• 제어로봇시스템학회논문지
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• 제22권2호
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• pp.133-138
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• 2016

This paper describes the track initiation and target-tracking filter for ship tracking in a marine environment by using Light Detection And Ranging (LiDAR). LiDAR with three-dimensional scanning capability is more useful for target tracking in the short to medium range compared to RADAR. LiDAR has rotating multi-beams that return point clouds reflected from targets. Through preprocessing the cluster of the point cloud, the center point can be obtained from the cloud. Target tracking is carried out by using the center points of targets. The track of the target is initiated by investigating the normalized distance between the center points and connecting the points. The regular track obtained from the track initiation can be maintained by the target-tracking filter, which is commonly used in radar target tracking. The target-tracking filter is constructed to track a maneuvering target in a cluttered environment. The target-tracking algorithm including track initiation is experimentally evaluated in a sea-trial test with several boats.

• 로봇학회논문지
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• 제10권1호
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• pp.33-41
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• 2015

Recent developments in robotics and intelligent vehicle area, bring interests of people in an autonomous driving ability and advanced driving assistance system. Especially fully automatic parking ability is one of the key issues of intelligent vehicles, and accurate parked vehicles detection is essential for this issue. In previous researches, many types of sensors are used for detecting vehicles, 2D LiDAR is popular since it offers accurate range information without preprocessing. The L shape feature is most popular 2D feature for vehicle detection, however it has an ambiguity on different objects such as building, bushes and this occurs misdetection problem. Therefore we propose the accurate vehicle detection method by using a 3D complete vehicle model in 3D point clouds acquired from front inclined 2D LiDAR. The proposed method is decomposed into two steps: vehicle candidate extraction, vehicle detection. By combination of L shape feature and point clouds segmentation, we extract the objects which are highly related to vehicles and apply 3D model to detect vehicles accurately. The method guarantees high detection performance and gives plentiful information for autonomous parking. To evaluate the method, we use various parking situation in complex urban scene data. Experimental results shows the qualitative and quantitative performance efficiently.

• 제어로봇시스템학회논문지
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• 제20권9호
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• pp.950-956
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• 2014

In this paper, the real-time detection method of a DDP (Driving Direction Point) is proposed for an unmanned vehicle to safely follow the center of the road. Since the DDP is defined as a center point between two lanes, the lane is first detected using a web camera. For robust detection of the lane, the binary thresholding and the labeling methods are applied to the color camera image as image preprocessing. From the preprocessed image, the lane is detected, taking the intrinsic characteristics of the lane such as width into consideration. If both lanes are detected, the DDP can be directly obtained from the preprocessed image. However, if one lane is detected, the DDP is obtained from the inverse perspective image to guarantee reliability. To verify the proposed method, several experiments to detect the DDPs are carried out using a 4 wheeled vehicle ERP-42 with a web camera.

• 전기학회논문지
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• 제63권6호
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• pp.797-803
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• 2014

In this paper, 3D face recognition system is designed by using polynomial based on RBFNNs. In case of 2D face recognition, the recognition performance reduced by the external environmental factors such as illumination and facial pose. In order to compensate for these shortcomings of 2D face recognition, 3D face recognition. In the preprocessing part, according to the change of each position angle the obtained 3D face image shapes are changed into front image shapes through pose compensation. the depth data of face image shape by using Multiple Point Signature is extracted. Overall face depth information is obtained by using two or more reference points. The direct use of the extracted data an high-dimensional data leads to the deterioration of learning speed as well as recognition performance. We exploit principle component analysis(PCA) algorithm to conduct the dimension reduction of high-dimensional data. Parameter optimization is carried out with the aid of PSO for effective training and recognition. The proposed pattern classifier is experimented with and evaluated by using dataset obtained in IC & CI Lab.

• 한국정보기술학회논문지
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• 제17권6호
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• pp.9-16
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• 2019

최근 제조 공정을 개선하려는 기업들은 스마트 팩토리를 도입, 이에 따른 도약이 특별히 눈에 띈다. 이는 최소한의 수동 제어를 통해 완벽하게 생산시설의 프로세스를 수행하는 스마트 팩토리의 영역을 최대화하고 추론의 오차를 최소화 하는 것이 최종 목적이다. 본 연구는 무인 생산, 관리, 포장, 배송 관리를 위한 프로젝트의 일부로써 무인생산의 자동화 설비의 철근 추적을 통해 롤러의 자동 교정을 수행하기 위해 철근 추적 시작점 검출에 대한 연구이며, 시작지점부터 끝점까지의 위치를 정확히 추적해야 하는 요구사항을 만족해야 한다. 추적성능을 높이기 위해서는 시작점 설정이 주요한데 기존의 시간 기반 검출방법을 통해서는 조도, 분진 등 환경에 따라 추적오류의 발생 확률이 높다. 본 논문에서는 환경에 따른 오차를 줄이기 위해 고속 IR카메라의 평균 밝기 변화를 이용한 시작점 검출 방법을 제안하며, 제안 사항을 통해 15%이상의 성능 향상을 확인하였다.

• 한국멀티미디어학회논문지
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• 제14권10호
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• pp.1229-1237
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• 2011

직접 볼륨 렌더링은 반투명한 물체에 대한 고화질 영상 생성이 가능한 기법으로 광선 투사법이 대표적이다. 이것은 각 화소별로 오브젝트 공간상의 관심 영역을 샘플링하기 때문에 높은 해상도의 영상을 생성할 수 있지만, 각 샘플점마다 반복적으로 수행하는 텍스처 참조와 누적연산 때문에 렌더링 성능이 저하되는 문제가 있다. 최근에는 연산 능력이 매우 커진 GPU를 이용해 광선 투사법을 가속화하는 기법들이 많이 연구되고 있지만 이들 역시 전처리 단계 및 추가적인 메모리 사용이 불가피하다. 본 논문에서는 반투명 물체의 표현이 가능하고, 전처리 과정 및 추가적인 텍스처 메모리를 사용하지 않으면서 기존의 방법들보다 고속으로 볼륨 데이터를 가시화할 수 있는 포인트 프리미티브 기반의 새로운 볼륨 렌더링기법을 제안한다. 이 방법은 볼륨 데이터를 샘플링하여 포인트 프리미티브를 생성하고 이를 이미지 평면상에 투영하는 방식으로 수행속도가 매우 빠르다. 또한, 생성된 포인트 프리미티브를 실행시간에 추가 및 삭제할 수 있기 때문에 OTF를 변경해도 실시간 대응이 가능하다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2003년도 추계학술대회 및 정기총회
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• pp.89-92
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• 2003

It is often that a large-scale linear programming(LP) problem may contain many constraints which are redundant or cause infeasibility on account of inefficient formulation or some errors in data input. Presolving or preprocessing is a series of operations which removes the underlying redundancy or detects infeasibility in the given LP problem. It is essential for the speedup of an LP system solving large-scale problems to implement presolving techniques. For the recovery of an optimal solution for the original problem from an optimal solution for the presolved problem, a special procedure, so called postsolving, must be applied. In this paper, we present how a postsolving procedure is constructed and implemented in LPABO, a interior-point based LP system. Briefly, all presolving processes are logged in a data structure in LPABO, and after the end of the solution method an optimal solution for the original problem is obtained by tracing the logs. In each stage of the postsolving procedure, the optimality of intermediate solutions is maintained. We tested our postsolving procedure on Netlib, Gondzio and Kennington LP data sets, and concluded that the computational burden of the procedure is relatively negligible compared with the total solving time.

• 디지털산업정보학회논문지
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• 제13권4호
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• pp.91-98
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• 2017

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2007년도 추계 종합학술대회 논문집
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• pp.501-505
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• 2007

최근에 한국과학기술정보연구원은 계량서지분석에 활용하기 위한 독립형 데이터 분석 시스템 Knowledge Matrix를 개발하였다. 본 논문에서는 개발된 시스템의 성능 수준을 이 분야에서 잘 알려진 분석툴인 VantagePoint와 BibTechMon과 비교분석 하였다. 기능 비교는 데이터, 행렬, 분석, 시각화, 데이터 전처리 부문에서 수행 하였다. 분석결과 각 분석툴의 특장점이 서로 다르지만 전반적으로 KnowledgeMatrix가 좀 더 우수한 기능을 보였다.

• 전기학회논문지
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• 제64권6호
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• pp.900-911
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• 2015

In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.