智慧城市建设水平对FDI的影响 -基于中国78个地级市的实证研究-

공준석,周健锋,이지행 한중사회과학학회 2023 한중사회과학연구 Vol.21 No.2

With the development of information technology, information technology is applied in the field of urban management and development. As the urban population is booming and the urban scale is expanding in China, a series of urban problems have become increasingly prominent, such as urban air pollution, traffic congestion, garbage siege, urban public facilities and other "urban diseases" that are difficult to solve in a short term. In order to solve these problems and challenges, China has built more than 150 smart cities and managed them with urban development strategies. Due to the rapid development of China's smart city construction, the proportion of foreign enterprises to domestic investment (hereafter refer to as “FDI”) has gradually increased, that is, FDI has a significant impact on the upgrading of the industrial structure, regional economic development and high-quality economic development in China. Under such circumstances, this paper is based on four assumptions: first, improving the level of smart city construction can significantly promote the inflow of FDI; second, smart transportation infrastructure can greatly promote the inflow of FDI; third, smart network can promote the inflow of FDI; fourth, smart economy can promote the inflow of FDI. This paper selects 78 prefecture-level cities as samples from the three batches of national smart city pilot list published by the Ministry of Housing and Urban-Rural Development during 2013 to 2019, constructs a smart city construction level evaluation system through smart network, smart transportation and smart economy, and analyzes the impact of smart city construction level on FDI by using a fixed response model. The results show that the improvement of smart city construction level can effectively attract FDI and the impact of smart network, smart economy and smart transportation on FDI is significantly positive.

The Significance of TROP2 Expression in Predicting BRAF Mutations in Papillary Thyroid Carcinoma

공준석,김현진,김민정,김아름누리,이달님,한강희,박선후,고재수,명재경 대한병리학회 2018 Journal of Pathology and Translational Medicine Vol.52 No.1

Background: Trophoblast antigen 2 (TROP2) is a human trophoblast cell-surface glycoprotein that is overexpressed in several types of epithelial cancers, and is suggested to be associated with an unfavorable prognosis. BRAF mutations are the most common genetic alteration in papillary thyroid carcinoma (PTC). We evaluated the correlation between TROP2 expression and BRAF mutation in PTC. Methods: First, we carried out pyrosequencing for BRAF mutations and immunohistochemistry for TROP2 expression with a tissue microarray consisting of 52 PTC cases. Membranous staining in at least 5% of tumor cells was designated as positive staining and we analyzed the relationship between TROP2 expression and diverse clinicopathological factors, including BRAF mutation. Second, we tested TROP2 mRNA expression in three thyroid cancer cell lines with BRAF mutations (BCPAP, SNU790, and 8505C) and a normal thyroid cell line. Additionally, we checked TROP2 protein levels in a normal thyroid cell line after introduction of the BRAF V600E mutation. Results: In this study, 21 of 26 cases with BRAF mutation showed TROP2 immunoreactivity, whereas all 26 cases without BRAF mutation showed no immunoreactivity for TROP2 with a statistically significant difference (p < .001). Upregulation of TROP2 mRNA was observed in all three thyroid cancer cell lines, but not in the normal thyroid cell line. Interestingly, however, the TROP2 expression was increased in the normal thyroid cell line after introduction of the BRAF V600E mutation. Conclusions: Based on these results, we concluded that TROP2 expression is significantly associated with BRAF mutation and that TROP2 immunohistochemistry could be used for predicting BRAF mutations or diagnosing papillary thyroid carcinoma.

동북아시아 국가 간 관계 변화에 관한 연구 – GDELT 기반 네트워크 분석

공준석 한국동북아학회 2023 한국동북아논총 Vol.28 No.4

Research on Northeast Asian international relations has expanded but often focuses narrowly on specific bilateral ties. With the advent of large-scale GDELT 2.0 data, this study conducted a comprehensive analysis on six countries in Northeast Asia and 17 OECD nations from 2015 to 2023. I categorized GDELT events as positive or negative and used network analysis to study inter-country relations, identifying shifts pre- and post-COVID-19. The results show a prevalence of negative networks, with both positive and negative connections weakening after COVID-19.

Preventative Effectiveness of Thoracic Side Airbags on Side Impact Crashes based on Korea In-Depth Accident Study(KIDAS) Database

공준석,김오현,이강현,강찬영,최두루 대한응급의학회 2022 대한응급의학회 학술대회초록집 Vol.2022 No.2

딥러닝 기법을 통한 공공분야 부패유형 비교

공준석 단국대학교 융합사회연구소 2024 공공정책과 국정관리 Vol.17 No.4

본 연구는 딥러닝 기법을 활용하여 공공분야(국가기관, 지방자치단체 및 공공기관)에서 발생하는 개인적 요인의 부패를 정리하고 비교하여 각 주체별 부패 감소를 위한 정책적 방향성을 제시하는 데 연구목적이 있다. 이를 위해 감사원의 공공감사 결과(2015~2023)를 수집하여 RoBERTa 모델을 통해 분류작업을 수행했다. 이를 기반으로 지방자치단체 > 공공기관 > 국가기관 순으로 개인차원 부패가 높음을 확인했다. 반면, 부패의 경중 관점에서는 공공기관 > 국가기관 = 지방자치단체 순으로 높음을 확인했다. 나아가 공통적으로 계약이나 입찰 관리관점에서도 ‘개인차원’의 부패가 빈번히 발생함을 확인하였으며, 개별적으로는 국가기관(부정청탁 및 금품수수), 지방자치단체(안전 및 환경), 공공기관(법인카드 사적사용) 등의 이슈 비중이 높음을 확인했다. 종합적으로 시스템적인 접근과 개인적인 접근을 통해 부패감소를 유도할 수 있을 것으로 기대할 수 있다. This study aims to organize and compare personal corruption in the public sector (nationalinstitutions, local governments, and public organizations) using deep learning techniques,providing policy direction. For this purpose, approximately 16,000 public audit results fromthe Board of Audit and Inspection were collected and classified using the RoBERTa model. Based on this, it was confirmed that personal corruption is higher in the order of local governments> public organizations > national institutions. Conversely, in terms of the severity of corruption,it was found to be higher in the order of public organizations > national institutions = localgovernments. Furthermore, it was observed that 'personal' corruption frequently occurs in contract andtender management across all sectors. Specifically, issues of solicitation and bribery in nationalinstitutions, safety and environmental concerns in local governments, and private use ofcorporate cards in public organizations were prominent. Based on these findings, the studyproposes policy directions focused on the continuous expansion of solicitation-related systemsin national institutions, improvement of project-specific systems coupled with education inlocal governments, and expanded legal education in public organizations.

지방자치단체 주요재정사업 성과와 예산집행 변수 간 관계성 연구: 서울시 주요재정사업평가 결과를 바탕으로

공준석(Kong, Joon Suk) 서울행정학회 2024 한국사회와 행정연구 Vol.34 No.4

본 연구는 예산집행 관련 변수가 사업성과에 미치는 영향을 파악하기 위해, 서울시의 일별 지출 자료 약 38만 건과 주요재정사업평가 결과 약 600건을 연결하여, 사업별 일별 지출과 주요재정사업평가 결과의 관계를 분석하였다. 이를 위해 일별 지출 자료에서 총집행액, 집행횟수, 집행기간, 상반기 집행률 및 집행속도지수 변수를 생성하여, 주요재정상업평가 결과(등급)에 대한 ANOVA 분석과 평가 결과에 대한 다중회귀분석을 실시하였다. ANOVA 분석결과 집행횟수가 5회 이상인 경우 선행연구와 같이 주요재정사업평가 등급에 따라 총집행액의 평균차이가 존재하며, 다중회귀분석 결과 집행횟수, 상반기 집행률 및 집행속도는 평가등급에 다소간의 영향력이 있는 것으로 나타났다. 본 연구를 통해 예산집행에 대한 정책적 방향성을 제시하고, 집행에 관련된 변수를 확장하는데 기여하기를 기대한다. This study examines the influence of budget execution factors on project performance in Seoul. It links around 380,000 daily expenditure records with about 600 financial project evaluations. Key variables include total execution amount, execution count, duration, first-half execution rate, and speed index. Using ANOVA and multiple regression analyses, it finds that while total execution amount varies by evaluation grade, first-half execution rate shows no significant difference. However, the regression analysis suggests that modifying the number of executions, first-half rate, and speed can improve performance. This research offers policy guidance on budget execution and broadens the scope of related variables.

딥러닝 기법을 이용한 공공기관 설문조사 주관식 문항 활용방안에 관한 연구

공준석(Joon Suk Kong),김유영(Yu Yeong Kim) 한국자료분석학회 2023 Journal of the Korean Data Analysis Society Vol.25 No.6

본 연구의 목적은 공공기관 설문조사에서 주관식 문항의 포함 여부가 만족도 조사 결과에 미치는 영향을 딥러닝 기법을 통해 분석하는 데 있다. 매년 약 1,000여 개의 공공기관이 만족도 조사를 시행하고 있으나, 비교가능성을 위해 보편화된 설문을 사용함에 따라 구체적 정보의 확인에 한계를 보이며, 이에 대한 대안으로 주관식 문항이 활용되고 있다. 그러나 주관식 문항에 대한 분석은 분절적으로 이루어져, 실질적으로 만족도 조사에 활용되지 못하고 있다. 이와 같은 문제를 해결하기 위해 K-공공기관 만족도 조사 결과 약 1,500명의 데이터를 통해 주관식 문항의 활용방안을 탐색했다. RoBERTa 알고리즘을 통해 주관식 문항을 Embedding하고, XGBoost, 인공신경망, 로지스틱 회귀분석 등의 알고리즘을 활용하여 만족도 예측 결과를 비교했다. 실험 결과, 알고리즘에 따른 성능 차이는 일반적으로 알려진 것과 같이 Table data에서는 XGBoost의 성능이 우수한 것으로 나타났다. 하지만 주관식 문항을 포함할 때는 결과가 상이할 수 있음을 확인하였다. 나아가 주관식 문항을 포함하여 만족도 점수를 예측할 때 정확도(accuracy)와 재현률(recall)에 긍정적인 영향을 미칠 수 있음을 확인하였다. 따라서 딥러닝 기반의 분석 방법을 통해 주관식 문항의 복잡성과 다양성을 더욱 정확하게 반영할 수 있으며, 이를 통해 만족도 조사의 효과성과 투명성을 높일 수 있다고 기대된다. The objective of this study is to assess the impact of incorporating subjective questions in public agency satisfaction surveys using deep learning techniques. Each year, around 1,000 public agencies carry out satisfaction surveys. Nevertheless, the reliance on standardized surveys for comparability restricts the gathering of nuanced information. In response, subjective questions have been introduced. Yet, the examination of these questions remains fragmented and hasn't been integrated into mainline satisfaction surveys. To address this gap, we evaluated feedback from roughly 1,500 participants associated with K-Public Agency. For handling these subjective questions, we leveraged the RoBERTa algorithm and employed a range of models, such as XGBoost, neural networks, and logistic regression. Preliminary findings underscore the effectiveness of XGBoost when dealing with structured data. Notably, its performance showed variations when considering subjective inputs. A significant finding was that integrating subjective questions enhanced both the accuracy and recall in predicting satisfaction levels. This indicates that deep learning offers a detailed interpretation of subjective inquiries, which elevates the overall quality of public satisfaction surveys.

Correlation between tSAB and Thoracic Injury in Side-Impact Collision based on the Korean In-Depth Accident Study(KIDAS) Database

Joon Seok Kong(공준석),Kang Hyun Lee(이강현),Oh Hyun Kim(김오현),Hyun Youk(육현),Sun Ju Kim(김선주),Hee Young Lee(이희영),Chan Young Kang(강찬영),Hae Ju Lee(이해주),Yeon Il Choo(추연일),Gun Kim(김건) 한국자동차공학회 2020 한국자동차공학회 학술대회 및 전시회 Vol.2020 No.11

Machine Learning Algorithms to Predict Traumatic Injury Severity of Occupants Engaged with Motor Vehicle Crashes

Joon Seok Kong(공준석),Kang Hyun Lee(이강현),Oh Hyun Kim(김오현),Dooruh Choi(최두루) 한국자동차공학회 2022 한국자동차공학회 부문종합 학술대회 Vol.2022 No.6

A Comparison of Severe Occupant Injury Risks according to Frontal Overlap and Oblique Crash Scenarios in Real-World Accident

Joon Seok Kong(공준석),Kang Hyun Lee(이강현),Oh Hyun Kim(김오현),Hyun Youk(육현),Hee Young Lee(이희영),Chan Young Kang(강찬영),Yoon Il Choo(추연일),Ho Yeon Jeong(정호연) 한국자동차공학회 2020 한국자동차공학회 부문종합 학술대회 Vol.2020 No.7