• Journal of Ecology and Environment
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• v.38 no.4
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• pp.505-515
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• 2015

The objectives of this study were to determine the effects of stream nutrient regime, N:P ratios and suspended solids on fish tolerance/trophic compositions and stream ecosystem health, based on multi-metric model, during 2008-2013. Also, stream ecosystem health was evaluated in relation to chlorophyll-a (CHL) as a measure of algal productivity or indicators of trophic state to water chemical parameters. Total number of sampled fish species were 50 and showed a decreasing trend from 2008 to 2013. The minnow of Zacco platypus, based on the catch per unit effort (CPUE), was the most dominant species (25.9%) among the all species. Spatial heterogeneity was evident in the fish tolerance guilds that showed the dominance of sensitive species (89%) in the headwaters (S1) and the dominance of tolerant species (57%) in the urban. These conditions were directly influenced by concentrations of nutrients and organic matter (COD). The N:P ratios, as a barometer of water pollution, had a negative linear function (R² = 0.40, P < 0.01) with CHL, and the ratios had an important role in changes of COD concentration (R² = 0.40, P < 0.01). Under the circumstances, the N:P ratio directly influenced the relative proportions of fish trophic/tolerance compositions. According to the regression analysis of omnivore (O_m) and insectivore sp. (I_n) on total nitrogen and total phosphorus, nitrogen had no significant influences (P > 0.05) to the two compositions, but phosphorus influenced directly the two guilds [slope (a) = -32.3, R² = 0.25, P < 0.01 in the I_n; a = 40.7, R² = 0.19, P < 0.01 in the O_m]. Such water chemistry and fish trophic guilds determined the stream ecosystem health, based on the multi-metric fish model.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.137-142
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• 2006

Cost and duration is important factors which directly affect profit therefore must be forecasted correctly to accomplish success of projects. So construction company uses EVMS(Earned Value Management System) to forecast final cost and duration. But previous forecasting model has low accuracy because of its linear forecasting method and can't reflect characteristic of company and project and changes as each progress. This paper presents cost and duration forecasting model using the slope prediction of cost and duration as each progress to reflect the various characteristics of construction industry. EVMS data of 23 road construction projects was used to make up regression analysis equation of slope forecasting model.

• Korean Medical Education Review
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• v.26 no.1
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• pp.70-76
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• 2024

This study investigates whether personality characteristics and learning behaviors can predict medical students' academic achievement in Korea, specifically in terms of successfully completing medical school without delays or achieving a high grade point average (GPA) in their final year. In May 2018, 316 medical students took the Multi-Dimensional Learning Strategy Test, 2nd edition, which provided data on their personality and learning behavioral characteristics. Their final year's GPA and any delays in completing medical school were ascertained by reviewing all electronic academic records of each semester they had been enrolled. The combination of personality and learning behavioral characteristics was significantly associated with completing medical school without delays, even after adjusting for sex and admission path. A multiple logistic regression analysis showed that the adjusted odds ratios and 95% confidence intervals for completing medical school without delays were 1.52 (95% confidence interval [CI], 0.83-2.78) and 3.64 (95% CI, 1.70-7.82) for "others" and "both high" categories, respectively, when compared with the "both low" category. For 235 students who completed medical school without delays, their learning behavioral characteristics (scores) were significantly associated with their final year's GPA even after adjusting for sex, admission path, and personality characteristics (scores) as determined by the multiple linear regression analysis. This study suggests that individual personality and learning behavior characteristics are predictors of medical students' academic achievement. Therefore, interventions such as personalized counseling programs should be provided in consideration of such student characteristics.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.5
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• pp.25-35
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• 2013

In swine house, dust generation comes from various sources and is known to be harmful both for the animals and the farmers because the dust contains biological and gaseous matters. When farmers are constantly exposed to the dusts, they can suffer chronic or acute respiratory symptoms and have high probability of manifesting various diseases. To address this problem, understanding of the mechanism of dust generation is very important. In this paper, the dust concentration of inhalable, respirable, TSP and $PM_{10}$ were monitored and analyzed according to the pig-activity level, ventilation quantity and feeding method in fattening pig house. From the measured results, in case of the concentration of TSP, an inverse-linear relation with ventilation rate ($R^2=0.88$) and linear relation with the installation height of feed supply pipe ($R^2=0.73$) were determined. However in case of the concentration of $PM_{10}$, no particular relationship with the variables was observed. Using the concentration of inhalable and respirable dust based on the pig-activity level, multi-variate regression analysis was conducted and results have shown that the movement of pigs can contribute to the dust generation (p＜0.05, $R^2=0.71$, 0.61). The relationship determined between dust generation and environmental variables investigated in this study is very significant and useful in conducting dust-reduction researches.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.701-702
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• 2021

The purpose of the study was to identify fiber changes induced by electrical stimulation of a certain neural substrate in the rat brain. In the stimulation group, the peripheral nerve was injured and the brain area associated to inhibit sensory information was electrically stimulated. There were sham and sham stimulation groups as controls. Then high-field diffusion tensor imaging (DTI) was acquired. 35 features were taken from the DTI measures from 7 different brain pathways. To compare the efficacy of the classification for 3 animal groups, the linear regression analysis (LDA) and the machine learning technique (MLP) were applied. It was found that the testing accuracy by MLP was about 77%, but that of accuracy by LDA was much higher than MLP. In conclusion, machine learning algorithm could be used to identify and predict the changes of the brain white matter in some situations. The limits of this study will be discussed.

• Korean Journal of Radiology
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• v.24 no.11
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• pp.1131-1141
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• 2023

Objective: Cortical iron deposition has recently been shown to occur in Alzheimer's disease (AD). In this study, we aimed to evaluate how cortical gray matter iron, measured using quantitative susceptibility mapping (QSM), differs in the clinical cognitive impairment spectrum. Materials and Methods: This retrospective study evaluated 73 participants (mean age ± standard deviation, 66.7 ± 7.6 years; 52 females and 21 males) with normal cognition (NC), 158 patients with mild cognitive impairment (MCI), and 48 patients with AD dementia. The participants underwent brain magnetic resonance imaging using a three-dimensional multi-dynamic multi-echo sequence on a 3-T scanner. We employed a deep neural network (QSMnet+) and used automatic segmentation software based on FreeSurfer v6.0 to extract anatomical labels and volumes of interest in the cortex. We used analysis of covariance to investigate the differences in susceptibility among the clinical diagnostic groups in each brain region. Multivariable linear regression analysis was performed to study the association between susceptibility values and cognitive scores including the Mini-Mental State Examination (MMSE). Results: Among the three groups, the frontal (P < 0.001), temporal (P = 0.004), parietal (P = 0.001), occipital (P < 0.001), and cingulate cortices (P < 0.001) showed a higher mean susceptibility in patients with MCI and AD than in NC subjects. In the combined MCI and AD group, the mean susceptibility in the cingulate cortex (β = -216.21, P = 0.019) and insular cortex (β = -276.65, P = 0.001) were significant independent predictors of MMSE scores after correcting for age, sex, education, regional volume, and APOE4 carrier status. Conclusion: Iron deposition in the cortex, as measured by QSMnet+, was higher in patients with AD and MCI than in NC participants. Iron deposition in the cingulate and insular cortices may be an early imaging marker of cognitive impairment related neurodegeneration.

• Journal of Soil and Groundwater Environment
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• v.13 no.4
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• pp.1-7
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• 2008

This study analyzed for hydrodynamic dispersion characteristics of multi-soil layer (Silt and clay, Find sand, Coarse sand), data of a field tracer test on the multi-soil layer and data of laboratory column experiments on the samples on each soil layers. Through the analysis of permeability and flow, MS (Silt and clay) and FS (Fine sand), which were low effective porosity, were higher average linear velocity while CS (Coarse sand), which was high effective porosity, was higher hydraulic conductivity. Hydraulic conductivity function based on average soil particle diameter was assumed Y=$3.49{\times}10^{-8}e^{15320x}$ and coefficient of determination was 0.90. Average linear velocity function based on average soil particle diameter was assumed Y=$1.88{\times}10^{-7}e^{11459x}$ and coefficient of determination was 0.81. Longitudinal dispersivity function based on average soil particle diameter was Y = 0.00256$e^{5971x}$ and coefficient of determination was 0.98. According to the linear regression analysis of average linear velocity and longitudinal dispersivity, assumed function was Y = 21.7527x + 0.0063, and coefficient of determination was 0.9979. The ratio of field scale/laboratory scale was 54.09, it exhibited scale-dependent effect of hydrodynamic dispersion. Field longitudinal dispersivity (1.39m) was 7.47 times as higher than longitudinal dispersivity estimated by the methods of Xu and Eckstein (1995). Hydrodynamic dispersion on CS layer was occurred mainly by diffusion flow in the test aquifer.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.8
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• pp.1606-1616
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• 2011

The blood pressure measuring equipment, which is being supplied and used most widely by being recognized convenience and accuracy now generally, is oscillometric blood pressure monitor. However, a change in blood pressure is basically influenced by diverse elements such as each individual's physiological status and physical condition. Thus, the measurement of blood pressure, which used single element called oscillation in blood pressure of being conveyed to cuff, is not considered on physiological elements such as cardiovascular system status and blood vessel stiffness index, and on external elements, thereby being quite in error. Accordingly, this study detected diverse bio-signals and body informations in each individual as the measurement subject such as ECG, PPG, and Korotkoff Sound in order to enhance convenience and accuracy of measuring blood pressure in the complex measurement equipment, thereby having extracted regression method for compensation in error of oscillometric blood pressure measurement on the wrist, and having improved accuracy of measuring blood pressure. To verify a method of improving accuracy, the blood pressure value in each of SBP, DBP, MAP was acquired through 4-stage experimental procedure targeting totally 51 subjects. Prior to experiment, the subjects were divided into two groups such as the experimental group for extracting regression method and the control group for verifying regression method. Its error was analyzed by comparing the reference blood pressure value, which was obtained through the auscultatory method, and the oscillometric blood pressure value on the wrist. To reduce the detected error, the blood pressure compensation regression method was calculated through multiple linear regression analysis on elements of blood pressure, individual body information, PTT, HR, K-Sound PSD change. Verification was carried out on improving significance and accuracy by applying the regression method to the data of control group. In the experimental results, as a result of confirming error on the reference blood pressure value in SBP, DBP, and MAP, which were acquired through applying regression method, the results of $-0.47{\pm}7.45$ mmHg, $-0.23{\pm}7.13$ mmHg, $0.06{\pm}6.39$ mmHg could be obtained. This is not only the numerical value of satisfying the sphygmomanometer reference of AAMI, but also shows the lower result than the numerical value in SBP : $-2.5{\pm}12.2$ mmHg, DBP : $-7.5{\pm}8.4$ mmHg, which is the mean error in the experimental results of Brram's research for verifying accuracy of Omron RX-M, which shows relatively high accuracy among wrist sphygmomanometers. Thus, the blood pressure compensation could be confirmed to be made within significant level.

• 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.

• Journal of Sensor Science and Technology
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• v.17 no.6
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• pp.406-413
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• 2008

We have been implemented simple statistical pattern recognition methods for harmful gases classification using gas sensors array fabricated by MEMS (Micro Electro Mechanical System) technology. The performance of pattern recognition method as a gas classifier is highly dependent on the choice of pre-processing techniques for sensor and sensors array signals and optimal classification algorithms among the various classification techniques. We carried out pre-processing for each sensor's signal as well as sensors array signals to extract features for each gas. We adapted simple statistical pattern recognition algorithms, which were PCA (Principal Component Analysis) for visualization of patterns clustering and MLR (Multi-Linear Regression) for real-time system implementation, to classify harmful gases. Experimental results of adapted pattern recognition methods with pre-processing techniques have been shown good clustering performance and expected easy implementation for real-time sensing system.