• 수산해양교육연구
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• 제27권1호
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• pp.160-169
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• 2015

The purpose of this study was to search and analyze the realities and needs of life long education, the necessity and perception of life competencies and the difference of competencies on experiences of education to support life long education system for adult learners in Busan and Ulsan. In this study, we conducted a questionnaire survey of 234 adult learners living in Busan and Ulsan. The results of this study were summarized as follows. First, adult learners perceived that life long education was very important to adults and appreciated an educational institution being managed by government. Second, adult learners thought that basic literacy competency and basic work competency were important and late fifties perceived that professional job is in important. Third, adult learners perceived that self directed learning and capacity of management are poor competencies. And they perceived that the government support adult learners who has economical difficulty. Forth, adult learners perceived that it was normal that 'professional job' and 'job transition abilities', 'women', 'the middle aged and old','the unemployed' and' low incomers' perceived that they have poor level. Fifth, according to the results of ${\chi}^2$ test for determining the statistical significance of differences in experience in lifelong learning among adult learner groups divided by their individual background, significant differences were observed in a few competencies(p<.05, p<.01).

• 한국도서관정보학회지
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• 제36권2호
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• pp.69-91
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• 2005

공공도서관은 지식정보사회의 전개와 함께 정보기술의 진보, 매스커뮤니티의 발달, 여가생활의 증대 등으로 인한 주변 환경의 변화에 적응하여 나가고 있다. 이러한 과정에서 정부나 지역사회는 공공도서관이 평생교육적 역할까지도 함께 담당하여 줄 것을 요청하고 있다. 지금도 많은 공공도서관들이 주민들의 생활과 밀접한 관계를 갖고 다양한 기능과 함께 평생학습관으로서의 역할까지 수행하고 있다. 하지만, 공공도서관의 주변 여건은 평생교육적 역할을 능동적으로 수행하기에 그리 좋지 않은 편이라고 할 수 있다. 더욱이 공공도서관들이 평생교육적 역할을 강화하기 위한 도서관 수, 전문인력, 예산 등의 면에서 상대적으로 부족한 실정에 있다. 따라서 본 고에서는 다음과 같이 평생교육의 활성화를 위한 공공도서관의 역할을 고찰하고자 한다. 첫째, 평생교육의 개념 및 방향성을 살펴보고 둘째, 공공도서관의 평생교육적 역할을 조명하며, 셋째, 공공도서관의 평생교육프로그램의 범주를 제시하고자 한다.

• 한국지구과학회지
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• 제24권2호
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• pp.108-116
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• 2003

Problem Based Learning (PBL) is one of methods which has been developed to promote student-centered learning and to pursue self-directed learning for life-long learning. The purpose of this study is exploring the possibility of Problem Based Learning (PBL) in college Earth science course. The participants of this study were fourteen students attending an Earth science class at Sookmyung Women's University in Seoul. PBL was implemented in the form of group project with utilizing Web-based course tool. We provided questionnaires and conducted interviews to figure out students' perception about PBL. The findings were as follows: Through a given experiences, (1) students participated more actively than LBL (Lecture Based Learning), (2) more students were engaged with self-directed learning, and (3) students made higher cognitive efforts. LBL seemed to be more efficient way to acquire factual knowledge. In the meanwhile, PBL did not seem to affect the improvement of communication skills. Students could not make use of Web-based course tool effectively in communicating with other team members. In this study, we found that college student participants preferred problems related to everyday life, environmental issues and interesting but unusual incidents. On the other hand, they felt difficult in open-ended problems, especially when they were asked to provide their own evaluation. On the basis of PBL experiment in this paper, we present one method of successful implementation of PBL and suggest topics which should be studied in the future.

• 동아시아경상학회지
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• 제2권4호
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• pp.26-47
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• 2014

The paper examined effect of age cohort on life cycle financial planning. A total of 990 questionnaires were distributed with a 55.2% return rate. Seven hypotheses were analysed using hierarchical and ordinary regression analysis. The results revealed that age cohort variables made significant contribution to life cycle financial planning as well as personal orientation towards retirement planning, particularly the younger age cohort. Age cohorts do affect personal orientation towards retirement planning with the confidence level making a significant impact. Current financial resources do have a strong positive impact on consumption for all age cohorts. On the other hand, no significant effect was found between age cohorts and current financial resources but older age cohorts were relatively more significant predictors. The implication was that not only should their individual perceptions of financial planning become an increasingly important part of people's long-term commitment throughout their life-cycle, it must also assume the role as a self-directed life-long learning process, in view of the ever-changing and complicated financial environment.

• 한국콘텐츠학회논문지
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• 제16권1호
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• pp.445-458
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• 2016

이 연구는 21세기 변화하는 미래사회를 준비하기 위해 국내 청소년들이 갖추어야 할 생애학습역량의 개념과 이를 구성하는 구성요인들을 선정하고 이에 대한 타당성과 신뢰성을 검증하는데 목적이 있었다. 이를 위해 청소년 생애학습역량을 개념화 하였으며, 이를 구성하는 하위 역량요인을 도출하였다. 도출한 생애학습역량의 구성체제의 타당성을 검증하기 위해 전문가 패널 및 중고등학생 대상으로 타당성 조사를 실시하였다. 전문가 패널 대상 생애학습역량지수의 타당성 조사는 청소년 관련 전문가 총 28명으로 구성하여 2차에 걸쳐 진행하였다. 전문가 패널 대상 타당성 조사를 통해 수정 보완된 생애학습역량지수의 타당성을 검증하기 위해 중고등학생을 대상으로 타당성 조사를 실시하였다. 타당성 조사에 참여한 연구대상은 서울, 경기, 인천지역 중고등학생 719명이었다. 연구결과 총 3개 역량에 9개의 하위역량이 개발되었는데 청소년 생애학습역량은 사고력(통합적 사고력, 비판적 사고력, 감성적 사고력), 지적도구활용(언어능력, 수리과학능력, 정보통신활용능력), 학습적응성(변화수용력, 지적호기심, 학습주도성) 등이 도출되었다. 도출된 지수의 구성체제에 대한 신뢰도와 타당도 또한 적합한 것으로 판명되었으며, 연구결과를 바탕으로 기존 생애학습역량과의 차별성, 생애학습역량을 바라보는 관점, 그리고 추후 연구에 대한 시사점을 제시하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2006년도 춘계학술발표대회
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• pp.303-306
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• 2006

유비쿼터스 컴퓨팅의 핵심은 네트워크 환경에 대한 고 가용성이라 할 수 있다. 이러한 사실은 사용자 컨텍스트(Context)가 반영된 서비스를 제공하기 위한 필수조건이 이미 갖추어져 있다는 것을 시사한다. 지금까지 상황인지(Context-Aware) 서비스를 위한 여러 응용들이 제시되어 왔지만, 동적으로 변화하는, 즉 예측하기 어려운 환경을 충분히 반영할 만큼의 유연성을 제공하지 못했다. 왜냐하면, 응용 태스크 시나리오가 시작단계부터 이미 정해져 있었기 때문이다. 여기에, 본 고는 평생동안 개인화된 태스크를 동적으로 생성, 제공할 수 있는 멀티 에이전트 시스템 구조를 제안하고자 한다. 평생 개인화 태스크(Life Long Personalized Task)는 끊임없이 변화하는 사용자의 행동패턴을 반영할 수 있도록, 동적으로 생성, 제공되는 태스크를 의미한다. 이는 태스크 시나리오가 컴파일 타임에 이미 결정되지 않고, 실행 시간 중에 자동으로 생성된다는 것을 의미한다. 이러한 유연성은 평생학습 엔진(Life Long Learning Engine)을 활용함으로써 가능하다. 이 엔진은 사용자의 행동패턴을 학습하며, 결과적으로 사용자 행동패턴 규칙들을 생성한다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.123-125
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• 2022

본 논문에서는 Yolo-pose를 이용하여 장단기 메모리(Long short-term Memory)에 적용하는 시스템을 소개한다. 영상데이터로부터 Yolo-pose를 이용하여 일상생활과 낙상으로 구분된 데이터를 추출하여 LSTM에 적용하여 학습시킨다. 학습은 오버피팅을 방지하기 위하여 8대2의 Validation을 진행하며 Confusion matrix로 나타낸다. Yolo-pose의 결과값은 sensitivity와 specificity 모두 100%를 기록하여 일상생활과 낙상을 잘 구분하는 것을 확인 하였다.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.119-130
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• 2022

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.

• Computers and Concrete
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• 제30권5호
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• pp.301-310
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• 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.

• 한국멀티미디어학회논문지
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• 제25권8호
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• pp.1233-1241
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• 2022

Tumors of the brain are the deadliest, with a life expectancy of only a few years for those with the most advanced forms. Diagnosing a brain tumor is critical to developing a treatment plan to help patients with the disease live longer. A misdiagnosis of brain tumors will lead to incorrect medical treatment, decreasing a patient's chance of survival. Radiologists classify brain tumors via biopsy, which takes a long time. As a result, the doctor will need an automatic classification system to identify brain tumors. Image classification is one application of the deep learning method in computer vision. One of the deep learning's most powerful algorithms is the convolutional neural network (CNN). This paper will introduce a novel deep learning structure and image gradient to classify brain tumors. Meningioma, glioma, and pituitary tumors are the three most popular forms of brain cancer represented in the Figshare dataset, which contains 3,064 T1-weighted brain images from 233 patients. According to the numerical results, our method is more accurate than other approaches.