Showing 18 results for Mohammadi
A. Moosavienia, K. Mohammadi,
Volume 1, Issue 1 (January 2005)
Abstract
In this paper we first show that standard BP algorithm cannot yeild to a uniform
information distribution over the neural network architecture. A measure of sensitivity is
defined to evaluate fault tolerance of neural network and then we show that the sensitivity
of a link is closely related to the amount of information passes through it. Based on this
assumption, we prove that the distribution of output error caused by s-a-0 (stuck at 0) faults
in a MLP network has a Gaussian distribution function. UDBP (Uniformly Distributed
Back Propagation) algorithm is then introduced to minimize mean and variance of the
output error. Simulation results show that UDBP has the least sensitivity and the highest
fault tolerance among other algorithms such as WRTA, N-FTBP and ADP. Then a MLP
neural network trained with UDBP, contributes in an Algorithm Based Fault Tolerant
(ABFT) scheme to protect a nonlinear data process block. The neural network is trained to
produce an all zero syndrome sequence in the absence of any faults. A systematic real
convolution code guarantees that faults representing errors in the processed data will result
in notable nonzero values in syndrome sequence. A majority logic decoder can easily detect
and correct single faults by observing the syndrome sequence. Simulation results
demonstrating the error detection and correction behavior against random s-a-0 faults are
presented too.
Shayegh, Mohammadi, Abdipour, Sedghi, Mirzavand,
Volume 2, Issue 1 (January 2006)
Abstract
A direct conversion modulator-demodulator with even harmonic mixers with emphasis on noise analysis is presented. The circuits consist of even harmonic mixers (EHMs) realized with antiparallel diode pairs (APDPs). We evaluate the different levels of I/Q imbalances and DC offsets and use signal space concepts to analyze the bit error rate (BER) of the proposed transceiver using M-ary QAM schemes. Moreover, the simultaneous analysis of the signal and noise has been presented.
M. R. Aghamohammadi,
Volume 4, Issue 3 (July 2008)
Abstract
This paper proposes a novel approach for generation scheduling using sensitivity
characteristic of a Security Analyzer Neural Network (SANN) for improving static security
of power system. In this paper, the potential overloading at the post contingency steadystate
associated with each line outage is proposed as a security index which is used for
evaluation and enhancement of system static security. A multilayer feed forward neural
network is trained as SANN for both evaluation and enhancement of system security. The
input of SANN is load/generation pattern. By using sensitivity characteristic of SANN,
sensitivity of security indices with respect to generation pattern is used as a guide line for
generation rescheduling aimed to enhance security. Economic characteristic of generation
pattern is also considered in the process of rescheduling to find an optimum generation
pattern satisfying both security and economic aspects of power system. One interesting
feature of the proposed approach is its ability for flexible handling of system security into
generation rescheduling and compromising with the economic feature with any degree of
coordination. By using SANN, several generation patterns with different level of security
and cost could be evaluated which constitute the Pareto solution of the multi-objective
problem. A compromised generation pattern could be found from Pareto solution with any
degree of coordination between security and cost. The effectiveness of the proposed
approach is studied on the IEEE 30 bus system with promising results.
M. Aghamohammadi, S. S. Hashemi, M. S. Ghazizadeh,
Volume 7, Issue 1 (March 2011)
Abstract
This paper presents a new approach for estimating and improving voltage stability margin from phase and magnitude profile of bus voltages using sensitivity analysis of Voltage Stability Assessment Neural Network (VSANN). Bus voltage profile contains useful information about system stability margin including the effect of load-generation, line outage and reactive power compensation so, it is adopted as input pattern for VSANN. In fact, VSANN establishes a functionality for VSM with respect to voltage profile. Sensitivity analysis of VSM with respect to voltage profile and reactive power compensation extracted from information stored in the weighting factor of VSANN, is the most dominant feature of the proposed approach. Sensitivity of VSM helps one to select most effective buses for reactive power compensation aimed enhancing VSM. The proposed approach has been applied on IEEE 39-bus test system which demonstrated applicability of the proposed approach.
M. R. Aghamohammadi, S. Hashemi, M. S. Ghazizadeh,
Volume 7, Issue 2 (June 2011)
Abstract
Abstract: Voltage instability is a major threat for security of power systems. Preserving voltage security margin at a certain limit is a vital requirement for today’s power systems. Assessment of voltage security margin is a challenging task demanding sophisticated indices. In this paper, for the purpose of on line voltage security assessment a new index based on the correlation characteristic of network voltage profile is proposed. Voltage profile comprising all bus voltages contains the effect of network structure, load-generation patterns and reactive power compensation on the system behaviour and voltage security margin. Therefore, the proposed index is capable to clearly reveal the effect of system characteristics and events on the voltage security margin. The most attractive feature for this index is its fast and easy calculation from synchronously measured voltage profile without any need to system modelling and simulation and without any dependency on network size. At any instant of system operation by merely measuring network voltage profile and no further simulation calculation this index could be evaluated with respect to a specific reference profile. The results show that the behaviour of this index with respect to the change in system security is independent of the selected reference profile. The simplicity and easy calculation make this index very suitable for on line application. The proposed approach has been demonstrated on IEEE 39 bus test system with promising results showing its effectiveness and applicability.
H. Mohammadian Bishe, A. Rahimi Kian, M. Sayyed Esfahani,
Volume 8, Issue 2 (June 2012)
Abstract
This paper proposes a Trust-Region Based Augmented Method (TRALM) to
solve a combined Environmental and Economic Power Dispatch (EEPD) problem. The
EEPD problem is a multi-objective problem with competing and non-commensurable
objectives. The TRALM produces a set of non-dominated Pareto optimal solutions for the
problem. Fuzzy set theory is employed to extract a compromise non-dominated solution.
The proposed algorithm is applied to the standard IEEE 30 bus six-generator test system.
Comparison of TRALM results with the various algorithms, reported in the literature shows
that the solutions of the proposed algorithm are very accurate for the EEPD problem.
S. Mohammadi, S. Talebi, A. Hakimi,
Volume 8, Issue 2 (June 2012)
Abstract
In this paper we introduce two innovative image and video watermarking
algorithms. The paper’s main emphasis is on the use of chaotic maps to boost the
algorithms’ security and resistance against attacks. By encrypting the watermark
information in a one dimensional chaotic map, we make the extraction of watermark for
potential attackers very hard. In another approach, we select embedding positions by a two
dimensional chaotic map which enables us to satisfactorily distribute watermark
information throughout the host signal. This prevents concentration of watermark data in a
corner of the host signal which effectively saves it from being a target for attacks that
include cropping of the signal. The simulation results demonstrate that the proposed
schemes are quite resistant to many kinds of attacks which commonly threaten
watermarking algorithms.
M. Mohammadian, H. R. Momeni, M. Tahmasebi,
Volume 11, Issue 3 (September 2015)
Abstract
Artificially regulating gene expression is an important step in developing new
treatment for system-level disease such as cancer. In this paper, we propose a method to
regulate gene expression based on sampled-data measurements of gene products
concentrations. Inherent noisy behaviour of Gene regulatory networks are modeled with
stochastic nonlinear differential equation. To synthesize feedback controller, we formulate
sampling process as an impulsive system. By using a new Lyapunov function with
discontinuities at sampling times, state feedback gain that guarantees exponential meansquare
stability and H&infin performance is derived from LMIs. These LMIs also determine the
maximum allowable time between sampling points. A numerical example and a practical
application are presented to justify the applicability of the theoretical results
A. A. Abedi, M. R. Mosavi, K. Mohammadi, M. R. Daliri,
Volume 12, Issue 3 (September 2016)
Abstract
One of the instruments for determination of position used in several applications is the Global Positioning System (GPS). With a cheap GPS receiver, we can easily find the approximate position of an object. Accuracy estimation depends on some parameters such as dilution of precision, atmospheric error, receiver noise, and multipath. In this study, position accuracy with GPS receiver is classified in three classes. Nine classification methods are utilized and compared. Finally, a new method is selected for classification. Results are verified with experimental data. Success rate for classificationis approximately 84%.
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.
R. Mohammadi, H. Rajabi Mashhadi,
Volume 15, Issue 1 (March 2019)
Abstract
Distribution system reliability programs are usually based on improvement of average reliability indices. They have weakness in terms of distinguishing between reliability of different customers that may prefer different level of reliability. This paper proposes a new framework based on game theory to accommodate customers’ reliability requests in distribution system reliability provision. To do this, distribution reliability equations are developed so that it is recognized how game theory is suitable for this purpose and why conventional methods could not provide customer reliability requirements appropriately. It would be shown that customer participation in distribution system reliability provision can make conflict of interest and leads to a competition between customers. So, in this paper a game theoretic approach is designed to model possible strategic behavior of customers in distribution system reliability provision. The results show that by implementing the proposed model, distribution utilities would have the capability to respond to customers’ reliability requirements, such that it is beneficial for both utility and customers.
M. Naderan, E. Namjoo, S. Mohammadi,
Volume 15, Issue 3 (September 2019)
Abstract
Social networks have become the main infrastructure of today’s daily activities of people during the last decade. In these networks, users interact with each other, share their interests on resources and present their opinions about these resources or spread their information. Since each user has a limited knowledge of other users and most of them are anonymous, the trust factor plays an important role on recognizing a suitable product or specific user. The inference mechanism of trust in social media refers to utilizing available information of a specific user who intends to contact an unknown user. This mostly occurs when purchasing a product, deciding to have friendship or other applications which require predicting the reliability of the second party. In this paper, first the raw data of the real world dataset, Epinions, is examined, and the feature vector is calculated for each pair of social network users. Next, fuzzy logic is incorporated to rank the membership of trust to a specific class, according to two-, three- and five-classes classification. Finally, to classify the trust values of users, three machine learning techniques, namely Support Vector Machine (SVM), Decision Tree (DT), and k-Nearest Neighbors (kNN), are used instead of traditional weighted sum methods, to express the trust between any two users in the presence of a special pattern. The results of simulation show that the accuracy of the proposed method reaches to 91%, and unlike other methods, does not decrease by increasing the number of samples.
Z. Rafiee, M. Rafiee, M. R. Aghamohammadi,
Volume 16, Issue 3 (September 2020)
Abstract
Improving transient voltage stability is one of the most important issues that must be provided by doubly fed induction generator (DFIG)-based wind farms (WFs) according to the grid code requirement. This paper proposes adjusted DC-link chopper based passive voltage compensator and modified transient voltage controller (MTVC) based active voltage compensator for improving transient voltage stability. MTVC is a controller-based approach, in which by following a voltage dip (VD) condition, the voltage stability for the WF can be improved. In this approach, a voltage dip index (VDI) is proposed to activate/deactivate the control strategy, in which, two threshold values are used. In the active mode, the active and reactive power are changed to decrease the rotor current and boost the PCC voltage, respectively. Based on the control strategy, in a faulty grid, DFIG not only will be able to smooth DC-link voltage fluctuations and reduces rotor overcurrents but also it will increase the voltage of point of common coupling (PCC). Therefore, it improves transient voltage stability. The simulation results show the effectiveness of the proposed strategy for improving voltage stability in the DFIG.
A. Mohammadi, S. Soleymani, B. Mozafari, H. Mohammadnezhad-Shourkaei,
Volume 17, Issue 2 (June 2021)
Abstract
This paper proposes an advanced distribution automation planning problem in which emergency-based demand response plans are incorporated during service restoration process. The fitness function of this planning problem consists of various costs associated with fault occurrence in electric distribution systems consisting of the total yearly cost of customers’ interruptions, the total annualized investment cost of control and protection devices deployment, including sectionalizing switches, circuit breakers, and fuses and the total annual cost of performing emergency-based demand response programs in the service restoration process. Moreover, the customers’ behavior in participating in the service restoration process is also modeled through using an S-function. The proposed advanced distribution automation planning method is implemented on the fourth bus of the Roy Bilinton test system in order to evaluate its efficacy. The obtained results show that the reliability indices and the total cost of distribution automation are reduced by about 9% and 12% more than the published methods for distribution automation, respectively.
M. Ghotbi-Maleki, R. Mohammadi Chabanloo,
Volume 17, Issue 4 (December 2021)
Abstract
Expansion of power system causes short-circuit currents (SCC) of networks to exceed the tolerable SCCs of equipment. The utilization of fault current limiter (FCL) in such networks is needed to address this issue. This paper presents a new method for optimal allocation of FCLs to restrain the SCCs under permissible value. In this method, it is suggested to select a line as FCL location where the addition of FCL to this line will have the greatest impact on reducing the SCC of buses which their SCCs exceed the permissible value (known as exceeded buses). Since the optimization algorithms are not capable for optimal allocation of FCL especially in large networks, therefore, the proposed FCL allocation method is presented in the form of a computational process. In this computational process, the candidate lines for FCL location are firstly prioritized by a new index based on the effect of location of FCL on the reduction of SCCs. Then, the FCL size is determined by solving a quadratic equation firstly presented in this paper. The proposed method is implemented on networks with different sizes, and the obtained results show the performance of the proposed method over previous FCL allocation methods.
H. Ghonoodi, M. Hadjmohammadi,
Volume 17, Issue 4 (December 2021)
Abstract
In this paper a novel design is presented for a dual-band LC oscillator, using an analytical approach. The core of the proposed circuit contains a cross-coupled CMOS LC oscillator with two serried LC tanks so that the inductors of these tanks have mutual inductance. There are some switches in the circuit that directly changes mutual inductance to produce two different frequencies. This technique increases the oscillation amplitude in the same power consumption that leads to the decrement of phase noise. In other words, using two serried LC tank compensates the injected phase noise from switches. The symmetrical structure is another advantage of the presented design that makes it possible to be used in multiphase oscillator. To assess the quality of the proposed circuit, a dual-band quadrature LC oscillator has been designed to oscillate at 3.6 GHz and 6.4 GHz with 1.5 V supply and 1 mA current consumption, with TSMC 0.18 CMOS practical model. Lastly, simulation results confirm the correctness of analytical results and high proficiency of the proposed design.
O. Mahmoudi Mehr, M. R. Mohammadi, M. Soryani,
Volume 19, Issue 3 (September 2023)
Abstract
Speckle noise is an inherent artifact appearing in medical images that significantly lowers the quality and accuracy of diagnosis and treatment. Therefore, speckle reduction is considered as an essential step before processing and analyzing the ultrasound images. In this paper, we propose an ultrasound speckle reduction method based on speckle noise model estimation using a deep learning architecture called “speckle noise-based inception convolutional denoising neural network" (SNICDNN). Regarding the complicated nature of speckle noise, an inception module is added to the first layer to boost the power of feature extraction. Reconstruction of the despeckled image is performed by introducing a mathematical method based on solving a quadratic equation and applying an image-based inception convolutional denoising autoencoder (IICDAE). The results of various quantitative and qualitative evaluations on real ultrasound images demonstrate that SNICDNN outperforms the state-of-the-art methods for ultrasound despeckling. SNICDNN achieves 0.4579 dB and 0.0100 additional gains on average for PSNR and SSIM, respectively, compared to other methods. Denoising ultrasound based on its noise model estimation is not only a novel approach in comparison to traditional denoising autoencoder models but also due to the fact that it uses mathematical solutions to recover denoised images, SNICDNN shows a greater power in ultrasound despeckling.
Eisa Zarepour, Mohammad Reza Mohammadi, Morteza Zakeri-Nasrabadi, Sara Aein, Razieh Sangsari, Leila Taheri, Mojtaba Akbari, Ali Zabihallahpour,
Volume 20, Issue 3 (September 2024)
Abstract
Using mobile phones for medical applications are proliferating due to high-quality embedded sensors. Jaundice, a yellow discoloration of the skin caused by excess bilirubin, is a prevalent physiological problem in newborns. While moderate amounts of bilirubin are safe in healthy newborns, extreme levels are fatal and cause devastating and irreversible brain damage. Accurate tests to measure jaundice require a blood draw or dedicated clinical devices facing difficulty where clinical technology is unavailable. This paper presents a smartphone-based screening tool to detect neonatal hyperbilirubinemia caused by the high bilirubin production rate. A machine learning regression model is trained on a pretty large dataset of images, including 446 samples, taken from newborns' sternum skin in four medical centers in Iran. The learned model is then used to estimate the level of bilirubin. Experimental results show a mean absolute error of 1.807 mg/dl and a correlation of 0.701 between predicted bilirubin by the proposed method and the TSB values as ground truth.
