• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.509-515
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• 2023

Despite the continued development of alternative energies, fuel consumption is increasing. In particular, the price of gasoline fluctuates greatly according to fluctuations in international oil prices. Gas stations adjust their gasoline inventory to respond to gasoline price fluctuations. In this study, news datasets is used to analyze the gasoline consumption patterns through fluctuations of the gasoline inventory. First, collecting news datasets with web crawling. Second, summarizing news datasets using KoBART, which summarizes the Korean text datasets. Finally, preprocessing and deriving the fluctuations factors through N-Gram Language Model and TF-IDF. Through this study, it is possible to analyze and predict gasoline consumption patterns.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.591-618
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• 2023

This paper evaluates the performance of various distribution assumptions in regression settings for estimating insurance loss. The gamma distribution is commonly used to handle the asymmetry property of loss distribution. However, recent studies highlight the significance of heavy-tailedness in loss distribution. Through an analysis of real home fire insurance data, we compare the effectiveness of different distribution assumptions in regression methods. Our findings show that the choice of parametric distributional assumption is crucial in determining premiums for various insurance products, including "excess of loss insurance" and "limit insurance". Additionally, we discuss practical considerations for applying our results in home fire insurance.

• Journal of Korean Society of Rural Planning
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• v.29 no.4
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• pp.27-34
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• 2023

Recently, worldwide to respond to climate change and secure sustainability. Korea aimed to increase water use efficiency by implementing integrated management according to the water management unification policy. Therefore, the necessity of establishing and operating governance is expanding to ensure the sustainability of agricultural water. In this study aims to evaluate the importance of agricultural water governance components and provide essential data for the participation of stakeholders in the efficient use of agricultural water in Korea. For this study, a total of 19 respondents to the ANP survey for this study were composed of experts in agricultural water and governance in Korea. As a result, the ranking for the main components was in the order of law, policy, and systems(0.222), core subjects(0.191), information sharing and communication(0.180), budget support(0.178), mutual learning(0.124), and external experts(0.105). The most important components for the operation of agricultural water governance are laws, policies, and systems. Since Korea's agricultural water management is a public management system, national standards are considered the first priority. This study, which is the purpose of the agricultural water governance model, evaluated the importance of the constituent components for participating in demand management with a sense of responsibility. Moreover, if agricultural water governance is expanded nationwide by reflecting agricultural and water resource policies in the future, it is believed that positive effects can be achieved in increasing utilization efficiency and securing sustainability through agricultural water saving.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.15-20
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• 2023

This paper is a study on the development of an artificial intelligence (AI) system algorithm that recommends indirect advertising products suitable for character web dramas. The goal of this study is to increase viewers' content immersion and help them understand the story of the drama more deeply by recommending indirect advertising products that are suitable for writing lines for web dramas. In this study, we analyze dialogue and plot using the natural language processing model GPT, and develop two types of indirect advertising product recommendation systems, including prop type and background type, based on the analysis results. Through this, products that fit the story of the web drama are appropriately placed, allowing indirect advertisements to be exposed naturally, thereby increasing viewer immersion and enhancing the effectiveness of product promotion. There are limitations of artificial intelligence models, such as the difficulty in fully understanding hidden meanings or cultural nuances, and the difficulty in securing sufficient data for learning. However, this study will provide new insights into how AI can contribute to the production of creative works, and will be an important stepping stone to expand the possibilities of using natural language processing models in the creative industry.

• The Mathematical Education
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• v.63 no.2
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• pp.139-163
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• 2024

The purpose of this study is to examine the capabilities of ChatGPT as a tool for supporting students in generating mathematical arguments that can be considered proofs. To examine this, we engaged students enrolled in a mathematics pathways course in evaluating and revising their original arguments using ChatGPT feedback. Students attempted to find and prove a method for the area of a triangle given its side lengths. Instead of directly asking students to prove a formula, we asked them to explore a method to find the area of a triangle given the lengths of its sides and justify why their methods work. Students completed these ChatGPT-embedded proving activities as class homework. To investigate the capabilities of ChatGPT as a proof tutor, we used these student homework responses as data for this study. We analyzed and compared original and revised arguments students constructed with and without ChatGPT assistance. We also analyzed student-written responses about their perspectives on mathematical proof and proving and their thoughts on using ChatGPT as a proof assistant. Our analysis shows that our participants' approaches to constructing, evaluating, and revising their arguments aligned with their perspectives on proof and proving. They saw ChatGPT's evaluations of their arguments as similar to how they usually evaluate arguments of themselves and others. Mostly, they agreed with ChatGPT's suggestions to make their original arguments more proof-like. They, therefore, revised their original arguments following ChatGPT's suggestions, focusing on improving clarity, providing additional justifications, and showing the generality of their arguments. Further investigation is needed to explore how ChatGPT can be effectively used as a tool in teaching and learning mathematical proof and proof-writing.

• Archives of design research
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• v.18 no.2 s.60
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• pp.243-254
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• 2005

Great Britain and the United States and Finland are having an interest in long policy subject about child design education through early design education. And they approaches and practices it systematically. The research about the design learning program instance of advanced nation of primary school's design education for various objective is necessary for use with the fundamental reference data for an elementary design education. And so, This research presented the program instance investigation and analysis result of British primary school's design education. U.K is teaching an primary design education from two subjects of Art & Design and Design and Technology which is a legal subject with national curriculum. The analysis result of design relation unit learning program of two subjects is: Design relation unit learning programs of 'Design and Technology' subject's 20 unit which except 4 food relation unit is largely scientific engineering contents that include utility function contents in part. The reason is as behavior styles based on Design process solve problems scientifically & rationally. Design relation 6 units in subject of Art & Design which except the units which relates with the pure fine arts and architecture in 19 units is aesthetic-symbolic and utility-functional contents largely. And so, the result was analyzed about relation of scientific-engineering content of 'Arts & Design' subject is insufficient comparing with 'Design and Technology' subject Specially, I think that the design relation's unit learning program instances of 'Design and Technology' subject of the British primary school which have been presented by this research paper is a possibility becoming one reference model for a program development. And so I expects that this research could be applied in the program development for the primary design education of primary teacher & education agency.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.603-616
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• 2023

In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, L_eq and L_max values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both L_eq and L_max analysis. Temporal characteristic clustered residential noise source into 9 clusters for L_eq and 11 clusters for L_max. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with L_eq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.641-648
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• 2020

This study was undertaken to provide basic data for the development of a mental health promotion intervention program, by confirming the mediating effects of social support in relation to the effect on the life stress and meaning of life in middle-aged women. The subjects of this study were 201 middle-aged women from D and K cities. The data were analyzed with descriptive statistics and Pearson's correlations using a statistical program for structural equation modeling (SEM); fitness of the final model was RMSEA 0.03, CFI 0.98, and NFI 0.95. The major learning from this study was that life stress has a direct effect on meaning of life and social support. Life stress (β=0.05, p< 0.001), meaning of life (β=0.05, p< 0.001), and social support (β=0.05, p< 0.001) have a significant and direct effect on depression. The findings also suggest that life stress indirectly affects the mediating effect between meaning of life and social support, and also depression of middle-aged women. We believe that results of this study encompass basic data that will aid in developing a program to promote the mental health of middle-aged women.