• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.255-262
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• 2015

In the absence of a deterministic algorithm for economic load dispatch optimization problem (ELDOP), existing algorithms proposed as solutions are inevitably non-deterministic heuristic algorithms. This paper, therefore, proposes a balance-and-swap algorithm to solve an ELDOP. Firstly, it balances the initial value to ${\Sigma}P_i=P_d$ by subsequently reducing power generation for each adult-step and baby-step and selects the minimum cost-generating method. Subsequently, it selects afresh the minimum cost-generating method after an optimization of the previously selected value with adult-step baby-step swap and giant-step swap methods. Finally, we perform the $P_i{\pm}{\beta}$, (${\beta}=0.1,0.01,0.001,0.0001$) swap. When applied to the 3 most prevalently used economic load dispatch problem data, the proposed algorithm has obtained improved results for two and a result identical to the existing one for the rest. This algorithm thus could be applied to ELDOP for it has proven to consistently yield identical results and to be applicable to all types of data.

• Electronics and Telecommunications Trends
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• v.37 no.5
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• pp.54-61
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• 2022

Data are an essential resource for artificial intelligence-based services. It is considered a vital resource in the 4th industrial revolution era based on artificial intelligence. However, it is well-known that only a few giant platforms that provide most of the current online services tend to monopolize personal data. Therefore, some governments have started enforcing personal data protection and mobility regulations to address this problem. Additionally, there are some notable activities from a technical perspective, and Web 3.0 is one of these. Web 3.0 focuses on distributed architecture to protect people's data sovereignty. An important technical challenge of Web 3.0 is how to facilitate the personal data storage technology to provide valuable data for new data-based services while providing data for producers' sovereignty. This study reviews some currently proposed personal data storage technologies. Furthermore, we discuss the domestic countermeasures from MyData perspective, which is a typical project for data-based businesses in Korea.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.895-902
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• 2011

This paper is mainly concerned about providing confidence prediction interval for exchange rate during economic crisis. Our proposed method is to use block bootstrap method for prediction interval for next day. It is shown that block bootstrap method is particularly effective for interval prediction of exchange rate during economic crisis.

• Environmental and Resource Economics Review
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• v.29 no.2
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• pp.273-292
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• 2020

본고는 코로나19라는 글로벌 팬데믹 상황에서 감염병 역학모형에 관한 내용과 기초재생산수, 집단면역임계, 백신비축 등의 주요 개념을 개론 수준에서 다루었다. 국내 첫 감염자 발생 이후 4월 12일까지의 데이터를 기준으로 분석해 볼 때 한국의 기초재생산수는 약 2의 값을 가지는데 이는 코로나19가 발생한 다른 나라에 비해 현저히 낮은 수치로 평가된다. 만일에 코로나19 백신이 개발되는 것을 가정하여 이의 비축규모를 추정해보면 인구의 최소 62%에 공급할 수 있는 수준이어야 하는 것으로 나타났다. 한편, 한국의 코로나19의 성공적 대응에는 사회적 거리두기 정책이 주된 요인 중의 하나라는 점도 발견하였다. 그러나 5월 이후 사회적 거리두기에 대한 다소 느슨해진 경향이 없지 않은데, 지역감염의 확산을 위해서는 원론적으로 대응할 필요가 있다. 본고는 학술적 관점이 아닌, 방역의 실무적 차원에서 역학모형, 그리고 경제-역학 모형을 활용하는 방법을 소개한 것 뿐이다. 보다 정교한 역학 모형을 제대로 연구하기 위해서는 상당한 규모의 팀워크가 필요하다. 2015년 메르스 이후 역학조사를 위한 자원이 보강되었다 하지만, 앞으로 역학조사 인력, 데이터 시스템 구축, 그리고 보건·경제·통계·수학 분야 등의 연구진이 보강되어야 할 것이다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.583-586
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• 2011

'저가의 고속 무선데이터 서비스'를 지향하는 이동통신 기술진화의 경제성, 최근 모바일 데이터 트래픽의 급증에 따른 3세대 이동통신 망으로의 대응 한계 등에 따라 국내외에서 LTE 및 4세대 이동통신으로 진화가 본격화되고 있다. 차세대 이동통신 망으로의 조기 진화 시 발생하는 참여주체간 효용의 trade-off 문제, 이동통신 사업자의 망 진화 요인분석 등의 차세대 이동통신 망으로의 진화과정에서 발생하는 주요 이슈를 검토하고, 4세대 이동통신용 주파수 조기 확보, 차세대 망 투자에 대한 정책적 인센티브 제공 등 국내 차세대 이동통신 망 투자를 촉진할 수 있는 정책방향을 제시한다.

• Journal of Technology Innovation
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• v.28 no.4
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• pp.165-175
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• 2020

In this viewpoint paper, we overlook the potential of the data industry and the strategies needed in order to survive in this new socio-economic order brought by COVID-19. The social distancing culture is leading to the expansion and centralization of data. The government established the development plan of the data industry ecosystem and the capital flow is following this stream, so this is an opportunity for those in the data business. To survive and grow in the data industry ecosystem, we need to identify quality characteristics that have a comparative advantage over competitors based on high data quality and need to determine the target business segmentation to avoid wasting resources and make efficient investments.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.151-160
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• 2016

The purpose of this research is to investigate Korean popular attitudes and social perceptions of 'sharing economy' terminology at the current moment from a creative or socio-economic point of view. In Korea, this study discovers and interprets the objective and tangible annual changes and patterns of sociocultural collective intelligence that have taken place over the last five years by applying text mining in the big data analysis approach. By crawling and Googling, this study collected a significant amount of time series web meta-data with regard to the theme of the sharing economy on the world wide web from 2010 to 2014. Consequently, huge amounts of raw data concerning sharing economy are processed into the value-added meaningful 'word clouding' form of graphs or figures by using the function of word clouding with R programming. Till now, the lack of accumulated data or collective intelligence about sharing economy notwithstanding, it is worth nothing that this study carried out preliminary research on conducting a time-series big data analysis from the perspective of knowledge management and processing. Thus, the results of this study can be utilized as fundamental data to help understand the academic and industrial aspects of future sharing economy-related markets or consumer behavior.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.71-78
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• 2013

The purpose of the paper is to empirically investigate the role of human capital and labour market conditions in the growth process. To do so, cross-sectional data for 3062 counties across 50 states of the US. Firstly, findings from the empirical estimation suggest income convergence among US counties. Secondly, the stock of human capital appears to have the growth enhancing effect while education expenditures turn out to retard economic growth. Thirdly, it is found that the unemployment rate would have a negative association with regional growth whereas the net migration rate is likely to have a positive relationship with growth. Once the sample counties are divided into both the poor group and the rich group, finally, such main empirical results overall remain unchanged and statistically significant.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.171-178
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• 2009

In order to make the high quality Korean cattle, it has been identified the gene which influence to various economic characters. In this paper, we introduce Restricted Partition Method for gene-gene interaction analysis. Further, economic traits, longissimus muscle dorsi area (LMA), carcass cold weight (CWT) and average daily gain (ADG) are applied with Restricted Partition Method (RPM). The SNP (19_1)$^*$SNP (28_2) was selected and was best marker on Single nucleotide polymorphisms (SNPs). It also influenced SNP (19_1)$^*$SNP (28_2) was an very important marker for economic character and to make the thing know it became.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.251-256
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• 2023

International diesel prices play a crucial role in various sectors such as industry, transportation, and energy production, exerting a significant impact on the global economy and international trade. In particular, an increase in international diesel prices can burden consumers and potentially lead to inflation. However, previous studies have primarily focused on gasoline. Therefore, this study aims to propose an international diesel price prediction model. To achieve this goal, we utilize various global economic indicators and train a linear regression model, which is one of the machine learning methodologies. This model clearly identifies the relationship between global economic indicators and international diesel prices while providing highly accurate predictions. It is expected to aid in understanding overall economic trends including market changes.