• International Journal of Advanced Culture Technology
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• 제9권4호
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• pp.9-17
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• 2021

This is a descriptive investigative study which attempts to confirm the mediating effect of learning motivation in the relationship between self-directed learning, learning commitment, and learning persistence intention of university students in an online learning environment. The questionnaires were randomly distributed online and the agreed questionnaires were retrieved, with a total of 338 copies used for analysis. The following is the summary of the findings. First, there were significant differences in learning persistence intention according to general characteristics depending on age, major, part-time job, and academic level. Second, the results showed a positive correlation between self-directed learning, learning commitment, learning motivation, and learning persistence intentions of the subjects were statistically significant. Third, after checking the mediating effect of learning motivation in relation to self-directed learning, learning commitment and learning motivation, the learning motivation has a partial mediating effect on learning and 23% explanatory power, and the learning commitment was found to have a complete mediating effect on the impact of learning motivation on learning intentions with 21% explanatory power. Based on these results, it is necessary to provide a more diverse educational environment, such as operating a motivation semester program that can improve learning motivations along with learning commitment, and the use of a variety of contents that can focus the learner's interest or attention.

• Current Photovoltaic Research
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• 제10권3호
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• pp.77-83
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• 2022

Renewable energy is receiving attention again as a way to realize carbon neutrality to overcome the climate change crisis. Among renewable energy sources, the installation of Photovoltaic is continuously increasing, and as of 2020, the global cumulative installation amount is about 590 GW and the domestic cumulative installation amount is about 17 GW. Accordingly, O&M technology that can analyze the power generation and fault diagnose about PV plants the is required. In this paper, a study was conducted to diagnose fault using I-V curves of PV strings and deep learning. In order to collect the fault I-V curves for learning in the deep learning, faults were simulated. It is partial shade and voltage mismatch, and I-V curves were measured on a sunny day. A two-step data pre-processing technique was applied to minimize variations depending on PV string capacity, irradiance, and PV module temperature, and this was used for learning and validation of deep learning. From the results of the study, it was confirmed that the PV fault diagnosis using I-V curves and deep learning is possible.

• 한국컴퓨터정보학회논문지
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• 제25권2호
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• pp.49-58
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• 2020

본 연구는 온라인 다중 객체 추적 환경에서 모든 객체의 상태(예. 위치 및 크기) 및 identifications (IDs)를 추적하는 문제를 다룬다. 프레임들 간 검출 결과들을 연관하여 객체들의 궤도를 점진적으로 완성하는 tracking-by-detection 접근법을 기반으로 온라인 다중 객체 추적 문제를 해결하고자 한다. 정확한 온라인 연관을 수행하기 위해 이산 푸리에 변환과 부분 최소 제곱법(partial least square, PLS) 분석을 기반으로 하는 새로운 온라인 외형 학습 방법을 제안한다. 즉, 먼저 주파수 도메인에서 추적에 용이한 객체 특징량을 추출하기 위해 추적 객체에 대한 이미지를 푸리에 이미지로 변환한다. 나아가 객체간의 주파수 특징을 보다 잘 구별할 수 있도록 PLS기반 부분 공간을 학습한다. 제안된 외형 학습을 최신 신뢰도 기반 연관 기법과 결합하였고, 다중 객체 추적평가 분야에서 국제적으로 공인된 MOT 벤치마크 챌린지 데이터 셋에서 최신 다중 객체 추적 알고리즘과 비교평가를 수행하였다.

• 공학교육연구
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• 제14권3호
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• pp.61-71
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• 2011

Korea has introduced the levy-grand system in the vocational learning finance. The standard unit price system of training cost was utilized in the distribution of training budget and the reimbursement system including total or partial training cost return has been operated in the corporate training after completing the learning course particularly. The standard unit price was calculated in the base of analyzing on supporting budget by the government per training institutions and corporate payment decision to learning institutions. The proposing standard unit price system of training cost was analyzed in the current standard price unit of training cost and then an improvement policy and the implication are derived from it. At the result of this study, the current government supporting level to e-learning and postal distance learning indicates good status.

• Asia pacific journal of information systems
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• 제17권1호
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• pp.33-58
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• 2007

Quiet often, an organization tries to grapple with inconsistent and partial information to generate relevant information to support decision making and action. As such, an organization scans the environment interprets scanned data, executes actions, and learns from feedback of actions, which boils down to computational interpretations and learning in terms of machine learning, statistics, and database. The ExOM proposed in this paper is geared to facilitate such knowledge discovery found in large databases in a most flexible manner. It supports a broad range of learning and classification styles and integrates them with traditional database functions. The learning and classification components of the ExOM are tightly integrated so that learning and classification of objects is less burdensome to ordinary users. A brief sketch of a strategy as to the expressiveness of terminological language is followed by a description of prototype implementation of the learning and classification components of the ExOM.

• International journal of advanced smart convergence
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• 제11권2호
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• pp.142-152
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• 2022

We study confirmed the relationship between the adversity index, entrepreneurial orientation, and organizational learning competency perceived by organizational members as follows. First, the adversity index showed a positive (+) effect on entrepreneurial orientation (hypothesis 1) and organizational learning competency (hypothesis 2). Second, the entrepreneurial orientation was statistically significant in organizational learning competency (hypothesis 3). Third, the partial mediating role of entrepreneurial orientation (Hypothesis 4) was confirmed in the process of the adversity index affecting organizational learning competency. Meanwhile, the main implications of this study are as follows. First, it is the aspect that provides additional theoretical implications in the reality that studies on the adversity index and entrepreneurial orientation that affect organizational learning competency are lacking. Second, it is the aspect that the importance of adversity index and start-up orientation was confirmed in improving organizational learning competency based on securing differentiated competitiveness for the advancement of the organization's sustainability management system. In addition, it is the aspect of drawing practical implications for strategic human resource management and human resource development to systematically improve it.

• 대한물리치료과학회지
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• 제9권4호
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• pp.83-94
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• 2002

Within physical therapy education, there has been increased attention to curricula and course that emphasize problem solving, clinical reasoning, and synthesis of information across traditional discipline-specific boundaries. This article describes the development implementation, and outcomes of a problem-based learning course in Physical therapy. The course was designed to help students to integrate the various elements of a physical therapy curriculum and to enhance their abilities to respond to an ever-changing health care environment. An evaluation of the course by the first 50 students who completed it revealed both strengths and weaknesses. Students responded that the course enhanced their professional behavior, including interpersonal communication skills, team work, and follow-through with professional responsibilities. The learning package was developed by the authors and implemented to a college students during three weeks of the first semester of 2001. Most studies which conducted PBL module development were short period or temporary PBL package application and evaluation rather than a whole semester's. While, this study carried on partial integrated PBL curriculum development and application with recomposing content of the two subjects to one subject Physical therapy which includes four PBL packages. This package was developed from a simple concept to complex and partial integrated PBL curriculum application systematically variable learning methods such as discussion, practice, lecture, video. There are 2 classes, each class has 25 students, in the college. Each class has 5 small groups consisting 5 students. Two tutors proceeded discussion charging each class also, they used multiple methods and materials like tutorials, self-directed learning, lecture, and video. The package is 5 grades and 5 hours per week and the rate of discussion, lecture is 4, 1 respectively. One of the most change is the increase of interaction between students and tutors. Whenever students need information and suggestion, they can visit tutors who provide reading materials and guide for the direction of self learning. Therefore, this study describes the PBL package development process and application during one semester recomposing contents of two subjects to Physical therapy concepts. Besides, it will contribute to active application of existing each subject to tutors who intend to convert as PBL methods. The study has significant meaning to show potentiality of partially integrated PBL application, using systematic PBL package development from two subjects contents. However, when students' need of yearning is over the extent of Introduction of Physical therapy and Rehabilitation medicine, tutors should set learning extent. So, there is limitation to attain completely integrated PBL education within one subject, therefore, it is high lighted to proceed development of integrated curriculum to maximize learning effects of PBL. It is exected that partial integrated PBL package development and application will distribute to prosper excellent physiotherapist in practice.

• 전기학회논문지
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• 제62권4호
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• pp.544-553
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• 2013

In this paper, we propose the pattern classifier of Radial Basis Function Neural Networks(RBFNNs) for diagnosis of 3-phase partial discharge. Conventional methods map the partial discharge/noise data on 3-PARD map, and decide whether the partial discharge occurs or not from 3-phase or neutral point. However, it is decided based on his own subjective knowledge of skilled experter. In order to solve these problems, the mapping of data as well as the classification of phases are considered by using the general 3-PARD map and PA method, and the identification of phases occurring partial discharge/noise discharge is done. In the sequel, the type of partial discharge occurring on arbitrary random phase is classified and identified by fuzzy clustering-based polynomial Radial Basis Function Neural Networks(RBFNN) classifier. And by identifying the learning rate, momentum coefficient, and fuzzification coefficient of FCM fuzzy clustering with the aid of PSO algorithm, the RBFNN classifier is optimized. The virtual simulated data and the experimental data acquired from practical field are used for performance estimation of 3-phase partial discharge pattern classifier.

• 한국응급구조학회지
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• 제25권3호
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• pp.49-61
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• 2021

Purpose: The study investigated the effects of self-directed learning, learning flow, and academic self-efficacy variables on academic achievement. Methods: This is a descriptive correlation study to understand the effects of self-directed learning, learning flow, and academic self-efficacy on academic achievement. Results: There is a significant positive correlation between the participants' self-directed learning, learning flow, academic achievement, and academic self-efficacy. Self-directed learning and learning flow influenced academic achievement, while academic self-efficacy was found to have a partial mediating effect. As indicated above, academic self-efficacy and self-directed learning were significant predictors of academic achievement. Conclusion: The study results can be used as basic data to conduct future studies. Furthermore, results can inform the development of educational programs that enhance self-directed learning, learning flow, and academic self-efficacy to improve students' academic achievement in the department of emergency technology.

• 전기학회논문지
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• 제67권1호
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.