• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.471-484
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• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.

• The Mathematical Education
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• v.63 no.2
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• pp.123-138
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• 2024

This study described the utilization of ChatGPT in teaching and students' learning processes for the course "Introductory Mathematics for Artificial Intelligence (Math4AI)" at 'S' University. We developed a customized ChatGPT and presented a learning model in which students supplement their knowledge of the topic at hand by utilizing this model. More specifically, first, students learn the concepts and questions of the course textbook by themselves. Then, for any question they are unsure of, students may submit any questions (keywords or open problem numbers from the textbook) to our own ChatGPT at https://math4ai.solgitmath.com/ to get help. Notably, we optimized ChatGPT and minimized inaccurate information by fully utilizing various types of data related to the subject, such as textbooks, labs, discussion records, and codes at http://matrix.skku.ac.kr/Math4AI-ChatGPT/. In this model, when students have questions while studying the textbook by themselves, they can ask mathematical concepts, keywords, theorems, examples, and problems in natural language through the ChatGPT interface. Our customized ChatGPT then provides the relevant terms, concepts, and sample answers based on previous students' discussions and/or samples of Python or R code that have been used in the discussion. Furthermore, by providing students with real-time, optimized advice based on their level, we can provide personalized education not only for the Math4AI course, but also for any other courses in college math education. The present study, which incorporates our ChatGPT model into the teaching and learning process in the course, shows promising applicability of AI technology to other college math courses (for instance, calculus, linear algebra, discrete mathematics, engineering mathematics, and basic statistics) and in K-12 math education as well as the Lifespan Learning and Continuing Education.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.252-262
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• 2020

In Remote Sensing, a machine learning based SVM model is typically utilized for land cover classification. And study using neural network models is also being carried out continuously. But study using high-resolution imagery of KOMPSAT is insufficient. Therefore, the purpose of this study is to assess the accuracy of land cover classification by neural network models using high-resolution KOMPSAT-3 satellite imagery. After acquiring satellite imagery of coastal areas near Gyeongju City, training data were produced. And land cover was classified with the SVM, ANN and DNN models for the three items of water, vegetation and land. Then, the accuracy of the classification results was quantitatively assessed through error matrix: the result using DNN model showed the best with 92.0% accuracy. It is necessary to supplement the training data through future multi-temporal satellite imagery, and to carry out classifications for various items.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.113-118
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• 2002

Research and Development(R&D) was once considered to be a unique, creative and unstructured process that was difficult, if not impossible, to manage and control. R&D decisions impact the entire enterprise. Therefore, decisions must not be based solely on R&D's perception of what is important or worthwhile. R&D contributions are difficult to measure separately from other functional organizations such as manufacturing and marketing. While some firms are attempting to overcome perceived limitations in traditional accounting-based performance measures using ROI, EVA, others are embracing the use of non-financial measures for decision making and performance evaluation. In particular, many firms are implementing 'Balanced Scorecard(BSC)' systems that supplement traditional accounting measures with non-financial measures focused on at least three other perspectives-customers, internal business processes, and learning and growth. AHP is a popular multi-attribute decision making model that allows for the development of importance rankings. The AHP has been applied in a wide variety of practical settings to model complex decision problems. The former, determine Perspectives and the Key Performance indicator(KPI) through the former research, the latter compose the questionnaire for determine the weight of perspectives and KPIs. And then, make a survey with researchers about 4 perspectives and 18 KPIs. The results will be simulate with Expert Choice 2000 for determine the weights. This results helps establish the firm's business strategy and technology strategy The firm should establish the business strategy to consider market position, business growth potential, and technological capabilities.

• Journal of Elementary Mathematics Education in Korea
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• v.15 no.2
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• pp.301-316
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• 2011

In this study, some discordance between curriculum and textbooks in usage of mathematics terms, dual meaning of some terms in the usage of those terms in textbooks, and inconsistency of defining methods of terms are discussed through some examples. Generally it can not be expected that there are any discordance between curriculum and textbooks, because textbooks are developed in the basis of curriculum. But actually, some discordance between curriculum and textbooks can be found out. Some terms are used with two different meaning, geometric figure and measure. It can be causative of troubles in teaching and learning mathematics. Terms of same kind can be expected to be consistent in the way of defining, but some examples defined inconsistently can be found out. The following four suggestions are offered as conclusions. First, textbooks must be consistent with curriculum. Second, The meaning of terms used in textbooks must be stipulated obviously. Third, terms of same kind must be defined consistently. Fourth, it is necessary to supplement a system for developing elementary mathematics textbooks. The result of this study can help develop new textbooks, and revise curriculum, and develop new curriculum.

• The Mathematical Education
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• v.56 no.2
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• pp.131-145
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• 2017

The representation of irrational numbers has a key role in the learning of irrational numbers. However, transparent and finite representation of irrational numbers does not exist in school mathematics context. Therefore, many students have difficulties in understanding irrational numbers as an 'Object'. For this reason, this research explored how mathematics textbooks affected to students' understanding of irrational numbers in the view of process and object. Specifically we analyzed eight textbooks based on current curriculum and used framework based on previous research. In order to supplement the result derived from textbook analysis, we conducted questionnaires on 42 middle school students. The questions in the questionnaires were related to the representation and calculation of irrational numbers. As a result of this study, we found that mathematics textbooks develop contents in order of process-object, and using 'non repeating decimal', 'numbers cannot be represented as a quotient', 'numbers with the radical sign', 'number line' representation for irrational numbers. Students usually used a representation of non-repeating decimal, although, they used a representation of numbers with the radical sign when they operate irrational numbers. Consequently, we found that mathematics textbooks affect students to understand irrational numbers as a non-repeating irrational numbers, but mathematics textbooks have a limitation to conduce understanding of irrational numbers as an object.

• School Mathematics
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• v.10 no.1
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• pp.1-22
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• 2008

The purpose of this study is to search the effective teaching-learning program by considering how affect on formation of linear function using Excel. This study was based on qualitative case study. The teaching experiment using Excel executed with five 8th graders' students for second research content. Teaching experiment was performed for two classes. Collecting the data was conducted via observations and interviews with students. The data include audio and video recording of the students' work, students' worksheets and detailed field notes. The conclusions drawn from teaching experiment are as follows: First, when students explored relevancy content of function in Excel environment, formation of concept of function was facilitated by experiencing operation of algebraic formulas, tables and graphs. We could infer that formation of concept was effected by conjecture activity and iterative process of feedback through Excel environment. Second, the students explored the changes very interestingly making algebraic formulas and presenting tables and graphs. The students were familiarized with observation on algebraic formulas, graphs and tables concurrently. Also, they tried to look for general rules through inductive observation. According to this study, we noticed that exploration teaming environment using Excel could supplement paper-and-pencil environment.

• Journal of Internet Computing and Services
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• v.7 no.3
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• pp.119-132
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• 2006

The network based security techniques well-known until now have week points to be passive in attacks and susceptible to roundabout attacks so that the misuse detection based intrusion prevention system which enables positive correspondence to the attacks of inline mode are used widely. But because the Misuse detection based Intrusion prevention system is proportional to the detection rules, it causes excessive false alarm and is linked to wrong correspondence which prevents the regular network flow and is insufficient to detect transformed attacks, This study suggests an Intrusion prevention system which uses Support Vector machines(hereinafter referred to as SVM) as one of rule based Intrusion prevention system and Anomaly System in order to supplement these problems, When this compared with existing intrusion prevention system, show performance result that improve about 20% and could through intrusion prevention system that propose false positive minimize and know that can detect effectively about new variant attack.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1960-1969
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• 2017

There have been ongoing researches to identify and analyze the patterns of electric power IoT data inside sensor nodes to supplement the stable supply of power and the efficiency of energy consumption. This study set out to propose an analysis process for electric power IoT data with the K-means algorithm, which is an unsupervised learning technique rather than a supervised one. There are a couple of problems with the old K-means algorithm, and one of them is the selection of cluster number K in a heuristic or random method. That approach is proper for the age of standardized data. The investigator proposed an analysis process of selecting an automated cluster number K through principal component analysis and the space division of normal distribution and incorporated it into electric power IoT data. The performance evaluation results show that it recorded a higher level of performance than the old algorithm in the cluster classification and analysis of pitches and rolls included in the communication bodies of utility poles.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.447-454
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• 2001

The automatic document classification is a method that assigns unlabeled documents to the existing classes. The automatic document classification can be applied to a classification of news group articles, a classification of web documents, showing more precise results of Information Retrieval using a learning of users. In this paper, we use the weighted Bayesian classifier that weights with keywords of a document to improve the classification accuracy. If the system cant classify a document properly because of the lack of the number of words as the feature of a document, it uses relevance word cluster to supplement the feature of a document. The clusters are made by the automatic word clustering from the corpus. As the result, the proposed system outperformed existing classification system in the classification accuracy on Korean documents.