• International journal of advanced smart convergence
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• v.12 no.4
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• pp.134-141
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• 2023

We present a novel method aimed at refining ground truth data through regularization and modification, particularly applicable when working with the original ground truth set. Enhancing the performance of deep neural networks is achieved by applying regularization techniques to the existing ground truth data. In many machine learning tasks requiring pixel-level segmentation sets, accurately delineating objects is vital. However, it proves challenging for thin and elongated objects such as blood vessels in X-ray coronary angiography, often resulting in inconsistent generation of ground truth data. This method involves an analysis of the quality of training set pairs - comprising images and ground truth data - to automatically regulate and modify the boundaries of ground truth segmentation. Employing the active contour model and a recursive ground truth generation approach results in stable and precisely defined boundary contours. Following the regularization and adjustment of the ground truth set, there is a substantial improvement in the performance of deep neural networks.

• Journal of Engineering Education Research
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• v.26 no.1
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• pp.27-36
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• 2023

The purpose of this study was to investigate high school students' competencies of creativity thinking, critical thinking, communication, collaboration, and digital competence by implementing science fiction STEAM program. Based on the story of 'Fritz Haber' and the 'Garden of Dawn', a STEAM program was developed according to the ADDIE model. In the analysis stage, the purpose of the teaching-learning program using novels was set, and learners and learning environments were analyzed. At the design stage, the novels 'Fritz Harbor' and 'Garden of Dawn' were selected, learning goals were set according to the achievement standards of the curriculum, and learning contents and learning activities were sequenced and designed. In the development stage, teaching and learning materials were developed in a module format, implemented to classes, and evaluated. Pre-test and post-tests were conducted to identify the five major competencies such as creativity thinking, critical thinking, communication, collaboration, digital competence. The collected data was verified by paired t-test using SPSS. The results of the study showed statistically significant results in creative thinking, critical thinking, and digital competency.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1051-1059
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• 2014

In this paper, we propose an intelligent situation recognition model by collecting and analyzing multiple sensor signals. Multiple sensor signals are collected for fixed time window. A training set of collected sensor data for each situation is provided to K2-learning algorithm to generate Bayesian networks representing causal relationship between sensors for the situation. Statistical characteristics of sensor values and topological characteristics of generated graphs are learned for each situation. A neural network is designed to classify the current situation based on the extracted features from collected multiple sensor values. The proposed method is implemented and tested with UCI machine learning repository data.

• Journal of Convergence for Information Technology
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• v.8 no.1
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• pp.201-206
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• 2018

Recently, as technologies for realizing artificial intelligence have become more common, machine learning is widely used. Machine learning provides insight into collecting large amounts of data, batch processing, and taking final action, but the effects of the work are not immediately integrated into the learning process. In this paper proposed an adaptive learning model to improve the performance of real-time stream analysis as a big business issue. Adaptive learning generates the ensemble by adapting to the complexity of the data set, and the algorithm uses the data needed to determine the optimal data point to sample. In an experiment for six standard data sets, the adaptive learning model outperformed the simple machine learning model for classification at the learning time and accuracy. In particular, the support vector machine showed excellent performance at the end of all ensembles. Adaptive learning is expected to be applicable to a wide range of problems that need to be adaptively updated in the inference of changes in various parameters over time.

• Educational Technology International
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• v.18 no.2
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• pp.249-263
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• 2017

The purpose of this study was to examine whether respondents showed gender differences in life- learning competency, self-directed learning ability, problem-solving ability, and academic achievement and to identify relationships among variables of university students in the context of higher education. To address those goal, the data set was analyzed that nationally collected from Korea Youth Competency Measurement and International Comparative Research III by National Youth Policy Institute in South Korea. 680 samples were used in the study that were 343 males and 337 females of university students. As results, statistically significant difference was showed in the participants' gender. Male university students were higher score than female university students in All variables. Also, learning agility in life-learning competency was strongly related to self-directed learning ability and problem-solving. Thinking skills in life-learning competency was strongly related to academic achievement in university students in higher education. In terms of learning strategy in the context of higher education, some suggestions have been made for university students.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.164-166
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• 2022

Long-tail problem은 class 별로 sample의 개수에 차이가 있어 성능에 안 좋은 영향을 미치는 것을 말한다. 본 논문에서는 cost-sensitive learning 중 Class-Balanced Loss를 이용해 성능을 개선하여 Long-tail problem을 해결하려고 한다. 먼저, balanced data set과 imbalanced data set의 성능 차이를 살펴보도록 할 것이다. 그 후, Class-Balanced Loss를 3가지 버전으로 이용해 그 성능을 측정하고 분석해 볼 것이다.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.82-89
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• 2017

The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the GradientDescentOptimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.120-125
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• 2020

Supply chain managers seek to achieve global optimization by solving problems in the supply chain's business process. However, companies in the supply chain hide the adverse information and inform only the beneficial information, so the information is distorted and cannot be the information that describes the entire supply chain. In this case, supply chain managers can directly collect and analyze supply chain activity data to find and manage the companies described by the data. Therefore, this study proposes a method to collect the order-inventory information from each company in the supply chain and detect the companies whose data characteristics are explained through deep learning. The supply chain consists of Manufacturer, Distributor, Wholesaler, Retailer, and training and testing data uses 600 weeks of time series inventory information. The purpose of the experiment is to improve the detection accuracy by adjusting the parameter values of the deep learning network, and the parameters for comparison are set by learning rate (lr = 0.001, 0.01, 0.1) and batch size (bs = 1, 5). Experimental results show that the detection accuracy is improved by adjusting the values of the parameters, but the values of the parameters depend on data and model characteristics.

• Journal of Information Technology Applications and Management
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• v.17 no.1
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• pp.35-54
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• 2010

Why does e-Learning service for individuals remain in the early development stage? To find the answers of this question, we adopted usefulness and intention to continued use as dependent variables based on technology acceptance model and inferred convenience, cost-effectiveness, social presence, interactivity, concentration, and procrastination as independent variables based on literature review and interview with e-Learning users. Convenience and cost-effectiveness of e-Learning tend to enhance usefulness and/or intention to continued use, while lack of social presence, interactivity, and concentration of e-Learning and academic procrastination tend to hinder usefulness and/or intention to continued use. To prove this research model, we used a data set collected from the survey. The respondents of survey were the undergraduate students who used voluntarily e-Learning. Data analysis was conducted using 275 respondents by partial least square. The analysis result of causal relation indicated that convenience and cost-effectiveness influenced both usefulness and intention to continued use, and that cost-effectiveness and concentration influenced only intention to continued use. But, interactivity and procrastination did not influence usefulness and intention to continued use.