• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.1
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• pp.21-25
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• 2009

The results of a recent case study for material characterizations and structural evaluation to design asphalt overlay thickness of William P Hobby airport in Houston, Texas are presented herein. The existing runway 12R-30L of Hobby airport consisted of thick asphalt overlay over Portland Cement Concrete (PCC) and the localized surface shoving as evident in the closure of surface groove has been observed recently. Using the field cored asphalt concrete mixtures, measurements of percent air voids, asphalt content and aggregate gradation were conducted to find out the causations of surface shoving and groove closure. The FAA layered elastic program, LEDFAA was utilized to evaluate pavement structural conditions for new asphalt overlay. Two different composition assumptions for existing pavement were made to evaluate the pavement as followings: 1) APC, Asphalt Concrete Overlay over PCC pavement and 2) AC, Asphalt Concrete pavement. Based on laboratory testing results, a ratio of percent passing #200 to asphalt content ranged 1.1 to 2.2, which is considered a high ratio and a tendency of tender mix design was observed. Thus, the localized surface shoving and groove closure of the runway 12R-30L could be attributed to the use of excessive fine contents and tender mix design. Based on the structural evaluation results, it was ascertained that the analysis assuming the pavement structure as AC pavement gives more realistic structural life when the asphalt overlay is thicker enough compared to PCC layer because the existing PCC pavement under asphalt overlay acts more like a high quality base material.

• Current Research on Agriculture and Life Sciences
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• v.5
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• pp.12-18
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• 1987

This study was performed to investigate the creeping habit in top-cross progeny, and to determine the relationship among the major agronomic characters in the top-cross progeny testing based on the The The conclusions of simple correlation coefficients. the study were summarized as follows ; The creeping type crossed with the non-creeping tester was the greatest in width and seed yield. For all three characters, the creeping type crossed with the non-creeping tester and the non-creeping type crossed with the creeping tester were greater than any other combination. The top-cross method was desirable for the study of general and specific combining ability. The sensitivity of the tester to differentiate the creeping and non-creeping types was better when a non-creeping tester was used.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.4
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• pp.1-10
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• 2008

Shear wave velocity($V_S$), which can be obtained using various seismic tests, has been emphasized as representative geotechnical dynamic characteristic mainly for seismic design and seismic performance evaluation in the engineering field. For the application of conventional geotechnical site investigation techniques to geotechnical earthquake engineering, standard penetration tests(SPT) and piezocone penetration tests(CPTu) together with a variety of borehole seismic tests were performed at many sites in Korea. Through statistical modeling of the in-situ testing data, in this study, the correlations between $V_S$ and geotechnical in-situ penetrating data such as blow counts(N value) from SPT and piezocone penetrating data such as tip resistance ($q_t$), sleevefriction($f_s$), and pore pressure ratio($B_q$) were deduced and were suggested as an empirical method to determine $V_S$. Despite the incompatible strain levels of the conventional geotechnical penetration tests and the borehole seismic tests, it is shown that the suggested correlations in this study are applicable to the preliminary estimation of $V_S$ for Korean soil layers.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• Journal of Aerospace System Engineering
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• v.15 no.5
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• pp.81-88
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• 2021

In general, a non-explosive nylon wire cutting-based holding and release mechanism has been used to store and deploy deployable solar panels of CubeSat. However, with this method, accessing the solar panel's access port for charging the cube satellite's battery and electrical inspection and testing of the PCB and payloads while the solar panel is in storage is difficult. Additionally, the mechanism must have a reliable release function in an in-orbit environment, and reusability for stow and deploy of the solar panel, which is a hassle for the operator and difficult to maintain a consistent nylon wire fastening process. In this study, we proposed a pin-puller-based solar panel holding and release mechanism that can easily deploy a solar panel without cutting nylon wires by separating constraining pins. The proposed mechanism's release function and performance were verified through a solar panel deployment test and a maximum separation load measurement test. Through this, we also verified the design feasibility and effectiveness of the pin-puller-based separation device.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.375-384
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• 2021

Deep learning techniques have been proven to have high performance in image processing and are applied in various fields. The most widely used methods for validating a deep learning model include a holdout verification method, a k-fold cross verification method, and a bootstrap method. These legacy methods consider the balance of the ratio between classes in the process of dividing the data set, but do not consider the ratio of various features that exist within the same class. If these features are not considered, verification results may be biased toward some features. Therefore, we propose a deep learning model validation method based on data feature coverage for image classification by improving the legacy methods. The proposed technique proposes a data feature coverage that can be measured numerically how much the training data set for training and validation of the deep learning model and the evaluation data set reflects the features of the entire data set. In this method, the data set can be divided by ensuring coverage to include all features of the entire data set, and the evaluation result of the model can be analyzed in units of feature clusters. As a result, by providing feature cluster information for the evaluation result of the trained model, feature information of data that affects the trained model can be provided.

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.610-621
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• 2021

The soil creep, primarily caused by earthquakes and torrential rainfall events, has widely occurred across the country. The Korea Forest Service attempted to quantify the soil creep susceptible areas using a discriminant value table to prevent or mitigate casualties and/or property damages in advance. With the advent of advanced computer technologies, machine learning-based classification models have been employed for managing mountainous disasters, such as landslides and debris flows. This study aims to quantify the soil creep susceptibility using several classifiers, namely the k-Nearest Neighbor (k-NN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) models. To develop the classification models, we downscaled 292 data from 4,618 field survey data. About 70% of the selected data were used for training, with the remaining 30% used for model testing. The developed models have the classification accuracy of 0.727 for k-NN, 0.750 for NB, 0.807 for RF, and 0.750 for SVM against test datasets representing 30% of the total data. Furthermore, we estimated Cohen's Kappa index as 0.534, 0.580, 0.673, and 0.585, with AUC values of 0.872, 0.912, 0.943, and 0.834, respectively. The machine learning-based classifications for soil creep susceptibility were RF, NB, SVM, and k-NN in that order. Our findings indicate that the machine learning classifiers can provide valuable information in establishing and implementing natural disaster management plans in mountainous areas.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.65-74
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• 2021

In order to efficiently inspect rapidly increasing old tunnels in many well-developed countries, many inspection methodologies have been proposed using imaging equipment and image processing. However, most of the existing methodologies evaluated their performance on a clean concrete surface with a limited area where other objects do not exist. Therefore, this paper proposes a 6-step framework for tunnel crack detection deep learning model development. The proposed method is mainly based on negative sample (non-crack object) training and Cascade Mask R-CNN. The proposed framework consists of six steps: searching for cracks in images captured from real tunnels, labeling cracks in pixel level, training a deep learning model, collecting non-crack objects, retraining the deep learning model with the collected non-crack objects, and constructing final training dataset. To implement the proposed framework, Cascade Mask R-CNN, an instance segmentation model, was trained with 1561 general crack images and 206 non-crack images. In order to examine the applicability of the trained model to the real-world tunnel crack detection, field testing is conducted on tunnel spans with a length of about 200m where electric wires and lights are prevalent. In the experimental result, the trained model showed 99% precision and 92% recall, which shows the excellent field applicability of the proposed framework.

• Korean Journal of Social Welfare Studies
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• v.47 no.3
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• pp.187-221
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• 2016

This study examines determining factors of effectiveness and satisfaction of Veteran Medical Service Delivery System. Especially, the association between the relevant variables of the effectiveness of the Veteran Medical Service Delivery System and the variables of the satisfaction from the perspective of beneficiaries was studied. Multi-level analysis was utilized to separate results of the evaluation of effectiveness in organizational-level and the evaluation of satisfaction in individual-level. This study tests key posited hypothesis by using survey data collected from 5 medical center of country(Seoul, Busan, Daejeon, Daegu, Gwangju). In terms of the result of the hypothesis testing on the effectiveness variable, integrity(${\beta}=.156$), accountability(${\beta}=.376$, financial sufficiency(${\beta}=.109$), and adequacy (${\beta}=.367$) are the determinants among various factors in evaluating veteran medical service delivery system, statistically reflecting the perception of directors of the veteran medical service delivery facilities on effectiveness. In other word, professionalism variable(${\beta}=0.99$) and effectiveness variable(${\beta}=-1.09$) are statistically reflecting the perception of directors of the beneficiaries satisfaction with employee. The findings suggests that the theoretical and practical implications will improve Effectiveness and Satisfaction of Veteran Medical Service Delivery System.

• Journal of IKEEE
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• v.23 no.2
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• pp.739-742
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• 2019

In this paper, we propose enhanced DAP(Dose Area Product). The development of enhanced DAP proposed in this paper has optimized the area dose meter that was developed previously. The development of enhanced DAP performed Optimized design of charge integrator and ADC circuit, optimization of line transceiver for RS-485 communication, optimization of display circuit, and optimization of PC-based control program for interlocking and aging. As a result of evaluating the performance of the proposed system in an accredited testing laboratory, Radiation dose dependence and Radiation quality dependence were measured to be 4.2%, which is below ${\pm}15%$ of international standard. Energy range/Tube voltage was confirmed in the range of 30~150kV. The sensitivity difference between sensor field and sensor field area dose sensitivity was measured to be 4.3%, and it was confirmed that it operates normally under ${\pm}15%$ of international standard. In order to measure the reproducibility of the area dosimeter, it was confirmed that it was 0% and it was operated normally at less than 2% of IEC60580 recommendation. Digital resolution was confirmed to be a minimum unit of $0.01{\mu}Gy{\cdot}m^2$ within the error range for the reference dose per hour.