• 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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• 제29권1호
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• pp.29-45
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• 2022

The Fourth Industrial Revolution era requires people to have the ability of integrated thinking, critics, sensitivity, and creativity in an integrated manner. Therefore, teaching methods are expected to become more suitable for the trend. In this belief, current teacher-leading education method should move to students' self motivating one and consist of programs in which students voluntarily involve. In this reason, this study suggests FPBL educational method model that is combines project-based learning with flipped learning by analysing preceding research and digital painting tool class was designed by applying it. As a result of applying the designed class model to the class, all of the class satisfaction, effectiveness, and interaction were evaluated positively. Problems such as limitations of project classes due to non-face-to-face classes, large amount of learning before class, and reduced concentration during class were found. Therefore, when the FPBL class model is conducted non-face-to-face, it will be necessary to further strengthen the role of the instructor, provide lecture videos summarizing the core contents, and improve concentration by providing active participation and fun using various digital tools. The result of the study looks significant by confirming the possibility of applying FPBL model not only in design education but also other educational settings.

• 대한공업교육학회지
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• 제31권2호
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• pp.332-349
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• 2006

이 연구에서는 성격유형 검사 도구와 e-러닝 학습양식 도구를 이용하여 대학생의 계열별, 성별, 인터넷 사용량과 e-러닝 학습양식의 관계를 알아보고, e-러닝 학습양식과 성격유형의 관계를 알아보았다. 연구 결과 다음과 같은 결론을 얻었다. 첫째, 계열별(공과계열, 인문 사회계열, 예 체능계열) 대학생간에 e-러닝 학습양식의 유형은 같다. 대학생들이 주로 사용하는 e-러닝 학습양식은 환경의존적 자기주도 학습형, 적극적 협동 학습형, 독자적 자율 학습형, 소극적 학습형의 순서이다. 둘째, 성별 e-러닝 학습양식은 다르다. 남,녀 모두 환경의존적 자기주도 학습형이 많지만 특히 여학생의 경우 더 높게 나타났으며 남학생은 적극적 협동학습형이 상대적으로 높다. 셋째, 인터넷 사용량에 따른 e-러닝 학습양식의 차이가 없다. 넷째, 성격유형 즉, 외향/내향, 감각/직관, 사고/감정, 판단/인식형에 따른 e-러닝 학습양식의 차이는 없다. 따라서 성격유형은 e-러닝 학습양식에 영향을 미치지 않는다.

• 반도체디스플레이기술학회지
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• 제22권4호
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• pp.136-141
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• 2023

The continuous shrinking of transistors in integrated circuits leads to difficulties in improving performance, resulting in the emerging transistors such as nanosheet field-effect transistors. In this paper, we propose a TCAD-machine learning framework of nanosheet FETs to model the current-voltage characteristics. Sentaurus TCAD simulations of nanosheet FETs are performed to obtain a large amount of device data. A machine learning model of I-V characteristics is trained using the multi-layer perceptron from these TCAD data. The weights and biases obtained from multi-layer perceptron are implemented in a PSPICE netlist to verify the accuracy of I-V and the DC transfer characteristics of a CMOS inverter. It is found that the proposed machine learning model is applicable to the prediction of nanosheet field-effect transistors device and circuit performance.

• 한국경영과학회지
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• 제33권3호
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• pp.93-105
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• 2008

Semi-supervised learning uses a small amount of labeled data to predict labels of unlabeled data as well as to improve clustering performance, whereas unsupervised learning analyzes only unlabeled data for clustering purpose. We propose a new clustering-based semi-supervised learning method by reflecting the initial predicted labels of unlabeled data on the objective function. The initial prediction should be done in terms of a discrete probability distribution through a classification method using labeled data. As a result, clusters are formed and labels of unlabeled data are predicted according to the Information of labeled data in the same cluster. We evaluate and compare the performance of the proposed method in terms of classification errors through numerical experiments with blinded labeled data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 하계학술대회 논문집 A
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• pp.331-333
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• 1994

A learning method for induction motor trajectory using neuro-fuzzy networks (NFN) based on fusion of fuzzy logic theory and neural networks is proposed. The premise and consequent parameters of the NFN affecting the controllers performances are modified during the learning stages by the proposed learning method to implement an optimal controller only with pre-determined target trajectory and the least amount of knowledge about an induction motor. The induction motor position control system is simulated to verify the effectiveness of the learned NF controller(NFC). The simulation results shows that the proposed learning method has good dynamic performance and small steady state error.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제22권4호
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• pp.305-318
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• 2019

To improve students' classroom engagement is not only the demand of curriculum revolution, but also the reflection of learning democracy. Students' responses and thinking are the main manifestations of students' participation in classroom learning. To reduce the amount of questions and increase the opportunities and time for students to think, this study, by employing SPSS, makes attempts to analyze the data by using multivariate GLM analysis to explore the effects of students' responses and thinking on learning results. The results indicated the students learning effect will be promoted through reducing the quantity and increasing the quality of question and adding the thinking opportunities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2942-2960
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• 2022

The core of cognitive radio is the problem concerning intelligent decision-making for communication parameters, the objective of which is to find the most appropriate parameter configuration to optimize transmission performance. The current algorithms have the disadvantages of high dependence on prior knowledge, large amount of calculation, and high complexity. We propose a new decision-making model by making full use of the interactivity of reinforcement learning (RL) and applying the Q-learning algorithm. By simplifying the decision-making process, we avoid large-scale RL, reduce complexity and improve timeliness. The proposed model is able to find the optimal waveform parameter configuration for the communication system in complex channels without prior knowledge. Moreover, this model is more flexible than previous decision-making models. The simulation results demonstrate the effectiveness of our model. The model not only exhibits better decision-making performance in the AWGN channels than the traditional method, but also make reasonable decisions in the fading channels.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2016년도 제54차 하계학술대회논문집 24권2호
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• pp.51-54
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• 2016

In computer vision especially in image processing, it has become popular to apply deep convolutional networks for supervised learning. Convolutional networks have shown a state of the art results in classification, object recognition, detection as well as semantic segmentation. However, supervised learning has two major disadvantages. One is it requires huge amount of labeled data to get high accuracy, the second one is to train so much data takes quite a bit long time. On the other hand, unsupervised learning can handle these problems more cheaper way. In this paper we show efficient way to learn features for classification in an unsupervised way. The network trained layer-wise, used backpropagation and our network learns features from unlabeled data. Our approach shows better results on Caltech-256 and STL-10 dataset.

• 산업경영시스템학회지
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• 제29권2호
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• pp.13-19
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• 2006

The instance-based learning is a machine learning technique that has proven to be successful over a wide range of classification problems. Despite its high classification accuracy, however, it has a relatively high storage requirement and because it must search through all instances to classify unseen cases, it is slow to perform classification. In this paper, we have presented a new data reduction method for instance-based learning that integrates the strength of instance partitioning and attribute selection. Experimental results show that reducing the amount of data for instance-based learning reduces data storage requirements, lowers computational costs, minimizes noise, and can facilitates a more rapid search.