• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.506-509
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• 2006

Extended Kalman Filter iterate the prediction and the filtering based on Initial state for the next time step. EKF method for the estimation of nonlinear parameters of a structural dynamic system is necessary that initial of state vector and error covariance matrix. Because those are unknown exactly, generally selected random values. That occasion observability problem appear because of unknown initial values. In this study, for the estimation of the nonlinear parameters, a simple one degree of Freedom example is carried out by Extended Kalman Filter. And initial value assumption for Parameter Estimation of Dynamic System are developed. The result of analysis is compared with calculated standard values.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.156.4-156
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• 2001

The uncertainties of an atmospheric re-entry flight with respect to stability and controllability are aerodynamic error, measurement error of the angle of attack, variation of dynamic pressure, wind, and trim position of the control surfaces, etc. During hypersonic flight, a future angle of attack is biased from a nominal schedule. In order words, because the angle of attack is estimated from the navigation data, estimation error occurs due to wind, atmospheric density variation, etc. Error models used in this study, include a standard deviation of +-3 sigma, and are the normal distribution of statistics. This paper shows the appraisement of tracking performance onto the reference trajectory, satisfaction of the initial condition of TAEM about the re-entry system.

• ETRI Journal
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• v.28 no.5
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• pp.574-582
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• 2006

In this paper, a new error concealment algorithm is proposed for the H.264 standard. The algorithm consists of two processes. The first process uses a fuzzy logic method to select the size type of lost blocks. The motion vector of a lost block is calculated from the current frame, if the motion vectors of the neighboring blocks surrounding the lost block are discontinuous. Otherwise, the size type of the lost block can be determined from the preceding frame. The second process is an error concealment algorithm via a proposed adapted multiple-reference-frames selection for finding the lost motion vector. The adapted multiple-reference-frames selection is based on the motion estimation analysis of H.264 coding so that the number of searched frames can be reduced. Therefore the most accurate mode of the lost block can be determined with much less computation time in the selection of the lost motion vector. Experimental results show that the proposed algorithm achieves from 0.5 to 4.52 dB improvement when compared to the method in VM 9.0.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.820-832
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• 2019

Estimation of accurate blood volume flow in ultrasound Doppler blood flow spectrograms is extremely important for clinical diagnostic purposes. Blood volume flow measurements require the assessment of both the velocity distribution and the cross-sectional area of the vessel. Unfortunately, the existing volume flow estimation algorithms by ultrasound lack the velocity space distribution information in cross-sections of a vessel and have the problems of low accuracy and poor stability. In this paper, a new robust ultrasound volume flow estimation method based on multigate (RMG) is proposed and the multigate technology provides detail information on the local velocity distribution. In this method, an accurate double iterative flow velocity estimation algorithm (DIV) is used to estimate the mean velocity and it has been tested on in vivo data from carotid. The results from experiments indicate a mean standard deviation of less than 6% in flow velocities when estimated for a range of SNR levels. The RMG method is validated in a custom-designed experimental setup, Doppler phantom and imitation blood flow control system. In vitro experimental results show that the mean error of the RMG algorithm is 4.81%. Low errors in blood volume flow estimation make the prospect of using the RMG algorithm for real-time blood volume flow estimation possible.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.527-539
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• 2023

In the positioning algorithm of two-dimensional planar sensor array, the estimation error of time difference-ofarrival (TDOA) algorithm is difficult to avoid. Thus, how to achieve accurate positioning is a key problem of the positioning technology based on planar array. In this paper, a method of sensor reliability discrimination is proposed, which is the foundation for selecting positioning sensors with small error and excellent performance, simplifying algorithm, and improving positioning accuracy. Then, a positioning model is established. The estimation characteristics of the least square method are fully utilized to calculate and fuse the positioning results, and the non-uniform weighting method is used to correct the weighting factors. It effectively handles the decreased positioning accuracy due to measurement errors, and ensures that the algorithm performance is improved significantly. Finally, the characteristics of the improved algorithm are compared with those of other algorithms. The experiment data demonstrate that the algorithm is better than the standard least square method and can improve the positioning accuracy effectively, which is suitable for vibration detection with large noise interference.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.101-108
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• 2010

In this paper, we proposed a new lazy classifier with fuzzy k-nearest neighbors approach and feature selection which is based on reconstruction error. Reconstruction error is the performance index for locally linear reconstruction. When a new query point is given, fuzzy k-nearest neighbors approach defines the local area where the local classifier is available and assigns the weighting values to the data patterns which are involved within the local area. After defining the local area and assigning the weighting value, the feature selection is carried out to reduce the dimension of the feature space. When some features are selected in terms of the reconstruction error, the local classifier which is a sort of polynomial is developed using weighted least square estimation. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods such as standard neural networks, support vector machine, linear discriminant analysis, and C4.5 trees.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.103-109
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• 2018

Purpose: This study has been conducted to develop a potato yield monitoring system, consisting of a segmentation algorithm to detect potatoes scattered on a soil surface and a counting system to count the number of potatoes and convert the data from two-dimensional images to masses. Methods: First, a segmentation algorithm was developed using top-hat filtering and processing a series of images, and its performance was evaluated in a stationary condition. Second, a counting system was developed to count the number of potatoes in a moving condition and calculate the mass of each using a mass estimation equation, where the volume of a potato was obtained from its two-dimensional image, and the potato density and a correction factor were obtained experimentally. Experiments were conducted to segment potatoes on a soil surface for different potato sizes. The counting system was tested 10 times for 20 randomly selected potatoes in a simulated field condition. Furthermore, the estimated total mass of the potatoes was compared with their actual mass. Results: For a $640{\times}480$ image size, it took 0.04 s for the segmentation algorithm to process one frame. The root mean squared deviation (RMSD) and average percentage error for the measured mass of potatoes using this counting system were 12.65 g and 7.13%, respectively, when the camera was stationary. The system performance while moving was the best in L1 (0.313 m/s), where the RMSD and percentage error were 6.92 g and 7.79%, respectively. For 20 newly prepared potatoes and 10 replication measurements, the counting system exhibited a percentage error in the mass estimation ranging from 10.17-13.24%. Conclusions: At a travel speed of 0.313 m/s, the average percentage error and standard deviation of the mass measurement using the counting system were 12.03% and 1.04%, respectively.

• Journal of electromagnetic engineering and science
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• v.18 no.3
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• pp.169-174
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• 2018

In this paper, we propose an estimation method using multiple in-phase and quadrature (IQ) signals of different frequencies to evaluate the sample-time errors in the sampling oscilloscope. The estimator is implemented by ODRPACK, and a novel iteration scheme is applied to achieve fast convergence without any prior information. Monte-Carlo simulation is conducted to confirm the proposed method. It clearly shows that the multiple IQ approach achieves more accurate results compared to the conventional method. Finally, the criteria for the frequency selection and the signal capture time are investigated.

• Korean Journal of Clinical Pharmacy
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• v.23 no.2
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• pp.137-141
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• 2013

Purpose: This study compared the performance of new NONMEM estimation methods using a population analysis dataset collected from a clinical study that consisted of 40 individuals and 567 observations after a single oral dose of glimepiride. Method: The NONMEM 7.2 estimation methods tested were first-order conditional estimation with interaction (FOCEI), importance sampling (IMP), importance sampling assisted by mode a posteriori (IMPMAP), iterative two stage (ITS), stochastic approximation expectation-maximization (SAEM), and Markov chain Monte Carlo Bayesian (BAYES) using a two-compartment open model. Results: The parameters estimated by IMP, IMPMAP, ITS, SAEM, and BAYES were similar to those estimated using FOCEI, and the objective function value (OFV) for diagnosing the model criteria was significantly decreased in FOCEI, IMPMAP, SAEM, and BAYES in comparison with IMP. Parameter precision in terms of the estimated standard error was estimated precisely with FOCEI, IMP, IMPMAP, and BAYES. The run time for the model analysis was shortest with BAYES. Conclusion: In conclusion, the new estimation methods in NONMEM 7.2 performed similarly in terms of parameter estimation, but the results in terms of parameter precision and model run times using BAYES were most suitable for analyzing this dataset.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9C
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• pp.720-724
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• 2008

Channel estimation in OFDM systems is usually carried out in frequency domain based on the least-squares (LS) method and the minimum mean-square error (MMSE) method with known pilot symbols. The LS estimator has a merit of low complexity but may suffer from the noise because it does not consider any noise effect in obtaining its solution. To enhance the noise immunity of the LS estimator, we consider estimation noise in time domain. Residual noise existing at the estimated channel coefficients in time domain could be reduced by reasonable selection of a threshold value. To achieve this, we propose a channel-estimation method based on a time-domain threshold which is a standard deviation of noise obtained by wavelet decomposition. Computer simulation shows that the estimation performance of the proposed method approaches to that of the known-channel case in terms of bit-error rates after the Viterbi decoder in overall SNRs.