• Korean Journal of Agricultural Science
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• v.27 no.1
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• pp.39-47
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• 2000

A large scale field test of prefabricated vertical drains is performed to analyze the effect of parameters of the very soft clay at a test site. Compression index and the coefficient of horizontal consolidation obtained by back-analysis from the settlement data were compared with those obtained by means of laboratory tests. The Hyperbolic, Asaoka's and The Curve fitting methods are used to estimate final settlements and coefficients of consolidation. 1. Final settlement predicted with the Hyperbolic method was the largest, and the settlements predicted with the Asaoka's and the Curve fitting methods were nearly the same range, and it was concluded that smear effect has to be considered on design in the case that spacing of drains is small 2. The relationships of the measured consolidation ratio (Urn) and the designed consolidation ratio($U_t$) were showed as $U_m$ = (1.13~1.17)$U_t$, $U_m$ = (1.07~1.20)$U_t$, $U_m$ = (1.13~1.17)$U_t$ on the Hyperbolic, Asaoka's and the Curve fitting methods, respectively. The relations on the Asaoka's and the Curve fitting methods were nearly the same range. 3. The relationships of the field compression index($C_{cfield}$) and virgin compression index($V_{cclab}$) were showed as $C_{cfield}$ = (1.26~1.45)$V_{cclab}$, $C_{cfield}$ = (1.08~1.15) $V_{cclab}$, $C_{cfield}$ = (1.04~1.21)$V_{cclab}$, on the Hyperbolic, Asaoka's and the Curve fitting methods, respectively. 4. The ratio ($C_h/C_v$) of the coefficient of vertical consolidation and the coefficient of horizontal consolidation that is obtained by back-analysis from the settlement data was $C_h$=(0.7~0.9)$C_v$, $C_h$=(0.9~1.5)$C_v$, $C_h$=(2.4~3.0)$C_v$ on the Hyperbolic, Asaoka's and the Curve fitting methods, respectively. 5. It was concluded that the exact consolidation coefficient must be determined after the final settlement is predicted again when the consolidation is finished, because the field consolidation coefficient is decreased as the time allowed to be alone is increased.

• Clean Technology
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• v.28 no.1
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• pp.79-93
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• 2022

In this study, an electronics industrial wastewater activated sludge model (e-ASM) to be used as a Water Digital Twin was calibrated based on real high-tech electronics industrial wastewater treatment measurements from lab-scale and pilot-scale reactors, and examined for its treatment performance, effluent quality prediction, and optimal process selection. For specialized modeling of a high-tech electronics industrial wastewater treatment system, the kinetic parameters of the e-ASM were identified by a sensitivity analysis and calibrated by the multiple response surface method (MRS). The calibrated e-ASM showed a high compatibility of more than 90% with the experimental data from the lab-scale and pilot-scale processes. Four electronics industrial wastewater treatment processes-MLE, A2/O, 4-stage MLE-MBR, and Bardenpo-MBR-were implemented with the proposed Water Digital Twin to compare their removal efficiencies according to various electronics industrial wastewater characteristics. Bardenpo-MBR stably removed more than 90% of the chemical oxygen demand (COD) and showed the highest nitrogen removal efficiency. Furthermore, a high concentration of 1,800 mg L^-1 T MAH influent could be 98% removed when the HRT of the Bardenpho-MBR process was more than 3 days. Hence, it is expected that the e-ASM in this study can be used as a Water Digital Twin platform with high compatibility in a variety of situations, including plant optimization, Water AI, and the selection of best available technology (BAT) for a sustainable high-tech electronics industry.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.2
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• pp.37-45
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• 2022

A simplified model using the lifetime distribution has been presented to estimate the Mean Residual Life (MRL) of rubble-mound breakwaters, which is not like a stochastic process model based on time-dependent history data to the cumulative damage progress of rubble-mound breakwaters. The parameters involved in the lifetime distribution can be easily estimated by using the upper and lower limits of lifetime and their likelihood that made a judgement by several experts taking account of the initial design lifetime, the past sequences of loads, and others. The simplified model presented in this paper has been applied to the rubble-mound breakwater with TTP armor layer. Wiener Process (WP)-based stochastic model also has been applied together with Monte-Carlo Simulation (MCS) technique to the breakwater of the same condition having time-dependent cumulative damage to TTP armor layer. From the comparison of lifetime distribution obtained from each models including Mean Time To Failure (MTTF), it has found that the lifetime distributions of rubble-mound breakwater can be very satisfactorily fitted by log-normal distribution for all types of cumulative damage progresses, such as exponential, linear, and logarithmic deterioration which are feasible in the real situations. Finally, the MRL of rubble-mound breakwaters estimated by the simplified model presented in this paper have been compared with those by WP stochastic process. It can be shown that results of the presented simplified model have been identical with those of WP stochastic process until any ages in the range of MTT F regardless of the deterioration types. However, a little of differences have been seen at the ages in the neighborhood of MTTF, specially, for the linear and logarithmic deterioration of cumulative damages. For the accurate estimation of MRL of harbor structures, it may be desirable that the stochastic processes should be used to consider properly time-dependent uncertainties of damage deterioration. Nevertheless, the simplified model presented in this paper can be useful in the building of the MRL-based preventive maintenance planning for several kinds of harbor structures, because of which is not needed time-dependent history data about the damage deterioration of structures as mentioned above.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.3
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• pp.89-98
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• 1965

The experimental studies were intended to clarify the effects of selection, and also aimed at estimating the heritabilities, the genotypic correlations among some agronomic characters, and at calculating the selection index on some selective characters for the selection of desirable lines, under different climatic conditions. Finally practical implications of these studies, especially on the selection index, were discussed. Twenty-two varieties, determinate growing habit type, were selected at random from the 138 soybean varieties cultivated the year before, were grown in a randomized block design with three replicates at Chinju, Korea, under May and June sowing conditions. The method of estimating heritabilities for the eleven agronomic characters-flowering date, maturity date, stem length, branch numbers per plant, stem diameter, plant weight, pod numbers per plant, grain numbers per plant and 100 grain weight, shown in Table 3, was the variance components procedures in a replicated trial for the varieties. The analysis of covariance was used to obtain the genotypic correlations and phenotypic correlations among the eight characters, and the selection indexes for some agronomic characters were calculated by Robinson's method. The results are summarized as follows: Heritabilities : The experiment on the genotype-environment interaction revealed that in almost all of the characters investigated the interaction was too large to be neglected and materially affected the estimates of various genotypic parameters. The variation in heritability due to the change of environments was larger in the characters of low heritability than in those of high heritability. Heritability values of flowering date, fruiting period (days from flowering to maturity), stem length and 100 grain weight were the highest in both environments, those of yield(grain weight) and other characters were showed the lower values(Table 3). These heritability values showed a decreasing trend with the delayed sowing in the experiments. Further, all calculated heritability values were higher than anticipated. This was expected since these values, which were the broad sense heritability, contain the variance due to dominance and epistasisf in addition to the additive genetic variance. Genotypic correlations : Genotypic correlations were slightly higher than the corresponding phenotypic correlations in both environments, but the variation in values due to the change of environment appeared between grain weight and some other characters, especially an increase between grain weight and flowering date, and the total growing period(Table 6). Genotypic correlations between grain weight and other characters indicated that high seed yield was genetically correlated with late flowering, late maturity, and the other five characters namely branch numbers per plant, stem diameter, plant weight, pod numbers per plant and grain numbers per plant, but not with 100 grain weight of soybeans. Pod numbers and grain numbers per plant were more closely correlated with seed yields than with other characters. Selection index : For the comparison and the use of selection indexes in the selection, two kinds of selection indexes were calculated, the former was called selection index A and the later selection index B as shown in Table 7. Selection index A was calculated by the values of grain weight per plant as the character of yield(character Y), but the other, selection index B, was calculated by the values of pod numbers per plant, instead of grain weight per plant, as the character of yield'(character Y'). These results suggest that selection index technique is useful in soybean breeding. In reality, however, as the selection index varies with population and environment, it must be calculated in each population to which selection is applied and in each environment in which the population is located. In spite of the expected usefulness of selection index technique in soybean breeding, unsolved problems such as the expense, time and labor involved in calculating the selection index remain. For these reasons and from these experimental studies, it was recognized that in the breeding of self-fertilized soybean plants the selection for yield should be based on a more simple selection index such as selection index B of these experiments rather than on the complex selection index such as selection index A. Furthermore, it was realized that the selection index for the selection should be calculated on the basis of the data of some 3-4 agronomic characters-maturity date(X$_1$), branch numbers per plant(X$_2$), stem diameter(X$_3$) and pod numbers per plant etc. It must be noted that it should be successful in selection to select for maturity date(X$_1$) which has high heritability, and the selection index should be calculated easily on the basis of the data of branch numbers per plant(X$_2$), stem diameter(X$_3$) and pod numbers per plant, directly after the harvest before drying and threshing. These characters should be very useful agronomic characters in the selection of Korean soybeans, determinate growing habit type, as they could be measured or counted easily thus saving time and expense in the duration from harvest to drying and threshing, and are affected more in soybean yields than the other agronomic characters.