• 대한조선학회논문집
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• 제38권4호
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• pp.30-38
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• 2001

조류를 고려한 조종운동방정식을 정립한 후 선박의 운동변수 뿐만 아니라 외력 및 조류의 방향과 속도도 상태변수로 설정하여 비선형 상태방정식과 측정방정식을 표현하였다. 여기서 외력은 3차의 Gauss-Markov 프로세스로 표시하고, 조류의 방향과 속도는 일정하다고 가정하였다. 상태 추정을 위하여 확장 Kalman-Bucy 필터와 고정간격 스무더를 이용하였다. 기존의 Hwang은 실선 시운전 계측값을 이용하여 동유체력미계수 및 조류의 영향을 동시에 확장 Kalman 필터를 이용하여 추정하였으므로 매개변수의 개수가 상당히 많아지는 반면 모델링 전 추정기법을 사용하면 각각의 동유체력미계수를 추정하는 대신에 3방향의 외력과 조류 변수만을 추정한다. 측정잡음이 포함된 시뮬레이션 측정값을 적용하여 조류 변수를 추정하는 경우 실제값이 잘 추정되는 것을 확인하였다.

• IEIE Transactions on Smart Processing and Computing
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• 제1권2호
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• pp.78-87
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• 2012

In this paper, we address the tracking problem caused by camera motion and rolling shutter effects associated with CMOS sensors in consumer handheld cameras, such as mobile cameras, digital cameras, and digital camcorders. A modified particle filtering method is proposed for simultaneously tracking objects and compensating for the effects of camera motion. The proposed method uses an elastic registration algorithm (ER) that considers the global affine motion as well as the brightness and contrast between images, assuming that camera motion results in an affine transform of the image between two successive frames. By assuming that the camera motion is modeled globally by an affine transform, only the global affine model instead of the local model was considered. Only the brightness parameter was used in intensity variation. The contrast parameters used in the original ER algorithm were ignored because the change in illumination is small enough between temporally adjacent frames. The proposed particle filtering consists of the following four steps: (i) prediction step, (ii) compensating prediction state error based on camera motion estimation, (iii) update step and (iv) re-sampling step. A larger number of particles are needed when camera motion generates a prediction state error of an object at the prediction step. The proposed method robustly tracks the object of interest by compensating for the prediction state error using the affine motion model estimated from ER. Experimental results show that the proposed method outperforms the conventional particle filter, and can track moving objects robustly in consumer handheld imaging devices.

• Smart Structures and Systems
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• 제30권6호
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• pp.583-599
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• 2022

In this paper, a multi-objective wireless sensor network configuration optimization method is proposed. The proposed method aims to determine the optimal information and lifespan wireless sensor network for structural health monitoring of large-scale infrastructures. In particular, cluster-based wireless sensor networks with multi-type of sensors are considered. To optimize the lifetime of the wireless sensor network, a cluster-based network optimization algorithm that optimizes the arrangement of cluster heads and base station is developed. On the other hand, based on the Bayesian inference, the uncertainty of the estimated parameters can be quantified. The coefficient of variance of the estimated parameters can be obtained, which is utilized as a holistic measure to evaluate the estimation accuracy of sensor configurations with multi-type of sensors. The proposed method provides the optimal wireless sensor network configuration that satisfies the required estimation accuracy with the longest lifetime. The proposed method is illustrated by designing the optimal wireless sensor network configuration of a cable-stayed bridge and a space truss.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2399-2410
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• 2017

In this paper, a linear matrix inequality-based sampled-data fuzzy observer design method is proposed based on the exact discretization approach. In the proposed design technique, a numerically relaxed observer design condition is obtained by using the discrete-time fuzzy Lyapunov function. Unlike the existing studies, the designed observer is robust to the uncertain premise variable because the fuzzy observer is designed under the imperfect premise matching condition, in which the membership functions of the system and observer are mismatched. In addition, we apply the proposed method to the state estimation problem of the attitude and heading reference system (AHRS). To do this, we derive a Takagi-Sugeno fuzzy model for the AHRS system, and validate the proposed method through the hardware experiment.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2014년도 전력전자학술대회 논문집
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• pp.401-402
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• 2014

State of Charge (SOC) and State of Health (SOH) are the key issues for the application of Absorbent Glass Mat (AGM) type battery in Idle Start Stop (ISS) system which is popularly integrated in Electric Vehicles (EVs). However, battery parameters strongly depend on SOC, current rate and temperature and significantly change over the battery life cycles. In this research, a novel method for SOC, SOH estimation which combines the Auto Regressive with external input (ARX) method using for online parameters prediction and Dual Extended Kalman Filter (DEKF) algorithm considering hysteresis is proposed. The validity of the proposed algorithm is verified by the simulation and experiments.

• 조명전기설비학회논문지
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• 제20권10호
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• pp.27-34
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• 2006

본 논문은 드라이브의 간적벡터제어에서 ANN을 이용하여 유도전동기의 회전자 저항을 온라인 추정하기 위한 새로운 기법을 제시한다. 약전파 알고리즘은 신경회로망의 학습을 위해 사용된다. 신경회로망의 실제 상태값과 유도전동기의 요구값 사이의 오차는 신경회로망 모델의 하중값 조절을 위하여 역전파 하여 실제값이 요구값을 추정하도록 한다. 드라이브의 회전자 저항, 토크, 자속응답 성능등 이러한 추정기의 성능은 고유값으로부터 회전자 저항을 연구하게 된다. 회전자 저항은 유도전동기 드라이브의 벡터제어에서 제시된 ANN을 사용하여 추정한다.

• 한국전자통신학회논문지
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• 제8권11호
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• pp.1681-1690
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• 2013

본 논문은 IPMSM(Interior Permanent Magnet Synchronous Motor)의 센서리스 운전을 위해 확장 칼만 필터를 기반으로 하는 속도와 위치 추정기의 설계방법을 제안한다. 제안된 방법은 상태 추정의 정밀도를 향상시키기 위해서 시스템 모델의 상태추정구간을 더욱 세분화하여 나누고, 세분화한 각 구간을 테일러급수 전개하여 일차항만 사용하여 추정하였다. 제안된 상태 추정기는 2차 확장칼만필터에 비해 사전추정의 연산의 양을 크게 하지 않고, 상태추정의 정밀도가 증가함을 시뮬레이션을 통해 보일 수 있었다.

• 한국측량학회지
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• 제34권4호
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• pp.383-390
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• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.245-248
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• 2006

A particle filter is one of the most famous filters. The reason why the particle filter is widely used is that particle deals with the state estimation problem for not only linear models with Gaussian noise but also the non-linear models with non-Gaussian noise and it receives great attention from many engineering fields. In the point of view state estimator, particle filter is feedforward observer. According to the characteristic of dynamic system, the feedforward observer can estimate real state. However, the speed of convergence of feedforward observer between the actual state and the estimated state cannot be satisfied. Since the particle filter is a sort of feedforward observer, the convergence speed of particle filter is slow, and the particle filter cannot estimate actual state like particle collapse problem. In order to overcome the limitation of particle filter as a kind of feedfoward estimator, we propose a new particle filter which has feedback term, called particle filter with feedback. Our proposed method is analyzed theoretically and studied by computer simulation. Comparisons are made with other filtering mehod.

• 드라이브 ㆍ 컨트롤
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• 제9권2호
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• pp.15-20
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• 2012

Pneumatic cylinder meter-out driving apparatus is used widely because it is clean, lightweight, and can be easily serviced. In this study an estimation method of energy consumption for pneumatic cylinder meter-out driving apparatus is proposed. The proposed method is derived from state equation and energy equation of air, and, the equation of motion of a moving part of a pneumatic cylinder reflecting the characteristics of the meter-out driving. The effectiveness of the proposed method is proved by simulation study and it demonstrates that the proposed method can evaluate the energy consumption quickly and easily when the parameters of the driving apparatus are changed.