• Korean Journal of Schizophrenia Research
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• v.21 no.2
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• pp.43-50
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• 2018

Objectives : Genome-wide association studies (GWASs) and meta-analyses indicate that single-nucleotide polymorphisms (SNPs) in the a-1C subunit of the L-type voltage-dependent calcium channel (CACNA1C) gene increase the risk for schizophrenia and bipolar disorders (BDs). We investigated the association between the genetic variants on CACNA1C and schizophrenia and/or BDs in the Korean population. Methods : A total of 582 patients with schizophrenia, 336 patients with BDs consisting of 179 bipolar I disorder (BD-I) and 157 bipolar II disorder (BD-II), and 502 healthy controls were recruited. Based on previous results from other populations, three SNPs (rs10848635, rs1006737, and rs4765905) were selected and genotype-wise association was evaluated using logistic regression analysis under additive, dominant and recessive genetic models. Results : rs10848635 showed a significant association with schizophrenia (p=0.010), the combined schizophrenia and BD group (p=0.018), and the combined schizophrenia and BD-I group (p=0.011). The best fit model was dominant model for all of these phenotypes. The association remained significant after correction for multiple testing in schizophrenia and the combined schizophrenia and BD-I group. Conclusion : We identified a possible role of CACNA1C in the common susceptibility of schizophrenia and BD-I. However no association trend was observed for BD-II. Further efforts are needed to identify a specific phenotype associated with this gene crossing the current diagnostic categories.

• Journal of the Korean Geotechnical Society
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• v.39 no.11
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• pp.85-95
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• 2023

This study implemented intelligent compaction technology at the construction site of the AY Highway in Gyeonggi Province, with a focus on obtaining the representative intelligent compaction value, CMV. The target CMV for quality control was established through trial construction, and the validation of the compaction quality control process based on intelligent compaction was conducted. The optimal approach for determining the target CMV was confirmed to be through linear regression of the average CMV measured within a 5-m radius from the plate load testing location. Upon assessing compaction quality against the target CMV, it was observed that the quality criteria outlined in the domestic intelligent compaction standard were met. However, the criteria outlined in Austria and the United States were not satisfied. Notably, indicators related to the variability of compaction quality did not meet the specified criteria, suggesting a stringent standard compared to the observed variability of CMV, ranging from 17% to 55%. Consequently, it is recommended to conduct additional field tests to further validate the compaction quality control process based on intelligent compaction. This will aid in confirming and enhancing the appropriateness of the regulations stipulated in each standard.

• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Korean Journal of Soil Science and Fertilizer
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• v.26 no.2
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• pp.111-120
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• 1993

An attempt was made to investigate the yield response to the N P K fertilizer application for barley, soybean, corn, garlic, onion and potato in the famers' fields from 1971 to 1989. 1. The yield index of without-fertilizers plot when yield of N P K plot is regarded as 100, were 43~92 in plot without N, 71~94 in plot without $P_2O_5$ and 88~96 in plot without $K_2O$. The effects of each fertilizer application for upland crops were greater in barley, potato and corn for N, barley and soybean cultivated in reclaimed soil for $P_2O_5$ and potato and barley for $K_2O$. 2. The optimum levels of N, P and K fertilizers varied with the kinds of soil and crop in the ranges of 4.0~23.2kg/10a for N, 6.0~45.0kg/10a for $P_2O_5$ and 6.6~21.9kg/10a for $K_2O$. 3. The efficiencies of applied fertilizers were increased in cultivated soil in comparison with the reclaimed soil and the efficiencies were in order of potato, onion>barley, corn>soybean. 4. The regression equations of fertilizer recommendation for 5 crops excepting onion were obtained from relations between the organic matter in soil and the amount of N fertilizer applied, between the available $P_2O_5$ in soil and the amount of P fertilizer applied, and between the exchangeable K of soil and the amount of K fertilizer applied. 5. The reduced rates of P and K fertilizer application based on soil testing varied with regions in the ranges of 11.8~80.0 and 0~62.5%, respectively. And the recommendation for N fertilizer amount was necessary to increase or decrease depending on regions.

• Journal of Animal Science and Technology
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• v.44 no.4
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• pp.453-458
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• 2002

In order to search for reliable rapid methods of estimating bacterial counts in pork, this study was tried to measure resazurin reduction time which is simple in experimental method, low in analytical cost, able to estimate bacterial count within short time. The results were summarized as follows; Correlation coefficient between initial bacterial log count(25$^{\circ}C$/72hr, Y) and resazurin reduction time(X) from blue color to pink color during incubation at 25$^{\circ}C$ and 30$^{\circ}C$ was higher than other conditions as -0.95 and -0.94, respectively. Considering correlation coefficient and reduction time, incubation temperature was compatible at 30$^{\circ}C$, and regression equation(RE) was Y = -0.4386X + 7.7870. At a bacterial load of $10^2$cfu/$cm^2$, $10^3$cfu/$cm^2$ and $10^4$cfu/$cm^2$ in pork, reduction time was 13.2hr, 10.9hr and 8.6hr, respectively. Correlation coefficient between initial bacterial log count(30$^{\circ}C$/72hr, Y) and resazurin reduction time(X) from blue color to pink color during incubation at 30$^{\circ}C$ was highest among other conditions as -0.93, and RE was Y = -0.4171X + 7.5540. At a bacterial load of $10^2$cfu/$cm^2$, $10^3cfu/$cm^2$ and $10^4cfu/$cm^2$ in pork, reduction time was 13.3hr, 10.9hr and 8.5hr, respectively. Correlation coefficient between initial bacterial log count(35$^{\circ}C$/72hr, Y) and resazurin reduction time(X) from blue color to pink color during incubation at 30$^{\circ}C$ was highest among other conditions as -0.93, and RE was Y = -0.3514X + 6.7513. At a bacterial load of $10^2$cfu/$cm^2$, $10^3$cfu/$cm^2$ and $10^4$cfu/$cm^2$ in pork, reduction time was 13.5hr, 10.7hr and 7.8hr, respectively.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.105-119
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• 2017

Smartphone users hit over eighty-five percentage of Korean populations and personal private items and various information are stored in each user's smartphone. There are so many cases to propagate malicious codes or spywares for the purpose of catching illegally these kinds of information and earning pecuniary gains. Thus, need of information security is outstanding for using smartphone but also user's security perception is important. In this paper, we investigate about how information security affects smartphone operating system choices by users. For statistical analysis, the online survey with questionnaires for users of smartphones is conducted and effective 218 subjects are collected. We test hypotheses via communalities analysis using factor analysis, reliability analysis, independent sample t-test, and linear regression analysis by IBM SPSS statistical package. As a result, it is found that hardware environment influences on perceived ease of use. Brand power affects both perceived usefulness and perceived ease of use and degree of personal risk-accepting influences on perception of smartphone spy-ware risk. In addition, it is found that perceived usefulness, perceived ease of use, degree of personal risk-accepting, and spy-ware risk of smartphone influence significantly on intention to purchase smartphone. However, results of independent sample t-test for each operating system users of Android or iOS do not present statistically significant differences among two OS user groups. In addition, each result of OS user group testing for hypotheses is different from the results of total sample testing. These results can give important suggestions to organizations and managers related to smartphone ecology and contribute to the sphere of information systems (IS) study through a new perspective.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Magazine of the Korean Society of Agricultural Engineers
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• v.26 no.1
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• pp.52-72
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• 1984

This study was performed to obtain the basic data which can be applied to the use of epoxy resin mortars. The data was based on the properties of epoxy resin mortars depending upon various mixing ratios to compare those of cement mortar. The resin which was used at this experiment was Epi-Bis type epoxy resin which is extensively being used as concrete structures. In the case of epoxy resin mortar, mixing ratios of resin to fine aggregate were 1: 2, 1: 4, 1: 6, 1: 8, 1:10, 1 :12 and 1:14, but the ratio of cement to fine aggregate in cement mortar was 1 : 2.5. The results obtained are summarized as follows; 1.When the mixing ratio was 1: 6, the highest density was 2.01 g/cm$^3$, being lower than 2.13 g/cm$^3$ of that of cement mortar. 2.According to the water absorption and water permeability test, the watertightness was shown very high at the mixing ratios of 1: 2, 1: 4 and 1: 6. But then the mixing ratio was less than 1 : 6, the watertightness considerably decreased. By this result, it was regarded that optimum mixing ratio of epoxy resin mortar for watertight structures should be richer mixing ratio than 1: 6. 3.The hardening shrinkage was large as the mixing ratio became leaner, but the values were remarkably small as compared with cement mortar. And the influence of dryness and moisture was exerted little at richer mixing ratio than 1: 6, but its effect was obvious at the lean mixing ratio, 1: 8, 1:10,1:12 and 1:14. It was confirmed that the optimum mixing ratio for concrete structures which would be influenced by the repeated dryness and moisture should be rich mixing ratio higher than 1: 6. 4.The compressive, bending and splitting tensile strenghs were observed very high, even the value at the mixing ratio of 1:14 was higher than that of cement mortar. It showed that epoxy resin mortar especially was to have high strength in bending and splitting tensile strength. Also, the initial strength within 24 hours gave rise to high value. Thus it was clear that epoxy resin was rapid hardening material. The multiple regression equations of strength were computed depending on a function of mixing ratios and curing times. 5.The elastic moduli derived from the compressive stress-strain curve were slightly smaller than the value of cement mortar, and the toughness of epoxy resin mortar was larger than that of cement mortar. 6.The impact resistance was strong compared with cement mortar at all mixing ratios. Especially, bending impact strength by the square pillar specimens was higher than the impact resistance of flat specimens or cylinderic specimens. 7.The Brinell hardness was relatively larger than that of cement mortar, but it gradually decreased with the decline of mixing ratio, and Brinell hardness at mixing ratio of 1 :14 was much the same as cement mortar. 8.The abrasion rate of epoxy resin mortar at all mixing ratio, when Losangeles abation testing machine revolved 500 times, was very low. Even mixing ratio of 1 :14 was no more than 31.41%, which was less than critical abrasion rate 40% of coarse aggregate for cement concrete. Consequently, the abrasion rate of epoxy resin mortar was superior to cement mortar, and the relation between abrasion rate and Brinell hardness was highly significant as exponential curve. 9.The highest bond strength of epoxy resin mortar was 12.9 kg/cm$^2$ at the mixing ratio of 1:2. The failure of bonded flat steel specimens occurred on the part of epoxy resin mortar at the mixing ratio of 1: 2 and 1: 4, and that of bonded cement concrete specimens was fond on the part of combained concrete at the mixing ratio of 1 : 2 ,1: 4 and 1: 6. It was confirmed that the optimum mixing ratio for bonding of steel plate, and of cement concrete should be rich mixing ratio above 1 : 4 and 1 : 6 respectively. 10.The variations of color tone by heating began to take place at about 60˚C, and the ultimate change occurred at 120˚C. The compressive, bending and splitting tensile strengths increased with rising temperature up to 80˚ C, but these rapidly decreased when temperature was above 800 C. Accordingly, it was evident that the resistance temperature of epoxy resin mortar was about 80˚C which was generally considered lower than that of the other concrete materials. But it is likely that there is no problem in epoxy resin mortar when used for unnecessary materials of high temperature resistance. The multiple regression equations of strength were computed depending on a function of mixing ratios and heating temperatures. 11.The susceptibility to chemical attack of cement mortar was easily affected by inorganic and organic acid. and that of epoxy resin mortar with mixing ratio of 1: 4 was of great resistance. On the other hand, when mixing ratio was lower than 1 : 8 epoxy resin mortar had very poor resistance, especially being poor resistant to organicacid. Therefore, for the structures requiring chemical resistance optimum mixing of epoxy resin mortar should be rich mixing ratio higher than 1: 4.

• Geophysics and Geophysical Exploration
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• v.9 no.3
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• pp.207-224
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• 2006

It has been widely known that the seismic piezo-cone penetration test (SCPTu) is one of the most useful techniques for investigating the geotechnical characteristics such as static and dynamic soil properties. As practical applications in Korea, SCPTu was carried out at two sites in Busan and four sites in Incheon, which are mainly composed of alluvial or marine soil deposits. From the SCPTu waveform data obtained from the testing sites, the first arrival times of shear waves and the corresponding time differences with depth were determined using the cross-over method, and the shear wave velocity $(V_S)$ profiles with depth were derived based on the refracted ray path method based on Snell's law. Comparing the determined $V_S$ profile with the cone tip resistance $(q_t)$ profile, both trends of profiles with depth were similar. For the application of the conventional CPTu to earthquake engineering practices, the correlations between $V_S$ and CPTu data were deduced based on the SCPTu results. For the empirical evaluation of $V_S$ for all soils together with clays and sands which are classified unambiguously in this study by the soil behavior type classification index $(I_C)$, the authors suggested the $V_S-CPTu$ data correlations expressed as a function of four parameters, $q_t,\;f_s,\;\sigma'_{v0}$ and $B_q$, determined by multiple statistical regression modeling. Despite the incompatible strain levels of the downhole seismic test during SCPTu and the conventional CPTu, it is shown that the $V_S-CPTu$ data correlations for all soils, clays and sands suggested in this study is applicable to the preliminary estimation of $V_S$ for the soil deposits at a part in Korea and is more reliable than the previous correlations proposed by other researchers.