• 한국추진공학회지
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• 제14권3호
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• pp.16-22
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• 2010

가스터빈 엔진의 성능은 구성품 성능 특성에 큰 영향을 받는다. 일반적으로 구성품 성능 특성은 여러 조건에서의 많은 실험을 통해서만 얻을 수 있어 많은 시간과 비용이 소모되므로 엔진 제작자는 엔진 구매자에게 고비용의 구성품 성능 특성맵의 제공을 하지 않는다. 따라서 일반적으로 유사한 성능맵을 스케일링하여 사용하는데, 이 방법은 탈설계점에서 큰 오차를 보이는데 특히 성층권 이상의 고도에서 운용되는 가스터빈 성능은 더욱 차이를 보인다. 이에 본 연구는 PT6A-67A 터보프롭 엔진을 대상으로 하여 제공된 성능 데이터로부터 구성품 성능맵을 역생성 하는 방법을 제안하였으며, C++를 이용한 대상 엔진 모델링 결과와 제공된 엔진 성능덱 결과를 비교하여 타당성을 검증하였다.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2009년도 제33회 추계학술대회논문집
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• pp.537-541
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• 2009

가스터빈 엔진의 성능은 그 구성품 성능 특성에 큰 영향을 받는다. 보통 이러한 구성품 성능 특성은 여러 조건에서의 수많은 실험을 통해 얻을 수 있지만 그것은 제작사의 고유 재산이기 때문에 쉽게 제공되지 않는다. 그렇기 때문에 보통 성능 덱을 이용하거나 소유하고 있는 엔진 성능 맵을 스케일링 하여 사용한다. 하지만 이러한 방법은 탈설계점에서 오차를 보인다. 이에 본 연구에서는 소형 가스터빈 엔진 실험 장치를 구성하여 몇 구간에서의 실험을 통해 데이터를 축적하고 이를 이용하여 구성품 성능 맵을 축척하는 방법을 제시하였다. 그리고 프로그램을 이용하여 대상 엔진의 정상상태 성능 모델을 구성하여 실제 측정 데이터와 새롭게 생성된 구성품 맵을 사용했을 경우, 그리고 기존의 방법을 이용한 경우를 비교하였다.

• Journal of information and communication convergence engineering
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• 제12권2호
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• pp.128-134
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• 2014

In this paper, we present a graphics processing unit (GPU)-based matching technique for the purpose of fast feature matching between different images. The scale invariant feature transform algorithm developed by Lowe for various feature matching applications, such as stereo vision and object recognition, is computationally intensive. To address this problem, we propose a matching technique optimized for GPUs to perform computations in less time. We optimize GPUs for fast computation of keypoints to make our system quick and efficient. The proposed method uses a self-organizing map feature matching technique to perform efficient matching between the different images. The experiments are performed on various image sets to examine the performance of the system under varying conditions, such as image rotation, scaling, and blurring. The experimental results show that the proposed algorithm outperforms the existing feature matching methods, resulting in fast feature matching due to the optimization of the GPU.

• 한국생산제조학회지
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• 제24권5호
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• pp.541-547
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• 2015

In this study, the performance of a low-floor midsize bus under development is predicted through simulations. To predict the vehicle's acceleration, maximum speed, and uphill driving performance, a forward simulator which calculates the vehicle power is developed. Also we verify the forward simulator by comparing simulations and test result for benchmarking vehicle. To predict the fuel consumption, we use a backward simulator for a specified road cycle. However, to predict the fuel consumption using the backward simulation the engine fuel-consumption map is needed. The engine fuel-consumption map extracting data from a similar sized diesel engine is used by re-scaling the maximum torque. As a result, we simulate the vehicle's forward performance with a new engine. Further, we simulated the backward performance to optimize the fuel efficiency and gearshift timing.

• 한국추진공학회지
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• 제17권1호
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• pp.70-79
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• 2013

본 연구에서는 중형 항공기의 추진 기관인 2 스풀 분리-배기 형식 터보팬 엔진 (BR715-56)의 성능 해석을 수행하기 위하여 상용코드인 MATLAB/SIMULINK를 이용하여 성능모델을 구성하였으며 유량 및 일 조합에 새로운 조합 서브시스템 블록을 새로이 개발하였다. 성능 모사는 먼저 팬, 고압압축기, 고압터빈, 저압터빈 구성품 성능 맵들을 축척방법을 이용하여 유사성능 맵들로 부터 생성하였고, 다음은 탈설계점 성능 해석을 할 수 있도록 구성품들 간 유량과 일 조합이 이루어 질수 있도록 하였다. 제안된 SIMULINK 성능모델은 정상 및 동적 모사와 사용자 편의의 장점을 가지고 있으며, 다양한 작동 조건들에서 개발된 프로그램을 이용한 탈설계점 해석 결과들이 GASTURB에 의한 해석 결과들과 잘 일치함이 확인 되었다.

• 방송공학회논문지
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• 제26권4호
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• pp.441-452
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• 2021

본 논문에서는 VVC의 LMCS에서 휘도 신호 매핑 방법의 부호화 효율을 향상시키기 위한 휘도 신호 매핑 함수 생성 방법을 제안한다. 본 논문에서 제안하는 방법은 기존 LMCS에서 지역적 특징을 반영하기 위하여 사용하는 지역적 공간 분산에 합성 곱 신경망의 어텐션 맵을 곱하여 인지 지각적 특징을 추가적으로 반영한다. 제안하는 방법의 성능 평가를 위하여 AI (All Intra) 조건에서 VVC 표준 실험 영상의 A1, A2, B, C, D 클래스를 이용하여 VTM-12.0과 BD-rate 성능을 비교한다. 실험 결과로서 본 논문에서 제안하는 방법이 VTM-12.0 대비 BD-rate 성능 관점에서 휘도 성분이 평균 -0.07%의 성능 향상을 보이고, 부/복호화 시간은 거의 동일하다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.40-43
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• 1997

This paper presents a 3D object recognition method for generation of 3D environmental map or obstacle recognition of mobile robots. An active light source projects a stripe pattern of light onto the object surface, while the camera observes the projected pattern from its offset point. The system consists of a laser unit and a camera on a pan/tilt device. The line segment in 2D camera image implies an object surface plane. The scaling, filtering, edge extraction, object extraction and line thinning are used for the enhancement of the light stripe image. We can get faithful depth informations of the object surface from the line segment interpretation. The performance of the proposed method has demonstrated in detail through the experiments for varies type objects. Experimental results show that the method has a good position accuracy, effectively eliminates optical noises in the image, greatly reduces memory requirement, and also greatly cut down the image processing time for the 3D object recognition compared to the conventional object recognition.

• Journal of Information Technology Applications and Management
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• 제15권1호
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• pp.225-242
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• 2008

PM (Inconsistency Pattern Modeling) is a hybrid supervised learning technique using the inconsistence pattern of input variables in mining data sets. The IPM tries to improve prediction accuracy by combining more than two different supervised learning methods. The previous related studies have shown that the IPM was superior to the single usage of an existing supervised learning methods such as neural networks, decision tree induction, logistic regression and so on, and it was also superior to the existing combined model methods such as Bagging, Boosting, and Stacking. The objectives of this paper is explore the characteristics of the IPM. To understand characteristics of the IPM, three experiments were performed. In these experiments, there are high performance improvements when the prediction inconsistency ratio between two different supervised learning techniques is high and the distance among supervised learning methods on MDS (Multi-Dimensional Scaling) map is long.

• 산업공학
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• 제22권1호
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• pp.17-25
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• 2009

In mobile device's user interface, menu organization is very important as well as menu structure because small display of mobile device. Menu items should be organized based on user knowledge structure to design user-centered interface. Traditionally, MDS (Multidimensional Scaling) have been most often used to expose users' perceived organization of menu items. But, information that MDS reveals is just relative spatial location of concepts and not relevant to concepts connection. Unlike MDS, Trajectory Mapping explicitly finds users' cognitive links between perceived concepts. This study proposes a Trajectory Mapping technique for eliciting knowledge structure, especially a set of cognitive pathways linking menu items, from end user. With twelve participants, MDS and Trajectory Mapping were conducted using cellular phone's menu items. And user knowledge structure was analyzed through Visual Concept Map that combination of results of MDS and Trajectory Mapping. After then, menu items were organized according to users' perceived organization. Empirical usability test was also conducted. The results of usability test showed that usability, in terms of task performance time, number of errors, and satisfaction, for newly organized interface was significantly improved compare to original interface. The methodology of this study is expected to be applicable to design a user-centered interface. In other words, Trajectory Mapping technique can be used as a design tool of user interface for imposing user knowledge structure on the interface.

• 한국멀티미디어학회논문지
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• 제19권12호
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• pp.1909-1918
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• 2016

This paper presents a unified framework for joint Convolutional Neural Network (CNN) based vehicle detection by leveraging multi-spectral image pairs. With the observation that under challenging environments such as night vision and limited light source, vehicle detection in a single color image can be more tractable by using additional far-infrared (FIR) image, we design joint CNN architecture for both RGB and FIR image pairs. We assume that a score map from joint CNN applied to overall image can be considered as confidence of vehicle existence. To deal with various scale ratios of vehicle candidates, multi-scale images are first generated scaling an image according to possible scale ratio of vehicles. The vehicle candidates are then detected on local maximal on each score maps. The generation of overlapped candidates is prevented with non-maximal suppression on multi-scale score maps. The experimental results show that our framework have superior performance than conventional methods with a joint framework of multi-spectral image pairs reducing false positive generated by conventional vehicle detection framework using only single color image.