• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.296-296
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• 2022

This study was investigated to compare the milling and physicochemical characteristics of six Korean wheat cultivars (Keumkang, KK; Jokyung, JK; Goso, GS; Joongmo2008, JM; Baekkang, BK; Saekeumkang, SKK) and five foreign wheat classes (Australian standard white wheat, ASW; Australian hard, AH; US northern spring, NS; US hard red winter, HRW; Soft wheat, SW). Korea and foreign wheat grains were milled using a Buhler MLU-202. Flour moisture, ash, protein, gluten, sedimentation, particle size, solvent retention capacity (SRC) and dough properties of flour were analyzed. Results showed that the hard wheats had a greater total flour yield and reduction fraction yield than the soft wheats regardless of the country. However, there were in the milling characteristics between the US and Korean soft wheats. GS, a soft wheat in Korea, had the lowest flour yield (59.6%) and the highest bran fraction yield (21.4%). The particle sizes of flour by milling fraction were B1>B2>B3 for the largest, and the R1〈R2〈R3 for the smallest. Particle size, ash, protein contents and the values of lactic acid SRC showed highly correlated with flour yield. The gluten-performance-index (GPI) is the ratio of the lactic acid SRC value to the sum of sodium carbonate and sucrose SRC values, and it has been used as a quality indicator for overall performance potential of flour. GPI values differed depending on the wheat variety or class, JM (0.82) was the highest value, and SKK (0.56) and SW (0.59) were low. The curve pattern of the Mixolab result also gives a quality indication of the flour sample. JM and NS flour had similar pattern at water absorption and gluten strength parameters and BK and HRW had similar viscosity patterns. These results will enable further study for blending Korean wheat cultivar to improve the flour quality.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.975-976
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• 1993

This talk presents the overview of the author's research and development activities on fuzzy inference hardware. We involved it with two distinct approaches. The first approach is to use application specific integrated circuits (ASIC) technology. The fuzzy inference method is directly implemented in silicon. The second approach, which is in its preliminary stage, is to use more conventional microprocessor architecture. Here, we use a quantitative technique used by designer of reduced instruction set computer (RISC) to modify an architecture of a microprocessor. In the ASIC approach, we implemented the most widely used fuzzy inference mechanism directly on silicon. The mechanism is beaded on a max-min compositional rule of inference, and Mandami's method of fuzzy implication. The two VLSI fuzzy inference chips are designed, fabricated, and fully tested. Both used a full-custom CMOS technology. The second and more claborate chip was designed at the University of North Carolina(U C) in cooperation with MCNC. Both VLSI chips had muliple datapaths for rule digital fuzzy inference chips had multiple datapaths for rule evaluation, and they executed multiple fuzzy if-then rules in parallel. The AT & T chip is the first digital fuzzy inference chip in the world. It ran with a 20 MHz clock cycle and achieved an approximately 80.000 Fuzzy Logical inferences Per Second (FLIPS). It stored and executed 16 fuzzy if-then rules. Since it was designed as a proof of concept prototype chip, it had minimal amount of peripheral logic for system integration. UNC/MCNC chip consists of 688,131 transistors of which 476,160 are used for RAM memory. It ran with a 10 MHz clock cycle. The chip has a 3-staged pipeline and initiates a computation of new inference every 64 cycle. This chip achieved an approximately 160,000 FLIPS. The new architecture have the following important improvements from the AT & T chip: Programmable rule set memory (RAM). On-chip fuzzification operation by a table lookup method. On-chip defuzzification operation by a centroid method. Reconfigurable architecture for processing two rule formats. RAM/datapath redundancy for higher yield It can store and execute 51 if-then rule of the following format: IF A and B and C and D Then Do E, and Then Do F. With this format, the chip takes four inputs and produces two outputs. By software reconfiguration, it can store and execute 102 if-then rules of the following simpler format using the same datapath: IF A and B Then Do E. With this format the chip takes two inputs and produces one outputs. We have built two VME-bus board systems based on this chip for Oak Ridge National Laboratory (ORNL). The board is now installed in a robot at ORNL. Researchers uses this board for experiment in autonomous robot navigation. The Fuzzy Logic system board places the Fuzzy chip into a VMEbus environment. High level C language functions hide the operational details of the board from the applications programme . The programmer treats rule memories and fuzzification function memories as local structures passed as parameters to the C functions. ASIC fuzzy inference hardware is extremely fast, but they are limited in generality. Many aspects of the design are limited or fixed. We have proposed to designing a are limited or fixed. We have proposed to designing a fuzzy information processor as an application specific processor using a quantitative approach. The quantitative approach was developed by RISC designers. In effect, we are interested in evaluating the effectiveness of a specialized RISC processor for fuzzy information processing. As the first step, we measured the possible speed-up of a fuzzy inference program based on if-then rules by an introduction of specialized instructions, i.e., min and max instructions. The minimum and maximum operations are heavily used in fuzzy logic applications as fuzzy intersection and union. We performed measurements using a MIPS R3000 as a base micropro essor. The initial result is encouraging. We can achieve as high as a 2.5 increase in inference speed if the R3000 had min and max instructions. Also, they are useful for speeding up other fuzzy operations such as bounded product and bounded sum. The embedded processor's main task is to control some device or process. It usually runs a single or a embedded processer to create an embedded processor for fuzzy control is very effective. Table I shows the measured speed of the inference by a MIPS R3000 microprocessor, a fictitious MIPS R3000 microprocessor with min and max instructions, and a UNC/MCNC ASIC fuzzy inference chip. The software that used on microprocessors is a simulator of the ASIC chip. The first row is the computation time in seconds of 6000 inferences using 51 rules where each fuzzy set is represented by an array of 64 elements. The second row is the time required to perform a single inference. The last row is the fuzzy logical inferences per second (FLIPS) measured for ach device. There is a large gap in run time between the ASIC and software approaches even if we resort to a specialized fuzzy microprocessor. As for design time and cost, these two approaches represent two extremes. An ASIC approach is extremely expensive. It is, therefore, an important research topic to design a specialized computing architecture for fuzzy applications that falls between these two extremes both in run time and design time/cost. TABLEI INFERENCE TIME BY 51 RULES {{{{Time }}{{MIPS R3000 }}{{ASIC }}{{Regular }}{{With min/mix }}{{6000 inference 1 inference FLIPS }}{{125s 20.8ms 48 }}{{49s 8.2ms 122 }}{{0.0038s 6.4㎲ 156,250 }} }}

• Journal of Korean Academy of Nursing Administration
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• v.6 no.3
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• pp.389-404
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• 2000

This study was designed to determine the cost and measurement of nursing care hours for hospice patients hostpitalized in one university hospital. 314 inpatients in the hospice unit 11 nursing manpower were enrolled. Study was taken place in C University Hospital from 8th to 28th, Nov, 1999. Researcher and investigator did pilot study for selecting compatible hospice patient classification indicators. After modifying patient classification indicators and nursing care details for general ward, approved of content validity by specialist. Using hospice patient classification indicators and per 5 min continuing observation method, researcher and investigator recorded direct nursing care hours, indirect nursing care hours, and personnel time on hospice nursing care hours, and personnel time on hospice nursing care activities sheet. All of the patients were classified into Class I(mildly ill), Class II (moderately ill), Class III (acutely ill), and Class IV (critically ill) by patient classification system (PCS) which had been carefully developed to be suitable for the Korean hospice ward. And then the elements of the nursing care cost was investigated. Based on the data from an accounting section (Riccolo, 1988), nursing care hours per patient per day in each class and nursing care cost per patient per hour were multiplied. And then the mean of the nursing care cost per patient per day in each class was calculated. Using SAS, The number of patients in class and nursing activities in duty for nursing care hours were calculated the percent, the mean, the standard deviation respectively. According to the ANOVA and the $Scheff{\'{e}$ test, direct nursing care hours per patient per day for the each class were analyzed. The results of this study were summarized as follows : 1. Distribution of patient class : class IN(33.5%) was the largest class the rest were class II(26.1%) class III(22.6%), class I(17.8%). Nursing care requirements of the inpatients in hospice ward were greater than that of the inpatients in general ward. 2. Direct nursing care activities : Measurement ${\cdot}$ observation 41.7%, medication 16.6%, exercise ${\cdot}$ safety 12.5%, education ${\cdot}$ communication 7.2% etc. The mean hours of direct nursing care per patient per day per duty were needed ; 69.3 min for day duty, 64.7 min for evening duty, 88.2 min for night duty, 38.7 min for shift duty. The mean hours of direct nursing care of night duty was longer than that of the other duty. Direct nursing care hours per patient per day in each class were needed ; 3.1 hrs for class I, 3.9 hrs for class II, 4.7 hrs for class III, and 5.2 hrs for class IV. The mean hours of direct nursing care per patient per day without the PCS was 4.1 hours. The mean hours of direct nursing care per patient per day in class was increased significantly according to increasing nursing care requirements of the inpatients(F=49.04, p=.0001). The each class was significantly different(p<0.05). The mean hours of direct nursing care of several direct nursing care activities in each class were increased according to increasing nursing care requirements of the inpatients(p<0.05) ; class III and class IV for medication and education ${\cdot}$ communication, class I, class III and class IV for measurement ${\cdot}$ observation, class I, class II and class IV for elimination ${\cdot}$ irrigation, all of class for exercise ${\cdot}$ safety. 3. Indirect nursing care activities and personnel time : Recognization 24.2%, house keeping activity 22.7%, charting 17.2%, personnel time 11.8% etc. The mean hours of indirect nursing care and personnel time per nursing manpower was 4.7 hrs. The mean hours of indirect nursing care and personnel time per duty were 294.8 min for day duty, 212.3 min for evening duty, 387.9 min for night duty, 143.3 min for shift duty. The mean of indirect nursing care hours and personnel time of night duty was longer than that of the other duty. 4. The mean hours of indirect nursing care and personnel time per patient per day was 2.5 hrs. 5. The mean hours of nursing care per patient per day in each class were class I 5.6 hrs, class II 6.4 hrs, class III 7.2 hrs, class IV 7.7 hrs. 6. The elements of the nursing care cost were composed of 2,212 won for direct nursing care cost, 267 won for direct material cost and 307 won for indirect cost. Sum of the elements of the nursing care cost was 2,786 won. 7. The mean cost of the nursing care per patient per day in each class were 15,601.6 won for class I, 17,830.4 won for class II, 20,259.2 won for class III, 21,452.2 won for class IV. As above, using modified hospice patient classification indicators and nursing care activity details, many critical ill patients were hospitalized in the hospice unit and it reflected that the more nursing care requirements of the patients, the more direct nursing care hours. Emotional ${\cdot}$ spiritual care, pain ${\cdot}$ symptom control, terminal care, education ${\cdot}$ communication, narcotics management and delivery, attending funeral ceremony, the major nursing care activities, were also the independent hospice service. But it is not compensated by the present medical insurance system. Exercise ${\cdot}$ safety, elimination ${\cdot}$ irrigation needed more nursing care hours as equal to that of intensive care units. The present nursing management fee in the medical insurance system compensated only a part of nursing car service in hospice unit, which rewarded lower cost that that of nursing care.

• Tuberculosis and Respiratory Diseases
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• v.42 no.4
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• pp.555-568
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• 1995

Background: It is most physiologic to measure the diffusing capacity of the lung by using oxygen, but it is so difficult to measure partial pressure of oxygen in the capillary blood of the lung that in clinical practice it is measured by using carbon monoxide, and single-breath diffusing capacity method is used most widely. However, since the process of withholding the breath for 10 seconds after inspiration to the total lung capacity is very hard to practice for patients who suffer from cough, dyspnea, etc, the intra-breath lung diffusing capacity method which requires a single exhalation of low-flow rate without such process was devised. In this study, we want to know whether or not there is any significant difference in the diffusing capacity of the lung measured by the single-breath and intra-breath methods, and if any, which factors have any influence. Methods: We chose randomly 73 persons without regarding specific disease, and after conducting 3 times the flow-volume curve test, we selected forced vital capacity(FVC), percent of predicted forced vital capacity, forced expiratory volume within 1 second($FEV_1$), percent of forced expiratory volume within 1 second, the ratio of forced expiratory volume within 1 second against forced vital capacity($FEV_1$/FVC) in test which the sum of FVC and $FEV_1$ is biggest. We measured the diffusing capacity of the lung 3 times in each of the single-breath and intra-breath methods at intervals of 5 minutes, and we evaluated which factors have any influence on the difference of the diffusing capacity of the lung between two methods[the mean values(ml/min/mmHg) of difference between two diffusing capacity measured by two methods] by means of the linear regression method, and obtained the following results: Results: 1) Intra-test reproducibility in the single-breath and intra-breath methods was excellent. 2) There was in general a good correlation between the diffusing capacity of the lung measured by a single-breath method and that measured by the intra-breath method, but there was a significant difference between values measured by both methods($1.01{\pm}0.35ml/min/mmHg$, p<0.01) 3) The difference between the diffusing capacity of the lung measured by both methods was not correlated to FVC, but was correlated to $FEV_1$, percent of $FEV_1$, $FEV_1$/FVC and the gradient of methane concentration which is an indicator of distribution of ventilation, and it was found as a result of the multiple regression test, that the effect of $FEV_1$/FVC was most strong(r=-0.4725, p<0.01) 4) In a graphic view of the difference of diffusing capacity measured by single-breath and intra-breath method and $FEV_1$/FVC, it was found that the former was divided into two groups in section where $FEV_1$/FVC is 50~60%, and that there was no significant difference between two methods in the section where $FEV_1$/FVC is equal or more than 60% ($0.05{\pm}0.24ml/min/mmHg$, p>0.1), but there was significant difference in the section, less than 60%($-4.5{\pm}0.34ml/min/mmHg$, p<0.01). 5. The diffusing capacity of the lung measured by the single-breath and intra-breath method was the same in value($24.3{\pm}0.68ml/min/mmHg$) within the normal range(2%/L) of the methane gas gradient, and there was no difference depending on the measuring method, but if the methane concentration gradients exceed 2%/L, the diffusing capacity of the lung measured by single-breath method became $15.0{\pm}0.44ml/min/mmHg$, and that measured by intra-breath method, $11.9{\pm}0.51ml/min/mmHg$, and there was a significant difference between them(p<0.01). Conclusion: Therefore, in case where $FEV_1$/FVC was less than 60%, the diffusing capacity of the lung measured by intra-breath method represented significantly lower value than that by single-breath method, and it was presumed to be caused largely by a defect of ventilation-distribution, but the possibility could not be excluded that the diffusing capacity of the lung might be overestimated in the single-breath method, or the actual reduction of the diffusing capacity of the lung appeared more sensitively in the intra-breath method.

• Journal of Korean Society of Environmental Engineers
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• v.34 no.2
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• pp.109-118
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• 2012

This study achieves results from 22 maternity breast milk samples in total to demonstrate exposure level and risk assessment of PBDEs in Seoul area. PBDEs were detected in all the breast milk samples of the present study, indicating that general population in these Seoul area are widely exposed to these chemicals. Residue levels of total PBDEs (sum PBDEs from tri- to hepta-BDE) ranged of 0.84~13.1 ng/g lipid with an arithmetic mean and median of 3.56 ng/g lipid and 2.6 ng/g lipid, respectively. Global comparison shows that the levels of total PBDEs were relatively similar to those of China, Taiwan and European country (Sweden), and somewhat higher than those in some Asian (Vietnam, Philippines, and Indonesia). In the present study, however, the levels of total PBDEs in human milk are still one to two orders of magnitude lower than those in North America. Contribution rate of each congener appeared to be predominant with BDE-47, followed by BDE-153, BDE-100, BDE-99, BDE-154 and BDE-183. The sum of BDE-47 and BDE-153 accounted for more than 65% of total PBDEs in most samples. From the result of the human risk assessment of infants for total PBDEs and BDE-47 by breast milk feeding, we could find out that the average daily doses and hazard index (95th percentile) were 16.5 ng/kg bw/day and $1.2{\times}10^{-1}$ each other when nursing for 6 months after infants born. However, it was concluded that the infant health hardly had adverse seriously effects under this research condition.

• Journal of Soil and Groundwater Environment
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• v.13 no.4
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• pp.40-53
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• 2008

Groundwater pollution prediction methods have been developed to plan the sustainable groundwater usage and protection from potential pollution in many countries. DRASTIC established by US EPA is the most widely used groundwater vulnerability mapping method. However, the DRASTIC showed limitation in predicting the groundwater contamination because the DRASTIC method is designed to embrace only hydrogeologic factors. Therefore, in this study, three different methods were applied to improve a groundwater pollution prediction method: US EPA DRASTIC, Modified-DRASTIC suggested by Panagopoulos et al. (2006), and LSDG (Land use, Soil drainage, Depth to water, Geology) proposed by Rupert (1999). The Modified-DRASTIC is the modified version of the DRASTIC in terms of the rating scales and the weighting coefficients. The rating scales of each factor were calculated by the statistical comparison of nitrate concentrations in each class using the Wilcoxon rank-sum test; while the weighting coefficients were modified by the statistical correlation of each parameter to nitrate concentrations using the Spearman's rho test. The LSDG is a simple rating method using four factors such as Land use, Soil drainage, Depth to water, and Geology. Classes in each factor are compared by the Wilcoxon rank-sum test which gives a different rating to each class if the nitrate concentration in the class is significantly different. A database of nitrate concentrations in groundwaters from 149 wells was built in Keumsan area. Application of three different methods for assessing the groundwater pollution potential resulted that the prediction which was represented by a correlation (r) between each index and nitrate was improved from the EPA DRASTIC (r = 0.058) to the modified rating (r = 0.245), to the modified rating and weights (r = 0.400), and to the LSDG (r = 0.415), respectively. The LSDG seemed appropriate to predict the groundwater pollution in that it contained land use as a factor of the groundwater pollution sources and the rating of each class was defined by a real pollution nitrate concentration.

• Journal of Distribution Research
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• v.17 no.3
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• pp.115-132
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• 2012

This study empirically examines the impact of SSM market entry on changes in market shares among retailing types. The data is monthly time-series data spanning over the period from January 2000 to December 2010, and the effect of SSM market entry on market shares of retailing types is analyzed by utilizing several key factors such as the number of new SSM monthly entrants, total number of SSMs, the proportion of new SSM entrant that is smaller than $165m^2$ to total new SSM entrants. According to the Korean Standard Industrial Classification codes, the retailing type is classified into 5 groups: department stores, retail sale in other non-specialized large stores(big marts), supermarkets, convenience stores, and retail sale in other non-specialized stores with food or beverages predominating (others). The market shares of retailing types are calculated by the ratio of each retailing type monthly sales to total monthly retailing sales in which total retailing sales is the sum of each retailing type sales. The empirical model controls for the size effects with the number of monthly employees for each retailing type and the macroeconomic effects with M2. The empirical model employed in this study is as follows; $$MS_i=f(NewSSM,\;CumSSM,\;employ_i,\;under165,\;M2)$$ where $MS_i$ is the market share of each retailing type (department stores, big marts), supermarkets, convenience stores, and others), NewSSM is the number of new SSM monthly entrants, CumSSM is total number of SSMs, $employ_i$ is the number of monthly employees for each retailing type, and under165 is the proportion of new SSM entrant that is smaller than $165m^2$ to total new SSM entrants. The correlation among these variables are reported in