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Showing 6 results for Behnam

V. Behnamgol, A. R. Vali,
Volume 11, Issue 2 (June 2015)
Abstract

In this paper, we extend the sliding mode idea to a class of unmatched uncertain variable structure systems. This method is achieved with introducing a new terminal sliding variable and the finite time stability of proposed method is proved using a new particular finite time condition in both reaching and sliding phases. In reaching phase new sliding mode controller is derived to guarantee the finite time stability of sliding surface with considering matched uncertainty. Also in sliding phase, because of introducing a new terminal sliding variable, the finite time stability of state variables with considering unmatched uncertainty has been guarantee. Therefore in proposed algorithm we are able to adjust reaching and sliding times in the presences of both matched and unmatched uncertainty. This algorithm is applied to designing control law for a moving cart system with bounded matched and unmatched uncertainties. Simulation results show the effectiveness and robustness of the proposed algorithm.

AWT IMAGE

AWT IMAGE


V. Behnamgol, A. R. Vali, A. Mohammadi,
Volume 14, Issue 3 (September 2018)
Abstract

In this paper, a new guidance law is designed to improve the performance of a homing missiles guidance system in terminal phase. For this purpose first of all, the two dimensions equations of motion are formulated, then the approximation dynamic of missile control loop is added to these equations which are nonlinear whit unmatched uncertainty. Then, a new adaptive back-stepping method is developed in order to control this system. An adaptive term is used in the control law that is converged to the uncertainty. This convergence is proved based on Lyapunov stability theorem. Therefore using this adaptive term in the control law can be eliminated the uncertainty. Based on this algorithm, a new guidance law is designed. Then its performance is compared with common guidance laws in a guidance loop simulation in the presence of control loop dynamics.

S. Fouladifard, H. Behnam, P. Gifani, M. Shojaeifard,
Volume 18, Issue 2 (June 2022)
Abstract

A semi-automatic method for the segmentation of the Left Ventricle in echocardiography images is presented. The manual segmentation of the left ventricle in all image sequences takes a lot of time. The proposed method is based on sparse representation and the design of overcomplete dictionaries based on prior knowledge of the intensity variation time curves (IVTC). We used the sparse recovery algorithm of orthogonal matching pursuit (OMP) to find the sparse coefficients of the IVTC signals. We obtained the histogram of non-zero sparse coefficients for all images. The binary images from successive frames were constructed via thresholding. In addition, we defined one image representing all the frames, dividing all the points of the heart into three groups. One group involved the points located inside the cavities in all frames. The second group included the points that belonged to the tissue in all frames. Points that in some frames are located inside the cavities and in some other frames are located inside the tissue. The results on 2D echocardiographic images acquired from both healthy and patient subjects showed good agreement with manual tracing and took a short time for the contour, including the whole left ventricle. According to the cardiology specialist, the value of ejection fraction is correctly calculated, and the error percentages were 0.83 and 2.33 for two healthy data samples. The proposed method can be applied to 3D echocardiography images to obtain the left ventricular volume. This approach also can be used for other types of medical images.

Reza Behnam, Gevork Gharehpetian,
Volume 18, Issue 4 (December 2022)
Abstract

State estimation is used in power systems to estimate grid variables based on meter measurements. Unfortunately, power grids are vulnerable to cyber-attacks. Reducing cyber-attacks against state estimation is necessary to ensure power system safe and reliable operation. False data injection (FDI) is a type of cyber-attack that tampers with measurements. This paper proposes network reconfiguration as a strategy to decrease FDI attacks on distribution system state estimation. It is well-known that network reconfiguration is a common approach in distribution systems to improve the system’s operation. In this paper, a modified switch opening and exchange (MSOE) method is used to reconfigure the network. The proposed method is tested on the IEEE 33-bus system. It is shown that network reconfiguration decreases the power measurements manipulation under false data injection attacks. Also, the resilient configuration of the distribution system is achieved, and the best particular configuration for reducing FDI attacks on each bus is obtained. 
 

M. Ehsani, A. Oraee, B. Abdi, V. Behnamgol, S. M. Hakimi,
Volume 19, Issue 1 (March 2023)
Abstract

A novel nonlinear controller is proposed to track active and reactive power for a Brushless Doubly-Fed Induction Generator (BDFIG) wind turbine. Due to nonlinear dynamics and the presence of parametric uncertainties and perturbations in this system, sliding mode control is employed. To generate a smooth control signal, dynamic sliding mode method is used. Uncertainties bound is not required in the suggested algorithm, since the adaptive gain in the controller relation is used in this study. Convergence of the sliding variable to zero and adaptive gain to the uncertainty bound are verified using Lyapunov stability theorem. The proposed controller is evaluated in a comprehensive simulation on the BDFIG model. Moreover, output performance of the proposed control algorithm is compared to the conventional and second-order sliding mode and proportional-integral-derivative (PID) controllers.


Neda Gorji Kandi, Hamid Behnam, Ali Hosseinsabet,
Volume 20, Issue 2 (June 2024)
Abstract

Cardiovascular diseases (CVD) are today a major cause of death globally that is diagnosed by measurement and quantification of left ventricle (LV) wall motion (WM) abnormality of the heart. The aim of this study was to assess the utility of left ventricular (LV) entropy, a novel measure of disease derived from two-dimensional (2D) echocardiography images that assesses the probability distribution of pixel intensities in the LV. The purpose of this research is to develop the method of LV entropy to predict heart diseases. In this algorithm, a frame is usually chosen as the reference frame to extract the region of interest (ROI) around LV and then it is mapped to all images in a cardiac cycle. Then Shannon Entropy transform was applied to calculate the distribution of pixel intensities across the LV so we obtained entropy curves and compared them. The main idea is to find a motion estimation accuracy. The results obtained by our method are quantitatively evaluated to those obtained by an experienced echocardiographer visually on 22 normal cases and 19 myocardial infarction (MI) cases in apical four-chamber (A4C) view. The entropy of diastole in MI cases was 0.50 (0.29-0.58) while in normal cases was 0.75 (0.64-1.13). The entropy of systole in MI cases was 0.64 (0.26-1.04) while in normal cases was 0.81 (0.63-1.26). The percent change of entropy for diastole and systole between normal and MI cases are 33.3% and 20.2%. The results indicate that the LV entropy curves of MI cases have less changes than normal cases.

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.