• 정보처리학회논문지:컴퓨터 및 통신 시스템
• /
• 제1권3호
• /
• pp.229-242
• /
• 2012

다수의 기업은 정보보호투자에 대한 필요성은 인식하고 있으나, 정보보호 투자로 인한 효과가 가시적으로 파악하기 힘들고, 침해사고로 인한 피해규모 또한 산정하기 매우 힘들다. 그렇기 때문에 기업은 정보보호에 관한 투자의사결정을 하기도 쉽지 않고 투자규모 또한 산정하기 쉽지 않은 실정이다. 물론 기업의 투자의사결정을 위한 전통적인 투자기법들은 많지만, 정보보호투자는 다른 실물자산에 대한 투자에 비하여 투자효과의 불확실성이 매우 높기 때문에 전통적인 투자분석 기법으로는 한계를 가지고 있다. 본 연구에서는 기업이 투자 의사결정을 함에 있어서 전통적으로 사용한 투자분석기법들에 대해 기술하고, 미래에 대한 불확실성이 큰 금융 선물(先物), 옵션(Option)의 평가기법에서 발전한 실물옵션(Real Option) 분석기법을 정보보호투자 분석에 활용하는 방법을 제시하고자 한다.

• 대한원격탐사학회지
• /
• 제24권2호
• /
• pp.205-211
• /
• 2008

현재까지 우리나라 하천관리는 기존의 도면관리 및 현장조사를 통한 정보관리에서 벗어나 WAMIS, RIMGIS와 같은 물 관련 하천기본지리정보 시스템을 구축하여 사용되어 왔다. 하지만 기 구축된 시스템들은 선(線) 중심의 9차원 하천 공간자료로 관리되어 실무자 중심의 친환경적 하천관리업무 지원을 위한 자료로서 활용성이 부족한 실정이다. 본 연구에서는 면(面) 중심의 입체적인 3차원 하천공간정보 구현과 체계적인 하천관리업무가 가능하도록 항공사진(아날로그, 디지털) 및 다양한 위성영상에 대한 해상도 비교와 시설물 판독 분석을 통한 하천 적용여부 분석을 수행하였다. 하천 적용성 분석을 통해 가장 효과적으로 판단되는 디지털 항공사진을 이용하여 하천관리 및 활용방안을 모색함으로써 효율적인 하천관리방안을 제시하고자 한다.

• Earthquakes and Structures
• /
• 제18권3호
• /
• pp.337-348
• /
• 2020

Experimental testing has been considered as one of the most straightforward approaches to realize the structural behavior for earthquake engineering studies. Recently, novel and advanced experimental techniques, which combine numerical simulation with experimental testing, have been developed and applied to structural testing practically. However, researchers have to take the risk of damaging specimens or facilities during the process of developing and validating new experimental methods. In view of this, a small-scale structural laboratory has been designed and constructed in order to verify the effectiveness of newly developed experimental technique before it is applied to large-scale testing for safety concerns in this paper. Two orthogonal steel reaction walls and one steel T-slotted reaction floor are designed and analyzed. Accordingly, a large variety of experimental setups can be completed by installing servo-hydraulic actuators and fixtures depending on different research purposes. Meanwhile, a state-of-the-art digital controller and multiple real-time computation machines are allocated. The integration of hardware and software interfaces provides the feasibility and flexibility of developing novel experimental methods that used to be difficult to complete in conventional structural laboratories. A simple experimental demonstration is presented which utilizes part of the hardware and software in the small-scale structural laboratory. Finally, experimental layouts of future potential development and application are addressed and discussed, providing the practitioners with valuable reference for experimental earthquake engineering.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2016년도 추계학술발표대회
• /
• pp.506-509
• /
• 2016

We present real-time pedestrian detection that exploit accuracy of Faster R-CNN network. Faster R-CNN has shown to success at PASCAL VOC multi-object detection tasks, and their ability to operate on raw pixel input without the need to design special features is very engaging. Therefore, in this work we apply and adjust Faster R-CNN to single object detection, which is pedestrian detection. The drawback of Faster R-CNN is its failure when object size is small. Previously, small sized object problem was solved by Scale-aware Network. We incorporate Scale-aware Network to Faster R-CNN. This made our method Scale-aware Faster R-CNN (DF R-CNN) that is both fast and very accurate. We separated Faster R-CNN networks into two sub-network, that is one for large-size objects and another one for small-size objects. The resulting approach achieves a 28.3% average miss rate on the Caltech Pedestrian detection benchmark, which is competitive with the other best reported results.

• 한국수소및신에너지학회논문집
• /
• 제22권6호
• /
• pp.868-877
• /
• 2011

This paper presents a parallel computing methodology for polymer electrolyte fuel cells (PEFCs) and detailed simulation contours of a real-scale fuel cell. In this work, a three-dimensional two-phase PEFC model is applied to a large-scale 200 $cm^2$ fuel cell geometry that requires roughly 13.5 million grid points based on grid-independence study. For parallel computing, the large-scale computational domain is decomposed into 12 sub-domains and parallel simulations are carried out using 12 processors of 2.53 GHz Intel core i7 and 48GB RECC DDR3-1333. The work represents the first attempt to parallelize a two-phase PEFC code and illustrate two-phase contours in a representative industrial cell.

• Genomics & Informatics
• /
• 제4권3호
• /
• pp.118-124
• /
• 2006

For the direct understanding of flow, pathway data are usually represented as directed graphs in biological journals and texts. Databases of metabolic pathways or signal transduction pathways inevitably contain these kinds of graphs to show the flow. KEGG, one of the representative pathway databases, uses the manually drawn figure which can not be easily maintained. Graph layout algorithms are applied for visualizing metabolic pathways in some databases, such as EcoCyc. Although these can express any changes of data in the real time, it exponentially increases the edge crossings according to the increase of nodes. For the understanding of genome scale flow of metabolism, it is very important to reduce the unnecessary edge crossings which exist in the automatic graph layout. We propose a metabolic pathway drawing algorithm for reducing the number of edge crossings by considering the fact that metabolic pathway graph is scale-free network. The experimental results show that the number of edge crossings is reduced about $37{\sim}40%$ by the consideration of scale-free network in contrast with non-considering scale-free network. And also we found that the increase of nodes do not always mean that there is an increase of edge crossings.

• Journal of the Korean Data and Information Science Society
• /
• 제25권6호
• /
• pp.1283-1291
• /
• 2014

본 논문에서는 대용량이거나 시간에 따라 순차적으로 들어오는 데이터의 분류를 위한 전진적 단계 알고리즘을 제안한다. Adaboost 알고리즘은 노이즈가 있는 데이터에 대하여 성능이 떨어지는 것으로 알려져 있다. 이를 해결하기 위한 한 가지 방법으로 전진적 단계 선형 회귀 방법을 사용한다. 대용량 데이터나 순차적 배치 데이터의 경우에도 이러한 상황을 극복하기 위해 전진적 단계 알고리즘 방법을 적용한 방법을 제안한다. 모의실험과 실제 자료 분석을 통해 제안된 알고리즘이 좋은 성능을 보임을 알 수 있었다.

• 한국지반공학회지:지반
• /
• 제13권6호
• /
• pp.147-164
• /
• 1997

본 논문은 절리 암반물성의 크기효과가 암반의 불균질성 및 불연속성에 기인한다고 가정하였다. 이를 위해 대표체적요소의 개념이 적용되는 일반적인 등가물성이론을 탈피하여 불규칙적인 절리기하와 임의의 절리수 및 임의의 절리 방향성을 고려할 수 있는 등가물성이론에 대해서 연구하였다. 이론적인 연구를 바탕으로 이 이론을 실제 문제에 적용한 결과 암반요소의 크기에 따라 달라지는 물성 변화를 관찰하고, 크기효과에 관한 여러 가지 연구를 수행하였다. 특히, 수치해석적인 방법으로 크기효과를 증명하는 과정의 타당성을 입증하고 크기효과의 구체적인 원인을 알아보기 위해서 4개의 모델에 대해서 전산실험을 수행하였다. 이 실험으로부터 증명된 내용을 토대로 실제 3차원 구조물을 대상으로 크기효과 실험을 수행하였다. 이 실험 과정 중 절리의 여러 역학적인 성질들이 암반강도 및 탄성계수의 크기효과에 미치는 영향을 관찰해 보았다. 또 크기효과가 특정 절리구조에서 발생되는 것이 아니고 절리를 포함하는 모든 경우에서 발생됨을 증명하기 위해 절리 구조가 다른 두 모델을 대상으로 크기효과 실험을 수행하였다.

• 제어로봇시스템학회논문지
• /
• 제6권12호
• /
• pp.1113-1119
• /
• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• 제어로봇시스템학회논문지
• /
• 제13권11호
• /
• pp.1074-1081
• /
• 2007

This paper presents a method for real-time face detection and tracking which combined Adaboost and Camshift algorithm. Adaboost algorithm is a method which selects an important feature called weak classifier among many possible image features by tuning weight of each feature from learning candidates. Even though excellent performance extracting the object, computing time of the algorithm is very high with window size of multi-scale to search image region. So direct application of the method is not easy for real-time tasks such as multi-task OS, robot, and mobile environment. But CAMshift method is an improvement of Mean-shift algorithm for the video streaming environment and track the interesting object at high speed based on hue value of the target region. The detection efficiency of the method is not good for environment of dynamic illumination. We propose a combined method of Adaboost and CAMshift to improve the computing speed with good face detection performance. The method was proved for real image sequences including single and more faces.