Othman S. Al-Heety^1,*, Zahriladha Zakaria^1,*, Ahmed Abu-Khadrah², Mahamod Ismail³, Sarmad Nozad Mahmood⁴, Mohammed Mudhafar Shakir⁵, Sameer Alani⁶, Hussein Alsariera¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2103-2127, 2024, DOI:10.32604/cmes.2023.029509

Abstract Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision. In this article, these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data. The framework integrates Kalman filtering and Q-learning. Unlike smoothing Kalman filtering, our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error. Unlike traditional Q-learning, our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road… More >

Shenkun Zhao, Chao Jiang^*, Zhe Zhang, Xiangyun Long

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1151-1173, 2020, DOI:10.32604/cmes.2020.08867

Abstract In the Prognostics and Health Management (PHM), remaining useful life (RUL) is very important and utilized to ensure the reliability and safety of the operation of complex mechanical systems. Recently, unscented Kalman filtering (UKF) has been applied widely in the RUL estimation. For a degradation system, the relationship between its monitored measurements and its degradation states is assumed to be nonlinear in the conventional UKF. However, in some special degradation systems, their monitored measurements have a linear relation with their degradation states. For these special problems, it may bring estimation errors to use the UKF method directly. Besides, many uncertain… More >

Raquel R. Pinho¹, João Manuel R. S. Tavares¹

CMES-Computer Modeling in Engineering & Sciences, Vol.46, No.1, pp. 51-76, 2009, DOI:10.3970/cmes.2009.046.051

Abstract This work addresses the problem of tracking feature points along image sequences. In order to analyze the undergoing movement, an approach based on the Kalman filtering technique has been used, which basically carries out the estimation and correction of the features' movement in every image frame. So as to integrate the measurements obtained from each image into the Kalman filter, a data optimization process has been adopted to achieve the best global correspondence set. The proposed criterion minimizes the cost of global matching, which is based on the Mahalanobis distance. A management model is employed to manage the features being… More >

Taoyun Zhou^1,2, Baowang Lian¹, Siqing Yang^2,*, Yi Zhang¹, Yangyang Liu^1,3

CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 225-245, 2018, DOI: 10.3970/cmc.2018.02671

Abstract For situations such as indoor and underground parking lots in which satellite signals are obstructed, GNSS cooperative positioning can be used to achieve high-precision positioning with the assistance of cooperative nodes. Here we study the cooperative positioning of two static nodes, node 1 is placed on the roof of the building and the satellite observation is ideal, node 2 is placed on the indoor windowsill where the occlusion situation is more serious, we mainly study how to locate node 2 with the assistance of node 1. Firstly, the two cooperative nodes are located with pseudo-range single point positioning, and the… More >