• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• Korean Journal of Food Science and Technology
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• v.42 no.4
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• pp.508-513
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• 2010

Ninety three strains (4.2%) of Vibrio parahaemolyticus were isolated from 2,239 samples of commercial marine products during 2006-2008 in Seoul, Korea. We examined 16 antimicrobial susceptibilities of 93 V. parahaemolyticus isolates. Antibiotic resistance of V. parahaemolyticus was most frequently observed to ampicillin (93.5%), followed by cephalothin (90.3%), streptomycin (87.1%), ticarcillin (55.9%), and amikacin (40.9%). Antibiotic susceptibility was most frequently observed to nalidixic acid, chloramphenicol and ampicillin/sulbactam (100%), followed by trimethoprim/sulfamethoxazole (98.9%), gentamicin and tetracycline (82.8%), and ceftriaxone (63.4%). In addition, the isolates also displayed intermediate resistance to kanamycin (79.6%), ciprofloxacin (64.5%), amikacin (47.3%) and cefoxitin (43.0%). All isolates were resistant to more than two drugs. The most prominent multiple drug resistance was 3 drug resistance (37.6%), followed by 4 drug (24.7%), 5 drug (17.2%), and 6 drug resistance (11.8%). The most prominent multiple drug resistance pattern was the cephalothin-streptomycin-ampicillin resistance pattern (22.6%), followed by cephalothinticarcillin-streptomycin-ampicillin (18.3%) and cephalothin-ticarcillin-streptomycin-ampicillin-amikacin (9.7%). Multiple drug resistance patterns of V. parahaemolyticus from marine products require continuous monitoring.

• BMB Reports
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• v.55 no.6
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• pp.275-280
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• 2022

The treatment of atopic dermatitis (AD) is challenging due to its complex etiology. From epidermal disruption to chronic inflammation, various cells and inflammatory pathways contribute to the progression of AD. As with immunosuppressants, general inhibition of inflammatory pathways can be effective, but this approach is not suitable for long-term treatment due to its side effects. This study aimed to identify a plant extract (PE) with anti-inflammatory effects on multiple cell types involved in AD development and provide relevant mechanistic evidence. Degranulation was measured in RBL-2H3 cells to screen 30 PEs native to South Korea. To investigate the anti-inflammatory effects of Parasenecio auriculatus var. matsumurana Nakai extract (PAE) in AD, production of cytokines and nitric oxide, activation status of FcεRI and TLR4 signaling, cell-cell junction, and cell viability were evaluated using qRT-PCR, western blotting, confocal microscopy, Griess system, and an MTT assay in RBL-2H3, HEK293, RAW264.7, and HaCaT cells. For in vivo experiments, a DNCBinduced AD mouse model was constructed, and hematoxylin and eosin, periodic acid-Schiff, toluidine blue, and F4/80-staining were performed. The chemical constituents of PAE were analyzed by HPLC-MS. By measuring the anti-degranulation effects of 30 PEs in RBL-2H3 cells, we found that Paeonia lactiflora Pall., PA, and Rehmannia glutinosa (Gaertn.) Libosch. ex Steud. show an inhibitory activity of more than 50%. Of these, PAE most dramatically and consistently suppressed cytokine expression, including IL-4, IL-9, IL-13, and TNF-α. PAE potently inhibited FcεRI signaling, which mechanistically supports its basophil-stabilizing effects, and PAE downregulated cytokines and NO production in macrophages via perturbation of toll-like receptor signaling. Moreover, PAE suppressed cytokine production in keratinocytes and upregulated the expression of tight junction molecules ZO-1 and occludin. In a DNCB-induced AD mouse model, the topical application of PAE significantly improved atopic index scores, immune cell infiltration, cytokine expression, abnormal activation of signaling molecules in FcεRI and TLR signaling, and damaged skin structure compared with dexamethasone. The anti-inflammatory effect of PAE was mainly due to integerrimine. Our findings suggest that PAE could potently inhibit multi-inflammatory cells involved in AD development, synergistically block the propagation of inflammatory responses, and thus alleviate AD symptoms.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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• pp.550-557
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• 2005

The multivariate statistical analysis methods, using both multiple linear regression(MLR) and partial least square(PLS), have been applied to predict the reactor properties and the product quality of a direct esterification reactor for polyethylene terephthalate(PET) synthesis. On the basis of the set of data including the flow rate of water vapor, the flow rate of EG vapor, the concentration of acid end groups of a product and other operating conditions such as temperature, pressure, reaction times and feed monomer mole ratio, two multi-variable analysis methods have been applied. Their regression and prediction abilities also have been compared. The prediction results are critically compared with the actual plant data and the other mathematical model based results in reliability. This paper shows that PLS method approach can be used for the reasonably accurate prediction of a product quality of a direct esterification reactor in PET synthesis process.

• Fashion & Textile Research Journal
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• v.18 no.6
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• pp.833-843
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• 2016

Due to the rapid expansion of fashion, consumers easily purchase fashion products, the period of wearing apparel is shortened, and the occurrence of clothes piling up in the closet is increasing. In order to induce and suggest rational consumption and disposal actions, research focused on the factors influencing difficulty discarding and disposal behavior toward a fashion product is needed. Thus, this study considered fashion-shopping orientation as a variable affecting difficulty discarding and disposal behavior toward fashion products. A total of 325 questionnaires were collected, and 11 were discarded due to partial responses or missing data. Finally, a total of 314 survey questionnaires were analyzed. Frequency, exploratory factor, reliability, and multiple regression analyses were employed for data analysis using SPSS 23.0. The study results were as follows. First, hedonic and economic shopping orientation positively affected difficulty discarding a fashion product, whereas rational shopping orientation negatively affected difficulty discarding a fashion product. Second, hedonic, economic, and conspicuous shopping orientation positively affected reuse behavior among disposal behavior toward a fashion product. Third, trend-seeking and convenient shopping orientation positively influenced handover behavior. Fourth, economic and conspicuous shopping orientation positively affected separation discard behavior. The results of this study provide various guidelines for manufacturers and retailers of fashion products.

• Journal of the Korean Society of Food Culture
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• v.29 no.6
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• pp.519-527
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• 2014

This study was conducted to analyze the differences in consumer behavior of coffee product according to monthly income in franchised coffee shop's consumer in Korea. A total of 293 questionnaires were used out of the 330 that had been distributed for analysis; those that lacked reliability were excluded. Frequency, ANOVA, and Duncan's multiple range test was conducted to analyze the hypotheses of the study. The findings are summarized as follows. There were significant differences in consumer behavior with regard to seeking health, eco-friendly products, product brand, and product taste as well as preferred coffee menu and coffee consumption expenditures. High income consumers rather than low income groups showed much higher score for concerning health, concerning diet, concerning calorie, concerning instant, concerning caffeine and concerning nature, organic in coffee consumer behavior. In amount spent on purchasing coffee products according to income, low income consumers rather than high income groups had much higher financial expense burden.

• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.7
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• pp.950-961
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• 2004

This study was attempted to examine consumers＇ pursuing benefits and satisfaction at outlet store, to analyze the differences of pursuing benefits and satisfaction at outlet store among the consumer groups segmented by the pursuing benefits, and to provide useful information for establishment of marketing strategies. The subjects were 500 female consumers experienced in purchasing clothes at outlet non. For analysis of questionnaires, $\chi$$^2$-test, ANOVA, cluster analysis, factor analysis, and Duncan＇s multiple range test were performed. The results were as follows: 1. Pursuing benefits at outlet store were composed of 6 factors: store＇s atmosphere, variety of products and brands, service and reputation of store, product quality, location＇s convenience, and price. Consumers were segmented into three groups by above 6 factors: product pursuing group, store convenience pursuing group, and price pursuing group. 2. Satisfaction at outlet store was composed of 5 factors: product, atmosphere, service and reputation of store, location convenience, and price. Satisfaction at outlet store such as service and reputation of store, location＇s convenience, and price were significantly different among the segmented groups. Product pursuing group was most satisfied with service and reputation of store, store convenience pursuing group was most satisfied with location＇s convenience and price, and price pursuing group was most satisfied with price. 3. The demographic variables such as an age, marriage, occupation, academic background, and total income were significantly different among the segmented groups.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.5 s.153
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• pp.818-828
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• 2006

The purpose of this study was to clarify differences in the product attribute evaluation in relation to the objective knowledge, subjective knowledge and involvement of apparel. The measurement instruments were developed by researcher on the basis of previous studies in the same field. The subjects of this study were female adults who lived in Seoul, Kyunggi or Incheon areas and quota sampling using age and residential areas was employed. The data were obtained from 603 questionnaires. Data were statistically analyzed using SPSS 10 and LISREL 7.0. Major statistical methods were factor analysis, Cronbach's a coefficient, multiple regression analysis, and structural equation model analysis. The results were as follows: 1. Involvement was related to the consumer knowledge and the knowledge influenced evaluation of intrinsic attributes, social attributes, and economic attributes. 2. The dimensions of objective knowledge significantly influenced intrinsic attributes and economic attributes. The dimensions of subjective knowledge significantly influenced intrinsic attributes, social attributes and economic attributes. 3. Apparel involvement significantly influenced intrinsic attributes, social attributes and economic attributes. Consumers who have higher interest in apparel product but not in trends considered intrinsic attributes more importantly, whereas consumers who care trends considered social attribute more.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.1
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• pp.7-16
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• 2003

At present, company has to produce a product that consumer like with a competitive price, a good quality, and a fitting time to supply. Process control and quality control are very important to supply with a product uniformly and inexpensively. Process control is given much weight in the quality control in manufacturing system. Statistical process controls(SPC) that are used in process generally have major impact on manufacturing, product design activities, and process development potentially. Control charts in statistical process control method can be interpreted the data from quality characteristics in production process and discriminated between chance variation and assignable variation in process. In addition, control chart can be used to monitor the process output and detect when changes in the inputs are required to bring the process back to an in-control state. The models that relate the influential inputs to process outputs help determine the nature and magnitude of the adjustments required. In this paper, the characteristic of product quality is monitored by control chart during the machining process and construction of quality control cycle is considered to divide into two types in this case that different assignable causes lead to shifts having different magnitudes. Then we are intended to find a process shift magnitude which has economical quality cost policy and are considered to quality cost functions to find a process shift magnitude. Those costs are categorized into the well-known categories of prevention, appraisal, and internal failure and external failure. This paper ends with numerical examples that demonstrate the usefulness of the model.