• 한국군사과학기술학회지
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• 제6권3호
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• pp.15-21
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• 2003

This paper contains a precise calibration technique for the position of seabed acoustic target and theoretical error analysis of calibration results. The target is deployed on seabed as a standalone transponder. The purpose of target is performing accuracy test for active sonar as well as position calibration itself. For the position calibration, relative range between target and test vessel should be measured using target's transponder function. The relative range data combined with vessel position can be converted into a estimated position of target by the application of nonlinear LSE method. The error analysis of position calibration was divided into two stages. One is for relative range estimator and the other for target position estimator. Numerical simulations for position calibration showed good matching between results and developed CRLB.

• 한국통신학회논문지
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• 제38C권12호
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• pp.1207-1215
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• 2013

이 논문에서는 장애물이 존재하는 환경에서 적외선 센서를 가진 다수의 감시 로봇이 획득한 정보를 융합하여 분산되어있는 표적의 위치 좌표를 추정하는 기법을 제안한다. 방위각(azimuth)과 표적을 대응시키는 방법으로는 장애물이 존재하지 않는 경우에서 제안되었던 깊이-우선(depth-first) 트리 탐색(tree search) 기법을 바탕으로, 우회경로 탐색, 중간 단계 탐색 종료, 하위 단계 부분 탐색, 결정기준 보완 등을 추가함으로써 트리 탐색을 확장한 새로운 기법을 제시하였다. 방위각과 표적이 대응된 후에는 하나의 표적을 가리키는 방위각들에 최소 제곱 오차(least square error) 알고리듬을 적용하여 최적 교점을 구함으로써 표적의 위치 좌표를 추정한다. 제안한 위치 추정 기법의 좌표 추정 성능과 복잡도를 모의실험으로 제시하고 분석한다.

• 한국지능시스템학회논문지
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• 제25권4호
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• pp.355-360
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• 2015

본 논문에서는 필기체 숫자를 인식하기 위해 주성분 분석법(PCA) 기반 방사형 기저함수 신경회로망(pRBFNNs) 패턴 분류기를 설계한다. 제안된 패턴 분류기는 PCA를 이용한 데이터 전처리 단계와 pRBFNNs를 이용한 분류 단계로 구성된다. 전처리 단계에서는 PCA를 사용하여 주어진 데이터의 정보손실을 최소화한 특징데이터를 생성하고, 이를 분류 단계인 pRBFNNs의 입력으로 사용한다. 제안된 분류기의 조건부에서는 Fuzzy C-Means(FCM) 클러스터링 알고리즘으로 구성하였고, 연결가중치는 1차 선형식을 사용하였다. 결론부에서는 최소자승법(LSE)을 사용하여 다항식 계수를 구하였다. 제안된 분류기의 성능평가를 위해 대표적인 필기체 숫자데이터인 MNIST 데이터를 사용하였으며, 제안된 분류기의 결과를 기존 다른 분류기들과 비교한다.

• Smart Structures and Systems
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• 제6권9호
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• pp.1057-1077
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• 2010

An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the adaptive LSE with unknown inputs (ALSE-UI) and the adaptive EKF with unknown inputs (AEKF-UI). Also, new analysis methods, referred to as the adaptive sequential non-linear least-square estimation with unknown inputs and unknown outputs (ASNLSE-UI-UO) and the adaptive quadratic sum-squares error with unknown inputs (AQSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of laboratory tests using a scaled 3-story building model with white noise excitations. The capability of the ALSE-UI, AEKF-UI, ASNLSE-UI-UO and AQSSE-UI approaches in tracking the structural damages will be demonstrated and compared.

• 제어로봇시스템학회논문지
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• 제2권4호
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• pp.330-336
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• 1996

An real-time active beacon localization system for mobile robots is developed and implemented. This system permits the estimation of robot positions when detecting light sources by PSD(Position Sensitive Detector) sensor which are placed sparsely over the robots work space as beacons(or landmarks). An LSE(Least Square Estimation) method is introduced to calibrate the internal parameters of a model for the beacon and robot position. The proposed system has two operational modes of position estimation. One is the initial position calculation by the detection of two or more light sources positions of which are known. The other is the continuous position compensation that calculates the position and heading of the robot using the IEKF(Iterated Extended Kalman Filter) applied to the beacon and dead-reckoning data. Practical experiments show that the estimated position obtained by this system is precise enough to be useful for the navigation of robots.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.916-919
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• 1996

An real-time active beacon localization system for mobile robots is developed and implemented. This system permits the estimation of robot positions when detecting light sources by PSD(Position Sensitive Detector) sensor which are placed sparsely over the robot's work space as beacons(or landmarks). An LSE(Least Square Estimation) method is introduced to calibrate the internal parameters of a model for the beacon and robot position. The proposed system has two operational modes of position estimation. One is the initial position calculation by the detection of two or more light sources positions of which are known. The other is the continuous position compensation that calculates the position and heading of the robot using the IEKF(Iterated Extended Kalman Filter) applied to the beacon and dead-reckoning data. Practical experiments show that the estimated position obtained by this system is precise enough to be useful for the navigation of robots.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.736-740
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• 1991

EEG(Electroencephalogram) background signals can be represented as the sun of a conventional AR(Autoregressive) process and an innovation process, or a prediction error process. We have seen that conventional estimation techniques. such as least square estimates(LSE) or Gaussian maximum likelihood estimates(MLE-G) are optimal when the innovation process satisfies the Gaussian or presumed distribution. But when the data are contaminated by outliers, or artifacts, these assumptions are not met and conventional estimation techniques can badly fall and be strongly biased. It is known that EEG can be easily affected by artifacts. So we suggest a robust estimation technique which considerably performs well against those artifacts.

• 대한의용생체공학회:의공학회지
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• 제13권2호
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• pp.125-132
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• 1992

Background EEG signals can be represented as the sum of a conventional AR process and an innovation process. It Is know that conventional estimation techniques, such as least square estimates (LSE) or Gaussian maximum likelihood estimates (MLE-G ) are optimal when the innovation process satisfies the Gaussian or presumed distribution. When the data are contaminated by outliers, however, these assumptions are not met and the power spectrum estimated by conventional estimation techniques may be fatally biased. EEG signal may be affected by artifacts, which are outliers in the statistical term. So the robust filtering estimation technique is used against those artifacts and it performs well for the contaminated EEG signal.

• 한국정보전자통신기술학회논문지
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• 제7권3호
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• pp.132-136
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• 2014

본 논문에서는 주어진 데이터 전처리를 통한 새로운 형태의 TSK기반 퍼지 추론 시스템을 제안한다. 제안된 모델은 주어진 데이터의 효율적인 처리를 위해 클러스터링 기법인 Fuzzy C-Means 클러스터링 방법을 이용하였다. 제안된 새로운 형태의 퍼지추론 시스템의 전반부는 FCM 을 통하여 정규화된 멤버쉽 함수와 클러스터 수를 결정하기 때문에, 멤버쉽함수의 형태 및 개수를 정의할 필요가 없어, 모델의 구조 또한 간단한 형태를 이룬다. 본 논문에서 사용된 후반부는 4가지 형태로-간략추론, 1차선형추론, 2차선형추론, 변형된 2차선형추론-가 있으며, 이는 효율적인 후반부구조를 찾는데 주도적인 역할을 한다. 또한 제안된 모델의 후반부 파라미터 계수는 Weighted Least Squares Estimation(WLSE)을 사용하여 동정하며, Least Squares Estimation(LSE)를 적용한 모델의 성능과 비교한다. 마지막으로, Boston housing 데이터를 사용하여 제안된 모델의 성능을 평가하였다.

• Smart Structures and Systems
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• 제15권2호
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• pp.425-445
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• 2015

Parametric identification of structures is one of the important aspects of structural health monitoring. Most of the techniques available in the literature have been proved to be effective for structures with small degree of freedoms. However, the problem becomes challenging when the structure system is large, such as bridge structures. Therefore, it is highly desirable to develop parametric identification methods that are applicable to complex structures. In this paper, the LSE based techniques will be combined with the substructure approach for identifying the parameters of a cable-stayed bridge with large degree of freedoms. Numerical analysis has been carried out for substructures extracted from the 2-dimentional (2D) finite element model of a cable-stayed bridge. Only vertical white noise excitations are applied to the structure, and two different cases are considered where the structural damping is not included or included. Simulation results demonstrate that the proposed approach is capable of identifying the structural parameters with high accuracy without measurement noises.