• 한국ITS학회 논문지
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• 제13권4호
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• pp.30-37
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• 2014

단차로 하이패스시스템의 지 정체로 인한 교통 혼잡, 안전사고, 유지보수 등 매년 발생하는 문제를 해결하기 위해 무정차 다차로 고속주행기반 시스템의 개발이 필요하게 되었다. 하이패스 통신시스템은 단차로 환경에서 선입선출 방식이므로, 다차로 환경에서 발생하는 군집주행 및 차로전환 등 다양한 주행패턴을 처리할 수 없다. 본 논문에서는, 기존의 하이패스 단말기를 수용하면서 다차로 주행이 가능한 통신시스템을 개발했다. 능동형 RF/IR 통신 안테나를 이용해 중첩 영역은 넓히고, 오류범위를 최소화 하는 기술을 구현했다. 체험도로를 다차로 실 환경처럼 구축하여 테스트한 결과 하이패스시험기준을 통과했을 뿐 아니라, 속도별, 차로별, 차로전환별 주행시험 및 단말기 위치별 시험 결과 통신 정확도도 99.9%로 하이패스시스템의 성능을 만족했다. 무정차 다차로를 위한 통신시스템은 향후 고속도로 및 유료도로에서도 사용할 수 있을 뿐 아니라, 주차장에서도 활용할 수 있을 것으로 기대한다.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.119-126
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• 2003

The problem of tracking moving objects in a video stream is discussed in this pa-per. We discussed the popular technique of optical flow for moving object detection. Optical flow finds the velocity vectors at each pixel in the entire video scene. However, optical flow based methods require complex computations and are sensitive to noise. In this paper, we proposed a new method based on the Hough transform and on voting accumulation for improving the accuracy and reducing the computation time. Further, we applied the Boo-lean based edge detector for edge detection. Edge detection and segmentation are used to extract the moving objects in the image sequences and reduce the computation time of the CHT. The Boolean based edge detector provides accurate and very thin edges. The difference of the two edge maps with thin edges gives better localization of moving objects. The simulation results show that the proposed method improves the accuracy of finding the optical flow vectors and more accurately extracts moving objects' information. The process of edge detection and segmentation accurately find the location and areas of the real moving objects, and hence extracting moving information is very easy and accurate. The Combinatorial Hough Transform and voting accumulation based optical flow measures optical flow vectors accurately. The direction of moving objects is also accurately measured.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 추계학술대회
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• pp.569-572
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• 2013

플로우 이론에 근거해서 본 연구는 개인의 성격이 UCC를 활용할 때 개인의 플로우 경험과 사용형태에 어떠한 영향을 미치는 가를 탐색하고자 한다. 연구결과는 외향성은 사용자가 오락의 목적으로 UGC를 활용할 때 긍정적인 영향관계를 보인다고 시사하고 있으며, 반면 커뮤니케이션이나 정보추구의 목적일 때는 부정적인 관계가 있음을 나타냈다. 한편, 신경성은 오락 및 커뮤니케이션 목적의 UGC 활용과 긍정적인 관계가 있었으며, 정신병리적 성향의 경우 정보추구적 목적의 UCC 활용과 부정적인 관계를 보였다. 본 연구는 플로우의 선행요인과 영향요인을 포함한 실증연구를 제시함으로써 플로우 이론을 확장함에 그 의의가 있다.

• 한국농공학회논문집
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• 제65권6호
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• pp.1-13
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• 2023

Irrigation return flow plays an important role in river flow forecasting, basin water supply planning, and determining irrigation water use. Therefore, accurate calculation of irrigation return flow rate is essential for the rational use and management of water resources. In this study, EPA-SWMM (Environmental Protection Agency-Storm Water Management Model) modeling was used to analyze the irrigation return flow and return flow rate of each intake work using irrigation canal network. As a result of the EPA-SWMM, we tried to estimate the quick return flow and delayed return flow using the water supply, paddy field, drainage, infiltration, precipitation, and evapotranspiration. We selected 9 districts, including pumping stations and weirs, to reflect various characteristics of irrigation water, focusing on the four major rivers (Hangang, Geumgang, Nakdonggang, Yeongsangang, and Seomjingang). We analyzed the irrigation period from May 1, 2021 to September 10, 2021. As a result of estimating the irrigation return flow rate, it varied from approximately 44 to 56%. In the case of the Gokseong Guseong area with the highest return flow rate, it was estimated that the quick return flow was 4,677 10³ m³ and the delayed return flow was 1,473 10³ m³ , with a quick return flow rate of 42.6% and a delayed return flow rate of 13.4%.

• 대한의용생체공학회:의공학회지
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• 제15권4호
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• pp.377-382
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• 1994

In the conventional Fourier imaging method in MRI (Magnetic Resonance Imaging), intramotion such as pulsatile flow makes zipper-like artifact along the phase encoding direction. On the other hand, line-integral projection reconstruction (LPR) method has advantages such as imaging of short T2, object and reduction of the flow artifact by elimination of the flow-induced phase fluctuation. The LPR, however, necessarily requires time consuming filtering and back-projection processes, so that the reconstruction takes long time. To overcome the long reconstruction time of the LPR and to obtain the flow artifact reduction effect, we adopted phase corrected concentric square raster sampling (CSRS) method and improved its imaging performance. The CSRS is a fast reconstruction method which has the same properties with the LPR. In this paper, we proposed a new method of flow artifact reduction using the CSRS method. Through computer simulations and experiments, we verified that the proposed method can eliminate phase fluctuations, thereby reducing the flow artifact and re- markably shorten the reconstruction time which required long time in the LPR.

• 문화기술의 융합
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• 제6권4호
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• pp.445-452
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• 2020

본 연구의 목적은 간호대학생의 눈치와 학습몰입도. 의사소통능력 간의 상관관계와 의사소통능력에 미치는 요인을 파악하기 위하여 실시되었다. 자료수집은 K광역시에 소재한 일 간호대학에 재학 중인 260명을 대상으로 연구기간은 2019년 3월 5일부터 4월 17일까지 구조화된 설문지를 이용하여 실시하였다. 자료분석은 SPSS WIN21을 이용하여 빈도, paired t-test. ANOVA, Pearson's correlation coefficients, 다중회귀분석을 이용하였다. 연구결과 간호대학생의 눈치와 학습몰입도는 유의한 순 상관관계를 보였으며(r=.502, p<.001), 눈치와 의사소통능력(r=.619, p<.001), 학습몰입도와 의사소통능력(r=.567, p<.001)에서도 유의한 순 상관관계가 있는 것으로 나타났다. 간호대학생의 의사소 능력에 영향을 미치는 요인은 눈치(β=.381, p<.001)와 학습몰입도(β=.243, p<.001), 실습만족도(β=.107, p<.028) 순으로 나타났다. 또한 이들 변수의 의사소통능력에 대한 설명력(R²)은 47.4% 이었다. 따라서 간호대학생의 의사소통능력 향상을 위한 눈치와 학습몰입도 강화 프로그램 개발 및 전략이 필요하다.

• Journal of information and communication convergence engineering
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• 제9권3호
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• pp.319-324
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• 2011

In computing the optical flow. Horn and Schunck's method which is a representative algorithm is based on differentiation. But it is difficult to estimate the velocity for a large displacement by this algorithm. To cope with this problem multigrid method has been proposed. In this paper, we have proposed a scaled multigrid algorithm which the initial flow for a level is calculated by the summation of the optimally scaled flow and error flow. The optimally scaled flow is the scaled expanded flow of the previous level, which can generate an estimated second image having the least RMS error with respect to the original second image, and the error flow is the flow between the estimated second image (generated by the optimally scaled flow) and the original second image. The flow for this level is then estimated using the original first and second images and the initial flow for that level. From among the various coarsest starting levels of the multigrid algorithm, we select the one that finally gives the best estimated flow. Better results were achieved using our proposed method compared with Horn and Schunck's method and a conventional multigrid algorithm.

• 디지털산업정보학회논문지
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• 제13권2호
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• pp.53-61
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• 2017

How to manage manhole is to estimate the part without observation and maintain the sewer. To get the information about current flow rate, visiting and measuring specific manhole is positively necessary. Also, there are some problems that accurate measurement of flow rate is difficult and obtaining the information about real-time whole flow rate is impossible. This thesis will easily grasp the accurate location and type of manhole to solve the problems, and provide the manhole system conveying information through direct radio communication with a manhole cover to renewal information of manhole properly. Besides, it intends to save the information about management of waterworks, maintenance of facilities, data for flow rate, and structure of manhole. Using these, it is supposed to offer how to handle manhole. Thus, this thesis delivers the information to manhole and into central servers directly without wire and provides the system and management method for effective maintenance of manhole.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2019년도 하계학술대회
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• pp.150-152
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• 2019

본 논문에서는 기존의 연구를 극복하여 단일 영상이 아닌 단안 비디오로부터 5D 라이트필드 영상을 합성하는 딥러닝 프레임워크를 제안한다. 현재 일반적으로 사용 가능한 Lytro Illum 카메라 등은 초당 3프레임의 비디오만을 취득할 수 있기 때문에 학습용 데이터로 사용하기에 어려움이 있다. 이러한 문제점을 해결하기 위해 본 논문에서는 가상 환경 데이터를 구성하며 이를 위해 UnrealCV를 활용하여 사실적 그래픽 렌더링에 의한 데이터를 취득하고 이를 학습에 사용한다. 제안하는 딥러닝 프레임워크는 두 개의 입력 단안 비디오에서 $5{\times}5$의 각 SAI(sub-aperture image)를 갖는 라이트필드 비디오를 합성한다. 제안하는 네트워크는 luminance 영상으로 변환된 입력 영상으로부터 appearance flow를 추측하는 플로우 추측 네트워크(flow estimation network), appearance flow로부터 얻어진 두 개의 라이트필드 비디오 프레임 간의 optical flow를 추측하는 광학 플로우 추측 네트워크(optical flow estimation network)로 구성되어있다.

• Journal of information and communication convergence engineering
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• 제7권4호
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• pp.539-544
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• 2009

An estimation of 3D velocity field including occluded parts without maxing tracer to the fluid had not only never been proposed but also impossible by the conventional computer vision algorithm. In this paper, we propose a new method of three dimensional optical flow of the fluid based on physical model, where some boundary conditions are given from a priori knowledge of the flow configuration. Optical flow is obtained by minimizing the mean square errors of a basic constraint and the matching error terms with visual data using Euler equations. Here, Navier-Stokes motion equations and the differences between occluded data and observable data are employed as the basic constrains. we verify the effectiveness of our proposed method by applying our algorithm to simulated data with partly artificially deleted and recovering the lacking data. Next, applying our method to the fluid of observable surface data and the knowledge of boundary conditions, we demonstrate that 3D optical flow are obtained by proposed algorithm.