• Coupled systems mechanics
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• 제7권4호
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• pp.421-440
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• 2018

In this study, a computational method for wall shear stress combined with an implicit direct-forcing immersed boundary method is presented. Near the immersed boundaries, the sub-grid stress is determined by a wall model in which the wall shear stress is directly calculated from the Lagrangian force on the immersed boundary. A coupling mathematical model of the transition process for a model Francis turbine comprising turbulent flow and rotating rigid guide vanes is established. The spatiotemporal distributions of pressure, velocity, vorticity and turbulent quantity are gained with the transient process; the drag and lift coefficients as well as other forces (moments) are also obtained as functions of the attack angle. At the same time, analysis is conducted of the characteristics of pressure pulsation, velocity stripes and vortex structure at some key parts of flowing passage. The coupling relations among the turbulent flow, the dynamical force (moment) response of blade and the rotating of guide vane are also obtained.

• 로봇학회논문지
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• 제13권1호
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• pp.45-54
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• 2018

This paper presents a vision-based fall detection system to automatically monitor and detect people's fall accidents, particularly those of elderly people or patients. For video analysis, the system should be able to extract both spatial and temporal features so that the model captures appearance and motion information simultaneously. Our approach is based on 3-dimensional convolutional neural networks, which can learn spatiotemporal features. In addition, we adopts a thermal camera in order to handle several issues regarding usability, day and night surveillance and privacy concerns. We design a pan-tilt camera with two actuators to extend the range of view. Performance is evaluated on our thermal dataset: TCL Fall Detection Dataset. The proposed model achieves 90.2% average clip accuracy which is better than other approaches.

• 한국전기전자재료학회논문지
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• 제11권7호
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• pp.571-580
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• 1998

The numerical model that can describe the ignition of pseudospark discharge using hybrid fluid-particle(Monte Carlo )method has been developed. This model consists of the fluid expression for transport of electrons and ions and Poisson's equation in the electric field. The fluid equation determines the spatiotemporal dependence of charged particle densities and the ionization source term is computed using the Monte carlo method. This model has been used to study the evolution of a discharge in Argon at 0.5 torr, with an applied voltage if 1kV. The evolution process of the discharge has been divided into four phases along the potential distribution : (1) Townsend discharge, (2) plasma formation, (3) onset of hollow cathode effect, (4) plasma expansion. From the numerical results, the physical mechanisms that lead to the rapid rise in current associated with the onset of pseudospark could be identified.

• 한국해양공학회지
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• 제21권6호
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• pp.59-63
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• 2007

This study discusses the spatiotemporal characteristics of the geomorphological changes in Gwangyang Bay. In this study, we analyzed four aerial photos and nautical charts. The geomorphological changes that have occurred over time were identified using an MGIS (Marine geographic information system) overlay analysis. As a result, we were able to identify the geomorphological changes that have resulted from the coastal development and fairway dredging of Gwangyang Bay. This paper demonstrates that the marine geographic information system can be effective in monitoring the geomorphological changes in coastal zones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권6호
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• pp.2450-2469
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• 2018

In this paper, we develop a spatiotemporal model to analysis of cellular user in underlay D2D communication by using stochastic geometry and queuing theory. Firstly, by exploring stochastic geometry to model the user locations, we derive the probability that the SINR of cellular user in a predefined interval, which constrains the corresponding transmission rate of cellular user. Secondly, in contrast to the previous studies with full traffic models, we employ queueing theory to evaluate the performance parameters of dynamic traffic model and formulate the cellular user transmission mechanism as a M/G/1 queuing model. In the derivation, Embedded Markov chain is introduced to depict the stationary distribution of cellular user queue status. Thirdly, the expressions of performance metrics in terms of mean queue length, mean throughput, mean delay and mean dropping probability are obtained, respectively. Simulation results show the validity and rationality of the theoretical analysis under different channel conditions.

• 경영정보학연구
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• 제11권1호
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• pp.181-196
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• 2009

최근 성능이 우수하고 가격이 저렴한 GPS수신기가 개발되면서 이동객체분석과 공간행태분석에 관한 관심이 높아지고 있다. 이러한 분석을 위한 정보기술과 방법에 관한 연구들이 다각적으로 이루어지고 있지만, 아직 실무에 적용하기에는 한계가 있다. 본 연구는 다차원 모델과 OLAP이라는 데이터분석 기법과 도구들을 이용하여 GPS를 활용한 공간행태분석 방법을 소개하고, 실제 사례를 통해 분석방법 및 분석결과를 제시하였다. 또한, GPS를 활용한 공간행태분석의 유용성과 한계점을 논하였다.

• 한국인터넷방송통신학회논문지
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• 제23권3호
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• pp.145-151
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• 2023

3D-CNN은 시계열 데이터 학습을 위한 딥 러닝 기법 중 하나이다. 이러한 3차원 학습은 많은 매개변수를 생성할 수 있으므로 고성능 기계학습이 필요하거나 학습 속도에 커다란 영향을 미칠 수 있다. 본 연구에서는 손의 동적인 제스처 동작을 시공간적으로 학습할 때, 3D-CNN 모델의 구조적 변화 없이 입력 영상 데이터의 시공간적 변화에 따른 학습 정확성을 분석함으로써, 3D-CNN을 이용한 동적 제스처 학습의 효율성을 높이기 위한 입력 영상 데이터의 최적 조건을 찾고자 한다. 첫 번째로 동적 손 제스처 영상 데이터에서 동적 이미지 프레임의 학습구간을 설정함으로써 제스처 동작간 시간 비율을 조정한다. 둘째로는 클래스간 2차원 교차 상관 분석을 통해 영상 데이터의 이미지 프레임간 유사도를 측정하여 정규화 함으로써 프레임간 평균값을 얻고 학습 정확성을 분석한다. 이러한 분석을 통하여, 동적 손 제스처의 3D-CNN 딥 러닝을 위한 입력 영상 데이터를 효과적으로 선택하는 두 가지 방법을 제안한다. 실험 결과는 영상 데이터 프레임의 학습구간과 클래스간 이미지 프레임간 유사도가 학습 모델의 정확성에 영향을 미칠 수 있음을 보여준다.

• 해양환경안전학회지
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• 제30권1호
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• pp.20-31
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• 2024

본 연구는 패류양식업이 밀집한 남해 강진만 해역에서 수하식 및 살포식 양식해역과 비양식해역을 구분하여 양식활동으로 인한 유기물 축적과 저서다모류군집 변화를 파악하고, 어장환경평가 기법을 활용하여 건강도를 평가하고자 수행되었다. 남해 강진만 해역의 평균 입도, 수온, 염분농도, 총유기탄소 등의 시·공간 분포는 정점간에 큰 차이가 없었던 반면, 출현 종수, 개체밀도와 종다양도 등은 살포식 양식 해역의 정점이 다른 해역의 정점과 비교해 상대적으로 낮은 경향을 보였다. 한편 집괴분석과 주요좌표분석 결과에서도 살포식 양식해역 정점의 저서다모류군집이 나머지 정점의 군집과 뚜렷하게 구분되었다. 수하식 양식해역의 정점과 비양식해역 정점에서는 오염지시종인 Scolectoma longifolia와 Sigambra tentaculata가 우점종으로 출현하였으나, 수심과 해수 유통 등의 물리적인 요인에 의한 영향을 받는 일부 정점을 제외하면 점유율이 높지 않았다. 강진만 해역의 저서생태계 건강도는 1~2등급으로 양호한 상태였다. 그러나, 다모류군집구조의 시·공간 분포와 저서생태계 건강도지수를 고려하면 살포식 양식해역의 퇴적환경은 양식으로 인한 물리적인 교란에 영향을 받는 것으로 보여진다.

• Parasites, Hosts and Diseases
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• 제60권2호
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• pp.97-107
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• 2022

This study was done to provide an overview of the latest trichomoniasis status in Korea by finding disease clusters and analyzing temporal trends during 2012-2020. Data were obtained from the Health Insurance Review & Assessment Service (HIRA) of Korea. SaTScan and Joinpoint programs were used for statistical analyses. Gyeonggi-do had the highest average population and highest number of cases. The high incidence of T. vaginalis infections were observed among women aged 40-49 and 30-39 years (33,830/year and 33,179/year, respectively). Similarly, the 40-49 and 30-39 age group in men showed the highest average cases (1,319/year and 1,282/year, respectively). Jeollabuk-do was the most likely cluster, followed by Busan/Gyeongsangnam-do/Ulsan/Daegu and Jeju-do and Gwangju. Urban and rural differences were prominent. Trichomoniasis has decreased significantly in most clusters, except for Incheon. Trichomoniasis was decreasing in women recently after peaking around 2014. Men showed different trends according to age. Trichomoniasis was increasing in the 10-39 age groups, but decreasing in the 40-59 age groups. This study might provide an analytic basis for future health measures, policy-makers, and health authorities in developing effective system for prevention of trichomoniasis.

• Architectural research
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• 제24권1호
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• pp.1-11
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• 2022

As the occurrence of a crime is dependent on different factors, their correlations are beyond the ordinary cognitive range. Owing to this limitation, systems face difficulty in correlating various factors, thereby requiring the assistance of artificial intelligence (AI) to overcome such limitations. Therefore, AI has become indispensable for crime prediction. Crimes can cause severe and irrevocable damage to a society. Recently, big data has been introduced for developing highly accurate models for crime prediction. Prediction of night crimes should be given significant consideration, because crimes primarily occur during nights, when the spatiotemporal characteristics become vulnerable to crimes. Many environmental factors that influence crime rate are applied for crime prediction, and their influence on crime rate may differ based on temporal characteristics and the nature of crime. This study aims to identify the environmental factors that influence sex and theft crimes occurring at night and proposes an artificial neural network (ANN) model to predict sex and theft crimes at night in random areas. The crime data of A district in Seoul for 12 years (2004-2015) was used, and environmental factors that influence sex and theft crimes were derived through multiple regression analysis. Two types of crime prediction models were developed: Type A using all environmental factors as input data; Type B with only the significant factors (obtained from regression analysis) as input data. The Type B model exhibited a greater accuracy than Type A, by 3.26 and 9.47 % higher for theft and sex crimes, respectively.