• 정보교육학회논문지
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• 제19권1호
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• pp.21-30
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• 2015

21세기 지식 정보 사회에 컴퓨터 교육이 매우 중요하다. 컴퓨터 교육에서 컴퓨터 프로그래밍 교육이 매우 중요하다. 컴퓨터 프로그래밍 교육에는 교수 학습 모델이 거의 없다. 본 연구에서는 학생들이 자기조절 학습을 할 수 있는 자기 조절 학습 모형을 개발한다. 본 연구에서는 자기 조절 학습 요소, 자기 조절 학습 단계와 자기 조절 학습 모형을 제안한다. 자기조절 학습 요소는 과제 수준, 일반화, 효율화이다. 자기조절 학습 단계는 문제이해, 설계, 코딩, 시험, 유지보수이다. 자기조절 학습 모델은 복사하기, 변형하기, 창조하기, 도전하기이다. 본 연구의 결과는 다음과 같다. 학습 요소들과 성취도간의 상관관계 분석은 효율화와 일반화가 과제 수준보다 더 높았다. 학습 단계에는 문제 이해와 설계 단계가 다른 단계보다 더 높았다. 학습 모형에서는 변형하기, 창조하기, 도전하기가 구현하기보다 상관관계가 더 높았다.

• 지식경영연구
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• 제17권2호
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• pp.205-229
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• 2016

Owing to the current economic downturn, one of the most important goals of the organizations who are actively involved in Information Technology Outsourcing (ITO) is the cost efficiency. We focus on supplier firm's domain understanding to make the cost efficiency; therefore, we examine how the disadvantages from lower domain knowledges affect outsourcing performance moderated by outsourcing ratio and knowledge change environments. That is, if clients can endure disadvantage from service providers' lower domain knowledge, they can achieve cost efficiency by choosing lower domain knowledge suppliers with less expensive cost. To examine performance gap depending on the environments, we applied 'A Learning Model of IT Outsourcing' which is suggested by previous literature. As a result, we suggest five strategies for clients to contract with suppliers which have lower domain knowledge: (1) Prepare the strategy to endure disadvantages from the early stage. (2) Make the strategy depending on outsourcing ratio. (3) Knowledge transfer between organizations is important. (4) Make a short-term contract if they do not have good environments for organizational learning. (5) Client's knowledge change environments are more important than those of supplier's. Finally, we offer various implications for clients and suppliers in IT outsourcing.

• 한국인터넷방송통신학회논문지
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• 제20권2호
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• pp.283-287
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• 2020

본 논문에서는 스마트폰 앱을 통하여 환풍기의 현재 상태 모니터링, on/off 기능 등 IoT를 지원하는 제어 시스템을 구현하였다. 기계학습(Machine Learning) 알고리즘 종류 중 하나인 지도학습에 포함되는 선형회귀(Linear Regression)를 적용하여 자율적으로 가정의 실내 온도, 습도의 데이터를 수집하여 상태를 진단하고 운전하면서 에너지를 최대한 효율적으로 사용하면서 사용자의 요구를 충족하도록 하였다. 구현한 시스템에서는 수동제어보다 같은 습도가 되는 데 필요한 환풍기의 작동 시간이 더 적다는 것으로 더 좋은 에너지 효율을 확인할 수 있었다. 이로 인해 사용자들은 기존의 환풍기보다 더욱 편리하고 효율적으로 사용할 수 있을 것으로 기대된다.

• ETRI Journal
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• 제45권6호
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• pp.963-973
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• 2023

Non-orthogonal multiple access (NOMA) is considered a key candidate technology for next-generation wireless communication systems due to its high spectral efficiency and massive connectivity. Incorporating the concepts of multiple-input-multiple-output (MIMO) into NOMA can further improve the system efficiency, but the hardware complexity increases. This study develops an energy-efficient (EE) subchannel assignment framework for MIMO-NOMA systems under the quality-of-service and interference constraints. This framework handles an energy-efficient co-training-based semi-supervised learning (EE-CSL) algorithm, which utilizes a small portion of existing labeled data generated by numerical iterative algorithms for training. To improve the learning performance of the proposed EE-CSL, initial assignment is performed by a many-to-one matching (MOM) algorithm. The MOM algorithm helps achieve a low complex solution. Simulation results illustrate that a lower computational complexity of the EE-CSL algorithm helps significantly minimize the energy consumption in a network. Furthermore, the sum rate of NOMA outperforms conventional orthogonal multiple access.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.224-230
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• 2022

The intent of this paper is to unveil the effectiveness of different learning environments (traditional, electronic, blended) in educational institutions through a set of dimensions: an introduction to traditional education and e-learning, the importance and objectives of e-learning, the difference between e-learning and traditional education and teachers' roles in e-learning, the challenges facing the use of e-learning. It also introduces blended learning, providing an account about its emergence, concept, importance, the difference between blended learning and e-learning, the advantages of blended learning, and the challenges confront using blended learning.

• 문화기술의 융합
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• 제7권1호
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• pp.309-316
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• 2021

본 연구의 목적은 지역사회경험학습(CBL)이 전문대학생의 진로결정 자기효능감에 미치는 효과를 검증하고 의미를 탐색하는 것이다. 본 연구는 부산광역시 D대학교 교수학습개발센터가 지원한 CBL에 참여한 10개 학과, 68명의 학생을 대상으로 진행되었다. 본 연구의 연구문제는 첫째, CBL이 전문대학생의 진로결정 자기효능감에 영향을 미치는가? 둘째, CBL이 전문대학생의 진로결정에 주는 의미는 무엇인가?이다. CBL의 적용 전·후 설문조사를 실시하여 효과성을 살펴본 결과 진로결정 자기효능감은 통계적으로 유의미한 변화를 보였다. CBL이 학습자들의 진로결정에 주는 의미는 '이론의 현장적용을 통해 이해력이 향상되고, 공부하는 계기가 되어 진로에 대한 확신과 다짐이 생김'으로 도출되었다. 이를 통하여 CBL은 전문대학생의 진로지도에 적합한 교수학습법으로 적용 가치가 있다는 것을 알 수 있다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.23-27
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• 2007

Many trials have been used to model room impulse responses, all attempting to provide efficient representations of room acoustics. The traditional model designs for room impulse response seem to fail in accuracy, controllability, or computational efficiency. In time domain, a room impulse response is generally considered as the combination of three parts having different acoustic characteristics, initial time delay, early reflection, and late reverberation. This paper introduces new learning hybrid model for the room impulse response. In this proposed model, those three parts are modeled using different models with learning algorithms that determine the length or boundary of each model in the hybrid model. By the simulation with measured room impulse responses, it was examined that the performance of proposed model shows the best efficiency in views of both the parameter numbers and modeling error.

• 한국소음진동공학회논문집
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• 제18권3호
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• pp.361-367
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• 2008

Many trials have been used to model room impulse responses, all attempting to provide efficient representations of room acoustics. The traditional model designs for room impulse response seem to fail in accuracy, controllability, or computational efficiency. In the time domain, room impulse responses are generally considered as combination of the three Parts having different acoustic characteristics, initial time delay, early reflection, and late reverberation. This paper introduces new learning hybrid model for room impulse responses. In this proposed model, those three parts are modeled using different models with learning algorithms that determine the boundary of each model in the hybrid model. By the simulation with measured room impulse responses, the performance of proposed model shows the best efficiency in views of computational burden and modeling error.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전력기술부문
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• pp.192-194
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• 2006

In this paper, the situation of energy efficiency standards for motors and diffusion states are analyzed and a new methodology is proposed using diffusion models and learning curves. The existing diffusion models could not explain affects from new appliances' penetration during the diffusion. But a mixed diffusion model with learning curves or learning ratio is studied to explain this penetration.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2009년도 정보통신설비 학술대회
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• pp.382-386
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• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.