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Consistency Analysis of Kalman Filter for Modal Analysis of Structures
(Institute of Electrical and Electronics Engineers Inc., 2009) Tanyer, İlker; Özen, Serdar; Dönmez, Cemalettin; Altınkaya, Mustafa Aziz
In this paper, Consistency Analysis of Kalman Filter for Modal Analysis of Structural Systems is made. As a future work, A fundamental Modal Analysis algorithm, Eigensystem Realization Algorithm(ERA) will be used with Kalman filters together to make a modal parameter estimation for a structural system. By applying ERA to the impulse response measurements taken from the structure, a state-space representation will be written. Kalman filter will be used as a state estimator in this study and it will have a critical role on minimizing the measurement noise. Before using Kalman filter with ERA, a consistency analysis of Kalman filter is made for artificial impulse response data of the structural system.
Alpha-Trimmed Means of Multiple Location Estimates
(Institute of Electrical and Electronics Engineers Inc., 2013) Altınkaya, Mustafa Aziz
Localization by distance measurements is a common technique for solving this contemporary problem. The methods which achieve the theoretically optimum solutions have generally iterative structures. That is why when limited computational load is required, suboptimum methods described by closed form formulas like the one of Coope which depends on orthogonal decomposition of sensor coordinates, are preferred. In this method, when there are more than necessary distance measurements required for localization, the location will be found as the arithmetic average of the estimates obtained using the all three-combinations of distance measurements. In the averaging, eliminating the outlier estimates will increase the performance. In this case discarding the estimates making the ratio of alpha which are farthest away from the arithmetic average, one attains the socalled alpha-trimmed mean of the estimates. Applying this technique, the disturbing effects of impulsive mixture of Gaussian contamination are eliminated and similar performances as in the case of Gaussian distance measurements are attained in localization.
A Bayesian Approach for Licence Plate Recognition Developed on a Realistic Simulation Environment
(Institute of Electrical and Electronics Engineers Inc., 2013) Efeler, Mahmut Cenk; Altınkaya, Mustafa Aziz; Gümüştekin, Şevket
Template matching is one of the most common methods for license plate recognition. This method discards prior probabilities of license plate codes. The posterior code class probabilities constructed by including the prior probability information are expected to improve the recognition performance. The probability information that needs to be included requires extensive training data, which is quite costly to obtain. In order to generate these training images a license plate image simulator is developed with a realistic noise model. Simulated license plate images are then used to test a Bayesian decision theory based recognition procedure. Test results indicate that, with the inclusion of prior information, significant recognition gain is obtained with respect to standard template matching method at high noise levels.
Citation - WoS: 2
Citation - Scopus: 3
Long Term Wind Speed Prediction With Polynomial Autoregressive Model
(Institute of Electrical and Electronics Engineers Inc., 2015) Karakuş, Oktay; Kuruoğlu, Ercan E.; Altınkaya, Mustafa Aziz
Wind energy is one of the preferred energy generation methods because wind is an important renewable energy source. Prediction of wind speed in a time period, is important due to the one-to-one relationship between wind speed and wind power. Due to the nonlinear character of the wind speed data, nonlinear methods are known to produce better results compared to linear time series methods like Autoregressive (AR), Autoregressive Moving Average (ARMA) in predicting in a period longer than 12 hours. A method is proposed to apply a 48-hour ahead wind speed prediction by using the past wind speed measurements of the (Cesme Peninsula. We proposed to model wind speed data with a Polynomial AR (PAR) model. Coefficients of the models are estimated via linear Least Squares (LS) method and up to 48 hours ahead wind speed prediction is calculated for different models. In conclusion, a better performance is observed for higher than 12-hour ahead wind speed predictions of wind speed data which is modelled with PAR model, than AR and ARMA models.

WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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