• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7635-7647
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• 2015

The purpose of this study is modeling nursing competencies and reasoning out the core competencies, the ability for 20% of important actions for nursing jobs which can manage the rest 80% so that the competency-based nursing curriculum can be developed. A literature review of the vast studies regarding competencies was done to understand the concepts of competency-based curriculum, competency, and nursing competencies, identifying the relationships among each nursing competency categorized in accordance with those concepts. An exemplified concept map of core competencies for the competency-based nursing curriculum is suggested based on a thorough review of various competency modeling methodologies. The core competencies consist of base competency (theoretical/practical nursing knowledge and skills), practical competency (clinical judgment, patient education, communication, etc.), and personality competency (leadership, sense of responsibility, cooperation, etc.). The circular relationship among them can remain consistent through self-directed learning and critical thinking. Therefore, a nurse who have those core competencies is a knowledge worker, a self-directed learner, and also an effective, professional communicator. Further studies which solidify the concept of nursing competencies should be done, as well as the feedback procedures which evaluate the program outcomes and then reflect the evaluation results in the curriculum should be followed continuously.

• The Journal of the Korea Contents Association
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• v.19 no.8
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• pp.10-22
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• 2019

The objective of this study is to analyze the relationships among manager' positive leadership, organizational member's organizational commitment and job performance. The questionnaire was performed for organizational members 387 in Seoul and metropolitan area. The collected data was analyzed by structural equation model and Sobel test. As a result of analysis, positive leadership factors such as positive climate, positive relationships, positive communication and positive meaning showed a statistically significant positive effects on the organizational member's organizational commitment. The organizational member's organizational commitment also showed a statistically significant positive effects on the job performance in company. The positive leadership factors such as positive climate, positive relationships, positive communication and positive meaning showed a no statistically significant effects on the organizational member's organizational commitment. But, they showed a statistically significant effects on job performance in indirect manner with the mediating effect of the organizational commitment. It is worthwhile that in this study, the effect of positive leadership of manage on the organizational member's organizational commitment and job performance was verified, and then, it is meaningful to present the role and importance of manager' positive leadership.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.83-97
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• 2021

Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.35-43
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• 2021

Recently, there has been a growing interest in zero-shot cross-lingual transfer, which leverages cross-lingual language models (CLLMs) to perform downstream tasks that are not trained in a specific language. In this paper, we point out the limitations of the data-centric aspect of quality estimation (QE), and perform zero-shot cross-lingual transfer even in environments where it is difficult to construct QE data. Few studies have dealt with zero-shots in QE, and after fine-tuning the English-German QE dataset, we perform zero-shot transfer leveraging CLLMs. We conduct comparative analysis between various CLLMs. We also perform zero-shot transfer on language pairs with different sized resources and analyze results based on the linguistic characteristics of each language. Experimental results showed the highest performance in multilingual BART and multillingual BERT, and we induced QE to be performed even when QE learning for a specific language pair was not performed at all.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.883-890
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• 2022

Purpose: Currently, in the case of domestic fire fighting facility design, it is difficult to secure highquality manpower due to low design costs and overheated competition between companies, so there is a limit to improving the fire safety performance of buildings. Accordingly, AI-based firefighting design solutions were studied to solve these problems and secure leading fire engineering technologies. Method: Through AutoCAD, which is widely used in existing fire fighting design, the procedures required for basic design and implementation design were processed, and AI technology was utilized through the YOLO v4 object recognition deep learning model. Result: Through the design process for fire fighting facilities, the facility was determined and the drawing design automation was carried out. In addition, by learning images of doors and pillars, artificial intelligence recognized the part and implemented the function of selecting boundary areas and installing piping and fire fighting facilities. Conclusion: Based on artificial intelligence technology, it was confirmed that human and material resources could be reduced when creating basic and implementation design drawings for building fire protection facilities, and technology was secured in artificial intelligence-based fire fighting design through prior technology development.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.499-508
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• 2022

A lot of research has been carried out on the Hangeul generation model using deep learning, and recently, research is being carried out how to minimize the number of characters input to generate one set of Hangul (Few-Shot Learning). In this paper, we propose a CKFont2 model using only 14 letters by analyzing and improving the CKFont (hereafter CKFont1) model using 28 letters. The CKFont2 model improves the performance of the CKFont1 model as a model that generates all Hangul using only 14 characters including 24 components (14 consonants and 10 vowels), where the CKFont1 model generates all Hangul by extracting 51 Hangul components from 28 characters. It uses the minimum number of characters for currently known models. From the basic consonants/vowels of Hangul, 27 components such as 5 double consonants, 11/11 compound consonants/vowels respectively are learned by deep learning and generated, and the generated 27 components are combined with 24 basic consonants/vowels. All Hangul characters are automatically generated from the combined 51 components. The superiority of the performance was verified by comparative analysis with results of the zi2zi, CKFont1, and MX-Font model. It is an efficient and effective model that has a simple structure and saves time and resources, and can be extended to Chinese, Thai, and Japanese.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.227-241
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• 2022

Full waveform inversion (FWI) in the field of seismic data processing is an inversion technique that is used to estimate the velocity model of the subsurface for oil and gas exploration. Recently, deep learning (DL) technology has been increasingly used for seismic data processing, and its combination with FWI has attracted remarkable research efforts. For example, DL-based data processing techniques have been utilized for preprocessing input data for FWI, enabling the direct implementation of FWI through DL technology. DL-based FWI can be divided into the following methods: pure data-based, physics-based neural network, encoder-decoder, reparameterized FWI, and physics-informed neural network. In this review, we describe the theory and characteristics of the methods by systematizing them in the order of advancements. In the early days of DL-based FWI, the DL model predicted the velocity model by preparing a large training data set to adopt faithfully the basic principles of data science and apply a pure data-based prediction model. The current research trend is to supplement the shortcomings of the pure data-based approach using the loss function consisting of seismic data or physical information from the wave equation itself in deep neural networks. Based on these developments, DL-based FWI has evolved to not require a large amount of learning data, alleviating the cycle-skipping problem, which is an intrinsic limitation of FWI, and reducing computation times dramatically. The value of DL-based FWI is expected to increase continually in the processing of seismic data.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.99-107
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• 2024

While the demand for extrapolating video content horizontally or vertically is increasing, even the most advanced techniques cannot successfully extrapolate all videos. Therefore, it is important to determine if a given video can be well extrapolated before attempting the actual extrapolation. This can help avoid wasting computing resources. This paper proposes a video classifier that can identify if a video is suitable for horizontal extrapolation. The classifier utilizes optical flow and an adaptive Gaussian blur network, which can be applied to flow-based video extrapolation methods. The labeling for training was rigorously conducted through user tests and quantitative evaluations. As a result of learning from this labeled dataset, a network was developed to determine the extrapolation capability of a given video. The proposed classifier achieved much more accurate classification performance than methods that simply use the original video or fixed blur alone by effectively capturing the characteristics of the video through optical flow and adaptive Gaussian blur network. This classifier can be utilized in various fields in conjunction with automatic video extrapolation techniques for immersive viewing experiences.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.6
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• pp.39-47
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• 2012

As a scheme to efficiently reduce the effects of multipath fading in next generation wireless communication systems, cooperative communication systems have recently come into the spotlight. Since these cooperative communication systems use cooperative relays with diverse fading coefficients to transmit information, having all relays participate in cooperative communication may result in unnecessary waste of resources, and thus relay selection schemes are required to efficiently use wireless resources. In this paper, we propose an efficient relay selection scheme through self-learning in cooperative wireless networks using Q-learning algorithm. In this scheme, we define states, actions and two rewards to achieve good SER (Symbol Error Rate) performance, while selecting a small number of cooperative relays. When these parameters are well-defined, we can obtain good performance. For demonstrating the superiority of the proposed Q-learning, We compared the proposed scheme with Q-learning and a relay selection scheme with a mathematical analysis. The simulation results show that, compared to a scheme that obtains optimum relays through a mathematical analysis, the proposed scheme uses resources efficiently by using smaller numbers of relays with comparable SER performance. According to these simulation results, the proposed scheme can be considered as a good attempt for future wireless communication.

• 한국공간정보시스템학회:학술대회논문집
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• 2001.11a
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• pp.275-286
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• 2001

습지 분류의 목표는 '목록작성(inventory)과 평가와 관리를 위해 자연적인 생태계에 범위를 설정하는 것'이다. 또한 등질적인 속성을 갖는 생태단위를 기술하고, 자원관리 의사결정에 도움을 줄 수 있는 체계로 단위를 만들어내고, 목록작성과 지도화에 필요한 단위를 제공하면, 습지에 관한 개념과 용어의 통일성을 제공하는 것 등이다. 해안지형 가운데 해안 습지의 분류에는 우선, 1) 형태, 2) 생성요인, 3) 자갈, 모래, 펄 같은 기질 물질과 4)현재의 환경이라는 요소가 모두 고려되어야만 하는데 아직 국내에는 이에 대한 연구가 절대적으로 부족하여 이에 대한 규정이 부족한 현실이다. 따라서 현 단계에서 ISO/IEC 규정대로 각 코드는 엄밀히 상호배타적인 개념일 것, 정수로 표시할 것과 순차적으로 증가하는 숫자로 표시할 것 등의 전제조건을 만족시키는 전제 하에서 해안습지를 분류하는 것은 매우 힘든 작업이라 생각한다. 하지만 국토공간의 효율적 관리와 보존을 위해서는 위치와 장소에 따라 차이를 보이는 지질, 지형, 토양, 식생, 수리 현상 등 제반 지표 환경요소에 대한 체계화된 정보의 축척이 있어야 가능하다. 우리나라의 경우 지질 정보는 지질자원연구원에서 발행하는 지질도와, 농촌진흥청에서 발행하는 토양도, 임업연구원에서 발행하는 임상도 등의 주제도가 있으나, 지표환경을 나타내주는 지형에 대한 정보체계는 아직 이루러진 바가 없고, 대학의 석사학위논문이나, 실험적인 수준의 연구에 머물고 있는 실정이다. 이번 연구에서는 지형분류도 작성과 관련한 외국의 사례를 집중적으로 분석하고, 지형정보의 체계적 관리를 위해 가장 필요한 해안습지 지형분류도를 작성하기 위해 가장 기초적인 단계인 해안습지 지형분류체계에 대한 국내외의 연구성과를 비교하여 시안을 작성 표준화를 위한 첫 단계 시도를 소개하였다.분석 결과는 문장, 그림 및 도표, 장 끝의 질문, 학생의 학습 활동 수 등이 $0.4{\sim}1.5$ 사이의 값으로 학생 참여를 적절히 유도하는 발견 지향적 인 것으로 조사되었다. 그러나 장의 요약은 본문 내용을 반복하는 내용으로 구성되었다. 이와 같이 공통과학 과목은 새로운 현대 사회에 부응하는 교과 목표와 체계를 지향하고 있지만 아직도 통합과학으로서의 내용과 체계를 완전히 갖추고 있지 못할 뿐만 아니라 현재 사용되고 있는 7종의 교과서가 교육 목표를 충분히 반영하지 못하고 있다. 따라서 교사의 역할이 더욱더 중요하게 되었다.괴리가 작아진다. 이 결과에 따르면 위탁증거금의 징수는 그 제도의 취지에 부합되고 있다. 다만 제도운용상의 이유이거나 혹은 우리나라 주식시장의 투자자들이 비합리적인 투자형태를 보임에 따라 그 정책적 효과는 때로 역기능적인 결과로 초래하였다. 그럼에도 불구하고 이 연구결과를 통하여 최소한 주식시장(株式市場)에서 위탁증거금제도는 그 제도적 의의가 여전히 있다는 사실이 확인되었다. 또한 우리나라 주식시장에서 통상 과열투기 행위가 빈번히 일어나 주식시장을 교란시킴으로써 건전한 투자풍토조성에 저해된다는 저간의 우려가 매우 커왔으나 표본 기간동안에 대하여 실증분석을 한 결과 주식시장 전체적으로 볼 때 주가변동율(株價變動率), 특히 초과주가변동율(超過株價變動率)에 미치는 영향이 그다지 심각한 정도는 아니었으며 오히려 우리나라의 주식시장은 미국시장에 비해 주가가 비교적 안정적인 수준을 유지해 왔다고 볼 수 있다.36.4%)와 외식을 선호(29.1%)${\lrcorner}$ 하기 때문에 패스트푸드를 이용하게 된 것으로 응답 하였으며, 남 여 대학생간에는 유의한 차이(p<0.05)가 인정되었다. 응답자의 체형은 ${\ulcorner}$