• 한국보건간호학회지
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• 제19권2호
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• pp.204-216
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• 2005

Purpose: This study evaluated the education and training for public health nursing personnel and we suggest a development plan for their education and training. Methods: The data was collected from the annual planning reports of the Department of Health and Welfare Training in the Korea National Institute of Health from 1985 to 2004. Results: The summary of problems on the education and training included 1) unspecific educational goals and objectives, 2) a shortage of education and training programs for improving practical skills and knowledge on health promotion and chronic disease management, and 3) ineffective teaching methods based on lecture. In order to overcome these problems, education & training for public health nursing personnel should 1) establish dear and specific goals and objectives, 2) develop educational programs that focus on the trainee's needs and develop a long term educational program for reinforcing practical competency along with elementary courses for novices & advanced courses for experts, 3) utilize effective teaching methods such as case study, e-learning and applied learning programs. Conclusion: The education and training for public health nursing personnel should be improved in order to reinforce their competency and their ability to cope with the changing health care environment of the 21st century.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2012년도 춘계학술발표대회
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• pp.1294-1297
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• 2012

기존의 이러닝에서는 교수자가 강의동영상을 통하여 주로 단방향으로 지식을 전달하였다. 이런 문제점을 해결하기 위해 본 연구에서논 실시간 상호작용이 가능한 이러닝시스템인 ECube을 제안하고 구현하였다. 교수자에게는 학습자와 실시간 소통을 위한 실시간 강의기능, 전문가의 도움없이 미디어 제작과 편집이 가능한 동영상삼 저작도구인 EMC(Effective Media Contents) 솔루션을 제공한다. ECube 시스템 안의 EMC 솔루션만으로도 자막, 이미지, 퀴즈, 비디오를 합쳐 통합된 콘텐츠의 제작이 가능하다. 학습자에게는 실시간 강의를 수강하는 동안에 발표수업에 참여할 수 있는 기능을 지원하고 자신의 학습에 관한 학습계획부터 학습성과까지의 내용을 문서화할 수 있는 기능을 제공한다. 이 기능을 활용하여 학습자는 과목별 포트폴리오 작성이 가능하여 자기주도적 학습을 수행할 수 있는 학습환경을 제공한다.

• 인터넷정보학회논문지
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• 제22권6호
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• 국제학술발표논문집
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• The 7th International Conference on Construction Engineering and Project Management Summit Forum on Sustainable Construction and Management
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• pp.80-88
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• 2017

Although hazard identification is one of the most important steps of safety management process, numerous hazards remain unidentified in the construction workplace due to the dynamic environment of the construction site and the lack of available resource for visual inspection. To this end, our previous study proposed the collective sensing approach for safety hazard identification and showed the feasibility of identifying hazards by capturing collective abnormalities in workers' walking patterns. However, workers generally performed different activities during the construction task in the workplace. Thereby, an additional process that can identify the worker's walking activity is necessary to utilize the proposed hazard identification approach in real world settings. In this context, this study investigated the feasibility of identifying walking activities during construction task using Wearable Inertial Measurement Units (WIMU) attached to the worker's ankle. This study simulated the indoor masonry work for data collection and investigated the classification performance with three different machine learning algorithms (i.e., Decision Tree, Neural Network, and Support Vector Machine). The analysis results showed the feasibility of identifying worker's activities including walking activity using an ankle-attached WIMU. Moreover, the finding of this study will help to enhance the performance of activity recognition and hazard identification in construction.

• International Journal of Contents
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• 제12권1호
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• pp.25-43
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• 2016

An agent-centric event planning method is proposed for providing pedagogical experiences in an immersed environment. Two-level planning is required at in a macro-level (i.e., inter-event level) and an intra-event level to provide realistic experiences with the objective of learning declarative knowledge. The inter-event (horizontal) planning is based on search, while intra-event (vertical) planning is based on hierarchical decomposition. The horizontal search is dictated by several realistic types of association between events besides the conventional causality. The resulting schematic plan is further augmented by conditions associated with those agents cast into the roles of the events identified in the plan. Rather than following a main story plot, all the events potentially relevant to accomplishing an initial goal are derived in the final result of our planning. These derived events may progress concurrently or digress toward a new main goal replacing the current goal or event, and the plan could be merged or fragmented according to their respective lead agents' intentions and other conditions. The macro-level coherence across interconnected events is established via their common background world existing a priori. As the pivotal source of event concurrency and intricacy, agents are modeled to not only be autonomous but also independent, i.e., entities with their own beliefs and goals (and subsequent plans) in their respective parts of the world. Additional problems our method addresses for augmenting pedagogical experiences include casting of agents into roles based on their availability, subcontracting of subsidiary events, and failure of multi-agent event entailing fragmentation of a plan. The described planning method was demonstrated by monitoring implementation.

• 생태와환경
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• 제43권1호
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.547-550
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• 2009

The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation (BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future.

• 국제교류와 융합교육
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• 제1권1호
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• pp.46-60
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• 2021

본 연구의 목적은 코로나 19로 인해 대학에서 전면 실시하였던 원격수업운영에 대해 교과목별 대학생들의 만족도와 단과대학에 따른 대학생의 원격수업운영의 만족도의 차이를 살펴보는데 있다. 이를 위해 경남의 K대학의 재학생 1,380명을 대상으로 설문한 자료로 원격수업 운영 만족도 분석을 실시하였다. 분석방법은 빈도분석과 일원변량분석을 실시하였고, 사후검증으로는 Scheffé 검증을 실시하였다. 그 결과 첫째, 교양과목과 전공과목 모두 만족도가 높은 편이었으나 과제의 양의 적절성에 대해서는 만족도가 낮았다. 둘째, 단과대학에 따른 과목별 원격수업운영의 만족도는 통계적으로 유의한 차이를 나타냈고, 특히 교양과목과 전공과목 모두 사범대학 학생들의 만족도가 높았고, 공과대학 학생들의 만족도는 낮게 나타났다. 셋째, 단과대학에 따른 학습자 영역인식의 만족도도 통계적으로 유의한 차이를 보였으나, 시스템접속과 e-class유용성 문항에서는 통계적으로 유의한 차이를 나타내지 않았다. 특히 시스템 접속문제에서는 모든 학생들의 만족도가 낮게 나타났다. 이상의 결과들을 바탕으로 효과적인 원격수업을 위해서는 학생들의 자기주도학습 역량을 함양할 수 있도록 지원해야 하며, 원격수업에 대한 효율적이며 융통성 있는 구체적인 지침이 마련되어야 하고, 서버확장 등 교수학습환경 인프라 구축 개선의 필요성에 대해 논의하였다.

• 한국융합학회논문지
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• 제6권3호
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• pp.103-109
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• 2015

기존 이러닝 시스템의 실감형 교육 및 교육적 체감문제를 해결하기 위하여 투명 디스플레이 기반 디바이스에 증강현실 기술을 적용한 실감형 에듀테인먼트 시스템이 연구되었다. 그러나 투명디스플레이를 이용한 에듀테인먼트 시스템의 경우 다중 마커 배열 및 회전 마커 배열의 미검출에 대한 문제점과 투명디스플레이를 투영한 현실 공간과 가상 객체간의 조명환경 차로 인한 부조화 현상에 대한 문제점과 다양한 디바이스를 통해 서비스를 제공받지 못하는 문제점을 가지고 있다. 따라서 본 논문에서는 회전 마커 검출이 가능한 향상된 마커 검출 기법을 통해 다수의 마커 배열과 회전 마커 배열을 인식하고 중첩 블록 레이어를 통해 현실 공간과 가상공간의 조명 환경을 통일하여 현실감 있는 융합형 에듀테인먼트 콘텐츠를 제공하는 시스템을 설계하였다.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제23권2호
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• pp.231-253
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• 2009

본 연구에서는 좋은 수학 수업에 대한 초등 교사들의 인식을 알아보고자, 4개의 대영역(교육과정과 교육내용, 교수 학습, 교실환경 및 수업 분위기, 평가)과 하위 48개 요소로 구성된 설문지를 이용하여 초등 교사 223명의 반응을 분석하였다. 분석 결과 초등 교사들은 학생들의 개인차를 고려하는 수업, 개념에 초점을 두는 수업 등을 좋은 수학 수업이라고 생각하였다. 그러나 상대적으로 공학을 사용하는 수업, 좋은 학습 환경에서 이루어지는 수업 등에 대해서는 낮은 인식을 드러냈다. 좋은 수학 수업의 구현에 핵심적인 역할을 담당하는 교사들의 인식을 바탕으로 좋은 수학 수업에 대한 시사점을 논의하였다.