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Mobile Edge Computing을 활용한 건물 재난 알림 시스템 구축 방안
하태영,김준성,정종문,Ha, Taeyoung,Kim, Jungsung,Chung, Jong-Moon 한국인터넷정보학회 2017 인터넷정보학회논문지 Vol.18 No.4
본 논문은 MEC (Mobile Edge Computing)기술을 이용하여 건물에 재난이 발생 하였을 때 건물 내 사람들에게 재난에 대해 알리는 건물재난 알림 시스템 구현 방안에 대하여 제안한다. MEC의 개요를 설명하고, MEC를 활용한 네트워크의 구조와 특성을 파악한다. 추가적으로 기업 통합 패턴기반의 Apache Camel의 특성을 파악하고, 이를 활용한 MEC 구현 방안에 대해서 설명한다. 마지막으로 Apache Camel 기반의 MEC를 활용하여 재난 발생시, 센서들을 통해 재난상황을 빠르게 인식하고, 건물 내 사람들을 신속하게 대피할 수 있도록 돕는 건물재난 알림 시스템 구현 방안을 제시한다. In this paper, a building disaster notification system with MEC (Mobile Edge Computing) technology is proposed, which informs people in a building about the disaster. The overview of MEC is presented, and the structure and characteristics of network using MEC are described. In addition, the characteristics of a enterprise integration pattern based Apache Camel is described, and how to implement MEC with Apache Camel is presented. Finally, an implementation method of building disaster notification system with Apache Camel based MEC is proposed to quickly recognize disasters through sensors and to rapidly evacuate people from buildings.
전국 지방자치단체의 주민자치회 조례 현황 분석에 관한 연구
하태영 ( Ha Taeyoung ),손정혁 ( Son Jeonghyuk ),오지은 ( Oh Jieun ) 한국지방행정연구원 2021 地方行政硏究 Vol.35 No.2
주민자치회는 읍·면·동에 설치되고, 주민으로 구성되어 주민자치센터를 운영하는 등 주민자치 활동 강화에 관한 사항을 수행하는 조직이다. 주민자치회를 설치하기 위해 지방자치단체는 조례를 제정한다. 이에 행정안전부는 지방자치단체의 조례 제·개정에 도움을 주고자 주민자치회 표준조례안을 만들어 안내한다. 주민자치회 표준조례안은 2013년 제정되어 2021년 5월 현재까지 6번 개정되었다. 지방자치단체에서는 표준조례안을 참고하고, 지역별 특성을 반영하여 지방자치단체별로 주민자치회 조례를 제·개정한다. 이에 본 연구에서는 주민자치회 그리고 주민자치회 표준조례에 대해 살펴보고, 전국 지방자치단체의 주민자치회 조례를 전수 조사하여(2021년 2월 기준), 조항별 어떻게 구성이 되어 있는지 실태를 분석하였다. 이를 통해 표준조례와 어떤 차이가 있는지 현황을 분석하여, 현재 주민자치회 조례를 제·개정하고자 하는 지방자치단체 및 주민자치 관계자, 기관에 도움을 주고자 한다. 현재 주민자치회 조례에 관한 연구는 전무하며, 전수 조사를 통한 현황 분석 연구는 없는 상황이다. The residents’ association is an organization to perform matters on strengthening citizen autonomy activities, such as operating a citizen autonomy center by being established in an eup division, myeon division or dong division, and consisting of residents. To establish a residents’ association, local governments enact ordinances for each local government. Accordingly, the Ministry of Public Administration and Security creates and informs standard ordinances on residents’ associations to help establish and amend ordinances of local governments. The standard ordinances on residents’ associations were enacted in 2013 and amended six times as of today, May 2021. The local governments reflect characteristics of each region based on the standard ordinance, and enact and amend ordinances on residents’ associations for each local government. Hence, this study investigated residents’ associations and standard ordinances on residents’ associations, performed a total investigation of ordinances on residents’ associations of national governments (as of February 2021), and analyzed actual status on how each article is structured. Based on these, this study aimed to analyze current status based on the difference from the standard ordinances, and help and contribute to local governments, and persons and institutions concerned with citizen Autonomy. Currently, there are no researches on ordinances of residents’ association, and researches on analysis of current status via total investigation.
Jaegeun Lee(이재근),Seung Woo Yang(양승우),Seunghee Lee(이승희),Yun Kyong Hyon(현윤경),Jinbum Kim(김진범),Long Jin(김용),Ji Yong Lee(이지용),Jong Mok Park(박종목),Taeyoung Ha(하태영),Ju Hyun Shin(신주현),Jae Sung Lim(임재성),Yong Gi 대한비뇨기종양학회 2019 대한비뇨기종양학회지 Vol.17 No.2
Purpose: The aim of this study was to evaluate the applicability of machine learning methods that combine data on age and prostate-specific antigen (PSA) levels for predicting prostate cancer. Materials and Methods: We analyzed 943 patients who underwent transrectal ultrasonography (TRUS)-guided prostate biopsy at Chungnam National University Hospital between 2014 and 2018 because of elevated PSA levels and/or abnormal digital rectal examination and/or TRUS findings. We retrospectively reviewed the patients’ medical records, analyzed the prediction rate of prostate cancer, and identified 20 feature importances that could be compared with biopsy results using 5 different algorithms, viz., logistic regression (LR), support vector machine, random forest (RF), extreme gradient boosting, and light gradient boosting machine. Results: Overall, the cancer detection rate was 41.8%. In patients younger than 75 years and with a PSA level less than 20 ng/mL, the best prediction model for prostate cancer detection was RF among the machine learning methods based on LR analysis. The PSA density was the highest scored feature importances in the same patient group. Conclusions: These results suggest that the prediction rate of prostate cancer using machine learning methods not inferior to that using LR and that these methods may increase the detection rate for prostate cancer and reduce unnecessary prostate biopsy, as they take into consideration feature importances affecting the prediction rate for prostate cancer.