• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2019.05a
• /
• pp.12-13
• /
• 2019

Maritime Big Data Linkage System is a system for linking maritime related internal and external data used in Korean e-Navigation. It is possible to create data acquisition, conversion, storage, analysis, management application service by technology of maritime data generated from inside and outside, and it can be configured and processed in units of components. It is also possible to monitor the flow of data defined by the workflow.. Through this study, it is expected that e-navigation service will be able to link data more efficiently and easily.

• Journal of Information Technology Services
• /
• v.18 no.1
• /
• pp.173-186
• /
• 2019

Recently, as the amount of fine dust has risen rapidly, our interest is increasing day by day. It is virtually impossible to remove fine dust. However, it is best to predict the concentration of fine dust and minimize exposure to it. In this study, we developed a mathematical model that can predict the concentration of fine dust using various information related to the weather and air quality, which is provided in real time in 'Air Korea (http://www.airkorea.or.kr/)' and 'Weather Data Open Portal (https://data.kma.go.kr/).' In the mathematical model, various domestic seasonal variables and atmospheric state variables are extracted by multiple regression analysis. The parameters that have significant influence on the fine dust concentration are extracted, and using ANN (Artificial Neural Network) and SVM (Support Vector Machine), which are machine learning techniques, we proposed a prediction model. The proposed model can verify its effectiveness by using past dust and weather big data.

• Annual Conference of KIPS
• /
• 2021.11a
• /
• pp.474-475
• /
• 2021

The accuracy of real-time video analysis system based on 3D skeleton data highly depends on the quality of data. This study proposes a methodology to distinguish noise in 3D skeleton frames using Intersection Over Union (IOU) method. IOU is metric that tells how similar two rectangles (i.e., boxes). Simply, the method decides a frame as noise or not by comparing the frame with a set of valid frames. Our proposed method distinguished noise in 3D skeleton frames with the accuracy of 99%. According to the result, our proposed method can be used to track noise in 3D skeleton frames.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.01a
• /
• pp.147-150
• /
• 2023

본 논문에서는 코로나 19 확산에 따른 마스크 생산량 급증에 따라 폐마스크 수거함을 배치하는 경기도 용인시에 데이터를 기반으로 한 수거함 위치 추천을 목표하는 프로젝트를 소개한다. 해당 프로젝트는 전국 표준노드 링크와 건축물 용도 API를 활용했으며 데이터 전처리와 메모이제이션 기법 등으로 적절한 시간 안에 결과가 도출되도록 위치 추천 엔진을 개발했다. 개발 완료된 엔진으로부터 도출된 결과를 바탕으로 폐마스크 수거함을 배치한다면 보다 효과적인 결과로 이어질 것으로 기대된다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.01a
• /
• pp.161-163
• /
• 2023

본 논문은 K-지방소멸지수를 바탕으로 지역 성장에 초점을 두어 지방소멸 대응 방향성을 제시한다. 연령별 추계인구 데이터와 총 요소 생산성 데이터를 비교하여 청년층의 감소가 지역 성장에 미치는 영향을 보여준다. 이에 따라 연도별 청년이 가장 많이 유출되는 지역을 샘플링하여 전출 사유를 알아보고 지역별 차별화된 대응 방향성을 제시한다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.01a
• /
• pp.165-166
• /
• 2023

물환경정보시스템에서 하천의 수질을 측정한 자료를 대상으로 DMS(Digital Mosquito Monitoring System)가 설치된 위치와 수질 측정지 위치가 가까운 4개 지점에 대한 수질 측정 성분과 모기지수의 연관성을 분석하였다. 수질 기준 외 측정 성분과 모기지수의 상관관계는 총질소(T-N), 용존총질소(TDN), 인산염인이 연관성이 높은 것으로 나타났다. 이 중에서 인산염인은 모기지수와 양적 선형관계를 이루는데, 인산염은 수중에 부영양화를 일으키는 성분 중 하나다. 수질 측정지는 비료의 영향보다는 오수의 유입으로 인산염이 과잉공급되는 것으로 보였다. 따라서 매년 바뀌는 모기지수 산출식의 지연일자 데이터에 부영양화 지수를 넣음으로써 모기지수의 정확도를 보완할 수 있을 것으로 판단된다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.07a
• /
• pp.321-324
• /
• 2023

본 연구는 광역시와 광역도 간의 개인적 요인과 건강수준 정도가 우울경험 여부에 영향을 미치는 변수의 중요도를 파악하고자 시도되었다. 본 연구의 자료는 질병관리청의 2021년 지역사회건강조사 데이터를 활용하였다. 광역시의 데이터는 4,602건을 이용하였고, 광역도는 19,545건의 데이터를 이용하였다. 자료 분석에 활용된 빅데이터는 R 4.3.0 for Windows를 활용하여 단어 빈도 분석과 machine learning기법인 Random Forest분석을 실시하였다. 연구결과, train 데이터와 test 데이터의 과적합(overfitting)의 문제는 발생하지 않았으며, machine learning 기법의 분류모델은 약 94% 수준으로 나타났다. 분석 결과 광역시와 광역도 간의 우울경험여부에 미치는 중요도가 각각 다르게 나타났다. 두 지역의 시민에게 미치는 우울경험의 원인을 다르게 접근함으로써 보다 더 효율적인 정책수립이 가능 할 것으로 판단된다.

• Management & Information Systems Review
• /
• v.39 no.3
• /
• pp.17-35
• /
• 2020

The purpose of this study is to develop business models for current situational scenarios reflecting customer needs emphasize the need for implementing a logistics cooperation system by analyzing big data to strengthen SCM competitiveness capacities. For healthcare SCM competitiveness needed for the logistics cooperation usage intent, they were divided into product quality, price leadership, hand-over speed, and process flexibility for examination. The wordcloud results that analyzed major considerations to realize work efficiency between medical institutes, words like unexpected situations, information sharing, delivery, real-time, delivery, convenience, etc. were mentioned frequently. It can be analyzed as expressing the need to construct a system that can immediately respond to emergency situations on the weekends. Furthermore, in addition to pursuing communication and convenience, the importance of real-time information sharing that can share to the efficiency of inventory management were evident. Accordingly, it is judged that it is necessary to aim for a business model that can enhance visibility of the logistics pipeline in real-time using big data analysis on site. By analyzing the effects of the adaptability of a supply chain network for healthcare SCM competitiveness, it was revealed that obtaining competitive capacities is possible through the implementation of logistics cooperation. Stronger partnerships such as logistics cooperation will lead to SCM competitive capacities. It will be necessary to strengthen SCM competitiveness by searching for a strategic approach among companies in a direction that can promote mutual partnerships among companies using the joint logistics system of medical institutes. In particular, it will be necessary to search for ways to utilize HCSM through big data analysis according to the construction of a logistics cooperation system.

• The Journal of Bigdata
• /
• v.6 no.1
• /
• pp.63-81
• /
• 2021

The performance of natural language processing is rapidly improving due to the recent development and application of machine learning and deep learning technologies, and as a result, the field of application is expanding. In particular, as the demand for analysis on unstructured text data increases, interest in NLP(Natural Language Processing) is also increasing. However, due to the complexity and difficulty of the natural language preprocessing process and machine learning and deep learning theories, there are still high barriers to the use of natural language processing. In this paper, for an overall understanding of NLP, by examining the main fields of NLP that are currently being actively researched and the current state of major technologies centered on machine learning and deep learning, We want to provide a foundation to understand and utilize NLP more easily. Therefore, we investigated the change of NLP in AI(artificial intelligence) through the changes of the taxonomy of AI technology. The main areas of NLP which consists of language model, text classification, text generation, document summarization, question answering and machine translation were explained with state of the art deep learning models. In addition, major deep learning models utilized in NLP were explained, and data sets and evaluation measures for performance evaluation were summarized. We hope researchers who want to utilize NLP for various purposes in their field be able to understand the overall technical status and the main technologies of NLP through this paper.

• The Journal of Bigdata
• /
• v.5 no.2
• /
• pp.77-84
• /
• 2020

In this study, we conducted a study on the win-loss predicton analysis of korean professional baseball by artificial intelligence models. Based on the model, we predicted the winner as well as each team's final rank in the league. Additionally, we developed a website for viewers' understanding. In each game's first, third, and fifth inning, we analyze to select the best model that performs the highest accuracy and minimizes errors. Based on the result, we generate the rankings. We used the predicted data started from May 5, the season's opening day, to August 30, 2020 to generate the rankings. In the games which Kia Tigers did not play, however, we used actual games' results in the data. KNN and AdaBoost selected the most optimized machine learning model. As a result, we observe a decreasing trend of the predicted results' ranking error as the season progresses. The deep learning model recorded 89% of the model accuracy. It provides the same result of decreasing ranking error trends of the predicted results that we observe in the machine learning model. We estimate that this study's result applies to future KBO predictions as well as other fields. We expect broadcasting enhancements by posting the predicted winning percentage per inning which is generated by AI algorism. We expect this will bring new interest to the KBO fans. Furthermore, the prediction generated at each inning would provide insights to teams so that they can analyze data and come up with successful strategies.