• Journal of Korea Water Resources Association
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• v.53 no.1
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• pp.15-27
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• 2020

Streamflow discharge as a fundamental riverine quantity plays a crucial role in water resources management, thereby requiring accurate in-situ measurement. Recent advances in instrumentations for the streamflow discharge measurement has complemented or substituted classical devices and methods. Among various potential methods, surface current meter using microwave has increasingly begun to be applied not only for flood but also normal flow discharge measurement, remotely and safely enabling practitioners to measure flow velocity postulating indirect contact. With minimized field preparedness, this method facilitated and eased flood discharge measurement in the difficult in-situ conditions such as extreme flood in active ways emitting 24.125 GHz microwave without relying on natural lights. In South Korea, a rectangular shaped instrument named with Microwave Water Surface Current Meter (MWSCM) has been developed and commercially released around 2010, in which domestic agencies charging on streamflow observation shed lights on this approach regarding it as a potential substitute. Considering this brand-new device highlighted for efficient flow measurement, however, there has been few noticeable efforts in systematic and comprehensive evaluation of its performance in various measurement and riverine conditions that lead to lack in imminent and widely spreading usages in practices. This study attempted to evaluate the MWSCM in terms of instrumen's monitoring configuration particularly regarding tilt and yaw angle. In the middle of pointing the measurement spot in a given cross-section, the observation campaign inevitably poses accuracy issues related with different tilt and yaw angles of the instrument, which can be a conventionally major source of errors for this type of instrument. Focusing on the perspective of instrument configuration, the instrument was tested in a controlled outdoor river channel located in KICT River Experiment Center with a fixed flow condition of around 1 m/s flow speed with steady flow supply, 6 m of channel width, and less than 1 m of shallow flow depth, where the detailed velocity measurements with SonTek micro-ADV was used for validation. As results, less than 15 degree in tilting angle generated much higher deviation, and higher yawing angle proportionally increased coefficient of variance. Yaw angles affected accuracy in terms of measurement area.

• The Korean Journal of Nuclear Medicine
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• v.34 no.5
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• pp.403-409
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• 2000

Objectives: Due to the heterogeneous receptor distribution and changes of receptor status over time, the biochemical measurement of estrogen receptor status of biopsy specimens is not sufficient to diagnose breast cancer. As a result, I-123 labeled estradiols have been applied for the diagnosis. The purpose of this study was to develop a suitable radioligand for imaging estrogen receptor-positive human breast tumors. Methods: Among the various estradiol derivatives, $17{\alpha}-[^{123}I]$iodovinyl estradiol ($[^{123}I]$IVE) has been prepared from $17{\alpha}$-ethynyl estradiol. Labeling of $E-17{\alpha}-[^{123}I]$iodovinyl estradiol (E-$[^{123}I]$IVE) was carried out using peracetic acid with $[^{123}I]NaI\;and\;Z-[^{123}I]IVE$ labelling was archived using chloamine-T/HCl solution with $[^{123}I]$NaI. Labeling yield was determined by silica thin-layer chromatography (TLC) and radiochemical purity was measured by high performance liquid chromatography (HPLC). The biodistribution of E-$[^{123}I]$IVE was measured in immature female rats at 60 min, 120 min and 300 min after injection. Results: The labeling yield of two isomers was 92% and 94% ($E-[^{123}I]IVE\;and\;Z-[^{123}I]IVE$, respectively). The radiochemical purity was more than 98% after purification. The highest uptake was observed at 120 min in uterus (3.11% ID/g for E-$[^{123}I]$IVE). Conclusion: These results suggest the possibility of using E-$[^{123}I]$IVE as an imaging agent for the evaluation of the evaluation of the presence of estrogen receptor in patients with breast cancer.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.47
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• pp.63-94
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• 2002

On the purpose to suggest an advanced scheme in assessing the domestic wheat quality, this paper reviewed the inspection systems of wheat in major wheat producing countries as well as the quality criteria which are being used in wheat grading and classification. Most wheat producing countries are adopting both classifications of class and grade to provide an objective evaluation and an official certification to their wheat. There are two main purposes in the wheat classification. The first objectives of classification is to match the wheat with market requirements to maximize market opportunities and returns to growers. The second is to ensure that payments to glowers aye made on the basis of the quality and condition of the grain delivered. Wheat classes has been assigned based on the combination of cultivation area, seed-coat color, kernel and varietal characteristics that are distinctive. Most reputable wheat marketers also employ a similar approach, whereby varieties of a particular type are grouped together, designed by seed coat colour, grain hardness, physical dough properties, and sometimes more precise specification such as starch quality, all of which are genetically inherited characteristics. This classification in simplistic terms is the categorization of a wheat variety into a commercial type or style of wheat that is recognizable for its end use capabilities. All varieties registered in a class are required to have a similar end-use performance that the shipment be consistent in processing quality, cargo to cargo and year to year, Grain inspectors have historically determined wheat classes according to visual kernel characteristics associated with traditional wheat varieties. As well, any new wheat variety must not conflict with the visual distinguishability rule that is used to separate wheats of different classes. Some varieties may possess characteristics of two or more classes. Therefore, knowledge of distinct varietal characteristics is necessary in making class determinations. The grading system sets maximum tolerance levels for a range of characteristics that ensure functionality and freedom from deleterious factors. Tests for the grading of wheat include such factors as plumpness, soundness, cleanliness, purity of type and general condition. Plumpness is measured by test weight. Soundness is indicated by the absence or presence of musty, sour or commercially objectionable foreign odors and by the percentage of damaged kernels that ave present in the wheat. Cleanliness is measured by determining the presence of foreign material after dockage has been removed. Purity of class is measured by classification of wheats in the test sample and by limitation for admixtures of different classes of wheat. Moisture does not influence the numerical grade. However, it is determined on all shipments and reported on the official certificate. U.S. wheat is divided into eight classes based on color, kernel Hardness and varietal characteristics. The classes are Durum, Hard Red Spring, Hard Red Winter, Soft Red Winter, Hard White, soft White, Unclassed and Mixed. Among them, Hard Red Spring wheat, Durum wheat, and Soft White wheat are further divided into three subclasses, respectively. Each class or subclass is divided into five U.S. numerical grades and U.S. Sample grade. Special grades are provided to emphasize special qualities or conditions affecting the value of wheat and are added to and made a part of the grade designation. Canadian wheat is also divided into fourteen classes based on cultivation area, color, kernel hardness and varietal characteristics. The classes have 2-5 numerical grades, a feed grade and sample grades depending on class and grading tolerance. The Canadian grading system is based mainly on visual evaluation, and it works based on the kernel visual distinguishability concept. The Australian wheat is classified based on geographical and quality differentiation. The wheat grown in Australia is predominantly white grained. There are commonly up to 20 different segregations of wheat in a given season. Each variety grown is assigned a category and a growing areas. The state governments in Australia, in cooperation with the Australian Wheat Board(AWB), issue receival standards and dockage schedules annually that list grade specifications and tolerances for Australian wheat. AWB is managing "Golden Rewards" which is designed to provide pricing accuracy and market signals for Australia's grain growers. Continuous payment scales for protein content from 6 to 16% and screenings levels from 0 to 10% based on varietal classification are presented by the Golden Rewards, and the active payment scales and prices can change with market movements.movements.

• Journal of Food Hygiene and Safety
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• v.31 no.4
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• pp.227-249
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• 2016

Arsenic and its compounds vary in their toxicity according to the chemical forms. Inorganic arsenic is more toxic and known as carcinogen. The provisional tolerable weekly intake (PTWI) of $15{\mu}g/kg$ b.w./week established by the Joint FAO/WHO Expert Committee on Food Additives (JECFA) has been withdrawn, while the EFSA panel suggested $BMDL_{0.1}$ $0.3{\sim}8{\mu}g/kg\;b.w./day$ for cancers of the lung, skin and bladder, as well as skin lesions. Rice, seaweed and beverages are known as food being rich in inorganic arsenic. As(III) is the major form of inorganic arsenic in rice and anaerobic paddy soils, while most of inorganic arsenic in seaweed is present as As(V). The inorganic arsenic in food was extracted with solvent such as distilled water, methanol, nitric acid and so on in heat-assisted condition or at room temperature. Arsenic speciation analysis was based on ion-exchange chromatography and high-performance liquid chromatography equipped with atomic absorption spectrometry and inductively coupled plasma mass spectrometry. However, there has been no harmonized and standardized method for inorganic arsenic analysis internationally. The inorganic arsenic exposure from food has been estimated to range of $0.13{\sim}0.7{\mu}g/kg$ bw/day for European, American and Australian, and $0.22{\sim}5{\mu}g/kg$ bw/day for Asian. The maximum level (ML) for inorganic arsenic in food has established by EU, China, Australia and New Zealand, but are under review in Korea. Until now, several studies have conducted for reduction of inorganic arsenic in food. Inorganic arsenic levels in rice and seaweed were reduced by more polishing and washing, boiling and washing, respectively. Further research for international harmonization of analytical method, monitoring and risk assessment will be needed to strengthen safety management of inorganic arsenic of foods in Korea.

• Journal of Life Science
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• v.24 no.3
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• pp.252-259
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• 2014

The leaves of Peatasites japonicus are a traditional oriental medicine with diverse biological activities. A simple and specific analytical method for the quantitative determination of bakkenolide B constituents from methanolic extract of the leaves of P. japonicus was developed. Bakkenolide B was isolated from the leaves of P. japonicus, and its structure was elucidated based on 1D, 2D NMR, and GC-MS spectral data. A liquid chromatographic method was developed to evaluate the quality of P. japonicus through determination of major active compound, bakkenolide B. The wavelengths at 254 and 215 nm were chosen to determine bakkenolide B. The recovery of the method was in the range of 98.6 to 103.1%, and bakkenolide B showed good linearity ($r^2$=0.999) within test ranges. The developed method was applied to the determination of bakkenolide B in the plant part and seasonal changes. The results showed that the content of bakkenolide B in the leaf was higher than in the petiole and rhizome. In this study, a simple, rapid, and reliable high-performance liquid chromatography method was used to determine the percentage and composition of bakkenolide B in P. japonicus procured from different Petasites species plants in South Korea. The method can be employed in routine quantitative analysis and quality control of different products in the market.

• Therapeutic Science for Rehabilitation
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• v.10 no.2
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• pp.109-128
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• 2021

Objective : The purpose of this study was to develop a Korean version of the Activities of Daily Living (ADL)-focused Occupation-Based Neurobehavioral Evaluation (A-ONE) through cross-cultural adaptation and examine its validity and reliability. Methods : This study translated the A-ONE into Korean and performed cross-cultural adaptation for the Korean population. After the development of the Korean version of the A-ONE, cross-cultural and concurrent validities were analyzed. Internal consistency, test-retest reliability, and inter-rater reliability were also evaluated. Results : We adapted three items to the Korean culture. The Korean version of the A-ONE showed high cross-cultural validity with a content validity index (I-CVI) >0.9. It correlated with the Functional Independence Measure (FIM) (r=0.52-0.77, p<0.001), except for communication. Cronbach's α was 0.58-0.93 for the functional independence scale (FI) and 0.42-0.93 for the neurobehavioral specific impairment subscale (NBSIS). Intraclass correlation coefficients (ICCs) indicated high test-retest and inter-rater reliability for FI (ICC=0.79-1.00 and 0.75-1.00, respectively) and NBSIS (ICC=0.74-1.00 and 0.72-1.00, respectively). Conclusion : The Korean version of the A-ONE is well adapted to the Korean culture and has good validity and reliability. It is recommended to evaluate ADL performance skills and neurobehavioral impairments simultaneously in Korea.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• Journal of Distribution Science
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• v.8 no.3
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• pp.27-35
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• 2010

As the population, buying power, and intensity of self-expression of the elderly generation increase, its importance as a market segment is also growing. Therefore, the mass marketing strategy for the elderly generation must be changed to a micro-marketing strategy based on the results of sub-segmentation that suitably captures the characteristics of this generation. Furthermore, as a customer access strategy is decided by sub-segmentation, proper segmentation is one of the key success factors for micro-marketing. Segments or sub-segments are different from sectors, because segmentation or sub-segmentation for micro-marketing is based on the homogeneity of customer needs. Theoretically, complete segmentation would reveal a single voice. However, it is impossible to achieve complete segmentation because of economic factors, factors that affect effectiveness, etc. To obtain a single voice from a segment, we sometimes need to divide it into many individual cases. In such a case, there would be a many segments to deal with. On the other hand, to maximize market access performance, fewer segments are preferred. In this paper, we use the term "sub-segmentation" instead of "segmentation," because we divide a specific segment into more detailed segments. To sub-segment the elderly generation, this paper takes their lifestyles and life stages into consideration. In order to reflect these aspects, various surveys and several rounds of expert interviews and focused group interviews (FGIs) were performed. Using the results of these qualitative surveys, we can define six sub-segments of the elderly generation. This paper uses five rules to divide the elderly generation. The five rules are (1) mutually exclusive and collectively exhaustive (MECE) sub-segmentation, (2) important life stages, (3) notable lifestyles, (4) minimum number of and easy classifiable sub-segments, and (5) significant difference in voices among the sub-segments. The most critical point for dividing the elderly market is whether children are married. The other points are source of income, gender, and occupation. In this paper, the elderly market is divided into six sub-segments. As mentioned, the number of sub-segments is a very key point for a successful marketing approach. Too many sub-segments would lead to narrow substantiality or lack of actionability. On the other hand, too few sub-segments would have no effects. Therefore, the creation of the optimum number of sub-segments is a critical problem faced by marketers. This paper presents a method of evaluating the fitness of sub-segments that was deduced from the preceding surveys. The presented method uses the degree of homogeneity (DoH) to measure the adequacy of sub-segments. This measure uses quantitative survey questions to calculate adequacy. The ratio of significantly homogeneous questions to the total numbers of survey questions indicates the DoH. A significantly homogeneous question is defined as a question in which one case is selected significantly more often than others. To show whether a case is selected significantly more often than others, we use a hypothesis test. In this case, the null hypothesis (H0) would be that there is no significant difference between the selection of one case and that of the others. Thus, the total number of significantly homogeneous questions is the total number of cases in which the null hypothesis is rejected. To calculate the DoH, we conducted a quantitative survey (total sample size was 400, 60 questions, 4~5 cases for each question). The sample size of the first sub-segment-has no unmarried offspring and earns a living independently-is 113. The sample size of the second sub-segment-has no unmarried offspring and is economically supported by its offspring-is 57. The sample size of the third sub-segment-has unmarried offspring and is employed and male-is 70. The sample size of the fourth sub-segment-has unmarried offspring and is not employed and male-is 45. The sample size of the fifth sub-segment-has unmarried offspring and is female and employed (either the female herself or her husband)-is 63. The sample size of the last sub-segment-has unmarried offspring and is female and not employed (not even the husband)-is 52. Statistically, the sample size of each sub-segment is sufficiently large. Therefore, we use the z-test for testing hypotheses. When the significance level is 0.05, the DoHs of the six sub-segments are 1.00, 0.95, 0.95, 0.87, 0.93, and 1.00, respectively. When the significance level is 0.01, the DoHs of the six sub-segments are 0.95, 0.87, 0.85, 0.80, 0.88, and 0.87, respectively. These results show that the first sub-segment is the most homogeneous category, while the fourth has more variety in terms of its needs. If the sample size is sufficiently large, more segmentation would be better in a given sub-segment. However, as the fourth sub-segment is smaller than the others, more detailed segmentation is not proceeded. A very critical point for a successful micro-marketing strategy is measuring the fit of a sub-segment. However, until now, there have been no robust rules for measuring fit. This paper presents a method of evaluating the fit of sub-segments. This method will be very helpful for deciding the adequacy of sub-segmentation. However, it has some limitations that prevent it from being robust. These limitations include the following: (1) the method is restricted to only quantitative questions; (2) the type of questions that must be involved in calculation pose difficulties; (3) DoH values depend on content formation. Despite these limitations, this paper has presented a useful method for conducting adequate sub-segmentation. We believe that the present method can be applied widely in many areas. Furthermore, the results of the sub-segmentation of the elderly generation can serve as a reference for mature marketing.

• Journal of Hospice and Palliative Care
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• v.9 no.2
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• pp.86-92
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• 2006

Purpose: This study was conducted to develop a measuring tool for spiritual care performance of hospice team members. The tool may be utilized for providing hospice patients with more systematic and standardized spiritual tares. Methods: The concept and questions of the tool were developed, and then its validity and reliability were tested. For the validity and reliability tests, a self-reported questionnaire comprising 33 questions with 4 point scale ($1{\sim}4$), was developed, and the data were collected from 192 hospice team members from December 2005 to February 2006. Results: Thirty three questions, drafted through literature review and professional consultation, were reviewed by 20 professionals for their validity, were revised and supplemented resulted in the final 33 questions. The questions with a correlation coefficient grater than .30 were selected: all the 33 questions were selected based on this criterion. The reliability coefficient, Cronbarh's ${\alpha}$, was 0.95. The 33 questions were analyzed for factors, and six factors were extracted: relationship formation and communication, encouragement and promotion of spiritual growth, linking with spiritual resources, preparation of death, evaluation and quality control for spiritual intervention, Intervention, and spiritual assessment for intervention. Conclusion: The tool developed in this study includes six factors and has high level of reliability. This tool Will greatly contribute to assess and improve hospice care services, providing systematic and standardized spiritual cares for terminally ill patients and their families.

• Horticultural Science & Technology
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• v.33 no.3
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• pp.403-408
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• 2015

Color is one of the quality determining factors for pepper powder. To measure the color of pepper powder, several methods including high-performance liquid chromatography (HPLC), thin layer chromatography (TLC), and ASTA-20 have been used. Among the methods, the ASTA-20 method is most widely used for color measurement of a large number of samples because of its simplicity and accuracy. However it requires time consuming preprocessing steps and generates chemical waste containing acetone. As an alternative, we developed a fast and simple method based on a visible/near infrared (Vis/NIR) hyperspectral method to measure the color of pepper powder. To evaluate correlation between the ASTA-20 and the visible/near infrared (Vis/NIR) hyperspectral methods, we first measured the color of a total of 488 pepper powder samples using the two methods. Then, a partial least squares (PLS) model was postulated using the color values of randomly selected 3 66 samples to predict ASTA values of unknown samples. When the ASTA values predicted by the PLS model were compared with those of the ASTA-20 method for 122 samples not used for model development, there was very high correlation between two methods ($R^2=0.88$) demonstrating reliability of Vis/NIR hyperspectral method. We believe that this simple and fast method is suitable for highthroughput screening of a large number of samples because this method does not require preprocessing steps required for the ASTA-20 method, and takes less than 30 min to measure the color of pepper powder.