• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.16-21
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• 2005

This paper considers the problem of state estimation in linear continuous-time systems with multi-sensor environment and observation uncertainties. We propose two suboptimal filtering algorithms for these types of systems. The filtering algorithms consist of two steps: The local optimal Kalman estimates are computed at the first step. And, these local estimates are lineally fused at the second step. The implementation of the two-step filtering algorithms needs a lower memory demand than the optimal Kalman and adaptive Lainiotis-Kalman filters. In consequence of parallel structure of the proposed filters, the parallel computers can be used for their design. The examples exhibit the effect of common noise on the performance of fusion of the local Kalman estimates based on observations from different sensors and in the presence of uncertainties.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.165-171
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• 2006

This paper proposes a data fusion algorithm of nonlinear multi sensor dynamic systems of synchronous sampling based on filtering step by step. Firstly, the object state variable at the next time index can be predicted by the previous global information with the systems, then the predicted estimation can be updated in turn by use of the extended Kalman filter when all of the observations aiming at the target state variable arrive. Finally a fusion estimation of the object state variable is obtained based on the system global information. Synchronously, we formulate the new algorithm and compare its performances with those of the traditional nonlinear centralized and distributed data fusion algorithms by the indexes that include the computational complexity, data communicational burden, time delay and estimation accuracy, etc.. These compared results indicate that the performance from the new algorithm is superior to the performances from the two traditional nonlinear data fusion algorithms.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.1
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• pp.138-149
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• 2014

This study proposes an estimation method of strain distribution for multi-span steel beam structure under unspecific loading conditions. The estimation method in this paper employs the curve fitting using the least square method from measured strain data, not analytical method. To verify the proposed estimation method, a static loading test for multi-span steel beam on which distributed and concentrated loads act was conducted. The strain data for verification was measured by FBG sensors that have multiplexing technology. The analysis of the accuracy of strain estimation for distributed and concentrated loads and the errors by considering the number of measured points used in the estimation were conducted. In the maximum strain points, the strains could be estimated with the errors of 5.89% (loading step 1) and 6.26% (loading step 2). In case of decreasing the number of sensors, it was also confirmed that the errors increased (0.26~0.37%). Through the curve fitting method, it is possible to estimate the strain distribution (maximum strains and their locations) of multi-span beam for unspecific loads and go over the limit of the analytical estimation method which is suitable for specific distributed loads.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.479-484
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• 2000

The reliable estimation of the system parameters and the accurate prediction of the system behavior are important to design tunnel safely and economically. Therefore, the back analysis using the field measurements data is useful to evaluate the geotechnical parameter for tunnel. In the back analysis method, the selection of initial value and uncertainty of field measurements influence significantly on the analysis result. In this paper, to overcome uncertainty of field measurements, we performed the back analysis using the displacement data gained at each step of excavation and support.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.435-436
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• 2006

When working with multi-view images, imbalances between multi-view images occur a serious problem in multi-view video coding because they decrease the performance of disparity estimation. To overcome this problem, we propose inter-view balanced disparity estimation for multi-view video coding. In general, the imbalance problem can be solved by a preprocessing step that transforms reference images linearly. However, there are some problems in pre-processing such as the transformation of the original images. In order to obtain a balancing effect among the views, we perform block-based disparity estimation, which includes several balancing parameters.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.65-71
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• 2014

This paper present the multi-modal user distance estimation system using mono camera and mono microphone basically equipped with a mobile device. In case of a distance estimation method using an image, we is estimated a distance of the user through the skin color region extraction step, a noise removal step, the face and eyes region detection step. On the other hand, in case of a distance estimation method using speech, we calculates the absolute difference between the value of the sample of speech input. The largest peak value of the calculated difference value is selected and samples before and after the peak are specified as the ROI(Region of Interest). The samples specified perform FFT(Fast Fourier Transform) and calculate the magnitude of the frequency domain. Magnitude obtained is compared with the distance model to calculate the likelihood. We is estimated user distance by adding with weights in the sorted value. The result of an experiment using the multi-modal method shows more improved measurement value than that of single modality.

• Nuclear Engineering and Technology
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• v.31 no.2
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• pp.192-201
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• 1999

This paper proposes an improved signal processing strategy for accurate feedwater flowrate estimation in nuclear power plants. It is generally known that ∼2% thermal power errors occur due to fouling Phenomena in feedwater flowmeters. In the strategy Proposed, the noises included in feedwater flowrate signal are classified into rapidly varying noises and gradually varying noises according to the characteristics in a frequency domain. The estimation precision is enhanced by introducing a low pass filter with the wavelet analysis against rapidly varying noises, and an autoassociative neural network which takes charge of the correction of only gradually varying noises. The modified multivariate stratification sampling using the concept of time stratification and MAXIMIN criteria is developed to overcome the shortcoming of a general random sampling. In addition the multi-stage robust training method is developed to increase the quality and reliability of training signals. Some validations using the simulated data from a micro-simulator were carried out. In the validation tests, the proposed methodology removed both rapidly varying noises and gradually varying noises respectively in each de-noising step, and 5.54% root mean square errors of initial noisy signals were decreased to 0.674% after de-noising. These results indicate that it is possible to estimate the reactor thermal power more elaborately by adopting this strategy.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.259-266
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• 2003

In this paper, we propose a multi-level block matching algorithm using motion information in blocks. In the proposed algorithm, the block-level is decided by the motion degree in the block before motion searching procedure, and then adequate motion searching performs according to the block-level. This improves computational efficiency by eliminating the unnecessary searching Process in no motion or low motion regions, and brings more accurate estimation results by deepening motion searching Process in high motion regions. Simulation results show that the proposed algorithm brings the lower estimation error about 20％ MSE reduction with the fewer blocks pet frame and the operation number was reduced to 56% compared to TSSA and 98% compared to FS -BMA with constant block size.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.359-362
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• 2001

본 논문에서는 광학센서와 적외선 센서를 사용하는 Multi-sensor 시스템에서 영상 정보를 통한 물체의 추적 및 인식에 필요한 영상을 분리하는데 필요한 전처리와 object 기반의 추적 방법을 제안하였다. 일반적인 추적 알고리즘의 목표는 consistency를 유지하는데 있다. 그러나 인식에 필요한 영상을 분리하기 위해서는 물체의 범위를 정확히 판단 할 수 있는 능력이 중요하다. 이를 위해 CCD와 IR영상에 동시에 적용 가능한 전처리 기법과 object 기반의 two-step 추적 알고리즘을 통해 consistency외에도, 물체의 범위를 estimation하여 인식에 필요한 범위를 분리해 낸다. 본 논문에서는 ITS 의 ETCS application을 위해 이종 센서인 CCD와 IR의 야간 차량 영상정보를 이용하여 알고리즘을 test 하였다.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.465-478
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• 2003

This paper presents two methods for automatic estimation of threshold values in unsupervised change detection of multi-temporal remote sensing images. The proposed methods consist of two analytical steps. The first step is to compute the parameters of a 3-component Gaussian mixture model from difference or ratio images. The second step is to determine a threshold value using Bayesian rule for minimum error. The first method which is an extended version of Bruzzone and Prieto' method (2000) is to apply an Expectation-Maximization algorithm for estimation of the parameters of the Gaussian mixture model. The second method is based on an iterative thresholding algorithm that successively employs thresholding and estimation of the model parameters. The effectiveness and applicability of the methods proposed here were illustrated by two experiments and one case study including the synthetic data sets and KOMPSAT-1 EOC images. The experiments demonstrate that the proposed methods can effectively estimate the model parameters and the threshold value determined shows the minimum overall error.