• Annual Conference of KIPS
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• 2023.05a
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• pp.636-638
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• 2023

Covid19의 세계적 유행 이래로 긴 일정의 해외여행이 감소하고 국내 여행의 수요가 꾸준히 증가하는 추세이다. 현재 다수의 국내 여행 숙박 플랫폼은 가성비 측면으로 이용자가 숙박업소를 선택하고 소비자와 업체를 연결해주는 과정에서 수수료를 얻는 상업적 모델이다. 본 논문에서는 가격 경쟁 중심의 기성 시스템이 아닌, 여행자 개인의 가치를 맞춤화하고 공익의 목적으로 업체를 홍보하는 시스템을 제안한다. 이 시스템은 웹 기반의 시스템을 구현하여 여행자에게 개인 가치에 맞는 업소를 맞춤형으로 추천하고 해당 업소에 대한 평가 지표를 시각화하여 제공한다. 본 시스템은 맞춤형 업소 추천과 평가 지표 제공을 위해 소비자의 리뷰 데이터를 사용한다. 텍스트 데이터를 분석하고 해당 데이터를 다중 분류를 통해 업소에 대한 평가 지표별 점수를 산정한다. 본 시스템은 여행자에게 다양한 관광지와 관광 업소를 추천함으로써 지역 관광을 유도하고 해당 여행지 업소와 지역 경제에 도움을 줄 것이라고 기대된다. 본 논문에서 제안된 기법은 오픈소스로 공개되었다[1].

• Annual Conference of KIPS
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• 2023.11a
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• pp.374-375
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• 2023

정부의 정책과 언론, 미세먼지 실황 서비스 등의 영향으로 미세먼지에 대한 사람들의 관심과 불안이 증가하고 있다. 본 연구에서는 미세먼지 발생에 화석연료 소비가 크게 작용한다고 보고 이 두 변수의 관계를 연구하기 위해 화석연료의 대리변수로 경제성장에 따라 증가하는 자동차 보유량을 채택하여 분석을 진행하였다. 시계열 데이터에 적합한 분석방법을 활용하여 VAR 모델에 적용시켜서 두 변수의 관계를 확인하였다.

• The Journal of Bigdata
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• v.5 no.2
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• pp.171-186
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• 2020

The proportion of micro-enterprises and self-employed in Korea is excessively high compared to that of major developed countries, and frequent start-ups and business closures are repeated, causing enormous damage to the national economy. In order to solve this problem, various studies are underway for micro-enterprises, and the government provides commercial district information analysis services using big data for micro-enterprises. Among the commercial district information analysis services, the commercial district information analysis of our village store operated by the Seoul Metropolitan Government is continuously improving its service to provide the big data analysis service related to micro-enterprises. Since the service was built by integrating big data provided by various organizations, however, there are limitations in data reliability, data analysis, and service composition. In order to overcome these limitations, this paper proposes a location-based survey system that can be analyzed in conjunction with big data-based commercial district services. The proposed questionnaire survey system established the basis for expending the big data commercial district analysis service by linking the survey information and commercial district information.

• Journal of Korea Port Economic Association
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• v.34 no.3
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• pp.159-182
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• 2018

The purpose of this study is to increase the competitiveness of big data in the maritime port organization, by understanding the expected performance and the intention to accept and use big data. In the empirical analysis of factors affecting the intention to use the big data technology for maritime port organizations, the variables employed are based on the Technology Organization Environment(TOE) and Diffusion of Innovations(DOI) theories, which are related to the acceptance of information and communication technologies. To achieve the objective of this study, an empirical analysis was conducted; this analysis targeted the personnel involved in the department of strategic planning and information technology in the related field. We set up eight hypotheses to examine the relevance between variables having three characteristics-technology, organization, and environmental characteristics. The empirical results are summarized as follows. First, it was seen that the technology characteristic, including relative advantage, complexity, and compatibility, has a significant effect on the expected performance. Second, the top management support of the organization characteristic has a significant effect, but the firm size of this characteristic has no significant effect on the expected performance. Third, the competitive pressure of the environment characteristic has a positive effect on the expected performance, while the regulatory support has no significant effect. Finally, the expected performance has a significant effect on the intention to use big data.

• The Journal of Bigdata
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• v.3 no.2
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• pp.19-33
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• 2018

Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.548-557
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• 2021

The purpose of this study was to determine the statistical usefulness of using atypical text data collected from media that are easy to collect to overcoming limits of the existing data related to economic activities of disabled people. In addition, by performing semantic network analysis, major issues by period that could not be grasped by statistical analysis were also identified. As a result, semantic network analysis revealed that the initiative of the public sector, such as the central and local government bodies, was strongly shown. On the other hand, in the private purchase sector, it was also possible to confirm the consumption revitalization trend and changes in production activities in the recent issue of Covid-19. While the term "priority purchase" had a statistically significant relation with the other two terms "vocational rehabilitation" and "employment for the disabled". For the regression results, while the term "priority purchase" had a statistically significant association with the other two terms "vocational rehabilitation" and "employment for the disabled". Further, some statistical analyses reveal that keyword data taken from media channels can serve as an alternative indicator. Implications for issue detection in the field of welfare economy for the disabled is also discussed.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.136-145
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• 2020

Agriculture is the backbone of the economy of most developing countries. In these countries, agriculture or farming is mostly done manually with little integration of machinery, intelligent systems and data monitoring. Irrigation is an essential process that directly influences crop production. The fluctuating amount of rainfall per year has led to the adoption of irrigation systems in most farms. The absence of smart sensors, monitoring methods and control, has led to low harvests and draining water sources. In this research paper, we introduce a 433 MHz Radio Frequency and 2G based Smart Irrigation Meter System and a water prepayment system for rural areas of Tanzania with no reliable internet coverage. Specifically, Ngurudoto area in Arusha region where it will be used as a case study for data collection. The proposed system is hybrid, comprising of both weather data (evapotranspiration) and soil moisture data. The architecture of the system has on-site weather measurement controllers, soil moisture sensors buried on the ground, water flow sensors, a solenoid valve, and a prepayment system. To achieve high precision in linear and nonlinear regression and to improve classification and prediction, this work cascades a Dynamic Regression Algorithm and Naïve Bayes algorithm.

• The Journal of the Korea Contents Association
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• v.17 no.7
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• pp.182-192
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• 2017

Recently, there is an increasing interest in analysis of big data that is coming from manufacturing industry. In this paper, we use PCB (Printed Circuit Board) manufacturing data to provide manufacturers with information on areas with high PCB defect rates, and to visualize them to facilitate production and quality control. We use the K-means and DBSCAN clustering algorithms to derive the high fraction of PCB defects, and compare which of the two algorithms provides more accurate results. Finally, we develop a system of MVC structure to visualize the information about bad clusters obtained through clustering, and visualize the defected areas on actual PCB images.

• Journal of Korean Society of Transportation
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• v.18 no.3
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• pp.43-54
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• 2000

이 연구는 버스정보 시스템 설계에 필요한 운행시격 결정과 통행시간 예측을 위한 알고리즘 개발을 다룬다. 운행시격 결정 문제는 버스와 같은 대중교통 수단을 운영하는데 중요한 요소 중에 하나이다. 기존 연구는 버스 운행비용과 승객비용의 합을 최소로 하는 운행시 격을 찾는데 초점을 두고 이다. 이때 승객비용이란 승객 대기비용과 승객 교통비용의 합으로 이루어진다. 그런데 우리나라와 같이 버스회사 수입이 전액 운행수입에만 의존하는 경우엔 이러한 접근 방식이 타당하지 않다. 기존의 방식과 다르게 승객비용으로 승객 이탈비용을 사용하여 버스의 최적 운행시 격을 구하는 것이 이 연구의 목적이다. 먼저 정류장이 하나인 경우에 대해 해석적 방법으로 풀고, 정류장이 여러 개인 경우에 대해서는 시뮬레이션 기법을 적용한다. 또한 이 연구는 신뢰성이 높고 정확한 통행시간 예측정보를 산출하기 위해 2 단계 예측 기법과 전문가시스템을 이용하는 자료융합 알고리즘을 개발한다. 정확한 정보를 제공하려면 교통정보 수집원을 통해 얻는 자료가 정확해야 하고, 또한 교통상황 변화에 따라 실시간으로 통행시간을 예측하는 것이 필요하다. 이 연구는 AVL(Automatic Vehicle Location)시스템을 이용한 버스정보시스템에서 실시간 데이터와 과거 데이터를 융합하여 통행시간을 예측하는 알고리즘을 개발한다. AVL 데이터를 수집하는 과정에서는 경제성을 고려하여 데이터를 수집한다. 그리고, 버스의 운행관리와 정확한 도착예정시간을 예측하기 위해 AVL시스템을 통해 얻은 데이터의 패턴을 분석하고 유고상황을 감지한다.

• Proceedings of the Korean Association for Survey Research Conference
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• 2000.06a
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• pp.1-14
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• 2000

We consider statistical methods for nonresponse problem in social and economic sample surveys. To create a complete data set, which does not include item nonresponse data, imputation methods are generally used. In this paper, we introduce some imputation methods and compare them with one another. Also, we consider some problems, which occur when an imputed data set is treated as a response data set. Due to the imputed values, the true variance of the estimator after imputation is increased by the imputation variance. However, since usual naive variance estimator constructed from the imputed data set does not estimate the imputation variance, the true variance of the estimator after imputation tends to be underestimated. Theoretical reason is investigated and serious results are explained through a simulation study. Finally, some adjusted variance estimation methods to compensate for underestimation are presented and discussed.