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Articles by M.H. Bahari
Total Records ( 2 ) for M.H. Bahari
  M.H. Bahari , A. Karsaz and M.B. Naghibi-S
  In this study, an intelligent approach is applied to reset the error covariance matrix of Kalman filter (KF) for high maneuver target tracking. In practice, the standard KF is used for non-maneuvering target tracking applications, which is optimal in the Minimum Mean Square Error (MMSE) sense. Furthermore, it has fast convergence rate. However, after some iterations the steps of the KF become very small. Because of small steps in KF, the accuracy of target tracking may be seriously degraded in presence of maneuver. This drawback can be overcome by resetting the error covariance matrix of the KF. Since the information of earlier updates will be partially lost by resetting the error covariance matrix, system should reset it just when the target maneuvers and KF steps are not large enough to track the target accurately. Moreover, resetting factor should be proportional to the maneuver. Therefore, we present an intelligent approach based on target maneuver detection to determine proper instants for resetting the error covariance matrix. In addition, the new scheme is enable to determine the optimal value of resetting factor in each iteration effectively. Simulation results illustrate that the tracking ability of the proposed scheme is more than conventional approaches, especially for high maneuvering target tracking applications.
  M.H. Bahari , A. Bahari , F. Nejati Moharrami and M.B. Naghibi Sistani
  In this research, GA is employed to determine constant coefficients of Bourgoyne and Young model and consequently predict drilling rate with high accuracy. Bourgoyne and Young model represents a general mapping between drilling rate and some drilling variables. There are eight unknown parameters in this model, which are dependent to the ground formation types. These eight parameters can be determined using previous drilling experiences. Previous drilling experiences include date sets of eight different drilling parameters such as depth, bit weight, rotary speed and pore pressure. In this research, sensory data of drilling nine different wells of Khangiran Iranian gas field has been collected to obtain needed sets. Bourgoyne and Young recommended multiple regression method to determine unknown coefficients. However, applying multiple regression method leads to physically meaningless values in some situations. Although, some new mathematical methods have recently been issued to reach meaningful results, applying them diminishes drilling rate prediction accuracy in practice. In order to reach more accurate prediction and physically meaningful coefficients, we applied Genetic Algorithm (GA) to determine unknown parameters of Bourgoyne and Young model. Afore-mentioned wells were considered for applying the new approach and testing it. Simulation results prove the proficiency of the new methodology to determine constant coefficients of Bourgoyne and Young model.
 
 
 
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