• Journal of Service Research and Studies
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• v.11 no.2
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• pp.106-117
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• 2021

This study is a descriptive survey study using a self-reported survey method to find out the job value and intention of career choice of college students in the medical management field. This study was conducted on university students in medical management at three four-year universities located in Busan Metropolitan City. A total of 139 effective questionnaires were used as statistical analysis data. As a result of the analysis, social dedication and stability were significantly displayed in the selection of jobs for hospital administration and administrative positions, and social dedication and stability were significantly displayed in the selection of jobs as medical recorders. In choosing a career as an international medical tourism coordinator, the focus on human relations, maintaining face, and pursuing stability have been significant. Only social commitment was significantly shown in the choice of occupation as a health educator. A comparison of job values according to general characteristics showed that there was a difference in the pursuit of knowledge and social commitment. In the case of grades, there was a difference in social dedication and stability. There was no significant difference in the case of religious or non-religious matters. In the case of economic level, only economic priorities differed. Through this study, we would like to present basic data so that college students in medical management who prepare to take the first step into a professional medical management society can recognize the need for recognition of job value and move in a better direction in choosing a job.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.161-168
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• 2023

This study attempted to determine the relationship between college students' motivation for major selection (personal motivation, social motivation), academic performance, and career maturity, and to identify the influencing factors of career maturity in order to provide basic data for improving career maturity. Data were collected through a structured questionnaire from 199 university students in C city. As a result of examining the correlation between personal motivation for major selection, social motivation, academic achievement, and career maturity, career maturity showed a significant positive correlation with personal motivation for major selection (r=.417, p=.00) and no significant correlation with social motivation for major selection and academic achievement. The influencing factors of career maturity were personal motivation for major selection, economic activity, and major department, and the explanatory power was 24%. Therefore, it seems that university-level support is needed to enable students to engage in economic activities in fields related to their majors. Since personal motivation is important in major selection, we should focus on increasing personal motivation for major selection by providing high school students with a wide range of opportunities, such as career experience and future work experience.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1202-1203
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• 2023

본 논문은 스팀(Steam) 게임 플랫폼을 기반으로 약 1000개의 게임 데이터를 활용하여 사용자들에게 알맞은 게임을 추천해주는 시스템을 제안한다. 게임 선택에 영향을 주는 요인들을 언어 객체로 설정하여 규칙 기반 추론 시스템을 구현했다. 선호도 정보는 게임 선택의 기준이 되는 세 가지 요소에 대한 질문에 답하는 방식으로 수집된다. 게임 추천 결과를 시각화하여 신규 유저를 게임에 유입하고 몰입을 촉진하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.1563-1566
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• 2008

본 논문에서는 데이터의 전처리과정으로 SNMP MIB 데이터에 대한 속성 부분집합의 선택 방법(attribute subset selection)을 사용하여 특징선택 및 축소(feature selection & reduction)를 실시하였다. 또한 데이터 마이닝의 대표적인 해석학적 분석 모델인 연관관계규칙기법(association rule mining)을 이용하여 트래픽 폭주 공격 및 공격유형별 SNMP MIB 데이터에 내재되어 있는 특징들을 규칙의 형태로 추출하여 분석하는 의미론적 심층해석을 실시하였다. 공격유형에 대한 패턴 규칙의 추출 및 분석은 공격이 발생한 프로토콜에 대해서만 서비스를 제한하고 관리할 수 있는 정책적 근거를 제공함으로써 보다 안정적인 네트워크 환경과 원활한 자원관리를 지원할 수 있다. 본 논문에서 제시한 트래픽 폭주 공격 및 공격유형별 데이터로부터의 자동적 특징의 규칙 추출 및 의미론적 해석방법은 침입탐지 시스템을 위한 새로운 방법론에 모멘텀을 제시할 수 있다는 긍정적인 가능성과 함께 침입탐지 및 대응시스템의 정책 수립을 지원할 수 있을 것으로 기대된다.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.23-31
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• 2022

This study analyzed the actual frozen container operation data of Starcool provided by H Shipping. Through interviews with H's field experts, only Critical and Fatal Alarms among the four failure alarms were defined as failures, and it was confirmed that using all variables due to the nature of frozen containers resulted in cost inefficiency. Therefore, this study proposes a method for detecting failure of frozen containers through characteristic importance and PCA techniques. To improve the performance of the model, we select variables based on feature importance through tree series models such as XGBoost and LGBoost, and use PCA to reduce the dimension of the entire variables for each model. The boosting-based XGBoost and LGBoost techniques showed that the results of the model proposed in this study improved the reproduction rate by 0.36 and 0.39 respectively compared to the results of supervised learning using all 62 variables.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.491-494
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• 2020

최근 언어 모델(Language model)의 기술이 발전함에 따라, 자연어처리 분야의 많은 연구들이 좋은 성능을 내고 있다. 정해진 주제 없이 인간과 잡담을 나눌 수 있는 오픈 도메인 대화 시스템(Open-domain dialogue system) 분야에서 역시 이전보다 더 자연스러운 발화를 생성할 수 있게 되었다. 언어 모델의 발전은 응답 선택(Response selection) 분야에서도 모델이 맥락에 알맞은 답변을 선택하도록 하는 데 기여를 했다. 하지만, 대화 모델이 답변을 생성할 때 일관성 없는 답변을 만들거나, 구체적이지 않고 일반적인 답변만을 하는 문제가 대두되었다. 이를 해결하기 위하여 화자의 개인화된 정보에 기반한 대화인 페르소나(Persona) 대화 데이터 및 태스크가 연구되고 있다. 페르소나 대화 태스크에서는 화자마다 주어진 페르소나가 있고, 대화를 할 때 주어진 페르소나와 일관성이 있는 답변을 선택하거나 생성해야 한다. 이에 우리는 대용량의 코퍼스(Corpus)에 사전 학습(Pre-trained) 된 언어 모델을 활용하여 더 적절한 답변을 선택하는 페르소나 대화 시스템에 대하여 논의한다. 언어 모델 중 자기 회귀(Auto-regressive) 방식으로 모델링을 하는 GPT-2, DialoGPT와 오토인코더(Auto-encoder)를 이용한 BERT, 두 모델이 결합되어 있는 구조인 BART가 실험에 활용되었다. 이와 같이 본 논문에서는 여러 종류의 언어 모델을 페르소나 대화 태스크에 대해 비교 실험을 진행했고, 그 결과 Hits@1 점수에서 BERT가 가장 우수한 성능을 보이는 것을 확인할 수 있었다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.291-300
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• 2023

The purpose of this study is to analyze the effect of teachers' motivation for choosing teaching on the relationship with career adaptability through self-directed learning in 271 pre-service early childhood teachers. As a result of the study, first, career adaptability, self-directed learning, and motivation for choosing teaching were high in order. Second, there was a positive correlation that the higher the motivation for choosing a teaching profession, the higher the self-directed learning and career adaptation, and the higher the self-directed learning, the higher the career adaptation. Third, it was found that self-directed learning of pre-service early childhood teachers partially mediated teachers' motivation for choosing teaching and career adaptability. This study is significant in that it sought various perspectives in practicing high-quality early childhood teacher education program and provided basic data on teacher education program.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.389-396
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• 2014

This study was conducted to analyze the satisfaction level that students in physical therapy departments have with their major after they enter college; it also aimed to present the factors that affect that satisfaction. With regard to satisfaction in relation to general characteristics, no significant difference in satisfaction was shown between the two genders or among the times of selection of the department. Significant differences in satisfaction with the department were found for high school courses, school systems, grades, and the influences on the selection of the department. The variables that significantly affected satisfaction with the department were: satisfaction with perception, satisfaction with curricula, general satisfaction, and satisfaction with relationships. In particular, satisfaction with perception showed the highest standardized regression coefficient value for satisfaction with the department. Based on the results of this study, to enhance the satisfaction levels of students in physical therapy departments, interest in physical therapy should be drawn from the students based on social perception and high level educational environments, and the relationship between the processors and the students should be improved.

• Korean journal of health promotion
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• v.18 no.4
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• pp.184-193
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• 2018

Background: Despite increasing the number of newly licensed nurses across Korea, shortages caused by geographical imbalances remains a significant concern. Therefore, understanding nursing students' attitudes to working and living, factors influencing where they first choose to work after graduation is useful in formulating appropriate interventions to retain nurses in regional areas. Methods: A total of 329 senior nursing students from areas outside Metropolitan Seoul completed self-report questionnaires. Data were analyzed using t-test, chi-square test and multiple logistic regression analysis. Results: Of the respondents, 57.8% reported that they planned to work in the region in which their school was located. The three factors ranked as having the greatest influence on their decision to work in non-metropolitan regions were: the cost of living, housing costs, and the proximity to family. Enjoyable aspects of rural life contributed positively to students' intentions to work in non-metropolitan regions, whereas isolation and socialization problems negatively affected their intentions to work in such areas. Conclusions: Greater consideration should be given to improving working conditions and housing environments in non-metropolitan regions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.342-345
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• 2014

Network intrusion detection system based on machine learning methods is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features from generally used features to detect network intrusion requires extensive computing resources. For instance, the number of possible feature combinations from given n features is $2^n-1$. In this paper, to tackle this problem we propose a optimal feature selection algorithm. Proposed algorithm is based on the local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In addition, the accuracy of clusters which obtained using selected feature components and k-means clustering algorithm is adopted to evaluate a feature assembly. In order to estimate the performance of our proposed algorithm, comparing with a method where all features are used on NSL-KDD data set and multi-layer perceptron.