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인공신경망 기반 벽 압력을 이용한 난류 채널 유동 내 벽 근처 속도 예측
윤진혁(Jinhyuk Yun),이정일(Jungil Lee) 대한기계학회 2017 대한기계학회 춘추학술대회 Vol.2017 No.11
It is well known that the near wall velocity is closely related to the streaky structure in the wall bounded flow and it is a useful information for the control of the flow. In the present study, we construct an artificial neural network (ANN) to predict near wall velocity in turbulent channel flow. As an input for ANN, wall pressure distribution below the near wall velocity to be predicted is chosen. For the learning process to build this ANN, instantaneous flow data sets are obtained from direct numerical simulation of turbulent channel flow at Reτ = 178. The performance of ANN is examined according to the plane size of wall pressure as an input, number of hidden layer, number of hidden nodes, and etc. It is found that the present ANN based on wall pressure successfully predicts the near wall velocity. Also, we apply this ANN to the control of turbulent channel flow for the skin friction reduction, and its results will be given in the presentation.
X-선 자유전자레이저를 이용한 철과 휘발성 물질의 반응 연구와 이의 초기 지구에서의 환원성 대기 형성 과정에 대한 함의
최진혁(Jinhyuk Choi),Rachel J. Husband,황희정(Huijeong Hwang),김태현(Taehyun Kim),방윤아(Yoonah Bang),윤서희(Seohee Yun),이정민(Jeongmin Lee),심희현(Heehyeon Sim),김상수(Sangsoo Kim),남대웅(Daewoong Nam),채복남(Boknam Chae),Hanns- 한국암석학회 2023 한국암석학회 학술발표회 논문집 Vol.2023 No.6
Analysis of risk propagation using the world trade network
Kim Sungyong,Yun Jinhyuk 한국물리학회 2022 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.81 No.7
An economic system is an exemplar of a complex system in which all agents interact simultaneously. Interactions between countries have generally been studied using the fow of resources across diverse trade networks, in which the degree of dependence between two countries is typically measured based on the trade volume. However, indirect infuences may not be immediately apparent. Herein, we compared a direct trade network to a trade network constructed using the personalized PageRank (PPR) encompassing indirect infuences. By analyzing the correlation of the gross domestic product (GDP) between countries, we discovered that the PPR trade network has greater explanatory power on the propagation of economic events than direct trade by analyzing the GDP correlation between countries. To further validate our observations, an agentbased model of the spreading economic crisis was implemented for the Russia–Ukraine war of 2022. The model also demonstrates that the PPR explains the actual impact more efectively than the direct trade network. Our research highlights the signifcance of indirect and long-range relationships, which have often been overlooked.
Crime and social environments: differences between misdemeanors and felonies
Kim Juyoung,Yun Jinhyuk 한국물리학회 2022 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.81 No.2
Owing to the growing population density of urban areas, many people are being increasingly exposed to criminal activity. Increasing crime rates raise the risk of both physical and psychological injury to law-abiding citizens, creating anxiety. From the viewpoint of complex systems, crime prevention through data science can be a solution to such issues. However, previous studies have focused only on a single aspect of crime, ignoring the complex interplay between the various characteristics, which must be considered in the analysis to understand the dynamics underlying criminal activities. In this study, we examined 12 features that have been identifed as important correlates of crime rates using state-level statistics from the USA. We found that the correlates of misdemeanors and felonies difer. The number of misdemeanors is strongly associated with the police precinct, whereas felony rates are strongly correlated with gun possession and happiness. To account for these results, we present a mechanistic model that commits the crime based on the motivation regarding the proft and the penalty of crimes. Our fndings suggest that the countermeasures for misdemeanors should be treated diferently from those for felonies.