• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.137-146
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• 2023

The dropout of university freshmen is a very important issue in the financial problems of universities. Moreover, the dropout rate is one of the important indicators among the external evaluation items of universities. Therefore, universities need to predict dropout students in advance and apply various dropout prevention programs targeting them. This paper proposes a method to predict such dropout students in advance. This paper is about a method for predicting dropout students. It proposes a method to select dropouts by applying logistic regression using a shift sigmoid classification function using only quantitative data from the first semester of the first year, which most universities have. It is based on logistic regression and can select the number of prediction subjects and prediction accuracy by using the shift sigmoid function as an classification function. As a result of the experiment, when the proposed algorithm was applied, the number of predicted dropout subjects varied from 100% to 20% compared to the actual number of dropout subjects, and it was found to have a prediction accuracy of 75% to 98%.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.109-115
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• 2021

The number of freshmen at universities is decreasing due to the recent decline in the school-age population, and the survival of many universities is threatened. To overcome this situation, universities are seeking ways to use big data within the school to improve the quality of education. A study on the prediction of dropout students is a representative case of using big data in universities. The dropout prediction can prepare a systematic management plan by identifying students who will drop out of school due to reasons such as dropout or expulsion. In the case of actual on-campus data, a large number of missing values are included because it is collected and managed by various departments. For this reason, it is necessary to construct a model by effectively reflecting the missing values. In this study, we propose a university student dropout prediction model based on eXtreme Gradient Boost that can be applied to data with many missing values and shows high performance. In order to examine the practical applicability of the proposed model, an experiment was performed using data from C University in Chungbuk. As a result of the experiment, the prediction performance of the proposed model was found to be excellent. The management strategy of dropout students can be established through the prediction results of the model proposed in this paper.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.190-200
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• 2014

The video-based system of the broadcasters and the video-related institutions have shifted from analogical to digital in worldwide. This migration process can generate a defect, digital dropout, in the quality of the contents. Moreover, there are limited researches focused on these kind of defects and those related have limitations. For that reason, we are proposing a new method for feature extraction emphasizing in the peculiar block pattern of digital dropout based on discrete cosine transform (DCT). For classification of error block, we utilize support vector machine (SVM) which can manage feature vectors efficiently. Further, the proposed method overcome the limitation of the previous one using continuity of frame by frame. It is using only the information of a single frame and works better even in the presence of fast moving objects, without the necessity of specific model or parameter estimation. Therefore, this approach is capable of detecting digital dropout only with minimal complexity.

• ETRI Journal
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• v.42 no.5
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• pp.790-798
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• 2020

Herein, a low-ripple coarse-fine digital low-dropout regulator (D-LDO) without ringing in the transient state is proposed. Conventional D-LDO suffers from a ringing problem when settling the output voltage at a large load transition, which increases the settling time. The proposed D-LDO removes the ringing and reduces the settling time using an auxiliary power stage which adjusts its output current to a load current in the transient state. It also achieves a low output ripple voltage using a comparator with a complete comparison signal. The proposed D-LDO was fabricated using a 65-nm CMOS process with an area of 0.0056 μ㎡. The undershoot and overshoot were 47 mV and 23 mV, respectively, when the load current was changed from 10 mA to 100 mA within an edge time of 20 ns. The settling time decreased from 2.1 ㎲ to 130 ns and the ripple voltage was 3 mV with a quiescent current of 75 ㎂.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.379-388
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• 2021

The purpose of this study is to identify the effect of parent-rearing attitudes on intention of school dropout of multicultural adolescent and the mediating effect of biculture acceptance between two variables. For the purpose, 1,323 subjects selected from the sixth year of the Multicultural Youth Panel (MCAPS) were analyzed. The data was verified by using technical statistics, correlation, and path analysis. The results showed that first, the positive parenting attitude and biculture acceptance decreased the intention of school dropout of multicultural adolescent, while negative parenting attitude increased it. Second, biculture acceptance had a partial mediating effect on the relationship between positive parenting attitude and intention of school dropout. Third, attitude the effect of negative parenting attitudes on intention of school dropout was partly mediated by biculture acceptance. On the basis of above findings, the practical engagements to enhance biculture acceptance and prevent the intention of school dropout of multicultural adolescent were suggested.

• ETRI Journal
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• v.37 no.5
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• pp.961-971
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• 2015

This paper proposes a 250 mV supply voltage digital low-dropout (LDO) regulator. The proposed LDO regulator reduces the supply voltage to 250 mV by implementing with all digital circuits in a$0.11{\mu}m$ CMOS process. The fast current tracking scheme achieves the fast settling time of the output voltage by eliminating the ringing problem. The over-voltage and under-voltage detection circuits decrease the overshoot and undershoot voltages by changing the switch array current rapidly. The switch bias circuit reduces the size of the current switch array to 1/3, which applies a forward body bias voltage at low supply voltage. The fabricated LDO regulator worked at 0.25 V to 1.2 V supply voltage. It achieved 250 mV supply voltage and 220 mV output voltage with 99.5% current efficiency and 8 mV ripple voltage at $20{\mu}A$ to $200{\mu}A$ load current.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.453-463
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• 2016

This study examined the relationship among dropout, organizational trust, and intention to transfer in the department affiliated with Physical Education. Data were collected from 480 students in the department affiliated with Physical Education from 6 institutions of a 4-year university located in Seoul, Gyeonggi-do, Incheon, and ChungChong-do. For the research model analysis, a structural equation model and bootstrapping were conducted. Results indicated that the academic domain, the social domain, and the environmental domain of dropout had a positive effect on organizational trust. But the only the academic domain in the relationship between dropout and intention to transfer affected intention to transfer positively. The social domain and the environmental domain were not statistically significant on intention to transfer. Moreover, organizational trust had a positive effect on intention to transfer. Finally, organizational trust partially mediated the path between the academic domain and intention to transfer. It fully mediated the paths between the social domain and the environmental domain and intention to transfer.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.257-264
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• 2021

Resident training is the process of training specialists with excellent competency. This study analyzed the intention of resident's training dropout and influencing factors by utilizing the results of a survey on the training and working environment of the residents conducted in April 2017. According to a multivariate logistic regression analysis of 1,748 residents, 340(19.5%) answered 'yes', 518(29.6%) 'normal', 890(50.9%) 'no'. Factors related to the dropout of training by residents were found to be whether the hospital was affiliated with a enterprise, training year, training hours, appropriate educational supervision and adequate of the training process. For a desirable Resident education and training, it is necessary to normalize of the resident's working environment, develop of a substantial training program, provide fair and systematic evaluation, and financial support.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.23-34
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• 2017

This study investigated relative explanation of social support, ego-resiliency and stress coping strategies to help adaptive coping style of school-dropout adolescents under stress situation. To this end, 101 school-dropout adolescents were surveyed by visiting and requesting the outofschool youth supporting project, youth detention center, and adolescent protective and treatment facilities in Daejeon, Cheongnam, and Chungbuk. As analysis methods, descriptive statistical analysis, pearson's correlation, and hierarchical analysis were conducted and the research results are as follows. First, stress coping strategies showed positive relationship with social support and ego-resiliency. Second, a variable that showed greater explanation power for stress coping strategies was the environmental variable, the social support. Third, it was identified that there was greater explanation power when the environmental variable, the social support, and the personal variable, the ego-resiliency, were put in at the same time for stress coping strategies. According to the result, this study implies that schools, community, national policy effort and systemetic approach are required as well as improvement of personal coping capabilities in order to overcome difficulties school-dropout adolescents face.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.124-129
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• 2020

Autonomous underwater vehicle makes attracts to many researchers. This paper proposes a convolutional neural network (CNN) based fish detection method. Since there are not enough data sets in the process of training, overfitting problem can be occurred in deep learning. To solve the problem, we apply the dropout algorithm to simplify the model. Experimental result showed that the implemented method is promising, and the effectiveness of identification by dropout approach is highly enhanced.