نشریه مهندسی برق
سال چهل و هفتم شماره 4 (پیاپی 82، زمستان 1396)

Transverse flux permanent magnet machines are known as machines with high power density and torque. This article presents a novel transverse flux permanent magnet disk-rotor generator. First, the initial design of the algorithm (sizing equation) for the proposed structure provide and then by (NPSO) combined with Local Random Search (LRS) method, optimized. In The optimization process, power density and efficiency should be maximum and total harmonic distortion (THD) of the internal voltage of the generator is minimized. Optimization process incorporated into the initial design of the algorithm and different dimensions and parameters of optimization generator is calculated. Electromagnetic field analysis of the optimized generator is subjected through 3D-FEA to obtain the generator’ characteristics. Finally, the results obtained from the optimization algorithm are compared with those of the 3D- FEM.

Transformer winding mechanical fault detection by Ultra Wide Band (UWB) Sensors has been recently proposed. In this paper, modal analysis technique has been used to detect and estimate radial deformation and axial displacement in transformer winding. In this technique, using the UWB wave propagation channel modeling by transfer function and knowing that mechanical changes affect the channel parameters, the features have been extracted for the fault diagnosis. By choosing the best method of estimating the transfer function, the zeroes and poles in different sound and defective mode has been determined. For diagnosing and determining the approximate amount of the faults, the amounts of Zeroes and Poles of the Transfer Function have been compared in different modes. The measurement results have been shown that the proposed method can be successful in the detection of the fault but it cannot determine the amount of the faults accurately. According to the capabilities of the proposed method, it can be used in combination with the previously introduced Radar Imaging Method.

In this paper, beside studying in fields of electronic, image processing, computer vision, face recognition, filtering, wavelet transform, linear discriminant analysis and support vector machine, a new method for face recognition has been proposed. First, feature vectors are obtained from raw face images using Gabor wavelets. Next, the extracted feature vectors are mapped to a low dimensional subspace using Haar wavelets. Then, direct linear discriminant analysis technique is used for more compressing and splitting data obtained from the previous part. Finally, these obtained data are sent to support vector machine classifier for classification and then recognition of new images. The proposed method was examined using the FERET database. Experimental results illustrate effectiveness of this method against other related works in terms of recognition rate.

Game theory is used as a new approach to model some problems of wireless sensor networks such as routing, data aggregation, and intrusion detection. These networks can be vulnerable to attacks by malicious nodes due to power and memory resource limitations of nodes. These nodes try to disrupt the network with unnecessary consumption of energy. Intrusion detection and prevention is run as a process of monitoring the events occurring in a network by the IDS (Intrusion Detection System). Because of high energy consumption by the IDS, selfish nodes in the cluster are unwilling to be a cluster head and turn IDS on. In this paper, we propose a mechanism based on game theory to enforce nodes to cooperate with other nodes in cluster head election and truthfully reveal their private information including cluster head cost. In this election, we use remaining energy, reputation and distance of node from base station to calculate cluster head cost. After selecting the appropriate cluster head, we propose a game based on BNE (Bayesian Nash Equilibrium) between cluster head and other nodes, such that the CH-IDS (Cluster Head-IDS) agent is not always in ‘on’ state. As a result, the power of CH can be saved. The implementation results show that our proposed mechanism reduces node energy consumption and increases the network life.

Today, a large number of multimedia data are easily accessible via the Internet and can be changed or modified simply by using image processing tools. Digital watermarking is a developing technology to ensure and facilitate data validation and security and also protect the copyright of digital media. One of the applications of digital watermarking is authenticating and detecting tampered region. Digital watermarking methods have been proposed for detection, are able to authenticate and check the integrity of the received image by utilizing the information that is already embedded in the media. In this paper, a new method is proposed for the detection of tampered region in the color and grayscale images. In the proposed method, at first, a lifting wavelet transform is applied to the image. Afterward, the horizontal edges are divided into the non-overlapping small blocks and then, the binary watermark is embedded in blocks by quantitating the two maximum factors. To extract the watermark, a set of statistical parameters are calculated for each block and considered as training data for the learning algorithm. For extracting the maximum correlation watermark, the nearest neighbor algorithm is used. Finally, the obtained watermark will be compared with the original watermark and the tampered region will be detected. The experimental results show that the proposed method is capable of detecting tampering area with high accuracy under various attacks such as histogram equalization, Gaussian filter, salt and pepper noise, sharpening, compression, and even combination attacks.

Estimation of aggregated electric vehicle charging demand is essential for better design of future distribution networks. Given that the penetration level of electric vehicles is currently very low, Hence, the precise statistical data are not available to determine the initial state of charge related to electric vehicles. this paper proposes a new model for calculating the initial state of charge in electric vehicles. The proposed model is a function of fuel consumption of the conventional gasoline vehicles and all driving behaviors such as the use of air conditioning, vehicle speed, road slope etc. have been considered. Also, unlike most of the papers that have specific outputs for the distribution network parameters, in this paper, the mentioned parameters are presented as probability functions. Finally, the proposed method is applied to the IEEE-37 node test feeder and simulation results are presented to illustrate its performance.

In recent years, the use of MIMO systems has been increased dramatically in telecommunication systems, because of their important advantages. In the 4GPP systems, the quality of service should be very high to provide video communications everywhere and every time. The MIMO technology can answer to this requirement. The performance of mobile communication systems is much related to the radio channel. Hence, the prediction of propagation characteristics between both antennas is important in wireless networks. In this paper, the radio channel is predicted between the transmitter and receiver by the use of Uniform Theory of Diffraction (UTD) and extract MIMO channel matrix. The UTD method helps that without the need for expensive measurements, the performance of MIMO systems are investigated. Moreover, the channel capacity is calculated using the channel matrix. To validate our proposed model, the simulation results are compared with the experimental data. There are good agreements between the measurements and the result obtained from the proposed model which justifies the theoretical work.

The design final step of a desired practical radar is analysis and evaluation of it in a closed-loop simulation loop. This analysis and evaluation from the error viewpoint is very important. The most important errors in monopulse radar systems are target angle measurement in azimuth and elevation directions for ground targets. In this article, in a closed-loop simulator, extraction, analysis and evaluation of these errors have been obtained. By software implementation of this radar system and extraction of target angles from this system, with regard to initial exact position of target, these errors have been achieved. In this way, three types of antennas have been considered for this purpose and also, radome effect has been considered in radar closed-loop simulation and extraction of errors. The antenna parameters are gain, beam squint and radiation pattern and the radame parameters are insetion loss and boresight error. Comparison of simulations results, achieve to this summary that the antenna beam squint can be compensated in monopulse computation unit but the boresight error of radome causes a non-compensable error in the target position measurement.

Since providing service quality for critical and real time systems is essential, online test and fault tolerance are considered to be of high significance in designing chips for these systems. The dominant routing algorithm of Network on Chip (NoC) in critical and real time systems is deterministic routing algorithm. Due to the effects of crosstalk, NoC communication infrastructure is highly prone to fault and failure. Thus, effective and efficient testing mechanisms should be designed for detecting faults in the form of both online testing and fault tolerance so that fault tolerance time is minimized. In this paper, a method was proposed for online testing of interconnections among non-adaptive routers with deterministic routing algorithms. Indeed, by embedding a test pattern generator and four comparators in each router and producing interconnection redundancy among routers, the need for stopping usual system operations while testing is obviated. In the proposed architecture, interconnections were tested with chip frequency. As faults and failures are detected, the opportunity for tolerating them is created simultaneously. The results of conduct simulations in this study revealed that the proposed router was able to significantly reduce test execution time with minimum area overhead and power consumption.

Mobile ad hoc networks (MANETs) are structure-less networks in which the mobile nodes are communicate wirelessly with each other’s. This wireless channel makes the MANETs vulnerable against different types of attacks. Wormhole is one of these attacks, according to which, two active attackers create a virtual private communication tunnel; reduce message normal stream and pretend two non-neighbored nodes as neighbors. In this way, the path between these two nodes is pretended as the shortest possible route between the source and the destination. Thus, all data packets passing through this route can be eliminated by these attacker nodes. This paper inspires from human immune system to design a defense mechanism against wormhole attack, called WAAIS (Wormhole Attack Artificial Immune System). In this proposed mapping, all unsecure routes, are considered as antigen. These unsecure routes are recognized by some rules, which are considered as antibody. The simulation results show that WAAIS outperforms WormPlanar and AODV in terms of packet delivery rate, average end-to-end delay and drop packets rate.

The ability to detect and recognize a variety of gaseous and dust pollutant under certain environmental condition is a significant potential which has received more attention in many areas such as academic studies, environmental problems and industrial domain. Up to now, identification of gaseous and dust species had been based on infra-red spectroscopy. Because of simple and unique spectral features of various pollutant in the THz range leading to finger-print characteristics, THz time-domain spectroscopy (THz-TDS) shows promise as a tool for gas detection and recognition. To this end, in this paper THz-TDS technique has been investigated in order to real time and fast dust detection. Additionally, detection of HMX and RDX dust has been modeled based on THz-TDS. The simulation of presented models shows that THz-TDS is a high sensitive and accurate technique for real-time dust and pollutant detection. So, detection of low concentration of dust pollutant can be identified in less than one second. Finally, based on this method the pollutant can be identified by comparing obtained absorption spectrum with database including spectral features of environmental pollutant.

In this article, the operational regimes and behavior of passively mode-locked quantum dot (QD) lasers have been investigated based on analyzing delay-differential rate equations and exploiting short and long saturable absorber criteria. Then, these criteria have been used for hybrid mode-locked QD lasers for the first time and it has been shown that elimination of unwanted Q-switching, Qs, Q-switching mode-locked, QS-ML regimes, decrease of the threshold current for all operation regimes, and enhancement of the extinction ratio for generated pulses can be obtained in short-absorber hybrid structure compared with passively mode-locked QD lasers. Also, by analyzing the pulse and harmonic pulse frequency generation mechanisms, proper approaches for generation of higher-frequency harmonic pulses especially third harmonic generation and increase of required dynamical regimes’ range have been discussed.

This paper represents two main contributions. Firstly, a new state space model is represented for doubly fed induction generators (DFIG) by adding the uncertainty in the state equation. Secondly, a direct active and reactive power control (DPC) is designed for wind power systems with DFIG. The proposed DPC strategy employs a nonlinear sliding mode control scheme to directly calculate the required rotor and grid control voltages so as to eliminate the instantaneous errors of active and reactive powers. Constant converter switching is achieved by using space vector modulation, which eases the design power converter and the ac harmonic filter. As a result, no extra current control loops are required, and hence the design is simplified and the transient performance is improved. Finally, the proposed controller is applied to control a 2-MW grid-connected DFIG system, and the simulation results effectiveness of proposed control system.

In this paper, the concept of SMIB is generalized for inter-area oscillation consisting of two or more oscillating areas. By using the generalized SMIB (GSMIB) model the nature of inter-area oscillation and the mechanism for its damping is investigated. It is shown that the PSS of power stations are not able to damp inter-area oscillation based on the local control signals which are commonly used for damping local oscillations. Based on the derived GSMIB model it is shown that only using global control signal provided based on the concept of center of inertia (COI) of oscillating areas is able to damp inter-area oscillation. The global signals provided based on the COI can be categorized into 1-global area signals and 2- global inter-area signals. Also it is shown that depending on the pattern of the oscillating areas proper combination of both types of global signals could be able for damping inter-area oscillations. The simulation studies on IEEE 39-buses test system show the effectiveness of the proposed signals for damping inter-area oscillations.

Bind signal identification is one of the necessities of intelligent receivers. Since burst-mode signal transmission is widely used, burst detection, i.e. detecting the beginning and the end of bursts, is an important problem. In this paper, a new method for burst detection is proposed. In this method, the beginning and the end of bursts are detected according to the energy ratio changes. In addition to Dice similarity coefficient, burst and silence error errors are proposed to evaluate detection methods. Simulation results demonstrate the superiority of the proposed method compared to the conventional methods. For example, for signal to noise ratio of 2 dB, Dice coefficient of the proposed method is around 0.999 which is much better compared to auto-correlation detector with dice coefficient of 0.96.

Ensemble classifier is an effective method in machine learning that attempted to provide a better approximation of an optimal classifier with combination of some classifiers results. To achieve better performance, the base classifiers should have acceptable efficiency and different classification error, also a suitable method used to combine their results. Various ensemble classification methods such as bagging, voting and strengthening methods have been presented. In this paper, we proposed the ensemble classifier based on weighted mean of the base classifiers output. The weights were estimated using a multi-objective genetic algorithm with taking classification error, sparsity, diversity and density criterion. The results of implementations on UCI datasets show that the proposed method causes more increasing classification accuracy related to other traditional ensemble classifiers.

Due to developing sensitive industries and increasing investments in these industries, the economic value of electricity has increased and therefore loss incurred to consumers has significantly increased by temporary and permanent outages. Failure of power transformer cause interruption of electricity services. Use of spare transformers is one of the most important approach to improve reliability of energy supplied to consumers. In this paper, a new Markov model is proposed to study impacts of a spare transformer on reliability of a network comprising of a number of two-transformer substations. In proposed model, it is demonstrated that the optimal capacity of spare transformer in an area is affected by both maximum loading percentage of substations in that area and the difference between the maximum and the minimum capacity of transformers in the area. Here, the expected energy not supplied (EENS) is used as the reliability index. A practical network in Mazandaran province consisting of 84 substations is selected and impacts of spare transformers with various capacities on system reliability and the related costs are studied. Moreover, the effects of value of lost load (VOLL) variations on utilities’ cost, caused by transformer outage, are investigated via sensitivity analysis.

A model is an abstract representation of software system and is an appropriate solution to cope with the complexity of software. Utilizing from this fact, Model-Driven Development is an approach to software development that employs models as main artifacts for building software systems. With the increase in the number of designers in a team, different versions of model arise at any time during the development process, especially in the design phase. To manage these versions it is necessary to be able to identify differences and reconcile them in a single, integrated model. As a consequence, there is a need for tools and techniques to support model merging with less user interference. In this paper, an algorithm and tool for three-way merging of UML models is presented. Before merging, subjective assumptions of each developer is received and at the end, a consistent merged model is provided. According to the evaluations, user and modeling environment independency with less running time than the existing approaches, shows the superiority of this work.

In this paper, considering the correlation of spectral bands of a hyperspectral image, first we cluster these bands based on correlation coefficients. Then, using spatial correlation among the pixels of a hyperspectral image and the mentioned clustering, we propose a spectral-spatial compressed sensing for hyperspectral images. For reconstruction of these images, we propose a spectral-spatial total variation regularization in which in addition to the vertical and horizontal discrete gradients, we incorporate the frequency discrete gradient as well. Using the mentioned clustering, reconstruction computations of spectral bands of clusters can be performed in parallel leading to a higher reconstruction speed. Also, in the case of spectral-spatial compressed sensing without clustering and performing reconstruction, the proposed method in comparison to the norm based and spatial total variation regularization methods improves the reconstruct quality in terms of PSNR. Computer simulations on actual hyperspectral images confirm the above results.

Operation scheduling of electrical distribution systems will be more challenging in the future thanks to evolution of this system. Presence of energy hub, natural gas infrastructure and distributed energy resources in this system will be the main challenge of the system operators. In this paper operation scheduling of energy carriers and reserves considering security constraints of networks in presence of interconnected energy hubs is proposed. Both electrical and Natural gas networks’ constraints are considered in this study. The objective function of this model is operation costs for supplying electrical and thermal loads. The MGO (Micro grid operator) is in charge of optimal scheduling of supplying loads as well as maintaining technical constraints. The optimal scheduling of resources is obtained. Also, Results show reduction of costs due to commitment of responsive loads. Moreover, interdependence of electricity price and natural gas consumption shows the importance of integrated optimization of electricity and gas networks.

Actuator saturation is one of the most common constraints of the system input. In this paper, a dynamic surface control scheme is presented for a class of uncertain nonlinear systems in the presence of actuator saturation and unknown external disturbance. In the proposed scheme, the multi-input multi-output adaptive wavelet network as a nonlinear-in-parameter approximator is designed to approximate the uncertain dynamics of the system without requiring any prior knowledge. Translation and dilation parameters of wavelets and weights of the network are updated based on the adaptive laws. The nonsmooth nonlinear saturation constraint is approximated by a smooth function of input signal. Also, a nonlinear disturbance observer is designed to estimate the unknown external disturbance. Then, the nonlinear disturbance observer-based dynamic surface control scheme is proposed. The proposed scheme guarantees that all signals of the closed-loop system are uniformly ultimately bounded and the tracking error can be made small by proper selection of the design parameters. In the proposed scheme, because of using wavelet network and dynamic surface control approach, both of “curse of dimensionality” and “explosion of complexity” problems are eliminated, simultaneously. Simulation results verify the proper performance and effectiveness of the proposed controller.

The main objective of this paper is to jointly estimate three parameters, power, frequency and location of primary users in cognitive radio communications based on cooperative compressed sensing. First, in addition to representing the fundamentals of compressive sensing and system model, three sub-Nyquist reconstruction methods (OMP, CoSaMP and IHT) are described which are based on compressive sensing. Using integrated and separate reconstructions on final decision-making, it is shown that OMP offers higher performance with respect to two other ones in noisy channels. Moreover, a new method for separate reconstruction is proposed which is based on majority decision manner. The new method shows higher performance for estimating three above-mentioned parameters compared to separate method with averaging and integrated one for noisy channels with shadowing. Furthermore, it is shown that it needs lower computational complexity compared to integrated decision-making and approximately the same complexity with respect to separate decision-making with averaging.

Restructuring in power systems has caused electricity price forecasting became one of the most important challenges facing electricity market participants. The precise electricity price forecasting helps both consumers and producers to make better decision in order to maximize their benefit. In this paper, the historical data of electricity price and energy consumption are utilized for prediction of electricity price for the next 24 hours. The proposed intelligent algorithm is realized through three main steps: 1- preprocessing step, 2- feature selection and 3- forecasting step. At first, the price signal is decomposed to different modes by using Empirical Wavelet Transform (EWT). Afterward, in the second step, the feature selection method based on mutual information is applied on input data to improve the performance of forecasting engine. In the third step, for day-ahead hourly electricity price forecasting, the Support Vector Regression (SVR) is trained by selected features. The performance of the proposed algorithm is evaluated using real data of two electricity markets i.e. Pennsylvania New-Jersey Maryland (PJM) and Operador del Mercado Ibérico de Energía-Polo Español (OMIE).

In this paper, the state space model predictive control system design for inverter switching pulses to produce the desired three-phase UPS outputs. Very fast dynamics and the ability to evaluate different cost functions of the advantages of predictive controller in designed. Relations in the system suggest a need to control the amount of time given the uncertain times, reduce costs and improve reliability, flow sensor, is designed to replace the observer. The common observers such as Luenberger Observer in terms of design, sustainability and complexity associated with difficulties. In this paper a novel observer based on fourier series and instatntius power theory in which desired operations have been achived, is designed. The proposed observer does not set specific parameters only measure of output voltage and filter current, ability to observation the load current with excellent accuracy. The proposed observer, a simulated 220-volt three-phase UPS tested and the results will be compared with Luenberger common observer.

In this paper the optimal reconfiguration and scheduling of a networked-Microgrids (networked-MGs) community for a 24 hours horizon is presents. Based on different fitness functions the configuration and commitment of the MGs are determined for any scheduling time slots. For any hours in scheduling horizon and for each network reconfiguration states the generation power of each distributed energy resource within MGs and the power flow exchange between MGs and main is obtained using Optimal Power Flow (OPF) considering the electrical and environmental constraints. In case of MGs operation in island mode, each MG is responsible for its load individually. To balance the total power in each MG, the price of the power exchanges with either the MGs or the utility grid is considered. The main results include the MGs reconfiguration state table for 24 hours scheduling of MGs which includes MGs resources output power and interconnection line flow. While the fitness function is a multi-objective function, to select the best and effective solution considering all fitness function the Data Envelope Analysis (DEA) method is adapted to select the most efficient solution for networked-MG optimal reconfiguration at each hour.

Understanding movies content and their genre, is always a complex and open issue to researchers. Several activities have been carried out by researchers to find out movies content. Most of the activities conducted in this area have been using audio processing or video processing. Recently a group of researchers have proposed the idea of using movies subtitle texts to understand movies content and considered text processing faster and easier than audio and image processing. In this paper a semantic orientation based method is presented for genre classification in multi-label data of movies subtitles. To do this, a feature extraction method is presented to extract unique features of each genre. Then a method is presented, in which with calculation of a subtitles semantic orientation to each genre, subtitles genres are predicted. Finally, using association rule mining methods, the relationship between genres in raw data is discovered and using these rules, predicted genres have been modified. Obtained results indicate significant improvement of proposed method compared to previous methods.

In this paper, a fuzzy adaptive control for SISO nonlinear systems is presented. The nonlinear system is modeled using Takagi-Sugeno (T-S) model. Then, a fuzzy adaptive control based on reference model (MRAS) is designed to track reference signal. The parameters of the controller are calculated on-line at each instant. For each bounded input reference signal, the states of the system follow the states of the reference model. In this paper, first a lemma called extended estimation lemma is introduced and proved. Then, to select the control law, adaptation law and to prove the overall system stability, this lemma is used. The stability proof of the adaptation algorithm with system parametric uncertainty is presented fully. Moreover, for two very popular linear systems, using the extended estimation lemma, adaptive controls are designed. The proposed controller is designed for inverse pendulum nonlinear system and the performance of the controller is verified with MATLAB software.

In this paper, we present a novel uni-directional microstrip-fed multi-layer wide tapered slot antenna with a fork-shaped tuning stub. The antenna is embedded in a multi-layer structure backed by a reactive high impedance surface (HIS) and a thick dielectric layer, and then is placed on top of a ferrite tile with a metal ground plane. The HIS layer is added for achieving ultra wideband operation, while the ferrite tile prevents back radiation. A parametric study is presented to show the effect of the various antenna dimensions on the antenna performance. The dimensions of the antenna are small enough along the x and y directions (10cm×10cm), and has ultra wide impedance bandwidth gained by the structure, ferrite and HIS layer. A prototype antenna was fabricated and measured. The measured and simulated results are in good agreement and show a positive broadside gain (>1 dB) over a 4:1 operating band (1 GHz-4 GHz) with a VSWR

The purpose of this paper is to reduce energy consumption and improve lifetime of a wireless sensor network (WSN) which is capable of covering all targets. Targets are special points of sensing field with determined locations that need to be constantly monitored. To this end, sensor nodes are categorized into number of groups called cover sets using an optimization approach. Each set alone, is able to cover all the targets. The operation of the network is divided into rounds with the same lengths. In the beginning of each round, one cover set is selected and will perform the monitoring task for current round while other nodes in other sets, are in sleep mode. Clustering and routing decisions of the network is calculated via a Mixed-Integer Linear Programming (MILP) model. Simulation results show that our proposed method not only fulfills the coverage requirements of the network, but also improves the network lifetime. Also, the impact of altering the number of cover sets reveals a tradeoff between the number of sets and the sensing radius adopted by sensors. Moreover, the proposed method outperforms similar algorithms in terms of network lifetime.

In this paper, the antenna phase center and its variations are discussed by using simulation data and genetic algorithm. Since the antenna phase center­ cannot be considered as a detailed assessment of the antenna phase pattern, it is required to evaluate the phase center variations. In fact, phase center variation shows the phase center stability which is used in many applications such as precise positioning. The new proposed approach can be used to calculate the antenna phase center and phase center variations in three dimensions by using simulation data and genetic algorithm optimization. To show the proposed method ability, some simple GPS microstrip antennas are selected and simulated to compare their phase patterns by phase center variation computation with the proposed method. Finally, the results are validated by using HFSS software.

When faced with a changing world, humans are apt to look not just to the future, but to the past. Drawing on knowledge from similar situations we have encountered helps us to decide what to do next. The more experience we’ve had with a particular situation, the better we can expect to perform. When solving dynamic problems using search, it may be enough to solve the problem completely from scratch when we encounter it again. An appropriate strategy for store past information is memory. In the researches shown that using standard memory with evolutionary algorithms for solving dynamic optimization problem is capable. Standard memory is containing infirmity point memory determinate capacity. In this paper presented a new memory namely Classifier-based memory, which solves standard memory problems. This memory combined with GA for solving dynamic scheduling. The dynamic job shop scheduling problem is one of the most complex forms of machine scheduling. Classifier-based memory is introduced to extend the use of memory to dynamic problems where solutions may become obsolete as the environment changes. Classifier-based memory creates an abstraction layer between feasible solutions and memory entries so that old solutions stored in memory may be mapped to solutions that are feasible in the current environment. The technique presented in this paper improves the ability of memories to guide search quickly and efficiently to good solutions as the environment changes.

Rule base is the most important part of a fuzzy inference system. Inconsistent data make some challenges in generating of fuzzy rules. In these cases, since there are multiple outputs for the same states, hence making decision for suitable consequence selection in each rule is a big challenge. Averaging of inconsistent states has been adopted by current methods and they create output with average of related consequences. The initialization of actions selection probability in fuzzy reinforcement learning based on architecture Actor-critic is used in this method. In this method, training data is clustered and zero order Sugeno method with number of candidate action in each rule are used for the initialization of the actor module parameters and they are online tuned with adopting actor-critic and reinforcement signal finally. There are many inconsistent challenges in robot navigation data in comparing other cases. Therefore the proposed method is used in robot navigation problem. The experiments are done for e-puck robot in Webots simulation. Results show that proposed method has reduced training time, collision to obstacle and fuzzy rule numbers.

The purpose of this paper is to address a shortfall in the literature in dealing with the congestion management problem through reinforcing a collaboration between ISO and Recos in implementing the DR program. Such reinforcement is developed based on defining some important economic signals which ISO submits to Recos for demand reduction when dealing with DRAs. A Stackelberg game is used to design a DR trade among Recos and DRAs, where Recos act as leaders, determining the values of demand reduction based on economical signals of ISO. DRAs as the follower of the game will compete based on Recos's strategy for maximizing their profits. In order to define the DR impact of each bus on congestion alleviation, the congestion elasticity concept is introduced and used in the Recos profit calculations.The performance of the proposed method is evaluated by using a test power system in different scenarios.

One of the most important optimization problem in combinatorial algorithms is Travelling Salesman Problem. This is due to its practical usages, especially in geospatial information technology. It is proved that the problem complexity is NP-Hard, but many heuristic methods are presented for practical applications. In this paper, the space transformation method is used to transform a geographic instance of travelling salesman problem into a simpler Euclidean one, such that traditional heuristics can be applied more successfully. Additionally, a heuristic technique to solve the transformed instance is introduced. The results confirm the superiority of the proposed method in terms of both the tour length and running time of the method in comparison with similar techniques.

Voltage flicker is referred to low frequency fluctuations modulated on the basis of voltage signal. Flicker mitigation needs identification of the flicker sources. In this paper a new method based on flicker power theory and wavelet transform and coherent phase demodulation is proposed to identify the locations of single and multiple flicker sources in a multiflicker source power network. The coherent phase demodulation and wavelet transform are utilized for demodulation flicker frequency components from main signal. Then by introducing of two parameters, “FPM” and “FPW”, in order to identify the exact location of flicker sources on the network and evaluation of their effects on the human vision and electronic equipment. The method also evaluates the contributions of individual flicker source to global voltage flicker level of a common coupling bus bar. To examine the proposed method, the IEEE 13-Bus system is simulated in Matlab/Simulink and this method is used to identify flicker sources. The simulation results in indicating the accuracy of the proposed method to identify the exact location of flicker sources, and determine the contribution of each sources in voltage flicker of each bus bar.

In this paper a novel class AB, ultra-low input impedance trans-linear based second generation current conveyor utilizing current positive feedback technique is proposed and the principle of its operation is discussed. In order to verify the performance of the proposed structure and also the achieved results the effect of temperature, threshold voltage, W/L and VDD variations is investigated on the circuit applying Monte Carlo analysis with thirty runs including 3 percent error which proves the robustness of the proposed technique. The input impedance of port X remains at relatively low values of about 0.83mΩ to frequencies up to 10MHz. The circuit has ultra-low offset current of 12.3nA and offset voltage of 17.2mV. It has current gain of 1.000 and current signal bandwidth of 900MHz. The voltage gain of 0.964 with voltage signal bandwidth of 600MHz is also achieved. The proposed CCII is simulated with HSPICE in TSMC 0.18µm CMOS technology. ±1.5V supply voltage with 10µA bias current is considered and the power dissipation of 304.28µW is measured.

By increasing the load of the double conversion uninterruptible power supply, their nominal power should increase, this could happen through paralleling of other UPS or changing the current one, which both of these have a cost effect. In this paper a new method for increasing the nominal power of the double conversion UPS is proposed, which could increase the UPS power up to 200%. This method is based on reconfiguration of the mentioned UPS. This proposed configuration not only could compensate the load and network perturbation during normal operation, but also in the absence of the network the two inverter could handle the load with each other. The proposed method is validate through simulation results.

In this paper, wound rotor induction modeling has been presented by modified Magnetic-Equivalent-Circuit (MEC). Generally, there are two separate sets of equations which are differential equations (relative to electrical part) and nonlinear algebraic equations (relative to core’s magnetic parts) which were solved separately in previous works. This caused to ambiguous manner and some of dynamic behaviors may be loss. In this paper the differential equations are converted to algebraic equations by trapezoidal method and a set of nonlinear equation are solved by Newton-Raphson technique. This manner is introduced as the paper novelty. Proposed manner is capable to modeling of a wound rotor machine with given slot and poles number and also arbitrary magnetization curve. Moreover, another machine types could be modeled by proposed method. Finally, performed validation by Finite-Element-Method shows the effectiveness of proposed method.

Traffic offloading (transferring a share of cellular network traffic to complementary network technologies such as Wi-Fi) is an economic and efficient mitigation for the ever-increasing congestion in cellular networks. In this regard, reducing fixed and variable costs of cellular operators in one side and providing some incentives for the users to participate in the offloading in the other side are among the main challenges to be addressed. In this way, delay tolerant offloading (in which users may tolerate some delay before receiving their needed data) is investigated and a new optimization mechanism in two stages is proposed. First, by utilizing user motion pattern prediction, a model is formulated to obtain the optimal placement of complementary WiFi networks and their bandwidth allocation. Second, cellular operator's overall variable cost (including data exchange and user incentive costs) is minimized. Minimizing the latter is performed using a reverse auction framework. Moreover, due to nonlinearity and high complexity of proposed models, heuristic algorithms are proposed to solve them. A numerical study and simulations of the algorithms show that the proposed mechanism outperforms previous methods in terms of balancing the load of cellular network and reducing overall cost of the cellular operators.

An explicit two dimensional (2D) analytical expression for potential of the channel of an undoped Dual-Material Double-Gate MOSFET has been presented which is applicable to asymmetric and symmetric devices. The 2D potential is based on the sum of 1D long channel potential along the channel and 2D potential variation. The 1D potential term depends on intrinsic Debye length and is analytically extracted from 1D Poisson’s equation. The 2D term is achieved by separation of variables method. Despite the existing models which use numerical calculations to achieve the long channel potential, the proposed model has been extracted analytically. For a symmetric device, using the proposed potential model and by means of virtual cathode, analytic expressions for the threshold voltage and drain induced barrier lowering have been derived. Simulations show good agreement between the results of the proposed model and those of TCAD software which guarantees the accuracy of the proposed model.