Iranian Journal of Electrical and Electronic Engineering
Volume:18 Issue: 2, Jun 2022

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.

In the new advanced drive schemes, identification and modeling of the load complex characteristics can play an important role to predict the dynamic performance of the proposed control strategy. The novelty of this paper consists in the classification of the different types of the nonlinear loads which the electrical drive systems may encounter. In this study, nonlinear components of mechanical loads are divided into two groups. The first type includes nonlinear phenomenon caused by the nature of load which is predictable and identifiable. Another type of loads nonlinear characteristic happens due to the occurrence of a mechanical fault in motor, coupling or load parts. Generally, this type of non-intended nonlinear effect is not predictable and often occurs in the installation and operation stage of the drive system utilization. In this paper, the performance of an induction servo drive system has been simulated under the influence of different types of non-linear industrial loads.

One of the major faults in Doubly-Fed Induction Generator (DFIG) is the Inter-Turn Short Circuit (ITSC) fault. This fault leads to an asymmetry between phases and causes problems to the normal state between current lines. Faults diagnosis from non-stationary signals for the Wind Turbine (WT) is difficult. Therefore, the strategy of fault diagnosis must be robust against instability. In this paper, a new intelligent strategy based on multi-level fusion is proposed for diagnosis of DFIG inter-turn stator winding fault. Firstly, to overcome the non-stationary nature of the vibration signals of the WT, empirical mode decomposition (EMD) method is performed in time-frequency domains to extract best fault features from information power sensor and information current sensor. Moreover, a feature evaluation technique is used for the input of the classifier to choose the best subset features. Secondly, Least Squares Wavelet Support Vector Machines (LS-WSVM) classifier is trained to classify fault types based on feature level fusion (FLF) from different sensors. The main parameters of SVM and the kernel function are optimized by Genetic Algorithm (GA). Finally, Dempster-Shafer evidential reasoning (DSER) is used for fusing the GA-LS-WSVM results based on decision level fusion (DLF) of individual classifiers. In order to evaluate the proposed strategy, a DFIG WT test rig is developed. The experimental results show the efficiency of the proposed structure compared to other ITSC fault diagnosis methods. The results show that the classification accuracy of DSER-GA-LS-WSVM is 98.27%.

Fair distribution of generated power has a significant impact on the performance of the power system. Many methods have been proposed for the safe and secure operation of power systems under the uncertainties of distributed generators and system load. In this paper, we present an optimal power distribution algorithm for distributed generators against uncertainties and load changes of direct-current and alternating-current transmission systems. In this optimal algorithm, considering the stable-state constraints for all uncertainties is performed. In order to establish these constraints at the lowest cost, the adaptive droop coefficients are employed to optimize the power sharing, reloading and modifying the power coefficient of each distributed generator in the power system. Simulation results show the efficiency of the proposed method to improve the performance of the system and reduce the total cost. The voltage/power deviation from reference value in the proposed method is about 1-1.5% where in the conventional droop control, it is more than 2-3%. In addition, in the same uncertainty of the load/distributed generator power in the test system, proposed method requires 20% less power redistribution compared to the conventional droop method. Also, total cost increasing (due to uncertainty increasing) in the conventional droop method is higher than the proposed method (about 10-15%) which shows the robustness of the suggested method against uncertainty changes.

This paper deals with the optimization of the CORDIC-based modified Gram-Schmidt (MGS) algorithm for QR decomposition (QRD) and presents a scalable algorithm with maximum throughput, the least possible latency, and hardware resources. The optimized algorithm is implemented on Xilinx Virtex 6 FPGA using ISE software as a fixed point with selected accuracy based on the results of MATLAB simulation. Using the loop unrolling technique with different coefficients, an attempt is made to reduce the latency and increase the throughput. In contrast, increasing the unrolling factor leads to a decrease in the frequency of the CORDIC unit as well as a decrease in the number of resources. As a result, there is a trade-off between the unrolling factor and the frequency of the CORDIC unit. By investigating the different unrolling factors, it is shown that the loop unrolling technique with a factor of 4 has the highest throughput with the value of 5.777 MQRD/s and the lowest latency with the value of 173 ns. Moreover, it is shown that throughput and latency are improved by 42.52% and 73.74% respectively compared to the not optimized case. The proposed method is also scalable for different sizes of m×m complex channel matrices, where log2 m ∈ N.

Multilevel optimal threshold selection is important and comprehensively used in the area of image processing. Mostly, entropic information-based threshold selection techniques are used. These methods make use of the entropy of the distribution of the grey levels of an image. However, entropy functions largely depend on spatial distribution of the image. This makes the methods inefficient when the distribution of the grey information of an image is not uniform. To solve this problem, a novel non-entropic method for multilevel optimal threshold selection is proposed. In this contribution, simple numbers (pixel counts), explicitly free from the spatial distribution, are used. A novel non-entropic objective function is proposed. It is used for multilevel threshold selection by maximizing the partition score using the adaptive equilibrium method. A new theoretical derivation for the fitness function is highlighted. The key to the achievement is the exploitation of the score among classes, reinforcing an improvised threshold selection process. Standard test images are considered for the experiment. The performances are compared with state-of-the-art entropic value-based methods used for multilevel threshold assortment and are found better. It is revealed that the results obtained using the suggested technique are encouraging both qualitatively and quantitatively. The newly proposed method would be very useful for solving different real-world engineering optimization problems.

In this article, a critical path identification method is proposed for ternary logic circuits. The considered structure for the ternary circuits is based on 2:1 multiplexers. Sensitization conditions for the employed ternary multiplexers are introduced. Moreover, static timing analysis and dynamic programming are utilized in the identification of true and false paths of the circuit for obtaining more realistic results in a reasonable time. An event-driven simulation engine is also developed for confirming the sensitization state of the identified paths. Some ternary arithmetic logic circuits are designed to depict the effectiveness of the proposed identification method. Simulation results show the correctness and efficiency of the proposed method.

Curtailment of the production of wind resources due to uncertainty can affect the expansion of the transmission networks. The issue that needs to be addressed is how to expand the transmission network, which is accompanied by increasing wind energy utilization. In this paper, a new framework is proposed to solve the transmission expansion planning (TEP) problem in the presence of wind farms, considering wind curtailment cost. The proposed model is a risk-constrained stochastic bi-level problem that, the difference between the expected social welfare and investment cost is maximized at the upper level where optimal decisions on expansion plans are adopted by the independent system operator (ISO). To make the best use of wind generation resources, a new term called wind power curtailment cost is added to the upper level. Also, the risk index is included in expansion decisions. The market-clearing is considered at the lower level, aiming at maximizing social welfare. Uncertainties relating to wind power and the forecasted demand are modeled by sets of scenarios. Using duality theory, the proposed framework is modeled as mixed-integer linear programming (MILP) problem. The model is examined using the classical Garver’s six-bus test system and the IEEE 24-bus reliability test system (RTS). The results show that by considering the wind curtailment cost, the transmission network is expanded in a way that increases the wind energy utilization factor from 92.05% to 95.17%.

Through beamforming, the desired signal is estimated by calculating the weighted sum of the input signals of an array of antenna elements. In the classical beamforming methods, computing the optimal weight vector requires prior knowledge on the direction of arrival (DoA) of the desired signal sources. However, in practice, the DoA of the signal of interest is unknown. In this paper, we introduce two different deep-neural-network-based beamformers which can estimate the signal of interest while suppressing noise and interferences in two/three stages when the DoAs are unknown. Employing deep neural networks (DNNs) such as convolutional neural networks (CNNs) and bidirectional long short-term memory (bi-LSTM) networks enables the proposed method to have better performance than existing methods. In most cases, the output signal to interference and noise ratio (SINR) of the proposed beamformer is more than 10dB higher than the output SINR of the classical beamformers.

Low-speed brushless permanent magnet machines are ideal for use in gearless propulsion systems. It is important to provide a precise analytical model to determine the performance characteristics of these machines. One of the challenges in designing permanent magnet machines is the elimination of the pulsating torque due to the presence of cogging torque and torque ripple components. The use of dummy slots (auxiliary teeth) is one of the most common methods of reducing pulsating torque phenomenon. In this paper, an accurate two-dimensional analytical model for calculating the magnetic vector potential in brushless permanent magnet machines is presented, taking into account the effect of stator slots, stator dummy slots, the magnetic direction of permanent magnets and phase winding style. The proposed analytical method is based on solving Laplace’s and Poisson’s equations using the separation of variables method for given regions in the subdomain approach. In the proposed method, to achieve a simpler analytical model, by changing the variable, the polar coordinate system is converted to a quasi-Cartesian coordinate system. Therefore, in mathematical terms, the hyperbolic functions are used instead of exponential ones. To validate the proposed model accuracy, the performance of a 14 kW low-speed brushless permanent magnet motor is calculated analytically and compared with the results of the numerical method and the experimental tests. Comparison of the performance results of this motor shows the consistency of analytical, numerical, and experimental results.

This paper proposes a grid-connected single-phase micro-inverter (MI) with a rated power of 300 W and an appropriate control strategy for photovoltaic (PV) systems. The proposed MI is designed based on a two-stage topology. The first stage consists of a SEPIC DC-DC converter with high voltage gain to step up the voltage of the PV panel and harness the maximum power, while the second stage includes a full-bridge DC-AC converter. The advantages of the proposed MI are the use of fewer components to provide suitable output voltage level for connection to a single-phase grid, continuous input current, limited voltage stress on the switch, high efficiency, long operational lifetime, and high reliability. Lower input current ripple and the presence of film capacitors in the power decoupling circuit increase the lifetime and reliability of the proposed MI. In the proposed MI, the active power decoupling circuit, which is normally used in a typical single-stage SEPIC-based MI, is eliminated to achieve both a long lifetime and high efficiency. The operating principles of the proposed MI are analyzed under different conditions. The results of design and simulation confirm the advantages and proper performance of the proposed MI.

Lightning is the main factor of outage and insulation breakdown of power system. The lightning event can produce dangerous overvoltage, equipment failures, and power supply interruption. In this paper, externally gapped Line arresters (EGLAs) performances have been investigated to evaluate the lightning performances of a typical 63 kV transmission line. A probabilistic analysis has been done to study the EGLA performance in transmission line by Monte-Carlo method. The results show the EGLA performance dependency to soil resistivity and lightning strike parameters.

Anisotropic media appear regularly in electromagnetic wave engineering. The finite-difference time-domain (FDTD) method is a robust technique to model such media. However, the value of the time step in the FDTD algorithm is bounded by the Courant-Friedrichs-Lewy (CFL) condition. In this paper, a simple analytical approach is developed using the Gershgorin circle theorem to derive a point-wise closed-form relation for the CFL condition in bounded inhomogeneous anisotropic media. The proposed technique includes objects of arbitrary shapes with straight, tilted, or curved interfaces located in a computational space with uniform or adaptive gridding schemes. Both axial and non-axial anisotropies are considered in the analysis. The proposed method is able to investigate the effect of boundaries and interfaces on the stability of the algorithm. It is shown that in the presence of an interface between two anisotropic media, the von-Neumann criterion is not able to predict the stability bound for specific ranges of the permittivity tensor components and unit cell aspect ratios. Exploiting the proposed closed-form formulations, it is possible to tune the CFL time step and avoid the temporal instability by the wise selection of the gridding scheme especially in curved boundaries where subcell modelings such as Yu-Mittra formalism are applicable. Some illustrative examples are provided to verify the method by comparing the results with those of the eigenvalue analysis and time-domain simulations.