• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.729-738
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• 2022

Existing wardrobes have been used only for storing simple clothes. Since it has a function to store clothes, there is only one way to control the environment such as humidity or temperature, and there is only one way to purchase and store items such as a desiccant. In this paper, by increasing the convenience in the existing wardrobe, automatic temperature and humidity control and various convenient functions were added. In line with the smart home market and smart phone application market that have grown over the past several years, along with the development of a wardrobe with sensors, the temperature and humidity control function and other functions inside the wardrobe through Bluetooth pairing between the wardrobe and the smartphone can be customized to the user using a smartphone. Through the clothing selection function and the weather data in the application, we want to implement convenient functions such as the function of recommending clothes in the closet to match the weather.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.57-66
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• 2023

Currently, a new energy system is emerging that implements consumption reduction by improving energy efficiency. Accordingly, as smart grids spread, the rate system by timing is expanding. The rate system by timing is a rate system that applies different rates by season/hour to pay according to usage. In this study, external factors such as temperature/day/time/season are considered and the time series prediction model, LSTM, is used to predict energy power usage data. Based on this energy usage prediction model, energy usage charges are reduced by analyzing usage patterns for each device and transferring power energy from the maximum load time to the light load time. In order to analyze the usage pattern for each device, a clustering technique is used to learn and classify the usage pattern of the device by time. In summary, this study predicts usage and usage fees based on the user's power data usage, analyzes usage patterns by device, and provides customized demand transfer services based on analysis, resulting in cost reduction for users.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.179-187
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• 2009

Most real world systems contain a series of dynamic and complex phenomena. One of common methods to understand these systems is to build a model and analyze the behavior of them. A two-step methodology comprised of clustering and then model creation is proposed for the analysis on time series data. An interface is designed for CRM(Customer Relationship Management) that provides user with 1:1 customized information using system modeling. It was confirmed from experiments that better clustering would be derived from model based approach than similarity based one. Clustering is followed by model creation over the clustered groups, by which future direction of time series data movement could be predicted. The effectiveness of the method was validated by checking how similarly predicted values from the models move together with real data such as stock prices.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.193-198
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• 2023

We study a learner-customized lecture recommendation project using deep learning. Recommendation systems can be easily found on the web and apps, and examples using this feature include recommending feature videos by clicking users and advertising items in areas of interest to users on SNS. In this study, the sentence similarity Word2Vec was mainly used to filter twice, and the course was recommended through the Surprise library. With this system, it provides users with the desired classification of course data conveniently and conveniently. Surprise Library is a Python scikit-learn-based library that is conveniently used in recommendation systems. By analyzing the data, the system is implemented at a high speed, and deeper learning is used to implement more precise results through course steps. When a user enters a keyword of interest, similarity between the keyword and the course title is executed, and similarity with the extracted video data and voice text is executed, and the highest ranking video data is recommended through the Surprise Library.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.2
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• pp.93-114
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• 2023

In accordance with the educational paradigm that values self-directed creative education, school libraries and public libraries emphasize self-directed learning support through curriculum-linked programs as their main tasks. For self-directed learning, it is essential to provide learner-centered educational knowledge information, and there should be abundant textbook-linked references that can deepen and expand the curriculum reflected in textbooks. This study established KDC-linked information related to unit and cross-curricular learning topics through the analysis of elementary school textbooks and curriculum-linked books, restructured KDC system based on major subjects in the elementary school curriculum, and established a curriculum-linked subject information. Libraries can strengthen support for self-directed creative learning for elementary school students in an online environment by linking library content targeted for each user with elementary school education content focusing on learning topics in the curriculum.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.229-238
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• 2023

This study is to identify marketing methods that are different from the existing ones on the Metaverse platform, where new business models and industrial ecosystems are being formed. The purpose of this study is to identify how brand marketing methods are differentiated according to the type of metaverse and the characteristics of the main users who use metaverse. The research method was a case analysis method, and Roblox, which currently has the largest number of active users, was selected. We closely analyzed how Nike and Gucci brands market by reflecting the characteristics and consumption patterns of Alpha Generation and Generation Z, the main users of Roblox. The results of the analysis are as follows. First, it is approaching with enjoyable content, including games, rather than direct marketing. Second, the content provided contains the brand's story and philosophy. Third, it takes a method of linking virtual and reality. Lastly, through Metaverse, Brands are identifying the tastes of future potential customers and collecting data for customized services.

• The Journal of Information Systems
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• v.32 no.1
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• pp.109-146
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• 2023

Purpose With Intelligent public service (IPS), it is possible to automate the quality of civil affairs, provide customized services for citizens, and provide timely public services. However, empirical studies on factors for the successful use of IPS are still insufficient. Hence, the purpose of this study is to empirically analyze the factors that affect the success of IPS with classification function. ISSM (Information System Success Model) is considered as the underlying research model, and how the algorithm quality, data quality, and environmental quality of the discrimination system affect the relationship between utilization intentions is analyzed. Design/methodology/approach In this study, a survey was conducted targeting users using IPS. After giving them a preliminary explanation of the intelligent public service centered on the discrimination system, they briefly experienced two types of IPS currently being used in the public sector. Structural model analysis was conducted using Smart-PLS 4.0 with a total of 415 valid samples. Findings First, it was confirmed that algorithm quality and data quality had a significant positive (+) effect on information quality and system quality. Second, it was confirmed that information quality, system quality, and environmental quality had a positive (+) effect on the use of IPS. Thirdly, it was confirmed that the use of IPS had a positive (+) effect on the net profit for the use of IPS. In addition, the moderating effect of the degree of knowledge on AI, the perceived accuracy of discriminative experience and IPS, and the user was analyzed. The results suggest that ISSM and TOE framework can expand the understanding of the success of IPS.

• Journal of Korean Society of Disaster and Security
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• v.16 no.3
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• pp.35-43
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• 2023

This study introduces a study on the adjustment methods of the Life Safety Index. The Life Safety Index is a service provided by the Life Safety Prevention Service System. It comprehensively evaluates individuals' levels of safety in their daily lives, continually monitors their safety status, and presents a comprehensive index to prevent safety accidents in advance. Previous studies have developed the Life Safety Index using evaluation criteria (items) for assessing life safety prevention services, incorporating both the AHP (Analytic Hierarchy Process) and Likert Scale techniques. In this study, we build upon this existing Life Safety Index and explore methods for applying adjustment factors based on individuals' characteristics to enhance its accuracy and customization. We develop adjustment factors using existing national statistics to provide personalized services tailored to individual profiles. Therefore, this paper proposes a method for providing customized services by applying adjustment factors to the Life Safety Index, contributing to the development and application of life safety index adjustment methodologies.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.119-131
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• 2024

In recent years, there has been a significant growth in loan comparson services offered by fintech platforms in South Korea. However, it has been reported that loan comparison platform users tend to have a higher risk of default compared to non-users. This paper investigates the difference in platform-specific credit risk factors using survival analysis models - Kaplan-Meier curves and Accelerated Failure Time (AFT) model. Our findings show that, relative to non-users, users of loan comparison platforms are characterized by elevated default rates, a greater propensity for home ownership, lower credit scores, and shorter loan durations. Furthermore, our AFT models elucidate the variance in default risk among the various loan comparison service platforms, highlighting the imperative for customized strategies that address the unique risk profiles of customers on each platform.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.197-203
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• 2024

Food allergies are common and accidents occur annually. However, many people lack knowledge of the severity of allergies and food ingredients. Allergy-related applications currently on the market have problems such as providing information by relying only on certain certified products, food ingredients, and barcodes. This design plans a customized service application for food allergy patients. In this application, after extracting the text of the image using OCR technology, the food ingredients were read and displayed in large letters. In addition, if the user selects an ingredient that cannot be consumed through filtering technology, the restricted food is quickly and conveniently shown when searching for food ingredients. Finally, when scanning a barcode or searching for a product, food ingredient information is provided through barcode scanning and search engine technology that provides ingredient information of the product. Therefore, the purpose of this paper is to design an app in which users with food allergies can easily check food ingredients and avoid allergic reactions using databases and various information search methods.