temperature, mean skin temperature, heart rate and total body mass loss between the four conditions. Microclimate clothing temperature on the back was greater for ORD than for HEAT1 and HEAT2. Subjects felt more comfortable with HEAT1 than for others at rest. HEAT2 was higher in microclimate humidity when compared to other conditions. The results suggest that thermoregulatory properties of 'heating underwear' in market did not differ from those of ordinary thermal underwear in terms of total thermal insulation and thermoregulatory responses in a cold environment.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.12
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• pp.1939-1949
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• 2008

The purpose of this study was to identify differences or similarities across the ensembles of 2006 F/W fashion trends in the big fashion centers such as Paris, London, New York, and Seoul, by street fashion research. The study focuses on understanding of localized fashion trend in the marketplace. We used photograph observation and analyzed data by SPSS program. We found there is a significant difference in winter outfits at these different global fashion mega cities. Most Korean women were wearing light colored outer jackets and blue jeans were dominant style for pants. The majority of Paris/London, New York and Seoul people on the street were wearing wool/wool like coat. Padded coats were worn more by New Yorkers than by people in Seoul. For the bottom, there is a similarity between Paris/London, and New York City, in that skinny pants were popular. Koreans were wearing skinny pants mostly, but the percentage of mini skirts/shorts was also higher than any other cities. We found that the cross-cultural fashion mega trend is similar in clusters, but there is a slight difference of trend in clothing color, style and design details, and accessories by localized fashion cities. Not only direct observation but also identification of cultural characteristics and consumer behavior through the years will bring much more contributions to apparel industries.

• Journal of Integrative Natural Science
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• v.4 no.2
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• pp.149-157
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• 2011

Urbanization of the world's population has given rise to more than 450 cities around the world with populations in excess of 1 million (megacity) and more than 25 so-called metacities with populations over 10 million (Brinkhoff, 2010). The United States today has a total resident population of more than 308,500,000 people, with 81 percent residing in cities and suburbs as of mid - 2005 (UN, 2008). Urban meteorology is the study of the physics, dynamics, and chemistry of the interactions of Earth's atmosphere and the urban built environment, and the provision of meteorological services to the populations and institutions of metropolitan areas. While the details of such services are dependent on the location and the synoptic climatology of each city, there are common themes, such as enhancing quality of life and responding to emergencies. Experience elsewhere (e.g., Shanghai, Helsinki, Tokyo, Seoul, etc.) shows urban meteorological support is a key part of an integrated or multi-hazard warning system that considers the full range of environmental challenges and provides a unified response from municipal leaders. Urban meteorology has come to require much more than observing and forecasting the weather of our cities and metropolitan areas. Forecast improvement as a function of more and better observations of various kinds and as a function of model resolution, larger ensembles, predicted probability distributions; Responses of emergency managers, government officials, and users to improved and probabilistic forecasts; Benefits of improved forecasts in reduction of loss of life, property damage, and other adverse effects. A national initiative to enhance urban meteorological services is a high-priority need for a wide variety of stakeholders, including the general, commerce and industry, and all levels of government. Some of the activities of such an initiative include: conducting basic research and development; prototyping and other activities to enable very--short and short range predictions; supporting and improving productivity and efficiency in commercial and industrial sectors; and urban planning for long term sustainability. In addition urban test-beds are an effective means for developing, testing, and fostering the necessary basic and applied meteorological and socioeconomic research, and transitioning research findings to operations. An extended, multi-year period of continuous effort, punctuated with intensive observing and forecasting periods, is envisioned.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.55-71
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• 2013

The prediction model is the main factor affecting the performance of a knowledge-based system for bankruptcy prediction. Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid-1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models. An ensemble is a technique that constructs a set of multiple models, combines their outputs, and produces one final prediction. The way in which the outputs of ensemble members are combined is one of the important issues affecting prediction accuracy. A variety of combination schemes have been proposed in order to improve prediction performance in ensembles. Each combination scheme has advantages and limitations, and can be influenced by domain and circumstance. Accordingly, decisions on the most appropriate combination scheme in a given domain and contingency are very difficult. This paper proposes a confidence-based selection approach as part of an ensemble bankruptcy-prediction scheme that can measure unified confidence, even if ensemble members produce different types of continuous-valued outputs. The present experimental results show that when varying the number of models to combine, according to the creation type of ensemble members, the proposed combination method offers the best performance in the ensemble having the largest number of models, even when compared with the methods most often employed in bankruptcy prediction.

• Advances in nano research
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• v.12 no.6
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• pp.605-616
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• 2022

We prepared two samples of ultrafine ferrihydrite (FH) nanoparticle ensembles of quite a different origin. First is the biosynthesized sample (as a product of the vital activity of bacteria Klebsiella oxytoca (hereinafter marked as FH-bact) with a natural organic coating and negligible magnetic interparticle interactions. And the second one is the chemically synthesized ferrihydrite (hereinafter FH-chem) without any coating and high level of the interparticle interactions. The interparticle magnetic interactions have been tuned by modifying the nanoparticle surface in both samples. The coating of the FH-bact sample has been partially removed by annealing at 150℃ for 24 h (hereinafter FH-annealed). The FH-chem sample, vice versa, has been coated (1.0 g) with biocompatible polysaccharide (arabinogalactan) in an ultrasonic bath for 10 min (hereinafter FH-coated). The changes in the surface properties of nanoparticles have been controlled by XPS. According to the electron microscopy data, the modification of the nanoparticle surface does not drastically change the particle shape and size. A change in the average nanoparticle size in sample FH-annealed to 3.3 nm relative to the value in the other samples (2.6 nm) has only been observed. The estimated particle coating thickness is about 0.2-0.3 nm for samples FH-bact and FH-coated and 0.1 nm for sample FH-annealed. Mössbauer and magnetization measurements are definitely shown that the drastic change in the blocking temperature is caused by the interparticle interactions. The experimental temperature dependences of the hyperfine field hf>(T) for samples FH-bact and FH-coated have not revealed the effect of interparticle interactions. Otherwise, the interparticle interaction energy E_int estimated from the hf>(T) for samples FH-chem and FH-annealed has been found to be 121k_B and 259k_B, respectively.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.177-189
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• 2022

The purpose of this study is to apply the d4PDF (Data for Policy Decision Making for Future Change) constructed from a large-scale ensemble climate simulation to estimate the probable rainfall with low frequency and high intensity. In addition, this study analyzes the uncertainty caused by the application of the frequency analysis by comparing the probable rainfall estimated using the d4PDF with that estimated using the observed data and frequency analysis at Geunsam, Imsil, Jeonju, and Jangsu stations. The d4PDF data consists of a total of 50 ensembles, and one ensemble provides climate and weather data for 60 years such as rainfall and temperature. Thus, it was possible to collect 3,000 annual maximum daily rainfall for each station. By using these characteristics, this study does not apply the frequency analysis for estimating the probability rainfall, and we estimated the probability rainfall with a return period of 10 to 1000 years by distributing 3,000 rainfall by the magnitude based on a non-parametric approach. Then, the estimated probability rainfall using d4PDF was compared with those estimated using the Gumbel or GEV distribution and the observed rainfall, and the deviation between two probability rainfall was estimated. As a result, this deviation increased as the difference between the return period and the observation period increased. Meanwhile, the d4PDF reasonably suggested the probability rainfall with a low frequency and high intensity by minimizing the uncertainty occurred by applying the frequency analysis and the observed data with the short data period.