• 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.

• 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.

• 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.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.57-79
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• 2022

COVID-19, which started in December 2019, has had a great impact on our lives in general, including politics, economy, society, and culture, and activities in sports and arts have also been significantly reduced. In the case of sports, sports for all fields in which ordinary citizens participate were particularly affected, and cases of infection in places closely related to people's lives, such as gyms, table tennis, and badminton clubs, also amplified the social fear of the spread of COVID-19. Therefore, in this study, we analyzed news articles related to sports for all at the time when COVID-19 was first spread, and investigated what issues were emerging and being discussed in the sports for all field under the COVID-19 situation. Specifically, we collected news articles dealt with sports for all issues under the COVID-19 situation from Korea's leading portal news sites and identified key sports for all issues by performing topic modeling on these articles. Through the analysis, we found meaningful issues such as COVID-19 outbreak in sports facilities and support for sports activities. In addition, through wordcloud analysis of these major issues, we visually understood the issues and identified the changes in these issues over time.

• Information Systems Review
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• v.19 no.2
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• pp.1-19
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• 2017

Original scripts of e-learning lectures for the CEOs of corporation S were analyzed using topic analysis, which is a text mining method. Twenty-two topics were extracted based on the keywords chosen from five-year records that ranged from 2011 to 2015. Research analysis was then conducted on various issues. Promising topics were selected through evaluation and element analysis of the members of each topic. In management and economics, members demonstrated high satisfaction and interest toward topics in marketing strategy, human resource management, and communication. Philosophy, history of war, and history demonstrated high interest and satisfaction in the field of humanities, whereas mind health showed high interest and satisfaction in the field of in lifestyle. Studies were also conducted to identify topics on the proportion of content, but these studies failed to increase member satisfaction. In the field of IT, educational content responds sensitively to change of the times, but it may not increase the interest and satisfaction of members. The present study found that content production for CEOs should draw out deep implications for value innovation through technology application instead of simply ending the technical aspect of information delivery. Previous studies classified contents superficially based on the name of content program when analyzing the status of content operation. However, text mining can derive deep content and subject classification based on the contents of unstructured data script. This approach can examine current shortages and necessary fields if the service contents of the themes are displayed by year. This study was based on data obtained from influential e-learning companies in Korea. Obtaining practical results was difficult because data were not acquired from portal sites or social networking service. The content of e-learning trends of CEOs were analyzed. Data analysis was also conducted on the intellectual interests of CEOs in each field.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.425-434
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• 2020

Background: Viral infection outbreaks are emerging public health concerns. They often exhibit seasonal patterns that could be predicted by the application of big data and bioinformatic analyses. Purpose: The purpose of this study was to identify trends in diarrhea-causing viruses such as rotavirus (Gr.A), norovirus G-I, and norovirus G-II in Cheonan, Korea. The identified related factors of diarrhea-causing viruses may be used to predict their trend and prevent their infections. Method: A retrospective analysis of 4,009 fecal samples from June 2010 to December 2019 was carried out at Dankook University Hospital in Cheonan. Reverse transcription-PCR (RT-PCR) was employed to identify virus strains. Information about seasonal patterns of infection was extracted and compared with local weather data. Results: Out of the 4,009 fecal samples tested using multiplex RT-PCR (mRT-PCR), 985 were positive for infection with Gr.A, G-I, and G-II. Out of these 985 cases, 95.3% (n = 939) were under 10 years of age. Gr.A, G-I, and G-II showed high infection rates in patients under 10 years of age. Student's t-test showed a significant correlation between the detection rate of Gr.A and the relative humidity. The detection rate of G-II significantly correlated with wind-chill temperature. Conclusion: Climate factors differentially modulate rotavirus and norovirus infection patterns. These observations provide novel insights into the seasonal impact on the pathogenesis of Gr.A, G-I, and G-II.

• Information Systems Review
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• v.19 no.1
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• pp.75-100
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• 2017

Today, firms are constantly transforming and innovating to survive under the rapidly changing business environment. The introduction of cloud computing services has become popular throughout society as a whole and is expected to result in many changes and developments not only in firms and but also in the public sector subject to innovation. The purpose of this study is to investigate the effect of the characteristics of cloud computing services on the perceived expected performance according to innovativeness based on innovation diffusion theory. The results of the analysis of the data collected from this research are as follows. The convenience and understanding of individuals' work as well as the benefits of cloud computing services to them depend on the innovative trend of cloud computing services. Further, the expectations for personal benefit and those for organizational benefit of cloud computing services are different from each other. Leading firms in the global market have been actively engaged in the utilization of cloud computing services in the public sector as well as in private firms. In consideration of the importance of cloud computing services, using cloud computing services as the target of innovation diffusion research is important. The results of the study are expected to contribute to developing future research models for the diffusion of new technologies, such as big data, digital convergence, and Internet of Things.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.25-31
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• 2023

The dispute over the Northern Limit Line in the West Sea has been sharply opposed since the U.N. commander set it in August 1953 with the aim of preventing accidental armed conflict between the two Koreas in the waters of the Korean Peninsula. In 2022, for the first time since the division, North Korea made a missile provocation beyond the NLL. The purpose of this study is to identify how the research on the NLL, which is under way by North Korea's actual provocation, has been conducted and to suggest a direction to proceed. This study examined the trend of research using a total of five academic information DBs, including RISS and Scholar, focusing on academic papers studied on NLL from 1998 to 2023. As a result of examining the current status of each year, field, and research method, significant differences in research volume were identified according to the government's relationship with North Korea, and the research field had the most introduction of the concept of NLL and historical background, confirming the need to expand to more diverse fields to have international legal justification and justification for the NLL, considering the changing international environment according to the logic of power. In terms of final research methods, most of them were literature studies, so the need for quantitative research using interviews, surveys, and big data was also found. It is hoped that the analysis results of this paper will play a positive role in setting the research direction for the international response of the NLL in the future amid the interests of the international political environment that is still ongoing.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.