Showing 77 results for Nn
F. Askarian, Dr. S.m. Razavizadeh, Dr. F. Haddadi,
Volume 11, Issue 4 (12-2015)
Abstract
In this channel,we study rate region of a Gaussian two-way diamond channel which operates in half-duplex mode. In this channel, two transceiver (TR) nodes exchange their messages with the help of two relay nodes. We consider a special case of the Gaussian two-way diamond channels which is called Compute-and-Forward Multiple Access Channel (CF-MAC). In the CF-MAC, the TR nodes transmit their messages to the relay nodes which are followed by a simultaneous communication from the relay nodes to the TRs. Adopting rate splitting method in the terminal encoders and then using Compute-and-Forward (CF) relaying and decoding the sum of messages at the relay nodes, an achievable rate region for this channel is obtained. To this end, we use a superposition coding based on lattice codes. Using numerical results, we show that our proposed scheme has better performance than other similar methods and achieves a tighter gap to the outer bound.
M. Rezaei, A. Falahati,
Volume 12, Issue 1 (3-2016)
Abstract
In this paper, a cooperative algorithm to improve the orthogonal space-timefrequency block codes (OSTFBC) in frequency selective channels for 2*1, 2*2, 4*1, 4*2 MIMO-OFDM systems, is presented. The algorithm of three node, a source node, a relay node and a destination node is formed, and is implemented in two stages. During the first stage, the destination and the relay antennas receive the symbols sent by the source antennas. The destination node and the relay node obtain the decision variables employing time-space-frequency decoding process by the received signals. During the second stage, the relay node transmits decision variables to the destination node. Due to the increasing diversity in the proposed algorithm, decision variables in the destination node are increased to improve system performance. The bit error rate of the proposed algorithm at high SNR is estimated by considering the BPSK modulation. The simulation results show that cooperative orthogonal space-time-frequency block coding, improves system performance and reduces the BER in a frequency selective channel.
H. Afkar, M. A. Shamsi Nejad, M. Ebadian,
Volume 12, Issue 2 (6-2016)
Abstract
Load balancing is an important issue in distributed systems. In addition, using distributed generation sources such as photovoltaic is increasing. Power electronic converters are main interfaces between the sources and the grid. In this paper, a method has been proposed to reduce the load imbalancing in distribution networks using PV Grid Interface Converter. Two DC/DC and DC/AC converters have been utilized for connecting PV to the grid. A control strategy is presented which enables the converter to compensate the load imbalancing by injecting power of solar cells to the load and grid. Simulation results by MATLAB/SIMULINK software indicate the ability of the proposed control method to reduce the load imbalancing.
S. Khosroazad, N. Neda, H. Farrokhi,
Volume 12, Issue 3 (9-2016)
Abstract
Physical-layer network coding (PLNC) has the ability to drastically improve the throughput of multi-source wireless communication systems. In this paper, we focus on the problem of channel tracking in a Decode-and-Forward (DF) OFDM PLNC system. We proposed a Kalman Filter-based algorithm for tracking the frequency/time fading channel in this system. Tracking of the channel is performed in the time domain while data detection is implemented in the frequency domain. As an important advantage, this approach does not need for training of some subcarriers in every OFDM symbols and this, results in higher throughput, compared to other methods. High accuracy, no phase ambiguity, and stability in fast fading conditions are some other advantages of this approach.
H. Yaghobi,
Volume 13, Issue 1 (3-2017)
Abstract
Condition monitoring and protection methods based on the analysis of the machine's current are widely used according to non-invasive characteristics of current transformers. It should be noted that, these sensors are installed by default in the machine control center. On the other hand, condition monitoring based on mathematical methods has been proposed in literature. However, they are model based and are too complex. Artificial neural network (ANN) methods are robust and less model dependent for fault diagnosis when the fault signature can be directly achieved using the sampling data. In this procedure, the state of internal process will be ignored. Therefore, generalized regression neural network (GRNN) based method is presented in this paper that uses negative sequence currents (calculated from the machine's currents) as inputs to detect and locate an inter-turn fault in the stator windings of the induction motor. Turn-to-turn fault by changing the contact resistance and various numbers of shorted turns for realizing the fault severity has been modeled by Matlab/Simulink. The simulation and experimental results show that the proposed method is effective for the diagnosis of stator inter-turn fault in induction motor under the supply voltage unbalances.
Y. Zehforoosh, M. Sefidi,
Volume 13, Issue 2 (6-2017)
Abstract
In this article, we present a new design of a coplanar waveguide fed (CPW-fed) ultra-wideband (UWB) antenna with dual band-notched characteristics. Two notched frequency bands are achieved by using two inverted U-shaped stepped impedance resonators. The proposed antenna can operate from 2.82 to 11 GHz (118%), defined by VSWR< 2, except two notched bands around 3.5 GHz (WiMAX) and 5.5 GHz (WLAN). The size of the antenna is 20×20×1.6 mm3. The experimental and simulated results of the prototyped antenna, including voltage standing wave ratio (VSWR), radiation pattern, and gain characteristics are presented and discussed. In addition, Analytical Hierarchy Process (AHP) method used for comparison the proposed antenna with previous designed structures.
M. Heidari,
Volume 13, Issue 3 (9-2017)
Abstract
In this paper, a new type of multi-variable compensation control method for the wind energy conversion systems (WECS) is presented. Based on wind energy conversion systems, combining artificial neural network (ANN) control and PID, a new type of PID NN intelligent controller for steady state torque of the wind generator is designed, by which the steady state torque output is regulated to track the optimal curve of wind power factor and the blade pitch angle is regulated to keep the stable power output. Also, the LPV model of the WECS, LPV compensator for the wind generator is designed to effectively compensate output of the wind generator torque and the blade pitch angle. Finally, simulation models of the control system based on a realistic model of a 8kw wind turbines are built up based on the Dspace platform. The results show that the proposed method can reduce interferences caused by disturbed parameters of the WECS, mechanical shocks of the wind generator speed are reduced while capturing the largest wind energyfluctuation range of wind generator power output is reduced, and the working efficiency of the variable pitch servo system is improved.
H. Benbouhenni,
Volume 14, Issue 1 (3-2018)
Abstract
In this paper, the author proposes a sensorless direct torque control (DTC) of an induction motor (IM) fed by seven-level NPC inverter using artificial neural networks (ANN) and fuzzy logic controller. Fuzzy PI controller is used for controlling the rotor speed and ANN applied in switching select stator voltage. The control method proposed in this paper can reduce the torque, stator flux and total harmonic distortion (THD) value of stator current, and especially improve system good dynamic performance and robustness in high and low speeds.
H. Sedighy,
Volume 14, Issue 2 (6-2018)
Abstract
A null steering GPS antenna array is designed in this paper. In the proposed method, the exact full wave antenna radiation properties with the effect of mutual couplings and nearby scatterers are considered to calculate the array steering vector, precisely. Although the proposed method is not constrained by the array geometry and the antenna element specifications, a five patch antenna elements with planar array geometry is designed and simulated as an anti jam GPS antenna example. The simulation results show the importance of the mutual coupling effects. Moreover, the results verify the proposed method ability to encounter with the multiple GPS jammer sources. Finally, the effect of jammer power is investigated which shown that the antenna performance is increased by jammer power enhancement.
S. Mirzakuchaki, Z. Paydar,
Volume 14, Issue 4 (12-2018)
Abstract
In this study a method has been introduced to map the features extracted from the recorded electromyogram signals from the forearm and the force generated by the fingers. In order to simultaneously record of sEMG signals and the force produced by fingers, 9 requested movements of fingers conducted by 10 healthy people. Estimation was done for 6 degrees of freedom (DoF) and generalized regression neural network (GRNN) was selected for system training. The optimal parameters, including the length of the time windows, the parameters of the neural network, and the characteristics of the sEMG signal were calculated to improve the performance of the estimate. The performance was obtained based on R2 criterion. The Total value of R2 for 6 DoF was 92.8±5.2% that obtained by greedy looking system parameters in all the subjects. The result shows that proposed method can be significant in simultaneous myoelectric control.
M. H. Lazreg, A. Bentaallah,
Volume 15, Issue 1 (3-2019)
Abstract
This article presents a sensorless five level DTC control based on neural networks using Extended Kalman Filter (EKF) applied to Double Star Induction Machine (DSIM). The application of the DTC control brings a very interesting solution to the problems of robustness and dynamics. However, this control has some drawbacks such as the uncontrolled of the switching frequency and the strong ripple torque. To improve the performance of the system to be controlled, robust techniques have been applied, namely artificial neural networks. In order to reduce the number of sensors used, and thus the cost of installation, Extended Kalman filter is used to estimate the rotor speed. By viewing the simulation results using the MATLAB language for the control. The results of simulations obtained showed a very satisfactory behaviour of the machine.
H. Kiani Rad, Z. Moravej,
Volume 15, Issue 3 (9-2019)
Abstract
In this paper, a new method is conducted for incorporating the forecasted load uncertainty into the Substation Expansion Planning (SEP) problem. This method is based on the fuzzy clustering, where the location and value of each forecasted load center is modeled by employing the probability density function according to the percentage of uncertainty. After discretization of these functions, the location and value of each of the new load centers are determined based on the presented fuzzy clustering based algorithm. A Genetic Algorithm (GA) is used to solve the presented optimization problem in which the allocations and capacities of new substations as well as the expansion requirements for the existing ones are determined. With the innovative presented method, the impact of uncertainty of the power and location of the predicted loads on the results of SEP is measured, and finally, it is possible to make a proper decision for the SEP. The significant features of this method can be outlined as its applicability to large-scale networks, robustness to load changes, the comprehensiveness and also, the simplicity of applying this method to various problems. The effectiveness of proposed method is demonstrated by application on a real sub-transmission system.
M. Monemizadeh, H. Fehri, Gh. Abed Hodtani, S. Hajizadeh,
Volume 16, Issue 2 (6-2020)
Abstract
Communication in the presence of a priori known interference at the encoder has gained great interest because of its many practical applications. In this paper, additive exponential noise channel with additive exponential interference (AENC-AEI) known non-causally at the transmitter is introduced as a new variant of such communication scenarios. First, it is shown that the additive Gaussian channel with a priori known interference at the encoder when the transmitter suffers from a fast-varying phase noise can be modeled by the AENC-AEI. Then, capacity bounds for this channel under a non-negativity constraint as well as a mean value constraint on input are derived. Finally, it is shown both analytically and numerically that the upper and lower bounds coincide at high signal to noise ratios (SNRs), and therefore, the capacity of the AENC-AEI at high SNRs is obtained. Interestingly, this high SNR-capacity has a simple closed-form expression and is independent of the interference mean, analogous to its Gaussian counterpart.
M. H. Refan, A. Dameshghi,
Volume 16, Issue 2 (6-2020)
Abstract
Geometric Dilution of Precision (GDOP) is a coefficient for constellations of Global Positioning System (GPS) satellites. These satellites are organized geometrically. Traditionally, GPS GDOP computation is based on the inversion matrix with complicated measurement equations. A new strategy for calculation of GPS GDOP is construction of time series problem; it employs machine learning and artificial intelligence methods for problem-solving. In this paper, the Time Delay Neural Network (TDNN) is introduced to the GPS satellite DOP classification. The TDNN has a memory for archiving past event that is critical in GDOP approximation. The TDNN approach is evaluated all subsets of satellites with the less computational burden. Therefore, the use of the inverse matrix method is not required. The proposed approach is conducted for approximation or classification of the GDOP. The experiments show that the approximate total RMS error of TDNN is less than 0.00022 and total performance of satellite classification is 99.48%.
A. Jelodar, M. Soleimani, S. H. Sedighy,
Volume 16, Issue 2 (6-2020)
Abstract
A new four elements compact antenna array is presented and discussed to achieve enhanced phase resolution without sacrificing the array output power. This structure inspired by the Ormia Ochracea’s coupled ears. The analogy between this insect acute directional hearing capabilities and the electrically compact antenna array is used to enhance the array sensitivity to direction of arrival estimation of an electromagnetic wave. This four elements biomimetic compact array is composed of four strongly coupled antenna elements and two external coupling networks which are designed to enhance the phase resolutions between all antenna element outputs without decrease in the array output power. In other words, this four elements compact array extracts the same power level from the incident EM wave compared with regular array, while the output phase sensitivity is significantly enhanced. The simulation results confirm the advantages of this new compact array compared with the previously reported ones in the literature.
D. Jamunaa, G. K. Mahanti, F. N. Hasoon,
Volume 16, Issue 2 (6-2020)
Abstract
This paper describes the synthesis of digitally excited pencil/flat top dual beams simultaneously in a linear antenna array constructed of isotropic elements. The objective is to generate a pencil/flat top beam pair using the excitations generated by the evolutionary algorithms. Both the beams share common variable discrete amplitude excitations and differ in variable discrete phase excitations. This synthesis is treated as a multi-objective optimization problem and is handled by Quantum Particle Swarm Optimization algorithm duly controlling the fitness functions. These functions include many of the radiation pattern parameters like side lobe level, half power beam width and beam width at the side lobe level in both the beams along with the ripple in the flat top band of flat top beam. In addition to it, the dynamic range ratio of the amplitudes excitations is set below a certain level to diminish the mutual coupling effects in the array. Two sets of experiments are conducted and the effectiveness of this algorithm is proved by comparing it with various versions of swarm optimization algorithms.
M. Khalaj-Amirhosseini, M. Nadi-Abiz,
Volume 16, Issue 2 (6-2020)
Abstract
Phase Perturbation Method (PPM) is introduced as a new phase-only synthesis method to design reflectarray antennas so as their sidelobe level is reduced. In this method, only the reflected phase of conventional unit cells are perturbed from their required values. To this end, two approaches namely the conventional Optimization method and newly introduced Phase to Amplitude Approximation (PAA) method are proposed. Finally, a reflectarray antenna is designed and fabricated to have a low sidelobe level and its performance is investigated.
A. Hassannejad Marzouni, A. Zakariazadeh,
Volume 16, Issue 3 (9-2020)
Abstract
State estimation is essential to access observable network models for online monitoring and analyzing of power systems. Due to the integration of distributed energy resources and new technologies, state estimation in distribution systems would be necessary. However, accurate input data are essential for an accurate estimation along with knowledge on the possible correlation between the real and pseudo measurements data. This study presents a new approach to model errors for the distribution system state estimation purpose. In this paper, pseudo measurements are generated using a couple of real measurements data by means of the artificial neural network method. In the proposed method, the radial basis function network with the Gaussian kernel is also implemented to decompose pseudo measurements into several components. The robustness of the proposed error modeling method is assessed on IEEE 123-bus distribution test system where the problem is optimized by the imperialist competitive algorithm. The results evidence that the proposed method causes to increase in detachment accuracy of error components which results in presenting higher quality output in the distribution state estimation.
M. Petrov,
Volume 17, Issue 1 (3-2021)
Abstract
The noise in reconstructed slices of X-ray Computed Tomography (CT) is of unknown distribution, non-stationary, oriented and difficult to distinguish from main structural information. This requires the development of special post-processing methods based on the local statistical evaluation of the noise component. This paper presents an adaptive method of reducing noise in CT images employing the shearlet domain in order to obtain such an estimate. The algorithm for statistical noise assessment takes into account the distribution of signal energy in different scales and directions. The method efficiently uses the strong targeted sensitivity of shearlet systems in order to reflect more accurately the anisotropic information in the image. Because of the complex characteristics of the noise in these images, the threshold constant is determined by means of the relative entropy change criterion. The comparative analysis, which has been conducted, shows that the proposed method achieves higher values for the Peak Signal-to-Noise Ratio (PSNR), as well as lower values for the Mean Squared Error (MSE), in comparison with the other methods considered. For the MATLAB’S Shepp Logan Phantom test image, the numerical value of this superiority is on average more than 23% for the first quantitative measure, and 37% for the second. Its efficiency, which is greater than that of the wavelet-based method, is confirmed by the results obtained – the edges have been preserved during noise reduction in real CT images.
H. Benbouhenni, Z. Boudjema, A. Belaidi,
Volume 17, Issue 1 (3-2021)
Abstract
The paper presents a super-twisting sliding mode (STSM) regulator with neural networks (NN) of direct power command (DPC) for controlling the active/reactive power of a doubly-fed induction generator (DFIG) using a two-level space vector pulse width modulation (2L-SVPWM). Traditional DPC strategy with proportional-integral (PI) controllers (DPC-PI) has significantly more active/reactive power ripples, electromagnetic torque ripple, and harmonic distortion (THD) of voltages. The proposed DPC strategy based on a neural super-twisting sliding mode controller (NSTSM) minimizes the THD of stator/rotor voltage, reactive/active power ripple, rotor/stator current, and torque ripples. Also, the DPC method with NSTSM controllers (DPC-NSTSM) is a simple algorithm compared to the vector control method. Both methods are developed and programmed in Matlab on a 1.5MW DFIG-based wind turbines. The simulation studies of the DPC technique with the NSTM algorithm have been performed, and the results of these studies are presented and discussed.
