نشریه مهندسی برق
سال چهل و پنجم شماره 4 (پیاپی 74، زمستان 1394)

Wide area measurement system (WAMS) prepares and transmits the measured remote signals of a whole power system to wide area controller (WAC) to achieve an effective inter-area oscillations damping. One of the main issue is the existence of transfer time delay in WAC input signals which can degrade the damping performance of WAC. One way to overcome this problem is to consider time delay in the controller design procedure to ensure effective control of WAC. In this paper, time delays are modeled by padé approximations and the time delay uncertainty is described by linear fractional transformations (LFT). The synthesis of the WAC is defined as a problem of H∞ mixed sensitivity control and is resolved by the linear matrix inequality (LMI) approach. The simulation results on 16-machine 68-bus system show that the WAC designed to provide a supplementary control signal of SVC, has satisfactory performance in a wide range of time delay.

Reflection guided-mode resonance (GMR) grating filters are the new type of the optical filters that are based on the guided-mode resonance phenomenon and have seen increasing demands in optical detectors, optical processors, couplers and so on. Careful selection of the structural parameters yields filters with high efficiency at a desired resonance frequency and low sidebands. Design of these elements with accurate spectral properties is a difficult task and is so interesting field for many researchers. In this paper a novel method using the meta-heuristic bat algorithm (BA) and efficient objective function is proposed for designing of this filter in the visible wavelength range. Simulation results show that the proposed method has high accuracy and reasonable computation time in comparison with other methods.

In this paper, a new approach is presented for identification of power quality (PQ) disturbances. At first, the proposed characteristics are extracted from the disturbances waveforms using S-transform. Then, based on the values of these characteristics, the type of the disturbance is recognized. This approach is evaluated for detection and classification of ten types of PQ disturbances including impulse, interruption, swell, sag, notch, oscillatory transient, harmonic, flicker, harmonic with swell and harmonic with sag. The results of this study demonstrate the suitable accuracy and speed of the proposed approach in identification of the mentioned disturbances. In addition, testing of this approach under various noisy conditions shows that its sensitivity to noise is too low.

One of the important problems in EEG signal analysis is epilepsy detection. In this paper, an epilepsy detection and classification algorithm is proposed based on fusion of the extracted Hartley transform features. In this algorithm, in addition to usual frequency and time features such as spectrum entropy and power spectral density, a novel feature is defined based on fusion of the extracted Hartley transform features. To define this feature, the extracted features from Hartley transform are fused based on a kernel matrixes scenario. To optimize and reduce the dimensions of extracted feature vector from EEG signals, a hybrid model is applied based on Genetic algorithm using Memetic learning strategy and multilayer back propagation neural network. The final classification is carried out on the optimal feature vectors by a Perceptron neural network with a hidden layer and average accuracy rate of 95.325% is obtained.

Side Channel Cube Attack (SCCA) is a kind of Algebraic Side Channel Attacks (ASCA). In recent years, this kind of attack is implemented on different light weight block ciphers, and it is known as the most powerful attack on these kinds of algorithms. In this paper, we proposed some advanced techniques such as locality space search, elimination of iterated chosen plaintexts, dynamic iteration, and choosing intelligent intermediate states to improve SCCA on block ciphers. Accordingly, the improved SCCA can mount efficiently on not only light weight but also high weight block ciphers. Our results show, the proposed model of SCCA can recover 80 bit key of PRESENT-80 only with 26.13 chosen plaintexts and recover 128 key bit of AES-128 only with 27.3 chosen plaintexts. To the best of our knowledge, there are the most efficient SCCA on PERESENT-80 and AES-128.

The goal of image fusion is integration of two or more images into a single image for increasing the accuracy and quality of interpretation of images. The functional imaging system such as PET (positron emission tomography) produces images with high spectral content but low spatial information. Images that achieved by the structural imaging systems such as MRI (magnetic resonance imaging) have good spatial resolution and low spectrum. The proposed fusion method in this paper extracts spatial features using Gabor wavelet from structural image and injects them into functional image. This algorithm is a combined method, which exploits the power of retina model in preserving spectral information. The proposed method is compared to IHS, Brovey, PCA, discrete wavelet transform, NSCT and retina model fusion techniques. For quantity assessment, four famed and traditional evaluation methods are used. The quality and quantity results show the proposed algorithm significantly improves the fusion performance.

Nowadays the phased array antennas are considered of the newest and most advanced military and industrial antennas. This paper presents a simple and practical method for the calibration process for the first phase of a planar phased array antenna with dimensions 21×21 (441single-antenna) in which the ferrite phase shifters were used. This method assumed that the ferrite phase shifters are selected from the same family and when embedded in antenna circuit, they have independent and random interior initial phase. In the case for the correct operation it is necessary in the first, these phases find coordination towards to each other or the system is calibrated in terms of phase, and then exploited. The simulation results show that the proposed phase calibration procedures able to make closing of radiation antenna due to lack of coordination initial phase of phase shifters and approaches to ideal radiation pattern for the average 3dB in the azimuth direction and 2.4dB in the elevation planes.

The rapid growth of e-commerce has led to introducing various products from companies which are active in this area. As a result, choosing an appropriate product has become difficult for a customer. Recommender systems aimed at offering the best and most appropriate items to users based on their interests and their previous activities. This paper propose a hybrid recommender system which combines two approaches: collaborative filtering and constraint satisfaction. The proposed system, on the one hand, solves the cold start problem of the collaborative filtering technique because of using constraints. On the other hand, it solves the problem of constructing the knowledge base, which is needed for constraint-based methods. Instead of constructing the knowledge base manually, which is time consuming, the knowledge base of the constraint-based approach is extracted from interactions among the target user’s neighbors. Using the "Movielens" dataset for evaluation, the experimental results show that the performance of the proposed approach is better than the collaborative filtering method.

The purpose of this paper is the optimal and constrained nonlinear optimization-based tracking strategy using Control Lyapunov Function (CLF) technique. The main approach is based on analytical solution and guarantees that the closed-loop system is stable, whilst presenting an explicit relation for the controller. A CLF method-base that considers the cost function in the design process is referred to as "Satisficing" method. In this method, optimal control problem is not solved directly. Instead, it's tried to design the control law point-wise so that a utility function is greater than a penalty function. Thus, its implication results in optimality of the performance objective in control strategies. The main disadvantage of Satisficing method is the lack of stability guarantee in systems with bounded control input. Referring to significance of bounded input in real systems, it's attempted to amend the method in order to overcome the said drawback. Therefore, after reviewing the Satisficing method, the modified method is proposed and attempted to prove its optimality and boundedness. Simulation results show that using the proposed method, a reference trajectory is well followed and while the input remains within its bounds.

With the deregulation of electric power systems, market participants are facing an important task of bidding energy to an Independent System Operator (ISO). Modeling and electricity prices forecasting in competitive market relative with its characteristics such as inability to store, non-stationary and time variant behavior and seasonality violation should be considered. A model with more accuracy and less error will be more efficient which a price forecast with a less prediction errors, yields maximum profits for market players. To achieve more accurate and robust price forecast, in this paper, a new hybrid forecast technique based on Wavelet Transforms (WT), feature selection technique, Artificial Neural Network (ANN) and Gravitational Search Algorithm (GSA) combined with Chaotic Local Search (CLS) model is proposed for day-ahead electricity price forecasting. The feature selection method is an improved version of the Mmutual Information (MI) technique. The superiority of this proposed method is examined by using the data acquired from the Iran and market clearing price (MCP) of Spanish market. Empirical results show that the proposed method performs better than some of the other price forecast methods. Also, the exploitation and exploration of the proposed intelligent algorithm for tuning weight and bias parameters of ANN is improved.

In this paper, a novel image fusion algorithm based on phase congruency is presented. The proposed algorithm has two steps. In the first step, the information content of the input images is evaluated by using phase congruency model. In this step, the main idea of the work is based on the fact that image features like edges are located in the points of image which frequency components have the maximum phase congruency. In the second step, the dominant features are selected and accordingly image fusion is done. Also, for reducing the computational complexity and improving the overall performance, the proper blocks are chosen by determining the appropriate threshold and the other blocks are chosen using K-means clustering algorithm through an iterative process. The experimental results on several images show that the proposed method has significant advantages compared to the other existing methods.

In grid-connected distributed energy resources (DERs), DC current of DERs is injected to a grid via a power processing unit. Function of this unit is to convert the input DC current to an AC current with acceptable specifications to the grid constraints. Performance of these systems is directly depended on their control systems methods. Recently introduced PR controllers are well suited for grid connected inverters due to their outstanding capability of tracking and disturbance rejection. In this paper, performance of these controllers on grid-connected inverters is studied, while the inductors of the output LCL filter have nonlinear behavior. By using a mixed feedback method, the apparent undesirable nonlinearity of inductors is used on the opposite direction to enhance the system performance. A 7kW inverter system performance is simulated and the results verify the designations.

Nowadays, transmitting of data through MIMO (Multi Input–Multi Output) systems is a common way for doing this kind of transmission. Some features of these systems like high data rate and more reliable connection are caused to use those for image transmission. In this paper, we use V-BLAST in MIMO system for transmitting of image with 2 suggestion algorithms. At first algorithm transmission power of data are distributed based on their importance. In second algorithm after transmitting some data, we are labeling channel based on pilot data. Then for remaining data we are allocating power transmission based on channel statue. Eventually, asymmetric transmitting power causes to increase PSNR (Peak Signal to Noise Ratio) of received image and decreases power transmission.

Curvelet transform is a new kind of multi-scale analysis algorithm which is veryuseful for SAR image processing. This transform is considered as geometric transform which is developed to overcame the vital limitations of wavelet and gabor transforms in edges and curves detection. The curvelet transform can be used with very high precision a better segmentation and target detection in SAR satellite images. In this paper, the main goal is to focus on the despeckling of SAR images which is the most important stage in the processing of SAR images. Because of excellent results of curvelet transform in the analysis of curved shape edges and its high accuracy, this transform is useful to approximate and describe the dispersals and orientations in comparison with the wavelet. In this paper, using fast discrete curvelet transform (FDCT) based on wrest and using an adaptive thresholding, a new despeckling algorithm is developed for SAR images; So that a comparison will be done on both fast curvelet based on USFFT and based on Wrapping. Using the results, a new method to take advantage of this kind of images is introduced. Then, the Fast Discrete Curvelet transform (FDCT) is used for noise reduction of SAR images and the Curvelet coefficients are decreased. Then, using the the proposed algorithm the curvelet coefficients can be extracted. The curvelet coefficients are affected by soft thresholding and this leads to reduce or eliminate the noise in SAR images. Finally, the proposed algorithm is simulated, and the results will be discussed. Simulation results demonstrate that the proposed algorithm has better performance than the other similar methods.

One of the best solutions to verify software systems (especially critical systems) is model checking in which all reachable states are generated from an initial state and the generated state space is searched to find errors or some desirable patterns. However, the problem for many real and complex systems is the state space explosion in which model checking cannot generate all the states. In this paper, a solution is presented to cope with this problem in systems modeled by graph transformation. Since meta-heuristic algorithms are proper solutions to search in very large problem spaces, they employed in this research to find errors (e.g. deadlocks) in systems which cannot be verified through existing model checking approaches because of state space explosion problem. To do so, a Particle Swarm optimization (PSO) algorithm is empployed to consider only a subset of states (called population) in each step of the algorithm. To avoid local optima problem, PSO is combined with Gravitational Search Algorithm (GSA). Our proposed approach is implemented in GROOVE –a toolset for designing and model checking graph transformation system-. The experiments show better results in terms of accuracy, speed and memory usage in comparison with the existing approaches.

In the recent years, the development of biomedical implantable devices has had a great role in treatment of diseases and disabilities. Cochlear implant, cardiac pacemaker, and retinal implant are some biomedical implantable devices that have progressed recently. Functional electrical or neural stimulation (FES/FNS) is the common purpose of these devices to restore lost functions of damaged tissues. Electrical stimulation is based on charge injection into the tissue, initiation action potentials and excitation neural reactions. Charge-balanced stimulation is necessary to achieve safe electrical stimulation. In this work, a new active charge balancing technique for functional electrical stimulation is presented where the voltage of the electrode is sensed to be used in tuning the current value of the following phase. This method was adopted in a conventional micro-stimulator. Simulations in high-voltage 0.18-μm CMOS technology validate the feasibility of the approach and its low power consumption. The power consumption of the proposed charge-balancing circuits is 2.178 μW which contributes 3. % of the overall power consumption of the system.

Control of nonholonomic mobile robots is more complex with respect to the holonomic type due to fewer degrees of freedom of their model. In addition, side slipping in a nonholonomic mobile robot disturbs the nonholonomic constraint adversely. In previous researches, the slip is either estimated by an estimator or is measured using sensors. In this paper, two robust tracking controllers are designed with considering the upper bound of the side slip value, using the Lyapunov redesign and nonlinear H∞ methods. Also, the angle of kanayama transformation is defined such that the model includes only matched disturbances in order to apply H∞ method. In the presented model, the slippage not only deviate the robot from its path, it also deviates the angle of robot’s main axis (the robot rotates around itself). Then, the performance of these two controllers is compared and the simulation results show the appropriate efficiency of the proposed controllers.

This paper proposes designing of adaptive sliding mode controller for two degree of freedom overhead crane system with uncertainty. Overhead crane is a nonlinear and dynamic system and the control of it is one of the important control issues. Control aim is obtaining a control law without high frequency chattering and with robustness versus unknown but bounded uncertainties. Among robust control approaches the sliding mode control is selected for the simplicity of it and small computational error. Also, for reducing chattering adaptive control is used. Knowing the upper bounds of uncertainties isn’t needed by applying adaptive control. This paper tries to reduce the undesired chattering phenomenon by adjusting the adaption gain properly. The simulation results show the effectiveness of the proposed controller for the overhead crane system.

With the advancement of CMOS technology power, supply voltage is reduced and switching speed is increased. Reducing the supply voltage makes it difficult to achieve high-precision for voltage-domain data converters. On contrary, increasing switching speed results in better performance in time-domain data converters. In VCO-based data converters, signals can be quantized in time domain. In addition, these converters have inherent noise shaping property. The benefit rose made these converters interesting for researchers. Several structures for improving performance of these converters have been presented. MASH structure increases the efficiency without need of analog components. In this paper, a two-stage MASH based on similar GRO is presented, in which a new approach is used for improvement of resolution. The main advantage of the proposed design is that not only resolution is improved by multiphase outputs, but also by offering a new technique for registration and extracting the data at the output circuit complexity is reduced. Therefore, the hardware and the power is decreased compared to the preceding works. Furthermore, in the proposed design employing equal employing GROs by PWM technique at the input prevents leakage of first stage to the next stages and also addition of distortion of first oscillator.

A traveling wave based algorithm for fault classification and faulted phases selection is presented in this paper. The proposed algorithm is composed of two major stages. In the first stage, after extraction of modal components from three phase signals and by means of Teager energy operator (TEO), traveling waves are extracted from modal signals. Then ratios of initial traveling wave in different modal components are used for the fault classification and faulted phases selection purpose. TEO is a very simple method which can extract traveling waves with very high speed and resolution. In the second stage, fault classification and faulted phases selection is performed based on the information extracted from the previous stage. Intelligent methods are the best choice to be used in the second stage and for the first time are utilized in this paper. Fuzzy system, which is used in this paper as a classifier, is able to classify the faulted signal with a high accuracy. Simulation results show that the proposed algorithm has an efficient and accurate performance in terms of close-in faults, high impedance faults and faults with very low inception angle.