• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.4
• /
• pp.187-193
• /
• 2020

In this study, we wanted to analyze the factors at the national university level that affect college students ' elimination. In addition, national universities, private universities, universities in Seoul and universities outside of Seoul were divided into more college-specific characteristics. Except for leave of absence and departure from school, it was defined as a middle school dropout among changes of students. The data were used for analysis by receiving raw data from "University Alerts," which are operated by the Ministry of Education and the Korean Council for Educational Universities. At the university notification, 222 universities out of the schools classified as "Universities" were utilized for final analysis, and jobs, credits, scholarships, tuition fees, students, independent students, and full-time teachers were secured through multiple education. Overall, the higher the average graduate level and employee-rate the lower the rate of elimination from the middle of college students, the analysis showed. Second, the higher the average tuition fees at private universities, the more negatively affects the rate of elimination of university students. Third, higher tuition fees at universities outside the Seoul metropolitan area have a negative impact on the rate of elimination of students.

• The Journal of the Korea Contents Association
• /
• v.17 no.8
• /
• pp.597-609
• /
• 2017

The aim of this study is to identify factors that the victimization by family violence has influence on peer violence perpetration through the mediation of anxiety and aggression in out-of-school youths. Subjects consist of 169 out-of-school youths in Chungbuk and data are analyzed by regression analysis with 18.0 version. The findings of this study are as follows. First, the victimization by family violence has positive influence on peer violence perpetration. Second, when the mediating effect of anxiety and aggression is tested, aggression only plays a mediating role between family violence victimization and peer violence behavior. On the basis of the results, this study suggest that we make efforts such as prompt intervention for out-of-school youths and their's family after school dropout, the reinforcement of family relationship, family function and youth competency, and the transition of social perception regarding out-of-school youths to decrease family and peer violence.

• Journal of the Korea Convergence Society
• /
• v.10 no.1
• /
• pp.265-275
• /
• 2019

The purpose of this study was to explore factors that physical education graduate students in graduate school of education drop out of teacher appointment examination of secondary school by using Ecological Model. The in-depth interview was conducted for 10 physical education graduate students studying in school of education. As an aspect of the intrapersonal component, the effort to apply how participants'current job positively affects their preparation for teacher appointment examination is required and they need to clearly decide academic priorities to prepare for teacher appointment examination. As an aspect of interpersonal component, participants are negatively affected by subjective norm, thereby providing personalized mentoring program is required. As an aspect of organizational component, limited tuition support and inappropriate place to study are emphasized, so practical helps like sports practice instructor or teacher appointment examination preparation class is required. Finally, this study suggests the introduction of integrated system for teacher appointment examination.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.5
• /
• pp.1431-1445
• /
• 2022

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.

• Geomechanics and Engineering
• /
• v.37 no.1
• /
• pp.49-64
• /
• 2024

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.

• Journal of Practical Engineering Education
• /
• v.12 no.1
• /
• pp.145-162
• /
• 2020

The dual system in Korea already plays an important role in quantitative terms in the Korean lifelong vocational competency development system. However, since most of the existing dual system performance management plans in Korea focus on qualification-linked dual system, research on the effective performance management of the four-year university-driven dual system is very insufficient. This paper presents multiple measures for developing a performance management system suitable for the university-driven dual system to achieve qualitative improvement of the contract departments of the dual colleges or universities. As an approach to the end, a performance evaluation system is established by developing the evaluation items and indicators for the dual colloeges' contract departments. Next, it analyzes the needs of various stakeholder groups such as field teachers of the involved companies, students in apprenticeship and OJT professors of KOREATECH through FGI's and polls to diagnose the current operational performance, especially the causes of high drpout rates of the contract departments. From these results, the paper presents firstly the development of measuring methods for the developed performance indicators of the evaluation system and then a systemic performance management system which is based on 'input-transformation-outcome-feedback' structure. In addition, some measures for improving the high dropout rate and performance are presented from the viewpoints of each stakeholder.

• Journal of The Korean Astronomical Society
• /
• v.38 no.2
• /
• pp.179-182
• /
• 2005

We conducted an extremely wide field survey of z ${\~}$ 6 Lyman break galaxies (LBGs) to precisely derive their bright end surface density overcoming the bias due to cosmic variance. We selected out LBG candidates in the Subaru/ XMM-Newton Deep Survey Field (SXDS) over the total of ${\~}1.0\;deg^2$ sky area down to $z_{AB} = 26.0 ({\ge}3{\sigma},\;2'.0 aperture)$ using i' - z' > 1.5 color cut. This sample alone is likely to be contaminated by M/L/T dwarfs, low-z elliptical galaxies, and z ${\~}$ 6 quasars. To eliminate these interlopers, we estimated their numbers using an exponential disk star count model, catalogs of old ellipticals in the SXDS and other field, and a z${\~}$6 quasar luminosity function. The finally derived surface density of z ${\~}$ 6 LBGs was 165 $mag^{-1}\;deg^{-2}$ down to $z_{AB}$ = 26.0 and shows good agreement with previous results from the narrower field survey of HST GOODS.

• Journal of Information Technology Applications and Management
• /
• v.28 no.6
• /
• pp.97-116
• /
• 2021

Due to the decrease in the school-age population and government regulations, universities have made great efforts to secure their own competitiveness. In particular, the selection of universities with financial support based on the recent evaluation of the Ministry of Education has become a major concern enough to affect the existence of the university itself. This paper extracts three-year data from 124 major private universities nationwide, and quantitatively analyzes the variables of major universities selected as self-improvement universities, competency reinforcement universities, and universities with limited financial support. As a result of estimating the selection of self-powered universities using the ordered logit model by hierarchically inputting 12 variables, student competitiveness in the metropolitan area (1.318^**), Educational Restitution Rate (4.078^***), University operation expenditure index rate (1.088^***) values were found. Significant positive coefficient values were found in the admission enrollment rate (45.98^***) and the enrollment rate (13.25^***). As a result of analyzing the marginal effects, the increase in the rate of reduction of education costs has always been positive in the selection of self-powered universities, but it was observed that the rate of increase decreases in areas of increase of 150% or more. On the contrary, the probability of becoming a Em-powered university was negative in all sectors, but on the contrary, it was analyzed that marginal effects increased at the same time point. On the other hand, the employment rate of graduates was not able to find direct significance with the result of the selection of Self powered universities. Through this paper, it is expected that each university will analyze the possibility and shortcomings of the selection of Self powered universities in policy making, and in particular, the risk of dropout of selection for the vulnerable field can be predicted using marginal effects. It can be used as major research data for both university evaluators, university officials and students.

• Computers and Concrete
• /
• v.30 no.5
• /
• pp.301-310
• /
• 2022

Cement-based sensors have been widely used as structural health monitoring systems, however, their long-term sensing performance have not actively investigated. In this study, a deep learning-based methodology is adopted to predict the long-term piezoresistive properties of cement-based sensors. Samples with different multi-walled carbon nanotube contents (0.1, 0.3, and 0.5 wt.%) are fabricated, and piezoresistive tests are conducted over 10,000 loading cycles to obtain the training data. Time-dependent degradation is predicted using a modified long short-term memory (LSTM) model. The effects of different model variables including the amount of training data, number of epochs, and dropout ratio on the accuracy of predictions are analyzed. Finally, the effectiveness of the proposed approach is evaluated by comparing the predictions for long-term piezoresistive sensing performance with untrained experimental data. A sensitivity of 6% is experimentally examined in the sample containing 0.1 wt.% of MWCNTs, and predictions with accuracy up to 98% are found using the proposed LSTM model. Based on the experimental results, the proposed model is expected to be applied in the structural health monitoring systems to predict their long-term piezoresistice sensing performances during their service life.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.28 no.6
• /
• pp.1463-1474
• /
• 2018

Fuzzing is one of the software testing techniques that find security flaws by inputting invalid values or arbitrary values into the program and various methods have been suggested to increase the efficiency of such fuzzing. In this paper, focusing on the existence of field with high relevance to coverage and software crash, we propose a new method for intensively fuzzing corresponding field part while performing field based fuzzing. In this case, we use a deep learning model called Variational Autoencoder(VAE) to learn the statistical characteristic of input values measured in high coverage and it showed that the coverage of the regenerated files are uniformly higher than that of simple variation. It also showed that new crash could be found by learning the statistical characteristic of the files in which the crash occurred and applying the dropout during the regeneration. Experimental results showed that the coverage is about 10% higher than the files in the queue of the AFL fuzzing tool and in the Hwpviewer binary, we found two new crashes using two crashes that found at the initial fuzzing phase.