• 한국디지털정책학회:학술대회논문집
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• 2006.06a
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• pp.363-375
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• 2006

본 연구에서는 청소년들을 대상으로 정의적 특성으로 분류해 온 도덕성의 성격과 발달 수준을 찾아 이를 기초하여 학생들의 자기 이해를 돕고 정의적 성숙을 가져올 수 있게 하는 자기 평가식 도덕성검사를 Kohlberg의 도덕성 발달 수준을 기반으로 한 온라인 도덕성 검사 시스템을 설계 및 구현하는 방법을 연구하였다. 구현된 시스템을 이용하여 학생들 스스로 자신의 도덕성을 진단하고 평가하여 자기중심성으로 부터 벗어나 협동과 상호존중의 관계를 지향할 수 있는 학생이 되도록 하는 것이 이 연구의 주요한 목적이다. 본 연구에서 설계하고 구현한 도덕성검사 시스템이 성공적으로 적용된다면 검사와 검사의 결과에 대한 진로안내가 한 시스템 내에서 이루어지기 때문에 학생들의 진로지도에 대한 효율적인 성과를 가져올 수 있을 것이며, 또한 쉽고 빠른 검사로 인해 지필검사를 통해 실시하는 것보다 훨씬 경제적인 효과를 올릴 수 있을 것이다.

• The Korean Journal of Archival Studies
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• no.71
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• pp.91-130
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• 2022

This article aims to (1) come up with a new way of the classification by analysing 2,475 of the documents in the Archives of the National Debt Redemption Movement(NDRM) recently registered as the Memory of the World; (2) make a proposal of their rules for archival description with reference to ISAD(G) 2ND EDITION, ISSAR, and NAK. This leads to the suggestion of necessity to make archivistically the rules for archival description of archives in the Memory of the World and manage them on the rules, in view of the recent trend of increasing the number of the records registered as the Memory of the World and adding the records even after their registration. The archives of NDRM is an artificial collection. It can be said that the classification of manuscript collection on the basis of the preparation subject according to Schellenberg's principle of the provenance is the most systematic. On the foundation of it, the suggestions of various classifications by activities, times, sorts of records, medias, topics, etc. would permit to search and interpret the archives in the Memory of the World with more efficiency.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.973-983
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• 2019

The importance of information security has been increasingly emphasized at the national, organizational, and individual levels due to the widespread adoption of software applications. High-safety software, which includes embedded software, should run without errors, similar to software used in the airline and nuclear energy sectors. Software development techniques in the above sectors are now being used to improve software security in other fields. Secure coding, in particular, is a concept encompassing defensive programming and is capable of improving software security. In this paper, we propose a software security vulnerability detection method using an improved coding standard searching technique. Public static analysis tools were used to assess software security and to classify the commands that induce vulnerability. Software security can be enhanced by detecting Application Programming Interfaces (APIs) and patterns that can induce vulnerability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.9 no.3
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• pp.115-123
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• 1989

This paper probabilisticly analyses the variance of the soil parameters on kinds of soil by conducting statistical analysis through the Unified Soil Classification System. Data used are the result of soil test which the Korea National Housing Corporation conducted in 176 sites of 74 cities throughout the country during the past 13 years from 1974 to 1986. In this paper, soil parameters such as natural water contents, specific gravity of soil particle, Atterberg limits, N-values, unconfined compression strength, compression index and shear strength parameter etc., is analysed. The result of the analysis is as follows. 1) The variance in physical properties of the soil is, when compared with coefficient of variation which is statistical variable, comparatively small. 2) The shear strength parameter is proved to be about 40% and compression index is about 32%. 3) The variance in specific gravity is 0.87-2.49% in granular soil and 0.91~5.03% in cohesive soil respectively. So, the degree of the variance is very small.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.53-62
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• 2017

In this paper, we present the score classification accuracy of BBS(Berg Balance Scale) which is the most commonly used balance evaluation tool using machine learning. Data acquisition was performed using the Noraxon system and an inertial sensor of Noraxon system was attached to the body in 8 locations (left and right ankle, left and right upper buttocks, left and right wrists, back, forehead). Based on the 3-axis accelerometer of the inertial sensor, the feature vector STFT(Short Time Fourier Transform) and SAM(Signal Area Magnitude) were extracted. Then, the items of the BBS were divided into static movement and dynamic movement depending on the operation characteristics, and the feature vectors were selected according to the sensor attachment positions which affect the score for each item of the BBS. Feature vectors selected for each item of BBS were classified using GMM(Gaussian Mixture Model). As a result of the accuracy calculation for 40 subjects, 55.5%, 72.2%, 87.5%, 50%, 35.1%, 62.5%, 43.3%, 58.6%, 60.7%, 33.3%, 44.8%, 89.2%, 51.8%, 85.1%, respectively.

• Smart Media Journal
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• v.10 no.2
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• pp.76-83
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• 2021

With the development and use of smart devices such as smartphones and tablets increases, the mobile application market based on mobile devices is growing rapidly. Mobile application users write reviews to share their experience in using the application, which can identify consumers' various needs and application developers can receive useful feedback on improving the application through reviews written by consumers. However, there is a need to come up with measures to minimize the amount of time and expense that consumers have to pay to manually analyze the large amount of reviews they leave. In this work, we propose to collect delivery application user reviews from Google PlayStore and then use machine learning and deep learning techniques to classify them into four categories like application feature advantages, disadvantages, feature improvement requests and bug report. In the case of the performance of the Hugging Face's pretrained BERT-based Transformer model, the f1 score values for the above four categories were 0.93, 0.51, 0.76, and 0.83, respectively, showing superior performance than LSTM and GRU.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.485-491
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• 2016

The purpose of this study was to compare of the fall risk factors for elderly in the city. 62 people aged 65 years or older were classified as fallers and nonfallers based on experience of their falls in the previous year. By comparing the difference between the groups via evaluations of general characteristics, health related behavior and chronic disease, balance-related psychological (K-ABC) and physical measurement (BBS), depression (SGDS), and the correlations between the significant differences in variables were identified. According to the results, K-ABC, BBS, and SGDS are statistically significant differences between fallers and nonfallers (P<0.05). Also it has positive correlations between BBS and K-ABC (r=0.499) whereas negative correlation between K-ABC and SGDS(r=-0.472).

• Korean Journal of Psychosomatic Medicine
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• v.11 no.1
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• pp.25-35
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• 2003

Objectives: The aim of this study was to investigate 1) the prevalence of depressive symptoms, 2) the severity of depressive symptoms, 3) the correlation of depressive symptoms with clinical variables, and 4) factors that contribute to depressive symptoms in patients with Parkinson's disease. Methods: One hundred eighteen patients with Parkinson's disease referred from the Parkinson's Disease Clinic of Dong-A University Hospital, Busan, Korea, completed a self-administered questionnaire package, which included basic demographic data, the Beck Depression Inventory, the Parkinson's disease quality of life questionnaire, the Symptom Checklist-90-Revision(SCL-90-R), and the Spielberger's State-Trait Anxiety Inventory. In addition, a structured interview and a complete neurological examination, including the Hoehn and Yahr stage, the motor part of the Unified Parkinson's Disease Rating Scale(some selected scales of UPDRS part III), the Schwab and England Activities of Daily Living scale(ADL), and the Korean version of Mini-Mental State Examination were performed. Results: 1) Based on BDI score, subjects were divided into four groups:severely(40.7%), moderately(13.6%) and mildly(12.7%) depressive and non-depressive(33.1%). 2) The severity of depressive symptom in Parkinson's disease was positively correlated with Hoehn and Yahr(H & Y) stage(r=0.34, p<0.0001), the severity of motor symptom(r=0.35, p<0.0001), and trait anxiety inventory(r=0.33, p<0.001). On the other hand, the severity of depressive symptom was negatively correlated with educational level(r=-0.34, p<0.001), ADL(r=-0.37, p<0.0001) and Parkinson's disease quality of life (PDQL)(r=-0.69, p<0.0001). Among several clinical variables, the PDQL was the most influential factor predicting whether the depressive symptom was present or not. Conclusion: This study suggests that depressive symptom is very prevalent among patients with Parkinson's disease. Data from this study indicate that medical staffs who take care of patients with Parkinson's disease should pay attention to finding and treating depressive symptom among their patients. With appropriate psychiatric intervention, patient's depressive symptom can be minimized or alleviated and thus, the quality of life in these patients is likely enhanced.

• Journal of Korean Philosophical Society
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• v.123
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• pp.87-110
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• 2012

The kitsch was emerged from the people's cultural desire in the conditions of the various duplicating technology, the capital economy system, and the civil revolution in the western modern mass society. But it is underestimated constantly because of the conspicious consumption and the aesthetic inadequacy. Even though some kitsches are elevated to the 'kitsch arts' in the historical description of the modern arts, still the most of kitsches are remained as 'just kitsches' and excluded from the aesthetic research according to the double standard. In this essay, I research for whether anti-kitsch theory is convincing theoretically and practically. Anti-kitsch theory criticizes the kitsch on the basis of the modernist aesthetics, in which the 'fine art' provokes the aesthetic pleasure in the disinterested contemplation. But kitsch purposes for the sensual gratification and the sentimentality. So the anti-kitish theorists conclude that the kitsch is the bad taste. In critically analyzing the argumentation of Greenberg's. Kaplan's and $C{\tilde{a}}linescu^{\prime}s$, I refute the privileged prejudice of the ideal critic. They don't justify the criteria of the classification of 'art'/ 'kitsch'. They supplement the economical and the political grounds for the evaluative theory of the kitsch. But the argumentation of the kitsch is consumed conspicuously and results in the unlettered masses is not sufficient. People produce and enjoy the kitsches in the various ways. People envelope the genres, styles and media of the kitsches and they try to suggest the new horizon of the popular aesthetics. So anti-kitsch theories cannot be accepted because they adhere to the elitism and formalism. The exclusion of the kitsch is the derogation for people's taste. Also they didn't reflect the contemporary cultural practice and the aesthetic needs in the system of post-art. The alternative aesthetics of the kitsch is the topic of my next essay.

• 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%.