• Journal of the Korean Society of Industry Convergence
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• v.25 no.4_2
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• pp.549-563
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• 2022

Continuous global warming is causing ecosystem destruction and direct damage to human life. The main cause of global warming is greenhouse gases, which account for more than 90 % of carbon dioxide. The leaders of each country signed the Paris Agreement at the United Nations Convention on Climate Change (UNFCCC) to reduce greenhouse gas emissions. Currently, the total amount of CO₂ emitted from South Korea is 664.7 million tons as of 2018, ranking eighth in the world. 37 % of South Korea's total CO₂ emissions come from the construction & building field, especially the cement production, which is a construction material. Carbon reduction technologies can be largely divided into four types: carbon reduction (CC), carbon reduction and storage technology (CCS), carbon reduction and utilization technology (CCU), and carbon reduction, storage and utilization technology (CCUS). Overseas, CCUS technology is mainly applied to reduce and store CO₂ emitted from construction and construction field. A technology for permanently storing CO₂ through mineralization by capturing CO₂ and utilizing CO₂ into a cement production process was developed, and this technology is applied to the entire cement industry. However, the development of CCUS technology applicable to the cement industry is still insignificant in South Korea. In this study, carbon dioxide reduction technology and methods for reducing carbon dioxide emitted during the cement manufacturing process, which is the main component of concrete mainly used in civil engineering construction, were investigated. Overseas, it has reached the commercialization stage beyond the demonstration stage as a way to reduce carbon dioxide by vomiting carbonation reactions. Accordingly, if carbon dioxide reduction plan technology generated during cement manufacturing is developed based on domestic technology differentiated from foreign technology, it is expected to contribute one more step to the carbon neutrality policy.

• Journal of Engineering Education Research
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• v.26 no.4
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• pp.22-35
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• 2023

As information and communication technology has developed remarkably, it has become possible to analyze various types of large-volume data generated at a speed close to real time, and based on this, reliable value creation has become possible. Such big data analysis is becoming an important means of supporting decision-making based on scientific figures. The purpose of this study is to develop a big data analysis tool that can analyze large amounts of data generated through engineering education. The tasks of this study are as follows. First, a database is designed to store the information of entries in the National Creative Capstone Design Contest. Second, the pre-processing process is checked for analysis with big data analysis tools. Finally, analyze the data using the developed big data analysis tool. In this study, 1,784 works submitted to the National Creative Comprehensive Design Contest from 2014 to 2019 were analyzed. As a result of selecting the top 10 words through topic analysis, 'robot' ranked first from 2014 to 2019, and energy, drones, ultrasound, solar energy, and IoT appeared with high frequency. This result seems to reflect the current core topics and technology trends of the 4th Industrial Revolution. In addition, it seems that due to the nature of the Capstone Design Contest, students majoring in electrical/electronic, computer/information and communication engineering, mechanical engineering, and chemical/new materials engineering who can submit complete products for problem solving were selected. The significance of this study is that the results of this study can be used in the field of engineering education as basic data for the development of educational contents and teaching methods that reflect industry and technology trends. Furthermore, it is expected that the results of big data analysis related to engineering education can be used as a means of preparing preemptive countermeasures in establishing education policies that reflect social changes.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.13-22
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• 2023

Recently, global luxury fashion brands such as Gucci, Saint Laurent, and Louis Vuitton have been showing special interest in Korean contemporary artists and actively collaborating with them. This can be said to be a new change that is distinguished from previous collaborations of fashion brands. Therefore, this study investigated the cases of collaboration between global luxury fashion brands and Korean contemporary artists, and analyzed the types and characteristics to examine the trends and characteristics and draw meaning. As a result, fashion brands combine the characteristics of Korean art works of various genres with the brand concept and pursued values to create new sensibility and high value-added products. It can be seen that the intention was to prepare a place to draw out. In addition, it was found that collaboration was also utilized in terms of marketing so that domestic customers could visit the brand store with a sense of familiarity by utilizing Korea's artistic and cultural sentiments. Based on the results of this study, we intend to provide basic data for the development of creative contents for various collaborations between fashion and art that can help develop the modern fashion industry.

• Asia Marketing Journal
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• v.7 no.1
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• pp.91-112
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• 2005

LG Household & Healthcare has achieved its goals of revenue growth and customer loyalty increase by applying new CRM strategy of OHUI brand, which is ranked in the middle of department store cosmetic channel. OHUI has set up detailed CRM strategy in order to solve AS-Is issues found by systematical review. First OHUI has gained critical mass and increased customer loyalty by developing customized loyalty program. OHUI also simplified customer types in order for employees to identify the customer type and apply the incentive program. As a result, the company has maximized the power of execution of its new strategy. Finally, OHUI has stabilized CRM by sharing best practice and implementing KPI. Throughout a series of CRM initiatives, OHUI has marked outstanding revenue growth and market share comparing to its competitors.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.429-434
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• 2023

Kitchen cabinets are essential furniture for storing the kitchen tools, but their high installed location makes it difficult for users to access the upper of the cabinets. Therefore, in this paper, we propose a new type of kitchen cabinet that allows users to easily take out or store items by adding new height adjustment features while maintaining the function of the existing cabinet. For convenience and safety, an appropriate complex link mechanism is designed so that the selected floor, not the entire cabinet, can come down to a desired height with one operation. Moreover, the optimal descent path is set to prevent the floor tilting or interfloor interference during descent, and appropriate link shapes, lengths, and joint types are selected to implement it. FEA analysis is performed to ensure that the stretched complex linkage can support the load of the stored items and the feasibility of the height adjustable kitchen cabinet is verified through fabrication.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.159-165
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• 2024

This study investigates the comprehensive system for collecting intelligent learning activity data tailored to learner-centered personalized education. We compared and analyzed the characteristics of xAPI, Caliper analytics, and cmi5, which are learning activity data collection standards, and established a system that allows not only standardized data but also non-standardized learning activity data to be stored as big data for artificial intelligence learning analysis. As a result, the system was structured into five stages: defining data types, standardizing learning data using xAPI, storing big data, conducting learning analysis (statistical and AI-based), and providing learner-tailored services. The aim was to establish a foundation for analyzing learning data using artificial intelligence technology. In future research, we will divide the entire system into three stages, implement and execute it, and correct and supplement any shortcomings in the design.

• Korean Journal of Heritage: History & Science
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• v.57 no.1
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• pp.6-19
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• 2024

The Korean Archaeological Dictionary, which represents Korean archaeological knowledge information, contains refined and high-quality information written by expert collective intelligence. This is a characteristic that clearly distinguishes it from overseas archaeological data archives, and can be called differentiated infrastructure data. However, it has not played a role as an information service or knowledge information platform reflecting the latest digital technology. As a way to maximize these strengths and compensate for weaknesses, it was proposed to develop and operate a GIS-based knowledge and information platform for Korean archaeology. To realize this, it is necessary to develop a title management system centered on repositories and metadata that can collect and store various information, link open linked data design and related systems, develop a search function that can analyze and visualize data in response to the big data era, and establish a WebGIS-based information service system. This will be a platform to continuously manage, supplement, and update Korean archaeological knowledge information, build a ubiquitous environment where anyone can use information anytime, anywhere, and create various types of business models.

• Journal of Fashion Business
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• v.28 no.2
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• pp.76-91
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• 2024

This study analyzed the colors used in the logos of infants' and children's clothing brands, comparing them based on brand type and the pursued image. A total of 34 brands with the highest recent sales rates were selected. Among these, 17 were large-sized enterprise brands found in department stores, and the other 17 were in-house production enterprise brands. All colors used in the logos of the selected brands were extracted from the brand logo images. In addition, the study classified the pursued images of the selected brands using emotional adjectives in order to assess variations in different color attributes of the logos according to the brand type and pursued image. The study found that in-house production enterprise brands used lighter and more varied colors compared to department store brands. For both brand types, reddish-to-yellowish hues were most commonly used. Brands projecting a 'cute' and 'gorgeous' image exhibited a greater array of colors in their logos, while brands with a 'neat', 'modern', and 'luxurious' image used less saturated colors. In terms of logo type, symbol logos used the most colors with a moderate level of lightness, while typographic logos used the fewest colors with a low level of lightness.

• Journal of Nutrition and Health
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• v.39 no.4
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• pp.417-425
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• 2006

The purpose of this study were to a) investigate operation of human resource in contract foodservice management company (CFMC), b) identify the staffing indices for the establishment an labor productivity for CFMC, and c) develop standardized model of staffing demand as foodservice's scale in CFMC. The data was collected using FS intra-net system from 138 contract-managed foodservice operations in A CFMC and statistical analysis was completed using the SAS/win package (ver. 8.0) for description analysis, ANOVA, Duncan multiple comparison, pearson correlation analysis, and regression analysis. The types of operation were included factory (45%), small scale operation (26%), office (11%), department store (10%), training institute (4%), and hospital (3%). The distribution of foodservice scale was classified by meal served was as follows; 'less than 500 meals (47%)', 'from 500 to 1500 meals (25%)', 'from 1500 to 2500 meals (17%)', and 'more than 2500 meals (12%)'. There was two types of contract method, fee-contract (53%) and profit-and-loss contract (46%) Some variables were significantly high operation indices such as selling price, food cost, monthly sales, net profit and others were significantly low operation indices such as labor, meal time a day in the small foodservice on meal scale (p<.001). The more foodservice was large, the more human resource was disposed on dietitian, cook, cooking employee altogether (p<.001). Foodservice in A CFMC was divided into 2 groups by 500 meals a day, according to comparative analysis of labor productivity as meal scale per working hour, meal scale a day and operation indices as meal per foodservice employee, meal per cooking employee (p<.001). The regression equation model was developed as 'the number of employees=1.82+0.014 ${\times}$ meal served' in the operation of less than 500 meals, 'the number of employees=9.42+0.013 ${\times}$ meal scale a day -0.94 ${\times}$ meal scale per working hour' in the operation over 500 meal scale using labor productivity indices and operation indices. Therefore, CFMC could be enhanced efficiency of human resource arrangement using the standardized model of staffing demand and would be increased effectiveness of profit.

• Journal of KIISE:Databases
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• v.37 no.2
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• pp.92-103
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• 2010

The Web Ontology Language (OWL) has become the W3C recommendation for publishing and sharing ontologies on the Semantic Web. To derive hidden information from OWL data, a number of OWL reasoners have been proposed. Since OWL reasoners are memory-based, they cannot handle large-sized OWL data. To overcome the scalability problem, RDBMS-based systems have been proposed. These systems store OWL data into a database and perform reasoning by incorporating the use of a database. However, they do not consider complete reasoning on all types of properties defined in OWL and the database schemas they use are ineffective for reasoning. In addition, they do not manage updates to the OWL data which can occur frequently in real applications. In this paper, we compare various database schemas used by RDBMS-based systems and propose an improved schema for efficient reasoning. Also, to support reasoning for all the types of properties defined in OWL, we propose a complete and efficient reasoning algorithm. Furthermore, we suggest efficient approaches to managing the updates that may occur on OWL data. Experimental results show that our schema has improved performance in OWL data storage and reasoning, and that our approaches to managing updates to OWL data are more efficient than the existing approaches.