• The Journal of Bigdata
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• v.8 no.1
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• pp.131-156
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• 2023

The traditional retail industry significantly changed over the past decade due to the mobile and online technologies. This change has been accompanied by a shift in consumer behavior regarding purchasing patterns. Despite the rise of online shopping, there are still specific categories of products, such as "Processed food" in Mongolia, for which traditional shopping remains the preferred purchase method. To prepare for the inevitable future of retail businesses, firms need to closely analyze the performance of their offline stores to plan their further actions in a new multi-channel environment. Retailers must integrate diverse channels into their operations to stay relevant and adjust to the shifting market. In this research, we have analyzed the performance data such as sales, profit, and amount of sales of offline stores by using clustering approach. From the clustering, we have found the several distinct insights by comparing the circumstances and performance of retail stores. For the certain retail stores, we have proposed three different strategies: a fulfillment hub store between online and offline channels, an experience store to elongate customers' time on the premises, and a merge between two non-related channels that could complement each other to increase traffic based on the store characteristics. With the proposed strategies, it may enhance the user experience and profit at the same time.

• Particle and aerosol research
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• v.19 no.4
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• pp.111-128
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• 2023

Chungcheongnam-do has various emission sources, including large-scale facilities such as power plants, steel and petrochemical industry complexes, which can lead to the severe PM pollution. Here, we measured concentrations of PM₁₀, PM_2.5, and its metallic elements at a suburban site in Taean, Chungcheongnam-do from September 2017 to June 2022. During the measurement period, the average concentrations of PM₁₀ and PM_2.5 were 58.6 ㎍/m³ (9.6~379.0 ㎍/m³) and 35.0 ㎍/m³ (6.1~132.2 ㎍/m³), respectively. The concentration of PM₁₀ and PM_2.5 showed typical seasonal variation, with higher concentration in winter and lower concentration in summer. When high concentrations of PM_2.5 occurred, particulary in winter, the fraction of Zn and Pb components considerably increased, indicating a significant contribution of Zn and Pb to high-PM_2.5 concentration. In addition, Zn and Pb exhibited the highest correlation coefficient among all other metallic elements of PM_2.5. A backward trajectory cluster analysis and CPF model were performed to examine the origin of PM_2.5. The high concentration of PM_2.5 was primarily influenced by emissions from industrial complexes located in the northeast and northwest areas.

• Korean Journal of Environmental Biology
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• v.41 no.4
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• pp.720-734
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• 2023

To understand the spatiotemporal distribution pattern of zooplankton and the environmental factors influencing zooplankton abundance in Gomso Bay, major harvesting area of Manila clam (Venerupis philippinarum) in South Korea, zooplankton sampling was conducted four times in autumn (October 2022), winter (January 2023), early spring (March 2023), and spring (May 2023). Among the environmental factors of Gomso Bay, water temperature, chlorophyll a concentration (Chl-a), dissolved oxygen (DO), and pH observed different patterns, while salinity and suspended particulate matter(SPM) showed no significant statistical differences between the survey periods. The zooplankton in Gomso Bay occurred 33, 29, 27, and 29 taxonomic groups during each respective survey period. In October 2022 and May 2023, arthropod plankton were dominated, while in January and March 2023, protozoa were primarily dominant. Among the Arthropods, copepods including Acartia hongi, Paracalanus parvus s. l., Corycaeus spp., and Oithona spp. commonly found along Korean coastal areas of the Yellow Sea, were dominated. Cluster analysis based on zooplankton abundance indicated a single community (stable condition) in each season, attributed to low dissimilarity distances, while three distinct clusters (autumn, winter-early spring, spring) between seasons indicated a highly seasonal environment in Gomso Bay.

• Journal of agricultural medicine and community health
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• v.30 no.2
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• pp.167-182
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• 2005

Objectives: This study was performed to investigate the prevalence of knee osteoarthritis according to the criteria of diagnosing knee osteoarthritis in rural women and the factors related with this disease. Methods: The data obtained from 200 women older than 40 years of age residing in 5 Ri's in Goryeong-gun. Gyeongsanbuk-do by random cluster sampling from September to October 2002. Knee osteoarthritis was determined positive according to the Kellgren and Lawrence classification and knee pain. Results: Among these subjects, 71.0% showed more than grade 2 in radiologic finding and the rate of knee pain according to the survey was 67.0%. The rate of subjects meeting the criteria of knee osteoarthritis was 54.0%. According to univariate analysis, the prevalence of knee osteoarthritis increased with age and those farming people and people working in household industry was significantly high at 58.9% compared with others. The prevalence of knee osteoarthritis showed a significant relationship with the family history and past history of knee injury and knee surgery(p<0.01), and diabetes mellitus(p<0.05). The score of ADL was significantly different in the subjects with knee osteoarthritis compared with normal group(p<0.05). When the presence of knee osteoarthritis and the period of the life style of seating down on the floor were compared, a significant difference was present between the osteoarthritis group and normal group. As for metabolic factors, the blood sugar level, bone density, and body mass index(BMI) were significantly different in the osteoarthritis group compared with normal group. When multiple logistic regression analysis was performed with the presence of knee osteoarthritis as the dependent variable, the prevalence of knee osteoarthritis was significantly affected by older age, subjects farming or working in household industry, the history of knee injury, the history of surgery, higher blood sugar level, and higher BMI. Conclusions: These subjects need an intervention through self-care programs such as exercise for preventing osteoarthritis, weight control programs, other exercise programs strengthening knee joints, and guidelines when working in vinyl houses.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.261-268
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• 2021

Cloud computing is a computing paradigm in which users can utilize computing resources in a pay-as-you-go manner. In a cloud system, resources can be dynamically scaled up and down to the user's on-demand so that the total cost of ownership can be reduced. The Modeling and Simulation (M&S) technology is a renowned simulation-based method to obtain engineering analysis and results through CAE software without actual experimental action. In general, M&S technology is utilized in Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Multibody dynamics (MBD), and optimization fields. The work procedure through M&S is divided into pre-processing, analysis, and post-processing steps. The pre/post-processing are GPU-intensive job that consists of 3D modeling jobs via CAE software, whereas analysis is CPU or GPU intensive. Because a general-purpose desktop needs plenty of time to analyze complicated 3D models, CAE software requires a high-end CPU and GPU-based workstation that can work fluently. In other words, for executing M&S, it is absolutely required to utilize high-performance computing resources. To mitigate the cost issue from equipping such tremendous computing resources, we propose HEMOS-Cloud service, an integrated cloud and cluster computing environment. The HEMOS-Cloud service provides CAE software and computing resources to users who want to experience M&S in business sectors or academics. In this paper, the economic ripple effect of HEMOS-Cloud service was analyzed by using industry-related analysis. The estimated results of using the experts-guided coefficients are the production inducement effect of KRW 7.4 billion, the value-added effect of KRW 4.1 billion, and the employment-inducing effect of 50 persons per KRW 1 billion.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.143-174
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• 2023

With the development of deep learning technologies, Artificial Intelligence powered Optical Character Recognition (AI-OCR) has evolved to read multiple languages from various forms of images accurately. For the financial industry, where a large number of diverse documents are processed through manpower, the potential for using AI-OCR is great. In this study, we present a configuration and a design of an AI-OCR modality for use in the financial industry and discuss the platform construction with application cases. Since the use of financial domain data is prohibited under the Personal Information Protection Act, we developed a deep learning-based data generation approach and used it to train the AI-OCR models. The AI-OCR models are trained for image preprocessing, text recognition, and language processing and are configured as a microservice architected platform to process a broad variety of documents. We have demonstrated the AI-OCR platform by applying it to financial domain tasks of document sorting, document verification, and typing assistance The demonstrations confirm the increasing work efficiency and conveniences.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.65 no.3
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• pp.231-240
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• 2020

This research was conducted to evaluate the physicochemical properties, antioxidant components, and their activities for more taking advantage of small redbean cultivars. Seed size, 100 seeds weight, and hardness on the 8 cultivars were measured. The free sugar and crude protein contents were evaluated using HPLC and protein analyzer, respectively. Amylose content, antioxidant components, and activities were analyzed by spectrophotometer. The range of 100 seeds weight and hardness were 12.55-18.81 g and 9,527.38-14,341.25 gf, respectively. Total free sugar, amylose, and crude protein were showed 22.49-31.07 mg/g, 13.53-15.67%, and 21.27-23.30%, respectively. The cultivar 'Hongeon' was higher antioxidant component and activity more than others. In clustering the cultivars based on the results, the tree showed four major clades. The 'Huinnarae' group was high in total free sugar and amylose content. The 'Hongeon' group were high in 100 seeds weight, antioxidant component. and activity, while amylose content was lower than that in the other groups. The results of the cultivars can be utilized for research of functional materials. The findings of this study will provide valuable information for expansion of functional food industry related on small redbean.

• Journal of the Korean Regional Science Association
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• v.36 no.2
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• pp.13-24
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• 2020

In modern society, the number of one-person households is increasing significantly. In particular, one-person households have rapidly increased around local small and medium-sized cities. This study examines the characteristics of local small and medium-sized cities by factor and cluster analysis. Analysis of variance are applied to the characteristics of one-person household in different local cities to find the relationship between different types of cities and the characteristics of one-person households. As a result of the study, local small and medium-sized cities are classified into growth stagnation cities, industrial leading cities, regional base cities, and population outflow cities. It is also found that there are several different types of local cities based on the characteristics of one-person households. The growth stagnation city is a city where the regional economy is revitalized due to the development of regional industries in the past. One-person households have a small age group in their 30s and 40s, which are the basis of industrial activities. They have a high proportion of older generation living in more than three rooms in their homes. It is necessary to supply long-term public rental housing and share houses for older generation. The leading city of the industry is a city where the local economy is revitalized as workers are concentrated. One-person households are evenly distributed among all age groups, and the apartment occupancy rate is the highest compared to other types. It is necessary to provide happy housing for youth generation and reconstruction or renovation housing of manhood generation. The regional base city leads the regional base function and the regional economy, but it has reduced workers. Many of one-person households are younger than 30 years old and college educated. They are also high rate of unmarried and live at one room as rental houses. It is needed to expand the supply of small houses such as apartments, officetels and rented houses for youth generation. The population outflow city has a slow local economy and a rural residential environment. It is found that the households of one-person households have high rate of bereavement and the age. They live more than four rooms in single-family homes. It is necessary not only to provide welfare housing but also to create a sound residential environment where cultural exchange is possible.

• 대한공업교육학회지
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• v.41 no.2
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• pp.48-68
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• 2016

This study was conducted to identify the NCS-related research trends, Keywords, the Keywords Networks and the extension of the Keywords using the sementic network analysis and to seek for the development plans about NCS. For this, the study searched 345 the papers, with the National Competency Standards or NCS as a key word, among master's theses, dissertations and scholarly journals that RISS provides, and selected a total of 345 papers. Annual frequency analysis of the selected papers was carried out, and Semantic Network Analysis was carried out for 68 key words which can be seen as key terms of the terms shown by the subject. The method of analysis were KrKwic software, UCINET6.0 and NetDraw. The study results were as follows: First, NCS-related research increased gradually after starting in 2002, and has been accomplishing a significant growth since 2014. Second, as a result of analysis of keyword network, 'NCS, development, curriculum, analysis, application, job, university, education,' etc. appeared as priority key words. Third, as a result of sub-cluster analysis of NCS-related research, it was classified into four clusters, which could be seen as a research related to a specific strategy for realization of NCS's purpose, an exploratory research on improvement in core competency and exploration of college students' possibility related to employment using NCS, an operational research for junior college-centered curriculum and reorganization of the specialized subject, and an analysis of demand and perception of a high school-level vocational education curriculum. Fourth, the connection forming process among key words of domestic study results about NCS was expanding in the form of 'job${\rightarrow}$job ability${\rightarrow}$NCS${\rightarrow}$education${\rightarrow}$process, curriculum${\rightarrow}$development, university${\rightarrow}$analysis, utilization${\rightarrow}$qualification, application, improvement${\rightarrow}$plan, operation, industry${\rightarrow}$design${\rightarrow}$evaluation.'