• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.205-215
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• 2008

As static traffic assignment has reached its limitation with ITS policy applications and due to the increase of interest in studies of ITS policies since the late 1980's, dynamic traffic assignment has been considered a tool to overcome such limitations. This study used the Dynameq program, which simulates route choice behavior by macroscopic modeling and dynamic network loading and traffic flow by microscopic modeling in consideration of the feasibility of the analysis of practical traffic policy. The essence of this study is to evaluate the feasibility for analysis in practical transportation policy of using the dynamic traffic assignment technique. The study involves the verification of the values estimated from the dynamic traffic assignment with South Korea's expressway network and dynamic O/D data by comparing results with observed link traffic volumes. This study used dynamic O/D data between each toll booth, which can be accurately obtained from the highway Toll Collection System. Then, as an example of its application, exclusive bus-lane policies were analyzed with the dynamic traffic assignment model while considering hourly variations.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.11
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• pp.915-924
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• 2014

In an aero gas-turbine engine, a surface air-oil heat exchanger (SAOHE) is used to cool the oil system for the gearboxes and electric generators. The SAOHE is installed inside the fan casing of the engine in order to dissipate the heat from the oil system into the bypass duct stream. The purpose of this study was to develop an effective numerical method for designing an SAOHE for an aero gas-turbine engine. A two-dimensional model using a porous medium was developed to evaluate the aero-thermal performance of the fins of the heat exchanger, and a one-dimensional flow and thermal network program was developed to save time and cost in the evaluation of the heat exchanger performance. Using this network program, the pressure drop and heat transfer performance of the heat exchanger were predicted, and the results were compared with two-dimensional computational fluid dynamics results and experiment data for validation.

• Korean Journal of Food Science and Technology
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• v.52 no.2
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• pp.125-129
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• 2020

In this study we developed a method for determining the geographic origin of soybeans by combining energy dispersive X-ray fluorescence spectrometry with statistical analysis. In 2018, 197 soybean samples (100 Korean domestic samples and 97 foreign samples) were collected for the construction of a geographic origin model. The mineral concentrations of 26 elements were measured and determined via the fundamental parameters approach. One-way analysis of variance, t-test, and canonical discriminant analysis were employed to reveal five elements (P, Ni, Br, Zn, and Mn) that could be used for the determination of geographic origins. The sensitivity, specificity, and efficiency for the above method were 91.0, 95.9, and 93.4%, respectively. Validation results from 60 samples collected in 2019 showed a predictive rate of 93.3% for Korean domestic soybeans and 100.0% for foreign soybeans. In conclusion, the combination of energy dispersive X-ray fluorescence spectrometry and chemometrics could be used to effectively determine the geographic origin of soybeans.

• 재활복지
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• v.21 no.1
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• pp.253-277
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• 2017

The purpose of this study was to develop the Constraints in Physical Activity Scale for people with physical Disabilities(CPASD) which measures the constraints in physical activity of people with physical disabilities. For this study, the 5 step analytic framework of unified validity developed by Messick (1995), the framework for conducting a strong program of construct validation by Benson (1998) method were applied. Furthermore, the validity of CPASD was systematically presented by applying common factor model and measurement model to 264 persons with physical disabilities. The conclusion based on the results and discussions of this study is as follows. First, CPASD presented evidence of job validity. Four factors (17 items) were developed, consisting of leader constraints, economic constraints, prejudice, and exercise environment constraints through the analysis of the factor structure and the fit of factor coefficients. Second, the factor structure of the developed CPASD (leader constraint, economic constraint, prejudice, exercise environment constraint) was statistically distinguished and stably reflected the existing exercise participation constraints theory. Third, the developed CPASD presented evidence of the validity of the criteria. Leader constraints and prejudice were negatively correlated with positive motor emotions, leader constraints, prejudice, and exercise environment constraints were positively correlated with negative motor emotions. Therefore, in future research, it is necessary to reevaluate the current system and actual condition related to leader constraints, economic constraints, prejudices, and exercise environment constraints derived as factors of CPASD. To do this, it is necessary to judge the degree of reality based on the causal relationship verification and IRT theory using CPASD.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.311-320
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• 2009

This study was carried out to estimate forest biomass and to produce forest biomass thematic map for Muju county by combining field data from the 5$^{th}$ National Forest Inventory (2006-2007) and satellite data. For estimating forest biomass, two methods were examined using a Landsat TM-5(taken on April 28th, 2005) and field data: multi-variant regression modeling and t-Nearest Neighbor (k-NN) technique. Estimates of forest biomass by the two methods were compared by a cross-validation technique. The results showed that the two methods provide comparatively accurate estimation with similar RMSE (63.75$\sim$67.26ton/ha) and mean bias ($\pm$1ton/ha). However, it is concluded that the k-NN method for estimating forest biomass is superior in terms of estimation efficiency to the regression model. The total forest biomass of the study site is estimated 8.4 million ton, or 149 ton/ha by the k-NN technique.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.97-117
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• 2021

Predicting term deposit subscriptions is one of representative financial marketing in banks, and banks can build a prediction model using various customer information. In order to improve the classification accuracy for term deposit subscriptions, many studies have been conducted based on machine learning techniques. However, even if these models can achieve satisfactory performance, utilizing them is not an easy task in the industry when their decision-making process is not adequately explained. To address this issue, this paper proposes an explainable scheme for term deposit subscription forecasting. For this, we first construct several classification models using decision tree-based ensemble learning methods, which yield excellent performance in tabular data, such as random forest, gradient boosting machine (GBM), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM). We then analyze their classification performance in depth through 10-fold cross-validation. After that, we provide the rationale for interpreting the influence of customer information and the decision-making process by applying Shapley additive explanation (SHAP), an explainable artificial intelligence technique, to the best classification model. To verify the practicality and validity of our scheme, experiments were conducted with the bank marketing dataset provided by Kaggle; we applied the SHAP to the GBM and LightGBM models, respectively, according to different dataset configurations and then performed their analysis and visualization for explainable term deposit subscriptions.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1149-1161
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• 2021

With a trend of the utilization of computer vision for satellite images, cloud detection using deep learning also attracts attention recently. In this study, we conducted a U-Net cloud detection modeling using SPARCS (Spatial Procedures for Automated Removal of Cloud and Shadow) Cloud Dataset with the image data augmentation and carried out 10-fold cross-validation for an objective assessment of the model. Asthe result of the blind test for 1800 datasets with 512 by 512 pixels, relatively high performance with the accuracy of 0.821, the precision of 0.847, the recall of 0.821, the F1-score of 0.831, and the IoU (Intersection over Union) of 0.723. Although 14.5% of actual cloud shadows were misclassified as land, and 19.7% of actual clouds were misidentified as land, this can be overcome by increasing the quality and quantity of label datasets. Moreover, a state-of-the-art DeepLab V3+ model and the NAS (Neural Architecture Search) optimization technique can help the cloud detection for CAS500 (Compact Advanced Satellite 500) in South Korea.

• 교육공학연구
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• v.33 no.4
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• pp.1001-1024
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• 2017

Flipped classroom has used widely in university in that its unique structure can facilitate learners' higher-thinking skills and promote competencies. Learners are expected to extend knowledge through performing online and offline, but they have difficulty in understanding their roles and specific behaviors to achieve the learning goals in the flipped learning. Therefore, a guidance for students has been required to support learners' mastery learning. The purpose of this study is to identify successful learners' characteristics in terms of "competency". For this, three-phased competency modeling was employed. In Phase I, Behavioral Event Interviews were conducted with eight learners of the flipped classroom. In Phase II for identifying competencies and developing a competency model, the data was coded, followed by testing reliability of the coding. Based on the meaning codes, competencies and behavioral indexes were developed. The final competencies consist of learning orientation, learning management, feedback seeking, peer interaction, and knowledge extension. In Phase III, validation of the competency model was conducted by explanatory factor analysis. As last, competencies were aligned by the two-phase of the flipped classroom. The finding will be used as the guidance for the learners and instructors in the flipped classroom.

• 재활복지
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• v.20 no.4
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• pp.107-134
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• 2016

The primary purpose of this study is to develop a quality of life scale for adults with developmental disabilities and to test its validity and reliability. For this purpose, the authors generated a initial item pool composed of 62 items based on the conceptual model of Felce and Perry (1995). The initial items were evaluated by three reviewers, and then the items were revised based on their feedbacks. Utilizing a survey questionnaire including the scale items, data on 430 adults with developmental disabilities were collected in collaboration with 33 social welfare agencies and residential facilities for people with disabilities. Through descriptive analysis, correlation analysis, and relevant theories, the qualities of each item were examined, and then the best 20 items were selected. Cronbach's ${\alpha}$ for the final scale was .87. The results of confirmatory factor analysis showed that the 5-factor model fitted the data reasonably well, In addition, criterion validity of each subfactor of the scale was successfully established, employing t-test, one-way ANOVA, and correlation analysis. In discussion, implications and limitations of this study were examined.

• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.11-19
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• 2019

In this study, the actual evapotranspiration products of Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM) and MOD16, which are satellite- and reanalysis-based dataset, were validated at the flux tower sites (i.e., CFK and SMK) managed by Korea Institute of Hydrological Survey, and the uncertainty and correlation analysis were conducted using Triple Collocation (TC) method. The result of validation with the flux tower showed better agreement in the order of GLEAM> GLDAS>MOD16. At the result of three combinations (S1: flux tower vs. GLDAS vs. MOD16, S2: flux tower vs. GLDAS vs. GLEAM, S3: flux tower vs. GLEAM vs. MOD16), the order of best to worst is GLEAM, GLDAS, MOD16, and flux tower for CFK (GLDAS> GLEAM>MOD16>flux tower for SMK). Since the error variance and correlation coefficients of the flux tower show relatively worse performance in TC analysis than the other products, By applying TC method to three products (GLDAS vs. GLEAM vs. MOD16), the uncertainty of each dataset were evaluated at the Korean Peninsula, As a results, the GLDAS and GLEAM performed reasonable performance (low error variance and high correlation coefficient), whereas results of MOD16 showed high error variance and low correlation coefficient at the cropland.