• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.1-11
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• 2023

COVID-19 has caused the dramatic reduction of public transportation demand in Busan Metropolitan City, that is, daily public transportation trips in 2020 dropped by approximately 920,000 trips from 2019 based on the public transportation card data. This study investigated the underlying factors affecting the public transportation demand discrepancy between before and after COVID-19 at the primary administration unit(i.e., Eup, Myeon, Dong) level with Ordered Logistic Regression model. Finding of this study is as follows. The primary administration units characterized with high ratio of welfare recipients, industrial area, and day boarders were heavily dependent on public transit, indicating little change in public transportation demand. On the other hands, the primary administration units which have high ratio of urban rail transit uses experienced significant reduction of public transportation demand. In conclusion, transportation policies taken under emergent situation such as COVID-19 need to take into account the region-based characteristics rather than unilateral ones.

• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.13-24
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• 2022

Construction projects are exposed to various types of risks, which tend to increase. The increasing risks call for contractors' more attentions to forecasting and dealing with these risks. One of the measures to deal with contractors' risks is to forecast or estimate risk cost and include it in the construction budget. Although various researches in relation to risk cost have been observed, little attention has been paid to general contractors' perceptions and current practices in estimating risk cost of construction budget. The objective of the study is to identify and discuss key characteristics and implications based on the survey and analysis of general contractors' perceptions and current practices in estimating risk cost of construction budget. The study shows that there is a gap between the perception and the practice of estimating risk cost, that is, high perception of the importance of risk cost and a relatively low level of practice. It suggests that historical cost data, guidelines and corporate-level standard procedures are required to improve the current practice in addition to sufficient time allocations for risk cost estimating. It discusses that there is a need for using sophisticated estimating techniques including bid data analytics despite a low level of the current adoption, and also proposes that research and development in the field of the sophisticated estimating techniques should be further implemented in order to increase their practicality.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.12 s.159
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• pp.1690-1696
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• 2006

The purpose of this study is to provide the basic data useful in designing apparel patterns for aged abdominal obese women. The body measurements of 318 women were taken at random, whose ages were over 60 and fields of action were colleges, sports centers, or business sites in Seoul and the neighboring districts. A total of 33 features in the upper body and lower body were used fer the anthropometric measurement and analysis using anthropometry. The collected measurement data were processed statistically using the SPSS 12.0 program for technical statistical analysis, t-test, frequency analysis, correlation analysis. The results of the study are as follows. 1. Subjects were classified into two groups as a result of analysis for measurement data. It was revealed that 251(about 79 percent) women of total subjects(n=318) have a characteristic of abdominal obese body type and elderly women of these group usually had big abdomen rather than hip. The criteria of abdominal obesity based on waist-hip ratio, WHR(=0.85). 2. Aged abdominal obese women have shown much larger size in most body measurements except items of some vertical length, such as bust ponit-bust point, font interscye, back interscye with circumference and depth of armscye, bust, waist, abdomen and hip while showing no difference in height, biacrominal breadth, hip width, neck shoulder point to breast point, crotch length. 3. Vervaeck index(=100.1) and Rohrer index(=1.7) indicated that the abdominal obese women were fat in overall body. And aspect ratio of waist(=0.86), abdomen(=0.92) and hip(=0.75) also appeared high that the shape of cross sections in those regions was similar to a figure of circle 4. In view of the correlation coefficient between hip circumference and the rest measurement items, and between hip circumference inclusively of the abdomen protrusion and the rest measurement items, there were found some differences for each group. In case of Group (abdominal obese group), the former is smaller than the other. 5. In case of Abdominal obese women, hip circumference inclusively of the abdomen protrusion is more mutually related to the rest items related to make apparel pattern as waist circumference, depth of armscye and so on than what hip circumference is. This result indicated which must be considered hip circumference inclusively of the abdomen protrusion to make apparel patterns for abdominal obese women unlike women of common body types.

• 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.

• Journal of Internet Computing and Services
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• v.16 no.3
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• pp.79-93
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• 2015

With the rapid development of information technology, the ways of usage have changed drastically. The ways and efficiency of traditional service application to data processing already could not satisfy the requirements of modern users. Nowadays, users have already understood the importance of data. Therefore, the processing and saving of big data have become the main research of the Internet service company. In China, with the rise and explosion of 115 Cloud leads to other technology companies have began to join the battle of cloud services market. Although currently Chinese cloud services are still mainly dominated by cloud storage service, the series of service contents based on cloud storage service have been affirmed by users, and users willing to try these new ways of services. Thus, how to let users to keep using cloud services has become a topic that worth for exploring and researching. The academia often uses the TAM model with statistical analysis to analyze and check the attitude of users in using the system. However, the basic TAM model obviously already could not satisfy the increasing scale of system. Therefore, the appropriate expansion and adjustment to the TAM model (i. e. TAM2 or TAM3) are very necessary. This study has used the status of Chinese internet users and the related researches in other areas in order to expand and improve the TAM model by adding the brand influence, hardware environment and external environments to fulfill the purpose of this study. Based on the research model, the questionnaires were developed and online survey was conducted targeting the cloud services users of four Chinese main cities. Data were obtained from 210 respondents were used for analysis to validate the research model. The analysis results show that the external factors which are service contents, and brand influence have a positive influence to perceived usefulness and perceived ease of use. However, the external factor hardware environment only has a positive influence to the factor of perceived ease of use. Furthermore, the perceived security factor that is influenced by brand influence has a positive influence persistent intention to use. Persistent intention to use also was influenced by the perceived usefulness and persistent intention to use was influenced by the perceived ease of use. Finally, this research analyzed external variables' attributes using other perspective and tried to explain the attributes. It presents Chinese cloud service users are more interested in fundamental cloud services than extended services. In private cloud services, both of increased user size and cooperation among companies are important in the study. This study presents useful opinions for the purpose of strengthening attitude for private cloud service users can use this service persistently. Overall, it can be summarized by considering the all three external factors could make Chinese users keep using the personal could services. In addition, the results of this study can provide strong references to technology companies including cloud service provider, internet service provider, and smart phone service provider which are main clients are Chinese users.

• Journal of Distribution Research
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• v.16 no.1
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• pp.1-27
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• 2011

I. Introduction: This study is to examine the structural relationships among exogenous variable (preliminary and post-support, franchisee's perceived business possibility, communication, moral hazard), the mediated variables(satisfaction, perceived risk, trust) and dependent variable(recontracting intention) in the food service franchise industry context. More specifically, this study has considered some realistic characteristics factors influencing satisfaction, perceived risk and trust between franchisors and franchisees and their further recontracting intention from the perspective of a practical approach. In this study, 437 data has been collected and used for the SPSS and AMOS analysis. The data were analyzed with structural equation modeling. Since the result of the overall model analysis demonstrated a good fit, we could further analyze our data. II. Research Model: This study is to examine the structural relationships among preliminary and post-support by franchisor, franchisee's perceived business possibility, and communication, moral hazard, has on effect on franchisee's satisfaction, perceived risk, trust and recontracting intention in the food service franchise industry context. Hypotheses are as following (Stern & EL-Ansary 1988; Oliver, 1997;Kee & Knox, 1970; Moorman, Deshpande & Zaltman, 1993; Perron, 1998; Zaheer, McEvily, Perrone, 1998). III. Result and Implication: We examined franchisee who have food service stores for samples of this study. The data were analyzed with structural equation modeling using path analysis. The result of the overall model analysis appeared as following: ${\chi}^2$ = 61.578 (d.f.=9, p<0.01), CFI =.990, GFI =.973, AGFI =.863, RMR =.019, RMSEA= .116, NFI = .988, TLI = .959. The findings can be summarized as follows: First, preliminary and post support of franchisor, perceived business possibility and communication positively influence to franchisee's satisfaction. Second, moral hazard of franchisor has negatively influence to franchisee's satisfaction and positively influence to perceived risk. Third, franchisee's satisfaction and trust has positively influence to recontracting intention. Fourth, franchisee's perceived risk has negatively influence to trust and recontracting intention. We can concluded that franchisor's preliminary and post support of franchisor, perceived business possibility and communication may be considered as the important factors influence to franchisee's satisfaction. Moral hazard has become a focused issue in franchise industry. Finally, the managerial implication has been stated as followings: First, in the process of building a systematic industry support franchise system and developing a creative business model, franchisee's stable profitability should be considered as the first important factor. The franchisee's trust to franchise may become a dominant factor that influence the business expansion of franchisor. Second, franchisor should communication with their franchisees and deal with the realistic difficulties faced by them with an effort. Third, the franchisor should achieve a synergy effect by utilizing the win-win strategy. The moral hazard strategy that achieving the profit through franchisee's damage will not be inadvisable to franchisor. Then the long-term oriented development and profitability can be maintained. To do so, the franchise industry may break away from the traditional business structure to improve management transparency and competitiveness on investment and organizational changing management. The conflict between franchisor and franchisee also can be reduced and big success can be achieved in the franchise industry.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.229-241
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• 2024

With the advancement of big data processing technology using cloud platforms, access, processing, and analysis of large-volume data such as satellite imagery have recently been significantly improved. In this study, the Change Detection Method, a relatively simple technique for retrieving soil moisture, was applied to the backscattering coefficient values of pre-processed Sentinel-1 synthetic aperture radar (SAR) satellite imagery product based on Google Earth Engine (GEE), one of those platforms, to estimate the surface soil moisture for six observatories within the Yongdam Dam watershed in South Korea for the period of 2015 to 2023, as well as the watershed average. Subsequently, a correlation analysis was conducted between the estimated values and actual measurements, along with an examination of the applicability of GEE. The results revealed that the surface soil moisture estimated for small areas within the soil moisture observatories of the watershed exhibited low correlations ranging from 0.1 to 0.3 for both VH and VV polarizations, likely due to the inherent measurement accuracy of the SAR satellite imagery and variations in data characteristics. However, the surface soil moisture average, which was derived by extracting the average SAR backscattering coefficient values for the entire watershed area and applying moving averages to mitigate data uncertainties and variability, exhibited significantly improved results at the level of 0.5. The results obtained from estimating soil moisture using GEE demonstrate its utility despite limitations in directly conducting desired analyses due to preprocessed SAR data. However, the efficient processing of extensive satellite imagery data allows for the estimation and evaluation of soil moisture over broad ranges, such as long-term watershed averages. This highlights the effectiveness of GEE in handling vast satellite imagery datasets to assess soil moisture. Based on this, it is anticipated that GEE can be effectively utilized to assess long-term variations of soil moisture average in major dam watersheds, in conjunction with soil moisture observation data from various locations across the country in the future.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.455-460
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• 2020

The governments of Korea, China, and Japan have operated comprehensive research institutes for railway technologies. Korea Railroad Research Institute (KRRI), China Academy of Railway Sciences Corporation Limited (CARS), and Railway Technical Research Institute (RTRI) are representatives of comprehensive railway research institutes in each country. KRRI was found to be the most advanced in the quantitative competitiveness of patents. In terms of qualitative competitiveness, KRRI has strength in civil engineering, whereas RTRI has strength in electricity. KRRI was found to have the greatest efforts in securing competitiveness in overseas property rights. By comparing the publication of papers, CARS published the most papers. On the other hand, from 2015, KRRI showed an upward trend and published the most papers. By examining the impact of the papers by the citation, KRRI was found to have higher competitiveness than the other two institutions. In the future, it will be necessary to perform big data analysis on patents and papers of the three organizations, derive the key research areas and promising technology areas for each institute, and establish a mid-to-long-term development plan for railway technology based on scientific evidence.