• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.687-700
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• 2019

Purpose: The purpose of this study is to investigate the interaction effects between price determinants of artworks. We expand the methodology in art market by applying machine learning techniques to estimate the price of artworks and compare linear regression and machine learning in terms of prediction accuracy. Methods: Moderated regression analysis was performed to verify the interaction effects of artistic characteristics on price. The moderating effects were studied by confirming the significance level of the interaction terms of the derived regression equation. In order to derive price estimation model, we use multiple linear regression analysis, which is a parametric statistical technique, and k-nearest neighbor (kNN) regression, which is a nonparametric statistical technique in machine learning methods. Results: Mostly, the influences of the price determinants of art are different according to the auction types and the artist 's reputation. However, the auction type did not control the influence of the genre of the work on the price. As a result of the analysis, the kNN regression was superior to the linear regression analysis based on the prediction accuracy. Conclusion: It provides a theoretical basis for the complexity that exists between pricing determinant factors of artworks. In addition, the nonparametric models and machine learning techniques as well as existing parameter models are implemented to estimate the artworks' price.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.864-875
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• 2018

In this paper, we propose a measurement method for height of white light scanning interferometer using deep learning. In order to measure the fine surface shape, a three-dimensional surface shape measurement technique is required. A typical example is a white light scanning interferometer. In order to calculate the surface shape from the measurement image of the white light scanning interferometer, the height of each pixel must be calculated. In this paper, we propose a neural network for height calculation and use virtual data generation method to train this neural network. The accuracy was measured by inputting 57 actual data to the neural network which had completed the learning. We propose two new functions for accuracy measurement. We have analyzed the cases where there are many errors among the accuracy calculation values, and it is confirmed that there are many errors when there is no interference fringe or outside the learned range. We confirmed that the proposed neural network works correctly in most cases. We expect better results if we improve the way we generate learning data.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1436-1442
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• 2020

Nuclear infrastructure systems play an important role in national security. The functions and missions of nuclear infrastructure systems are vital to government, businesses, society and citizen's lives. It is crucial to design nuclear infrastructure for scalability, reliability and robustness. To do this, we can use machine learning, which is a state of the art technology used in various fields ranging from voice recognition, Internet of Things (IoT) device management and autonomous vehicles. In this paper, we propose to design and develop a machine learning algorithm to perform predictive maintenance of nuclear infrastructure. Support vector machine and logistic regression algorithms will be used to perform the prediction. These machine learning techniques have been used to explore and compare rare events that could occur in nuclear infrastructure. As per our literature review, support vector machines provide better performance metrics. In this paper, we have performed parameter optimization for both algorithms mentioned. Existing research has been done in conditions with a great volume of data, but this paper presents a novel approach to correlate nuclear infrastructure data samples where the density of probability is very low. This paper also identifies the respective motivations and distinguishes between benefits and drawbacks of the selected machine learning algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3966-3991
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• 2018

Due to the lack of visualization services and organic combinations between public and private buildings data, the usability of the basic map has remained low. To address this issue, this paper reports on a solution that organically combines public and private data while providing visualization services to general users. For this purpose, factors that can affect building prices first were examined in order to define the related data attributes. To extract the relevant data attributes, this paper presents a method of acquiring public information data and real estate-related information, as provided by private real estate portal sites. The paper also proposes a pretreatment process required for intelligent machine learning. This report goes on to suggest an intelligent machine learning algorithm that predicts buildings' value pricing and future value by using big data regarding buildings' spatial information, as acquired from a database containing building value attributes. The algorithm's availability was tested by establishing a prototype targeting pilot areas, including Suwon, Anyang, and Gunpo in South Korea. Finally, a prototype visualization solution was developed in order to allow general users to effectively use buildings' value ranking and value pricing, as predicted by intelligent machine learning.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.17-29
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• 2017

IRecently, researches on the recognition of indoor user situations through various sensors in a smart home environment are under way. In this paper, the case study was conducted to determine the operation of the robot vacuum cleaner by inferring the user 's indoor situation through the operation of home appliances, because the indoor situation greatly affects the operation of home appliances. In order to collect learning data for indoor situation awareness model learning, we received feedbacks from user when there was a mistake about the cleaning situation. In this paper, we propose a semi-supervised learning method using user feedback data. When we receive a user feedback, we search for the labels of unlabeled data that most fit the feedbacks collected through genetic algorithm, and use this data to learn the model. In order to verify the performance of the proposed algorithm, we performed a comparison experiments with other learning algorithms in the same environment and confirmed that the performance of the proposed algorithm is better than the other algorithms.

• Research in Mathematical Education
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• v.27 no.3
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• pp.253-265
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• 2024

This editorial explores the transformative potential of technology in advancing equitable teaching and learning in mathematics education. The COVID-19 pandemic has underscored the need for innovative approaches to education, particularly in leveraging technology to create more inclusive and effective learning environments. This special issue focuses on how emerging technologies can deepen students' mathematical proficiencies, shape students' identities, and promote equitable teaching practices. The EqT-tech framework is introduced, highlighting six key dimensions that enhance equitable mathematics education through technology: inquiry-based learning, mathematical identity and agency, formative assessment, collaborative learning, amplification of cognitive processes, and insights into social justice issues. Through a review of seven manuscripts, three recurring themes are identified: the use of technology to develop students' mathematical identity and agency, the role of collaborative platforms in enhancing collective learning, and the expanding nature of emergent technology to increase mathematical rigor as well as awareness for teaching mathematics for social justice exploring inequities within our communities. These studies imply an emphasis on the importance of task design and teacher knowledge in implementing equitable teaching practices, suggesting that technology, when used thoughtfully, can significantly advance equity in mathematics education.

• Journal of the Korean earth science society
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• v.22 no.5
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• pp.360-376
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• 2001

This qualitative research investigated in-service science teachers' perceptions about cooperative learning and their perceived barriers in implementing cooperative learning in their classrooms. The underlying premise for cooperative learning is founded in constructivist epistemology. Cooperative learning (CL) is presented as an alternative frame to the current educational system which emphasizes content memorization and individual student performance through competition. An in-depth interview was conducted with 18 in-service science teachers who enrolled in the first-class teacher certification program during 2001 summer vacation. These secondary school teachers's interview data were analyzed and categorized into three areas: teachers' definition of cooperative learning, issues with implementing cooperative learning in classrooms, and teachers' and students' responses towards cooperative learning. Each of these areas are further subdivided into 10 themes: teachers' perceived meaning of cooperative learning, the importance of talk in learning, when to use cooperative learning, how to end a cooperative class, how to group students for cooperative learning, obstacles to implementing cooperative learning, students' reactions to cooperative learning, teachers' reasons for choosing (not choosing) student-centered approaches to learning/teaching, characteristics of teachers who use cooperative learning methods, and teachers' reasons for resisting cooperative learning. Detailed descriptions of the teachers' responses and discussion on each category are provided. For the development and implementation of CL in more classrooms, there should be changes and supports in the following five areas: (1) teachers have to examine their pedagogical beliefs toward constructivist perspectives, (2) teacher (re)education programs have to provide teachers with cooperative learning opportunities in methods courses, (3) students' understanding of their changed roles (4) supports in light of curriculum materials and instructional resources, (5) supports in terms of facilities and administrators. It's important to remember that cooperative learning is not a panacea for all instructional problems. It's only one way of teaching and learning, useful for specific kinds of teaching goals and especially relevant for classrooms with a wide mix of student academic skills. Suggestions for further research are also provided.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.31-42
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• 2024

Different industries mostly rely on quality certification for promoting their products or brands. Although getting quality certification, specifically by human experts is a tough job to do. But the field of machine learning play a vital role in every aspect of life, if we talk about quality certification, machine learning is having a lot of applications concerning, assigning and assessing quality certifications to different products on a macro level. Like other brands, wine is also having different brands. In order to ensure the quality of wine, machine learning plays an important role. In this research, we use two datasets that are publicly available on the "UC Irvine machine learning repository", for predicting the wine quality. Datasets that we have opted for our experimental research study were comprised of white wine and red wine datasets, there are 1599 records for red wine and 4898 records for white wine datasets. The research study was twofold. First, we have used a technique called backward elimination in order to find out the dependency of the dependent variable on the independent variable and predict the dependent variable, the technique is useful for predicting which independent variable has maximum probability for improving the wine quality. Second, we used a robust machine learning algorithm known as "XGBoost" for efficient prediction of wine quality. We evaluate our model on the basis of error measures, root mean square error, mean absolute error, R2 error and mean square error. We have compared the results generated by "XGBoost" with the other state-of-the-art machine learning techniques, experimental results have showed, "XGBoost" outperform as compared to other state of the art machine learning techniques.

• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.658-671
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• 2015

The present study explores the effect of blended action learning program on the improvement of the self-directed learning skills of university students. For this, students in the college of education at D University located in the capital area were selected to form 30 students of the experimental group and 30 of the control group. The average age of the subjects is 22.3. They are students in the $2^{nd}$ to $4^{th}$ year of university and the demographic background is relatively homogeneous. The experiment was conducted in the Sociology of Education class (a teacher training course). The self-directed learning skills were pre-tested at the beginning of the term. After that, the experimental group had the class with blended action learning program, and the control group had the class with action learning program. The post-test was carried out at the end of the term. For data analysis, ANCOVA was carried out to compare the scores on post-test of the two groups in consideration of the scores on the pre-test. The results show that first, for the improvement of self-directed learning skills of university students, blended action learning program is more effective than action learning program. Second, in the class for learners with lower degree of cognition and experience, blended action learning program was more effective than action learning program. Third, in terms of management of learning resources and utilization of learning strategies, both action learning program and blended action learning program were not effective. In summary, for improvement of the self-directed learning skills of university students, action learning program, which is a learning method focused on learner's participation and practice, is more effective than the traditional collective lecture among diverse teaching methods. Yet in consideration of the elementary level of university students in terms of intelligence and experience, active use of blended action learning program is required.

• Journal of KIISE
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• v.44 no.6
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• pp.607-612
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• 2017

This paper presents an attempt to apply Deep Learning technology to solve the problem of forecasting floods in urban areas. We employ Recurrent Neural Networks (RNNs), which are suitable for analyzing time series data, to learn observed data of river water and to predict the water level. To test the model, we use water observation data of a station in the Trinity river, Texas, the U.S., with data from 2013 to 2015 for training and data in 2016 for testing. Input of the neural networks is a 16-record-length sequence of 15-minute-interval time-series data, and output is the predicted value of the water level at the next 30 minutes and 60 minutes. In the experiment, we compare three Deep Learning models including standard RNN, RNN trained with Back Propagation Through Time (RNN-BPTT), and Long Short-Term Memory (LSTM). The prediction quality of LSTM can obtain Nash Efficiency exceeding 0.98, while the standard RNN and RNN-BPTT also provide very high accuracy.