• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.175-180
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• 2022

To develop computational thinking skills, university students are learning how to solve problems with algorithms, program commands and grammar, and program writing. Because non-computer majors have difficulty with computer programming-related content, they need a learning method to acquire coding knowledge from the process of understanding, interpreting, changing, and improving source codes by themselves. This study explored clone coding, refactoring coding, and coding methods using reconstruction tools, which are practical and effective learning methods for improving coding skills for students who are accustomed to coding. A coding learning process with the code reconstruction principle was designed to help non-computer majors use it to understand coding technology and develop their problem-solving ability and applied the coding technology learning method used in programmer education.

• International Journal of Internet, Broadcasting and Communication
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• 제14권3호
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• pp.149-154
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• 2022

In recent years, the application of deep learning method to computer vision has shown to achieve great performances. Thus, many research projects have also applied deep learning technology to railroad defect detection. In this paper, we have reviewed the researches that applied computer vision based deep learning method to railroad defect detection and inspection, and have discussed the current trend and the direction of those researches. Many research projects were targeted to operate automatically without visual inspection of human and to work in real-time. Therefore, methods to speed up the computation were also investigated. The reduction of the number of learning parameters was considered important to improve computation efficiency. In addition to computation speed issue, the problem of annotation was also discussed in some research projects. To alleviate the problem of time consuming annotation, some kinds of automatic segmentation of the railroad defect or self-supervised methods have been suggested.

• 전기학회논문지
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• 제65권9호
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• pp.1541-1550
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• 2016

In this paper, we introduce a novel learning methodology of fuzzy clustering-based neural network pattern classifier. Fuzzy clustering-based neural network pattern classifier depicts the patterns of given classes using fuzzy rules and categorizes the patterns on unseen data through fuzzy rules. Least squares estimator(LSE) or weighted least squares estimator(WLSE) is typically used in order to estimate the coefficients of polynomial function, but this study proposes a novel coefficient estimate method which includes advantages of the existing methods. The premise part of fuzzy rule depicts input space as "If" clause of fuzzy rule through fuzzy c-means(FCM) clustering, while the consequent part of fuzzy rule denotes output space through polynomial function such as linear, quadratic and their coefficients are estimated by the proposed local least squares estimator(LLSE)-based learning. In order to evaluate the performance of the proposed pattern classifier, the variety of machine learning data sets are exploited in experiments and through the comparative analysis of performance, it provides that the proposed LLSE-based learning method is preferable when compared with the other learning methods conventionally used in previous literature.

• Nuclear Engineering and Technology
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• 제53권12호
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• pp.4072-4079
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• 2021

This article presents a study on the state-of-the-art methods for automated radioactive material detection and identification, using gamma-ray spectra and modern machine learning methods. The recent developments inspired this in deep learning algorithms, and the proposed method provided better performance than the current state-of-the-art models. Machine learning models such as: fully connected, recurrent, convolutional, and gradient boosted decision trees, are applied under a wide variety of testing conditions, and their advantage and disadvantage are discussed. Furthermore, a hybrid model is developed by combining the fully-connected and convolutional neural network, which shows the best performance among the different machine learning models. These improvements are represented by the model's test performance metric (i.e., F1 score) of 93.33% with an improvement of 2%-12% than the state-of-the-art model at various conditions. The experimental results show that fusion of classical neural networks and modern deep learning architecture is a suitable choice for interpreting gamma spectra data where real-time and remote detection is necessary.

• 한국정보통신학회논문지
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• 제16권1호
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• pp.167-175
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• 2012

본 논문은 컴퓨터과학 교육에서 협동학습과 개별학습이 학습자에게 어떠한 효과가 있는 지에 대해 연구하였다. 협동학습은 우리나라 IT 기업들에서는 프로젝트 방식의 업무 처리 비중이 높아지고 있기 때문에 컴퓨터 과학 분야의 학습자들에게 협동학습의 훈련은 매우 중요시되고 있다. 본 연구는 개별학습과 협동학습에 대해 학습자들이 어떠한 견해를 가지고 있는지를 연구하고 학습 유형 별 실험을 통해 어떠한 학습의 차이를 가져오는지 설문과 실험을 통해 분석하였다. 이 연구는 서울 소재의 한 대학교에 컴퓨터 관련학과 전공과목을 수강하는 학생들을 학습 방법에 기초해서 두 집단으로 나누어 실험하였고 그 결과를 비교 분석하여 효과 및 문제점을 논의한다.

• 간호행정학회지
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• 제18권4호
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• pp.442-451
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• 2012

Purpose: The purpose of this study was to identify effects of action learning approaches on learning outcomes of students taking nursing management courses. Methods: The questionnaire surveys were completed between March 2011 and June 2012 by 109 undergraduate seniors in the nursing department of C University. Survey data were obtained 3 times: before, in and after the study of nursing management. The course consisted of lectures and clinical practices. Learning outcomes were measured through problem solving skills, team efficacy, and class satisfaction. Collected data were analyzed using repeated measures ANOVA with the SPSS 20.0 program Results: Scores for problem solving skills (F=13.67, p<.001) and team efficacy (F=4.49, p=.012) showed statistically significant increases after the course. The scores also increased significantly after the lectures for 5 of 9 problem solving skill subscales: analysis skill, divergent thinking, decision making, assessment, feedback, and after the clinical practices for 2 subscales: divergent thinking, and execution and risk taking. Class satisfaction score also increased after both the lectures and the clinical practices. Conclusion: The findings from this study suggest that an action learning approaches for nursing management courses would be a useful teaching and learning method to achieve learning outcomes.

• 산업기술연구
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• 제33권A호
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• pp.31-39
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• 2013

There are two methods to make a distinction of deterioration of high-speed railway track. One is that an administrator checks for each attribute value of track induction data represented in graph and determines whether maintenance is needed or not. The other is that an administrator checks for monthly trend of attribute value of the corresponding section and determines whether maintenance is needed or not. But these methods have a weak point that it takes longer times to make decisions as the amount of track induction data increases. As a field of artificial intelligence, the method that a computer makes a distinction of deterioration of high-speed railway track automatically is based on machine learning. Types of machine learning algorism are classified into four type: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. This research uses supervised learning that analogizes a separating function form training data. The method suggested in this research uses SVM classifier which is a main type of supervised learning and shows higher efficiency binary classification problem. and it grasps the difference between two groups of data and makes a distinction of deterioration of high-speed railway track.

• Journal of Information Technology Applications and Management
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• 제24권4호
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• pp.57-69
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• 2017

Due to the development of technology even in learning and education area, many studies have begun to make a new attempts to research by using SNS, breaking away from traditional learning methods. However, the limitations of these studies are restricted only to the use of wireless Internet and writing on Web sites. This study aims to conduct a research on English learning activities that utilize various technologies such as Bigdata, Facebook, Social Network Services (SNS) and English applications. In addition, this study looks into how these modern technologies can be integrated in the classrooms and which activities can be applied in the English classroom. This research is to suggest effective English learning methods through a thorough investigation on the effectivity of various technologies based on the Web 2.0 such as Flickr, blogs, MySpace, and online discussion board within the context of the English learning. To verify the effect of the study, the subjects are divided into experimental and control group. The experiment is proceeded with pre- and post-test. The experimental group is designed to verify the effects using SNS tools such as Facebook, Bigdata, and Online Massive Learning. A survey is conducted to determine the preference of utilizing social networking sites and to analyze the effects in class. The result is that the average scores for experimental group have improved more than the average of control group. The comparison of pre and post-test of the experimental group shows that the significance of the higher and median group was statistically significant at the p<0.01.

• Smart Structures and Systems
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• 제24권6호
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Journal of Korea Artificial Intelligence Association
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• 제1권1호
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.