• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.670-681
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• 2016

Recently, based on Internet and mobile environments, the Fintech industry that fuses finance and IT together has been rapidly growing and Fintech services armed with simplicity and convenience have been leading the conversion of all financial services into online and mobile services. However, despite the rapid growth of the Fintech industry, few studies have classified Fintech technologies into detailed technologies, analyzed the technology development trends of major market countries, and supported technology planning. In this respect, using Fintech technological data in the form of unstructured data, the present study extracts and defines detailed Fintech technologies through the topic modeling technique. Thereafter, hot and cold topics of the derived detailed Fintech technologies are identified to determine the trend of Fintech technologies. In addition, the trends of technology development in the USA, South Korea, and China, which are major market countries for major Fintech industrial technologies, are analyzed. Finally, through the analyses of networks between detailed Fintech technologies, linkages between the technologies are examined. The trends of Fintech industrial technologies identified in the present study are expected to be effectively utilized for the establishment of policies in the area of the Fintech industry and Fintech related enterprises' establishment of technology strategies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.68-78
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• 2017

The purpose of this study was to identify the influence of the individual-level and community-level factors in the ecological model on walking and to provide the basic data for a strategy that can increase walking for health promotion of adult workers. By combining the primary data of community health survey (CHS) (2011-2013) with the Korea national statistics annual book (2011-2013), the regional level variables were extracted from 253 municipal districts and the convergent big data with the hierarchical structure was produced. As a result, the increase in budget expenditure for public order and safety in social and cultural environment factors, the increase in budget expenditure for national and community land development in the leisure environment factors, and the number of buses in the transportation environment were increased by walking. In conclusion, walking was increased by the development of a community environment and bus transportation besides individual characteristics and behavior. Therefore, improving environment and public transportation will increase physical activity, such as walking, which will increase the health expectancy in community citizen workers.

• Spatial Information Research
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• v.20 no.1
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• pp.47-57
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• 2012

An Administrative Boundary is the basic of spatial information to cover geographical and regional area. Its importance has arisen in our society at the Smart world era. However, it is difficult to serve exact boundary's lines as administrative boundaries are based on the cadastre lines of land register ; these partly are overlay each other or has gaps. So, it Should be adjusted. But, the maintenance work of administration boundaries causes a conflict or confusion unless we offer concrete procedures and detailed plans previously. Therefore, a rational method is required to prevent side-effects such as confusion, disagreem ent and a conflict etc. In this Study, we present a method and 5 step procedures to make better use in a practical maintenance work. we researched on basic studies of Administrative boundary's concept, history. And we performed a field survey as well as analysis of current problems. considering these results, we suggest usage of various spatial data sources, stake-holders' participation, a method of Nearest district's boundaries to maintain administrative boundaries. Throughout the method, we expect it to serve correct boundary-data to various fields without a big confusion. it is also useful to apply its results not only for re-surveying our land but for recording appropriate boundary-data as rational lines.

• Journal of the Economic Geographical Society of Korea
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• v.23 no.1
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• pp.1-17
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• 2020

The study aims to identify economic interdependencies between regions and define functional economic areas of Korea by analyzing inter-firm transaction networks. Previous research has relied on pre-given administrative boundaries or cultural homogeneity and used data such as commuting, population movement, and cargo flows which could not fully explain economic activities. To overcome the limitations, this study applies a community detection method to inter-firm transaction networks derived from the CRETOP+ database of Korean corporate data. The novel dataset and the network analysis enables us to identify Korea's functional economic areas based on actual inter-firm linkages. The result shows that there are six to seven economic blocs in the networks as of 2018. In particular, one huge economic bloc is formed integrating the Seoul metropolitan area, Chungcheong, and Gangwon provinces. Meanwhile, North Jeolla and South Jeolla provinces form two economic blocs separately rather than being tied up in one bloc due to the low frequency of transactions between each other. The two big economic blocs of Daegu-Gyeongbuk and Busan-Gyeongnam exist, and interestingly, Ulsan, Gyeongju, and Pohang form a separate middle-sized bloc across the administrative boundaries. The results reveal that the future balanced national development policies should be implemented based on functional economic areas derived from empirical data.

• Korea journal of population studies
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• v.16 no.2
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• pp.125-139
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• 1993

Korea has successfully achieved a lowered fertility level owing to the strong population control policy and effective family planning program. Along with fertility decline and decreased number of children in family, average number of household members has decreased and nontraditional households such as one person household and households composed of unrelated individuals have prolifirated, even though the absolute number of them are found minimal in Korea. However in recent years several data and survey results suggest that one person households are gradually in the increasing trend. The study aimed at investigating the real state of one person households in Korea and next analyzing the proportional distribution of one person households by a few socioeconomic characteristics, thus providing basic for eatablishing far-singhted population and social welfare policy in the future. Korea has experienced high growth rate of economy through government-led development plans starting from the 1960s. During the past three decades, Korea has shifted from the agricultural state to the industrialized one. In compliance with the economic growth, urbanization and industrialization have brought about rural-to-urban migration and a great bulk of young population migrated to urban areas, who are seeking for educational and job opportunities. Korean society has also been under drastic change in every aspect of life involving norms, tradition, and attitude, etc. Therefore, in spite of the prejudice on 'living alone' still remaining, young people gradually leave parents and home, and further form nontraditional households in urban areas. Current increase in the number of one person households is partly attributable to the increase in high female educational attainment and female participation in economic activities. As the industrial structure in Korea changes from primary into secondary and tertiary industries, job opportunities for service/sales and manufacturing are opened to young female labor force in the process of industrialization. Contrary to the formation of one person households by young people, the aged single households are composed when children in family leave one by one because of marriage, education, employment. In particular, a higher proportion of aged female single households occur in rural areas due to the mortality difference by sex. Based on the data released form the 1990 Population and Housing Census and National Fertility and Family Health Survey in 1985 and 1991, the study tried to examine the state of one person households in Korea. According to Census data, the number of one person households increased to 1, 021, 000 in 1990, comprising 9.0 percent of total households. And the survey reveal that among total 11, 540 households, 8.0 percent, 923 households, are composed of one person households. Generally, the proportion of female single households is greater than that of male ones, and a big proportion of one person households is concentrated in the 25-34 age bracket in urban areas and 65 years and more in rural areas. It is shown than one person householders in urban areas have higher educational attainment with 59.2 percent high schooling and over in 1991, Job seeking proved to be the main reason for leaving home and forming one person households. The number of young female single households with higher education and economic self-reliance are found nil and the study did not allow to analyze the causal realtionship between female education and employment and one person household formation. However more research and deep analysis on the causal facors on one person household formation using statistical method are believed to be necessary.

• Journal of Wetlands Research
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• v.22 no.4
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• pp.312-320
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• 2020

Various LIDs with natural water circulation function are applied to reduce urban environmental problems and environmental impact of development projects. However, excessive Infiltration and evaporation of LID facilities dry the LID internal soil, thus reducing plant and microbial activity and reducing environmental re duction ability. The purpose of this study was to develop a real-time measurement system with complex sensors to derive the management plan of LID facilities. The test of measurable sensors and Internet of Things (IoT) application was conducted in artificial wetlands shaped in acrylic boxes. The applied sensors were intended to be built at a low cost considering the distributed LID and were based on Arduino and Raspberry Pi, which are relatively inexpensive and commercialized. In addition, the goal was to develop complex sensor measurements to analyze the current state o f LID facilities and the effects of maintenance and abnormal weather conditions. Sensors are required to measure wind direction, wind speed, rainfall, carbon dioxide, Micro-dust, temperature and humidity, acidity, and location information in real time. Data collection devices, storage server programs, and operation programs for PC and mobile devices were developed to collect, transmit and check the results of measured data from applied sensors. The measurements obtained through each sensor are passed through the Wifi module to the management server and stored on the database server in real time. Analysis of the four-month measurement result values conducted in this study confirmed the stability and applicability of ICT technology application to LID facilities. Real-time measured values are found to be able to utilize big data to evaluate the functions of LID facilities and derive maintenance measures.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.197-207
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• 2010

Using a variety of data-mining methods on high-throughput cDNA microarray data, the level of gene expression in two different tissues can be compared, and DEG(Differentially Expressed Gene) genes in between normal cell and tumor cell can be detected. Diagnosis can be made with these genes, and also treatment strategy can be determined according to the cancer stages. Existing cancer classification methods using machine learning select the marker genes which are differential expressed in normal and tumor samples, and build a classifier using those marker genes. However, in addition to the differences in gene expression levels, the difference in gene-gene correlations between two conditions could be a good marker in disease diagnosis. In this study, we identify gene pairs with a big correlation difference in two sets of samples, build gene classification modules using these gene pairs. This cancer classification method using gene modules achieves higher accuracy than current methods. The implementing clinical kit can be considered since the number of genes in classification module is small. For future study, Authors plan to identify novel cancer-related genes with functionality analysis on the genes in a classification module through GO(Gene Ontology) enrichment validation, and to extend the classification module into gene regulatory networks.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.63-79
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• 2022

Recently, various studies have been conducted on stock price prediction using machine learning and deep learning techniques. Among these studies, the latest studies have attempted to predict stock prices using limit order books, which contain buy and sell order information of stocks. However, most of the studies using limit order books consider only the trend of limit order books over the most recent period of a specified length, and few studies consider both the medium and short term trends of limit order books. Therefore, in this paper, we propose a deep learning-based prediction model that predicts stock price more accurately by considering both the medium and short term trends of limit order books. Moreover, the proposed model considers news headlines during the same period to reflect the qualitative status of the company in the stock price prediction. The proposed model extracts the features of changes in limit order books with CNNs and the features of news headlines using Word2vec, and combines these information to predict whether a particular company's stock will rise or fall the next day. We conducted experiments to predict the daily stock price fluctuations of five stocks (Amazon, Apple, Facebook, Google, Tesla) with the proposed model using the real NASDAQ limit order book data and news headline data, and the proposed model improved the accuracy by up to 17.66%p and the average by 14.47%p on average. In addition, we conducted a simulated investment with the proposed model and earned a minimum of $492.46 and a maximum of $2,840.93 depending on the stock for 21 business days.

• Therapeutic Science for Rehabilitation
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• v.11 no.2
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• pp.63-76
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• 2022

Objective : The purpose of this study is to examine the relationship with employment of the disabled considering the severity and the type of disability. Methods : Data from the 4th data of the 2nd wave Panel Survey of Employment for the Disabled (PSED) by Korea Employment Agency for Persons with Disabilities (KEAD) were used. The odds ratio of employment in disability types according to severity of disability was calculated by logistic regression analysis. Results : When the related variables were adjusted, the employment of internal disability type was significantly lower than that of external disability type by 0.413(95% CI:0.271-0.629) times in the group with severe disability. On the other hand, in the group with less severe disability, internal disability was 0.475(95% CI:0.327-0.690) times lower than that of external disability (p=<.001). Conclusions : Employment may vary depending on the type of disability, even if the disability severity level is the same. It is necessary to prepare judgment criteria that can reduce the variation in employment by considering both the type and severity of the disability.