Showing 3 results for Shabani
Sh. Kasaei, E. Shabani Nia,
Volume 7, Issue 3 (September 2011)
Abstract
Multicamera vehicle tracking is a necessary part of any video-based intelligent transportation system for extracting different traffic parameters such as link travel times and origin/destination counts. In many applications, it is needed to locate traffic cameras disjoint from each other to cover a wide area. This paper presents a method for tracking moving vehicles in such camera networks. The proposed method introduces a new method for handling inter-object occlusions as the most challenging part of the single camera tracking phase. This approach is based on coding the silhouette of moving objects before and after occlusion and separating occluded vehicles by computing the longest common substring of the related chain codes. In addition, to improve the accuracy of the tracking method in the multicamera phase, a new feature based on the relationships among surrounding vehicles is formulated. The proposed feature can efficiently improve the efficiency of the appearance (or space-time) features when they cannot discriminate between correspondent and non-correspondent vehicles due to noise or dynamic condition of traffic scenes. A graph-based approach is then used to track vehicles in the camera network. Experimental results show the efficiency of the proposed methods.
M. R. Mosavi, M. Khishe, Y. Hatam Khani, M. Shabani,
Volume 13, Issue 1 (March 2017)
Abstract
Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorithms have been conventional to training RBF network in the recent years. This study uses Stochastic Fractal Search Algorithm (SFSA) for training RBF NNs. The particles in the new algorithm explore the search space more efficiently by using the diffusion property, which is observed regularly in arbitrary fractals. To assess the performance of the proposed classifier, this network will be evaluated with the two benchmark datasets and a high-dimensional practical dataset (i.e., sonar). Results indicate that new classifier classifies sonar dataset six percent better than the best algorithm and its convergence speed is better than the other algorithms. Also has better performance than classic benchmark algorithms about all datasets.
S. Shabani, M. Asadi, A. Zakipour,
Volume 19, Issue 1 (March 2023)
Abstract
In this paper, parasitic capacitors and common mode voltage (CMV) are modeled in a delta connection multilevel cascaded STATCOM. In high frequency and high voltage applications the parasitic capacitors play important role in common mode voltages. In this paper, parasitic capacitors and CMV are modeled in a multilevel cascaded STATCOM and also parasitic currents are calculated, then a method will be proposed to reduce the effects of the parasitic capacitors. The values of parasitic capacitors are calculated by finite element software. Finally, a delta connection 13-level cascaded STATCOM with parasitic capacitors will be simulated in MATLAB Simulink and then simulation results will be presented.
