• 보건의료산업학회지
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• 제8권1호
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• pp.135-150
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• 2014

The purpose of this study was to identify the effects of nursing simulation-based practice education on learning outcome and teaching experience in nursing students. Pretest-posttest design with nonequivalent control group was utilized to analyze the effects of nursing simulation-based practice education. The subjects were 96 students of a nursing college. All subjects participated in 6 week. The data were analyzed by the SPSS win 17.0 program. The results were as follows; There were statistically significant differences in learning outcome and teaching experience effects of nursing simulation-based practice education in nursing students. In conclusion, we required nursing simulation-based practice education and small group discussion analysis of factors are associated with goal-setting skills and self-presentation skills, goal setting skills. We required nursing simulation-based practice education training to strengthen the ability of self-directed learning program utilizing the repeated study.

• 디지털산업정보학회논문지
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• 제7권2호
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• pp.1-8
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• 2011

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The finite failure non-homogeneous Poisson process models presented and the life distribution applied extreme distribution which used to find the minimum (or the maximum) of a number of samples of various distributions. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than automatic error that is generally efficient model could be confirmed. This paper, a numerical example of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error.

• 디지털산업정보학회논문지
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• 제13권2호
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• pp.1-11
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• 2017

In this paper, we design and implement PADIL(Prediction And Detection of Information Leakage) system that predicts and detect information leakage behavior of insider by analyzing network traffic and applying a variety of machine learning methods. we defined the five-level information leakage model(Reconnaissance, Scanning, Access and Escalation, Exfiltration, Obfuscation) by referring to the cyber kill-chain model. In order to perform the machine learning for detecting information leakage, PADIL system extracts various features by analyzing the network traffic and extracts the behavioral features by comparing it with the personal profile information and extracts information leakage level features. We tested various machine learning methods and as a result, the DecisionTree algorithm showed excellent performance in information leakage detection and we showed that performance can be further improved by fine feature selection.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2006년도 춘계학술대회
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• pp.182-187
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• 2006

The learning, which is an extended concept from the concept of continuos improvement and replace it, has become a very important core concept in Malcom Baldridge National Quality Award these days. The most potent core value among 11 core values is 'organizational and personal learning' Embedded learning in the organization is also a critical part to get a high score in approach scoring criteria for 6 categories except results category. But the concept of learning is often overlooked for the people who have interests in MB award or TQM. This paper review and analyze the concept of learning which has appeared In MB criteria since it's first appearance.

• 품질경영학회지
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• 제35권1호
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• pp.113-123
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• 2007

This paper considers the development of e-learning training program for the six sigma green belt. Comparative studies of existing e-learning programs are performed and a new one is proposed. A catapult simulator is developed and the automatic grading function which immediately computes the result of the catapult simulation and gives feedback to the trainees is presented. An illustrative example is also given.

• 디지털산업정보학회논문지
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• 제17권4호
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• pp.85-94
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• 2021

The LPR system's trigger sensor makes problem occasionally due to the heave weight of vehicle or the obsolescence equipment. If we replace the hardware sensor to the deep-learning based software sensor in order to generate the trigger signal, LPR system maintenance would be a lot easier. In this paper we proposed the deep-learning sliding window based object detection and tracking algorithm for the LPR system's trigger signal generation. The gate passing vehicle's license plate recognition results are combined into the normal tracking algorithm to catch the position of the vehicle on the trigger line. The experimental results show that the deep learning sliding window based trigger signal generating performance was 100% for the gate passing vehicles including the 5.5% trigger signal position errors due to the minimum bounding box location errors in the vehicle detection process.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2020년도 가을 학술논문 발표대회
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• pp.121-122
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• 2020

Universities that offer architectural engineering programs in Korea are making efforts to introduce innovation teaching methods to cultivate teamwork, creativity, flexibility of thought and practical skills needed for the Fourth Industrial Revolution. However, there is a lack of specific measures to support them. In this regard, this study investigated a method of application development for building construction courses applied with the innovation teaching methods. It mainly focused on 'improvement directions for existing learning management systems' and 'online learning support plans using the innovation teaching method' as research contents. It is expected that these improvement directions can be applied to the field of education through the development of mobile and web-based applications. In the follow-up research, the development of specific software for field application will be carried out.

• Educational Technology International
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• 제6권2호
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• pp.101-111
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• 2005

This study was to examine effects of On-Line Community Assisted Small Group Peer Tutoring(OCSPT) on university students' learning strategies. To achieve the purpose, twenty-eight university students were randomly selected. Fourteen students participated in OCSPT and they were divided into small groups consisted of 2 to 5. Students in experimental group participated in OCSPT for total thirty-four hours during sixteen weeks. There is no treatment for the other fourteen students in control group. To measure students' learning strategies, Motivated Strategies for Learning Questionnaire (MSLQ) shorts has been used. The result revealed that students in experimental group showed higher possession than control group in resource-management strategy(p<.05). However, there were no significant difference between both groups in cognitive and motivative strategies.

• 대한산업공학회지
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• 제32권1호
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• pp.61-73
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• 2006

This paper deals with traveling salesman problem(TSP) with the stochastic travel time. Practically, the travel time between demand points changes according to day and time zone because of traffic interference and jam. Since the almost pervious studies focus on TSP with the deterministic travel time, it is difficult to apply those results to logistics problem directly. But many logistics problems are strongly related with stochastic situation such as stochastic travel time. We need to develop the efficient solution method for the TSP with stochastic travel time. From the previous researches, we know that Q-learning technique gives us to deal with stochastic environment and neural network also enables us to calculate the Q-value of Q-learning algorithm. In this paper, we suggest an algorithm for TSP with the stochastic travel time integrating Q-learning and neural network. And we evaluate the validity of the algorithm through computational experiments. From the simulation results, we conclude that a new route obtained from the suggested algorithm gives relatively more reliable travel time in the logistics situation with stochastic travel time.

• 정보기술응용연구
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• 제1권
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.