TABRIZ JOURNAL OF ELECTRICAL ENGINEERING
Year:2020 | Volume:50 | Issue:3 (93)|Papers Count:40

Stability and damping oscillations during fault occurrence are two important challenges which have attracted the attention of the power systems designers. In this paper Improvement of the Damping of Multi Machine Power System Oscillations is considered by using UPFC. The applied control algorithm for the damping oscillations is the Multi-input Nonlinear Back-stepping Controller. The injection model of UPFC is used for modeling of UPFC, Using of this model decreases 4 input control of UPFC to two inputs and the performance of power system is improved by this control method. Performance of this control algorithm in comparison with classical method is shown, more robustness, more independent to the work point and using of whole control variables of UPFC. The proposed control method, is simulated for a single machine connected to infinite bus and a multi-machine(9 bus IEEE) power system. The results of simulation, is illustrated that the performance of designed multi-input Back-stepping controller rather is better than conventional controller.

In this article, design and simulation of an optical memory based on slow light phenomenon is presented for the first time in a hexagonal rod-type photonic crystal. The write, storage, and read processes are controlled independently by refractive index change. The proposed flash memory is capable of operating in either parallel form or by taking advantage of wavelength division multiplexing technique. Here, the memory cell is designed to function at  =1550nm, however, the scalability rule in photonic crystals aids to adjust the operating wavelength in a wide optical communication frequency range. The Q-factor of the proposed memory cell is ~3. 4 105 and this can be enhanced considering larger memory structure. The photon lifetime of 0. 6ns is achieved for the memory cell for Q-factor of about 3. 4 105. The fascinating characteristics of the presented optical memory include independently controlled write and read processes, compact size, high-speed operation, long photon lifetime, and perfect group index matching between the memory cell and the input/output ports which enhances the coupling efficiency.

In recent years, Massive Multiple-Input Multiple-Output (MMIMO) systems have emerged as a promising enabling technology to address the ever-growing demand of data traffic in the next generation of wireless networks. These systems received a great deal of interest due to their promise of high performance gains. These gains are mainly achieved at the expense of having a tremendous number of antenna elements within a relatively small physical platform. The growth in the number of antennas, however, significantly increases the Radio Frequency (RF) expenses. Therefore, developing solutions to alleviate this problem has become a major topic of interest. In this paper a stepwise regression approach is proposed to find an effective subset of transmit antennas. In each step of our scheme, we choose the antenna that maximizes the achievable rate and discard the antenna that has been selected in the previous step. Our numerical results show that the proposed antenna selection scheme outperforms the other schemes in the literature.

Deep packet inspection is an unavoidable task to prevent the web-based attacks. HTTPS traffic constitutes a significant percentage of web traffic. In order to perform the deep packet inspection on HTTPS traffic, searchable encryption is used. Using searchable encryption, there is no need to decrypt the packet contents; hence the privacy is preserved. Besides, a significant percentage of HTTPS traffic is compressed before reaching the SSL layer. Compression consists of two stages: LZ77 compression and Huffman encoding. For compressed traffic, required tokens for performing searchable encryption are not accessible and the full decompression should be applied to access the tokens. In this case, token extraction is done by parsing a nondeterministic finite automata (NFA) on the decompressed contents. The goal of this paper is to decrease the time complexity of NFA parsing process. In the proposed method, instead of full decompression of the compressed traffic, at first Huffman decoding is applied on the packets to access the LZ77 compressed content. Then, the LZ77 pointers can be used to recognize the repeated substrings and to skip the NFA parsing process. Hence, the token extraction process is accelerated. The evaluations show that the proposed method decreases 65% of time of compressed HTTPS token extraction by skipping the 44% of characters.

This paper addresses the problem of fixed-time control for a class of second-order nonlinear systems in the presence of model uncertainty and external disturbance. By introducing a novel form of non-singular terminal sliding mode control, a fixed-time control is designed to obtain acceptable performance, rapid convergence of the system states, high robustness and singularity elimination. Guaranteeing fixed-time convergence is a significant feature of the proposed control law under which the convergence time of the proposed surface is independent of the initial conditions. Since the upper bound of the system uncertainty and disturbance is quite difficult to obtain, an adaptive mechanism is presented under which there is no need to know this upper bound. Lyapunov analysis proves that the system states converge to small neighborhood of the origin within a fixed time. To assess efficiency of the suggested method, a flexible spacecraft attitude control system is considered and a fixed-time attitude control system is derived. Simulation results verify the effectiveness and performance of the presented approach.

The aim of interaction prediction in videos is to predict the interaction before it actually happens. Recently, this task has been important in computer vision domain and is gaining a lot of attention due to its challenges. In this paper, a deep neural network using fuzzy relationship and optical flow is proposed to deal with the problem. In this approach for each frame of a given video, first, two fuzzy images are obtained based on the gradient and the optical flow of the frame. Then, two set of features are extracted by a convolutional neural network trained on these images. Final prediction is made by aggregating the two outputs of the network. The proposed method shows promising results on two interaction datasets, namely BIT-Interaction and UT-Interaction.

First step in designing smart homes is energy efficiency analysis including energy optimization and consumption analysis, This paper incorporates the uncertainty of the effective parameters and probabilistically optimizes the energy consumption in a building, To this end, probability density functions (PDFs) of building uncertain parameters are modeled by point estimate method and the energy usage in buildings is optimized, EnergyPlus and MATLAB software are respectively used to compute the energy consumption in a building and to optimize the energy consumption, Energy consumption of the building and the thermal comfort are considered as objective functions of the proposed multi-objective optimization problem, The 12-story commercial building is used as a case study, To assess the proposed method, the proposed probabilistic method is compared with the deterministic method, The results show a significant difference between the deterministic and probabilistic cases, The maximum difference between the mean optimal values of variable parameters in these cases is about 28%, Finally, sensitivity of the results to the climate changes is also investigated in this paper,

This paper presents a 0. 18um CMOS Ku-band 6-bit phase shifter for phased array transceiver applications. Design specifications of proposed phase shifter are less than 2. 5o RMS phase error (less than half LSB) in 15. 3 GHz to 18. 3 GHz frequency band (16. 8 GHz center frequency) and less than 12 dB insertion loss for phased array aplications. Phase shifter blocks are designed in high-pass and low-pass topology and ADS software has been used for conducting electromagnetic simulations. Designed phase shifter has been fabricated in 180nm TSMC process and measured. There is excellent matching between simulation and measured results.

In this article the structural analysis of a single-input-multiple-output DC-DC boost converter including only one inductor is presented. The outputs of the converter are independent and detached from one another and, hence, the converter can be employed in such applications that require multiple and independent voltage levels (individual) with unidirectional current flow. Moreover, the size of the proposed converter is decreased because of having only one inductor. The main advantages of the proposed structure is the lack of transformer which leads to allowing for modular construction and the possibility for switching at high frequencies. In this paper, the operating principles of mentioned structure are explained and accurate analysis of this structure are conducted. Finally, the experimental results of this single-input-multiple-output DC-DC multiplier converter are presented in detail verifying the operation of this structure.

In this paper, a new method is presented for discrimination of internal fault other abnormal conditions in power transformers. Since the differential current signals are non-stationary, a powerful tool such as S transform is needed for time-frequency analysis of them. First, by considering the effective factors, different simulations for internal fault, inrush current, external fault, over-excitation and ultrasaturation conditions are performed via PSCAD/EMTDC software in different operating conditions of transformer. Then, the obtained differential current signals are analyzed by modified hyperbolic S transform. Important characteristics are extracted from modified hyperbolic S transform and according to them, different conditions are distinguished and classified. These features include: energy contour of first level, variance index, standard deviation of the fundamental frequency and second harmonic component and three special criteria. The process of modified hyperbolic S transform and performing the proposed algorithm are performed in MATLAB software. In this paper, all of possible transient conditions and effects of the saturation of current transformers are considered. Finally, the accuracy and precision of the proposed method are compared with different methods in conditions with noise and without noise. The results show good performance of the proposed method.

This paper presents an active down-conversion mixer for wireless local area networks (WLAN) application. The proposed down-conversion mixer is designed for 2-3 GHz radio frequency (RF) band and an intermediate frequency (IF) of 100 MHz using RFTSMC CMOS 0. 18 μ m technology. A new fully differential Darlington cell is introduced in the RF transconductance stage to suppress third-order nonlinearity and improve mixer linearity. In addition, the conversion gain (CG) and noise performance of the proposed mixer are improved by using a diode-connected active load and current bleeding technique. The proposed mixer has been simulated by Advanced Design System (ADS) and Spectre-RF softwares. The results of post-layout simulation show the third-order input intercept point (IIP3) can be improved up to 12. 5 dBm by optimum biasing of the Darlington cell. The proposed mixer achieves high isolation between ports, the high conversion gain of 14 dB and the low double side-band noise figure (DSB-NF) of 5 dB at the input frequency of 2. 4 GHz. The mixer operates at the supply voltage of 1. 8 V with power consumption of 17 mW.

This paper proposes a new method to allocate the transmission fixed costs among the network participants in a pool based electricity market. The allocation process relies on the circuit laws, utilizes the modified impedance matrix, and is performed in two individual steps for generators and loads. First: to identify the generators contributions into the usage of the transmission facilities, the generators are modelled as current injections and the loads as equivalent impedances. Second: to identify the load's contributions into the usage of the transmission facilities, the loads are modelled as current injections and the generators as impedances. To split the mutual terms between the involved components, the equal sharing principle is used and proved to be rational by the Shapley and Aumann-Shapley values as two preferred game-theoretic solutions. The proposed approach is also applied to determine the generators contributions into the loads, thus, a new concept, named, circuit-theory based equivalent bilateral exchange, is introduced. Numerical results are reported and discussed to validate the proposed cost allocation method.

In recent years, electric vehicles have attracted significant attention. For proper use of electric vehicles, determining the location and size of charging stations is essential. In this paper, the problem of fast charging station planning is modeled as a mixed integer nonlinear programming (MINLP). In the proposed method, network reconfiguration possibility is considered. In addition, for the installation planning of fast charging stations, the uncertainties associated with the conventional load level, the charging stations load level and the price of energy are considered. In the proposed method, a scenario-based approach is used to consider the abovementioned uncertainties. In addition, network reconfiguration is considered as a tool to optimize the objective functions of distribution company. Finally, the efficiency of the proposed method is demonstrated by numerical results.

The use of visual information for voice recognition is an important solution in the absence of audio information. This paper presents a method for speech recognition using visual information by describing spatial-temporal changes in the lobe of the lips. The gradient of the image was used for feature extraction. In the proposed method, after lobe area detection and extraction of key points, the gradient was extracted to describe the spatial information of the key points. To describe the key areas of the lip during speaking, the 3D histogram of gradients path curve fitting was used. The main focus of this research was to provide an adequate description of speech. For this purpose, different classifiers were tested and the best one was recognized. To evaluate the proposed method, the MIRACL-VC1 database was used and the results were compared with previous methods for speech recognition which had an improvement about 11 to 17 percent.

In the mid-term operational management of multi-carrier energy systems (MCES) in a deregulated system, coordination among owners of production units is very important. Scheduling the CCHP resources and also the behavior of the owners of production resources are the main coordinating challenges. The cogeneration resources play a key role in energy generation networks and are affected by the behavior of the owners and their operation policies. In this paper, a model is presented that coordinates resources in MCES in competitive conditions. It takes the objectives of key players, including system operator into account. The proposed model is a multi-objective model and aims to minimize mid-term costs for the resource owners and ensures the reliability of the network over the medium term from the perspective of the operator of the whole system. To solve the multi-objective problem, the Epsilon-constraint method has been used and the Pareto front has been solved. The Fuzzy method was used to find a unique answer to the problem. The proposed model is tested on a local MCES (Special Economic Zone in Assaluyeh), and the simulation results indicate that coordinated outage window, eliminates the scheduling challenge for the output of concurrent production units in these networks.

With increasing demand of bandwidth-intensive application in cellular networks, coexistence of Device-to-Device (D2D) communications with cellular subscribers is a promising solution for high spectrum efficiency and network throughput. In cellular D2D communications, intelligent resource sharing among the network subscribers and paired devices is of significant importance. The most state-of-the-art works are relied on the exact values of Channel State Information (CSI) and subscribers’ transmission rate feedback which are not available in the real cases. In this paper, we propose a novel reinforcement-learning-based approach for mode selection and spectrum allocation called RL-D2D which shares efficiently resources amongst the D2D users and cellular subscribers with the need for CSI, achieving high network throughput. The results of evaluations show that RL-D2D achieves near-optimal performance and low outage rate in despite of lack of CSI and users’ transmission rate feedback.

This paper presents a mixed linear programming optimization model for determining the optimal path and location of sinks with respect to dead time and energy consumption, in order to maximize the amount of collected data in underwater wireless sensor networks. Solving this problem in polynomial time is not possible due to be centralizing. So, to solve this problem, a heuristic and greedy and fully distributed algorithm are proposed to determine the movement of sinks and their location based on maximizing the amount of collected data and the type of dead time of data. As long as be in a place data and energy, sinks can be repeatedly traveled to increase the amount of collected data. By comparing and simulating different routing methods with proposed model and algorithm, the collected data in the proposed method for sparse and dense networks and in the soft and hard dead time is at least 77% higher than the unaware heuristic methods such as TSP and LM and is 80 percent more than the RS method, and the energy consumption is about 45% lower than the TSP method, and the end-to-end delay is about 69% less than the TSP method, and network efficiency is about 23% more than the TSP method.

A multi-processor architecture was proposed to address power consumption limitations in chips in order to increase performance and lower power consumption. In this architecture, the highest power consumption comes from the chip connections. Optical connections can reduce power consumption and increase performance via a new architecture called photonic network-on-chips, which are capable of utilizing the benefits of optical signals and elements for data transfer. This paper proposes a 4×4, non-blocking photonic switch to improve physical layer parameters by increasing photonic network performance. The insertion loss and energy dissipation of the proposed switch were compared to three switches: Original, StraightPath, and Symmetric switches. The results indicated that, in a mesh topology, the proposed switch reduced loss for the GTC, Cactus, Tornado, and MADbench by 28. 22, 24. 46, 28. 72, and 29. 20%, respectively, when compared to the Original switch. According to PhoenixSim simulation results, the proposed switch reduced insertion loss and energy dissipation in photonic network-on-chips when compared to the three comparison switches.

This paper presents a new approach for a Piezoelectric Energy Harvester (PEH) with actuation piezoelectric layer to shift system natural frequency. Beams are consisted of Si and AlN piezoelectric based on MEMS technology and piezoelectric is deposited on Fixed-Fixed beams with mass loading that produces more stress points and generates more power in comparison to other cantilever beam PEHs. This PEH with ability of shifting system natural frequency is designed in the size of 0. 25 cm2 using optimum available space. In addition to piezoelectric generating layers, piezoelectric layers for actuation purpose is added on both sides of the beams for possibility of reducing system natural frequency to less than 10 Hz. Accomplished simulation also confirms theoretical calculation done by Partial Differential Equation (PDE) method for a Fixed-Fixed beam with mass loading in the center to estimate natural frequency of the system and generated voltage from piezoelectric effect. Natural frequency of the system in the designed structure without piezoelectric actuation voltage is about 127 Hz that with 1 g acceleration generated ± 3. 35 volts and 45 μ W electrical powers which can be used in wireless sensor network and biosensing applications.

In the past decade, the study of human opinions, feelings and tendencies has been very effective in the decision-making of managers and individuals. Machine learning algorithms play an important role in the field of opinion mining, but they suffer from a big problem: most of the machine learning algorithms assume that the feature dimensions and data distribution are equal, but most of real-world applications don't follow these assumptions. In fact, the data that the algorithm will receive in the future may have different dimensions or distributions. In this article, a new method for improving sentiment analysis of opinions is proposed by the aid of featurebased transfer learning. In the proposed method, initially, the feature or topic of the opinion in the source language domain is identified. Then, by collecting adjectives, adverbs and totally a package of probabilities about that feature and by translating it into the target language, learning from the source language is transferred into the target language. An analysis of the proposed method on the data available at the Amazon store as the source domain indicates that by creating a pattern of feature transferring in English, the polarity of 77% of the opinions in Persian (recorded at the Digikala store) can be extracted that outperforms the SCL, SFA and TCA models with 9, 5 and 5 percent respectively.

Feature selection is the process of selecting a subset of features among the set of primary features, so that, by removing the redundant and irrelevant features, the accuracy of the classification increases. Because of the low computational complexity, scalability in term of data dimensions and independence of any classifier, filter selection methods are very important. But one of the weaknesses of these methods is the lack of information about the interaction and communication between the features which leads to select redundant and irrelevant features. Selection of redundant and irrelevant features is due to the inappropriate selection of an objective function which estimates the significance and redundancy of the features. In this paper, a nonlinear filter feature selection method, based on conditional mutual information and Pareto set is presented and to prove the efficiency of it a series of experiments are performed on twelve widely used datasets. According to the results, the proposed method is more accurate than a number of recently feature selection methods.

In this paper, a hierarchical Bayes model is introduced which models local word relationships in a language. The model can be considered as a language model. The proposed model does not suffer from sparseness because it does not rely on the exact word orders. However, the model does not completely ignore the word orders. The proposed generative model assumes that each word is a distribution over words and the current word is generated from the distribution of one of its previous words located in a fixed-size window. Contrary to an n-gram model which is a distribution over word sequences and so takes the exact sequences of words into account, the proposed model considers ordered pairs of words which can occur at different distances in the subject text data. Because of this, the sparseness problem is not severe for the proposed model. The model is compared with and outperformed n-gram model according to its ability to model text data which is evaluated by perplexity.

Installation of active power filters (APFs) is one of the most efficient ways to reduce harmonic pollution in distribution networks. In previous researches, APFs are considered with integrated control system and local control system of APFs is one of the items that has been ignored in optimal allocation of these devices. Integrated central control systems need powerful hardware and software infrastructures and since there is no such infrastructure in Iran distribution networks, such methods would be usefulness for optimal allocation of APFs. In this paper allocation of locally controlled APFs has been studied. Local control system of APFs make the current of upstream branch from their connection point sinusoidal by sensing the current passing through the connection point and injecting equal but opposite current to that point. Genetic algorithm has been used as the optimization tool and finally, the numerical results are discussed in two, 9-bus and 33-bus test systems.

Cloud computing is a new technology which its popularity increases every day, a popularity due to its elasticity. On the other words, cloud computing takes into account an unlimited capacity of the resource for the consumer, and the consumer can take resources in demand based on competitive rates and increase or decrease the amount of resources. There have been many improvements to elasticity management by previous researches. However, further reasearches are necessary to manage elasticity more efficiently. In this paper, a model for the elasticity improvement using a colored Petri network is proposed to provide resources in cloud computing. In the proposed model, elasticity management is performed using a colored Petri net in the form of control of the M/M/N queues. In this way, there is a horizontal queue for each request or service in the vertical queue for the need to increase or decrease the virtual machine. The results of the proposed method show an improvment in elasticity, accuracy and speed, compared with the other approaches.

In this paper, a conical horn antenna is introduced in the X-band and in the presence of a metamaterial lens. The metamaterial lens is located at the aperture of a short horn antenna that the length of the horn is half the one of the corresponding optimum versions. The phase velocity in the metamaterial lens is more than the phase velocity in an environment without lens, so the phase distribution in the aperture of antenna is uniform and the gain of the short horn antenna increases. Metamaterial cells are designed in the form of printed circuit board that have easy to build and suitable cost. The geometrical characteristics of metamaterial lens is calculated by designed formulas and finally; particls swarm optimazition algoritm is used to achieve proper gain and radiation characteristics in the desired frequency band. The radiation characteristics of an antenna with a metamaterial lens are similar to that of an antenna with optimum length in the desired frequency band; the difference is that the weight and volume of these antennas are reduced, so they have better performance in satellite systems and radars.

Nowadays, due to the dynamic environment of service-based applications, the ability to adapt to environmental and contextual changes has become a crucial quality of such applications. However, providing these applications with adaptability capability at runtime so as to supports service-oriented development characteristics and to be able to run on a service integration infrastructure is a complex and difficult task. This paper presents a method to support realizing adaptability requirements in the development and evolution of service-based applications. To this end, the business process meta-model has been extended to monitoring and adaptation concerns. In addition, a set of mechanisms to transform these requirements to monitoring checkpoints and adaptation actions are presented in such a way that they can be deployed on enterprise service bus. The proposed method is evaluated on the basis of the several scenarios in a case study based on quantitative and qualitative experiments. The results of the evaluation demonstrate that the proposed method can effectively provide the possibility of developing adaptive behaviors.

With the expansion of social networks, relationship between people has taken a new form. One of the important issues in social networks is social influence. Research on social influences and how information is disseminated in social networks, indicates that accepting or rejecting a new pattern by a person depends on the acceptance or rejection of the friends of that person. That is, because the people usually trust their friends more than other sources of advertising. As a result, many companies are focused on this type of advertisement which is called viral marketing. Given a large number of users in a social network, selecting the most influential users as target users, through which a company can reach the highest expansion in the network with the lowest cost, is of great importance. In this paper, a new method for identifying the influential nodes in social networks is proposed which is called MOSI (Multi-Objective algorithm based on Structured Information). The proposed method has two goals: "maximize profit" and "minimize similarity among selected users". The evaluation of the proposed method on real datasets indicates that our method has a greater expansion power in comparison with other similar methods.

In this paper by electromagnetic modeling of neurons in brain, the brain waves have been derived in a full-wave way. Now, in all clinics and research centers, traditionally, it has been done by using the quasi-static approximation of the Maxwell equation in electromagnetic. However, the error rate resulting from the approximation has not been studied upon the final results. This issue becomes more noticeable due to increasing the sensitivity of today's modern sensors. In this paper, first, with an overview of the basics of applying quasi-static approximation in the analysis brain waves, ambiguities about the suitability of this approximation are presented and the necessity of full-wave solution of the problem is expressed. Then, in the simplest form, the electromagnetic fields aroused from a current dipole where is located in the center of a sphere with known conductivity is written in terms of Bessel and Hankel function expansion; and the problem has been solved in a full-wave way by using of scattering theories in electromagnetic. Finally, the curve of relative difference measure (RDM) between quasi-static and full-wave solution has been drawn in terms of frequency conductivity. One of the important achievements of full-wave modeling is enriching the information resulted from EEG and MEG and consequently extracting more accurate patterns from brain activities.

The Residue Number System (RNS) hardware structure consists of several components, including forward converter, separate arithmetic units for performing modular addition and multiplication, and reverse converter. Forward and reverse converters, essential in any RNS to interface with other digital circuits, represent overhead, resulting in larger chip-area and power-consumption. This work, for the first time, proposes a hybrid converter for RNS, which unifies forward and reverse converters by re-using hardware. To achieve this aim, the mixed-radix conversion (MRC) algorithm has been used for putting up the reverse conversion formulas in a similar format to forward conversion formulas. The VLSI implementation results of the proposed hybrid converter based on TSMC-65nm technology for the moduli set {2n− 1, 22n, 2n+1− 1} show a reduction up to 19% of the required area in comparison to the total area of the forward and reverse converters. However, the delay of the proposed hybrid converter is just 10% higher than individual reverse converter delay.

Elasticity is considered as one of the most important features that distinguishes cloud computing from other distributed system approaches. This feature takes into account the fact that the resource allocation process is considered as a process that can be implemented dynamically. Providing an efficient solution for improving elasticity will be useful for both providers and users of cloud computing services. Using the proposed solution in this paper, providers will be able to evaluate and improve the quality of their services, and increase their qualitative or quantitative advantage in competing with other competitors. In this paper, we present a hybrid solution for improving elasticity through using buffer management and centralized elastic management. Buffer managment controls the input queue of the request and the elastic management by using the reinforcement learning controls the elasticity of the system. Finally, we evaluate the effectiveness of our approach under three real workload traces, namely, Yahoo Cluster, Wikipedia, and Google Cluster workload traces. The experimental results show that the proposed approach reduces the response time by up to 15. 2%, and increases the resource utilization by up to 13. 2 % and the elasticity by up to 19. 8 % compared with the CTMC and ControCity approaches.

In this paper, a dissipativity-based fuzzy integral sliding mode control (FISMC) is proposed for a class of nonlinear systems with matched/unmatched uncertainties and external disturbance and with consideration actuator saturation. The control aim is to design a robust controller such that guarantees the stability of the state variables in the presence of uncertainties and applying the constraint on the control input amplitude, to overcome the practical limitation. To do this, Takagi-Sugeno fuzzy model is used to represent the nonlinear behavior of a saturated actuator. Ability to appropriate select the maximum control input amplitude is another advantage of the proposed idea. By using Lyapunov theory, stability conditions are derived with a strictly dissipative performance and expressed as linear matrix inequality (LMI) conditions. Finally, simulation results illustrates the effectiveness and priority of the proposed approach.

The complexity of power systems increases with the advent of intelligent electrical power systems and the use of renewable resources. In order to successfully carry out control and management tasks, the accuracy of the estimated electrical quantities will be an important issue. State estimation plays an important role in this view and is considered as the final loop of the measurement chain. The effect of the errors of measuring devices is such that it directly affects the accuracy of the results, so providing methods to improve robustness of the estimation algorithm is necessary in relation to the errors in the inputs of this problem. In this paper, a method is proposed to improve the distribution system state estimation (DSSE) algorithm, which provides real and accurate analyzes of the operating conditions of an active distribution system. Thus, in this paper is presented an algorithm using exponential function for weighting tuning of measurement sets of the power system in the form of measurement errors in the presence of wind power plants and loads by non-Gaussian uncertainties and probability functions. For analysis of proposed algorithm, simulations results are carried on IEEE 33-bus and 50-bus distribution systems and demonstrate the effectiveness of the proposed method.

Using full duplex communication systems called FD, is a new technique for increasing the spectral efficiency. That`s why it has been considered seriously in the new generation of communication systems. The main challenge of the full-duplex system is the self-interference, which has to be decreased properly. In this paper, we consider a full-duplex cellular network base station and using the antenna selection technique to reduce its self-interference issue. For appropriate antenna selection, we present a new criterion. In this new criterion, antenna selection has been modeled and solved as a multi-objective optimization problem. In this new criterion, antennas which simultaneously minimizes interference channel gain and maximizes uplink (UL) and downlink (DL) channel gains are selected for transmission and reception. Since the goal of the base station is a good performance in both the UL and DL channels and reduction of the self-interference simultaneously, the multi-objective criterion has a better performance when compared to single-objective criteria. In the common single-objective criteria, only the UL and DL channels or interference channel is considered. Finally, simulation results show that the new criterion has more throughput rate than the other single-objective antenna selection technique in the full duplex systems.

Finding a graph-based clustering is an applied method for detecting the relationship between nodes in complex networks, which has attracted considerable attention. Since recognizing different communities in large-scale data is a challenging task, by understanding the relationship between the behavior of elements in a society (cluster), we can predict the general characteristics of the clusters. Graph-based clustering techniques have played an important role in the clustering of gene expression data due to their ability to show the relationship between data. In order to detect effective genes in the development of diseases, it is necessary to achieve the relationship between cells or tissues. The interaction between cells or different tissues can be demonstrated by expressing different genes between them. In this research, the problem of cell-to-cell and tissue-to-cell communication is expressed as a graph and is extracted by the recognition of relationships. The Phantom 5 database is used to simulate and calculate the similarity between cells and tissues. After preprocessing and normalizing the data, for the conversion of these data to the graph, the expression of the gene in different cells and tissues has been examined and considering the threshold and the Wilcoxon test, using clustering of communications They were identified.

In this paper, a Decision Tree-based method of Loss of Excitation (LOE) Protection of Synchronous Generators in Presence of Unified Power Flow Controller (UPFC) is presented. The presence of UPFC causes the Berdy relay to be delayed in detecting LOE. In the proposed method, different variables are considered and in the various test and training patterns and using intelligent algorithms, the most suitable variables selected to increases the accuracy in detecting the time of LOE. The simulation results in Matlab/Simulink software validate the accuracy of the proposed method in various loadings, different percentages of loss of excitation, various scenarios of fault and noisy conditions. The results reveal that the proposed method has a better classification accuracy and a higher identification speed under new and unseen operating conditions of LOE and external faults compared to traditional methods.

Typically, when someone wants to buy an online product, he/she reviews the comments and notes written by others about the product. Clearly, it has a profound impact on the person's decision to buy or not to buy the product. Sentiment analysis or opinion mining is one of the hot research topics in computer science. The main purpose of sentiment analysis is to extract opinions of individuals about the characteristics of an entity such as a product. In this research, an unsupervised opinion mining is proposed for Persian products based on implicit feature extraction which is a critical step in sentiment analysis. In most previous studies, statistical information is utilized to create a co-occurrence matrix and determine the implicit features. In this paper, we benefit from syntactic rules and sentiment clustering in conjunction with statistical information to construct an efficient co-occurrence matrix between features and sentiment words. The evaluation results provided on a real-world dataset, extracted from Digikala website, indicates that the proposed method achieves higher recall and precision compared to the previous studies.

In this paper, a new approach of modeling and fault-tolerant control is presented for multi-rate networked control system (MRNCS) with considering long time delay. Firstly, the MRNCS is modeled as a switched system with linear subsystems and a random switching signal. By considering the switching signal (as the result of random induced delay) as a Markov chain, the model of MRNCS is obtained as a Markovian jump linear system. Then a mode-independent dynamic output feedback controller is designed to stabilize the closed-loop system. In continuation, with the purpose of system tolerance against the actuator fault (or load disturbance), a virtual actuator is used so that the reconfiguration is performed without needing for any changes in the main controller. Finally, the quadrupletank process is used to validate the proposed modeling and control approaches.

In this paper a wideband vector-sum based phase shifter with 6-bits accuracy is presented. In this architecture the desired phase shift is obtained by adding quadrature signals with appropriate amplitude. To generate quadrature signals with phases of 0, 90, 180, and 270o an active balun and gm-based integrator are deployed. Finally an active adder has been used to add up the quadrature signals. In this paper in order to widen the frequency bandwidth of the phase shifter and hence reduce the root mean square (RMS) phase error, a feedback path including two power detectors and an amplifier is deployed, so that the quadrature signals have minimal amplitude and phase mismatch in frequency range of 2 – 4 GHz. The RMS phase error varies between 1. 092 and 4. 02o for simulation of corners of (typical transistors and 27o) and (fast transistors and 120o), respectively. The RMS amplitude error varies between 0. 25 and 0. 35dB for simulation of corners of (typical transistors and 27o) and (fast transistors and 120o), respectively. The proposed phase shifter draws 9. 28 mA from 1. 8V supply voltage.

For correct performance of protection and control systems in microgrids, the islanding conditions should be identified as soon as possible. In this paper, an optimal method is proposed to determine the input parameters of classification method by using local information. The goal of optimization is to minimizie the time and maximize the percision of microgrid connection state detection. Also, in classification method, three states, i. e. islanding, reconnecting to the utility and other events are considered. The objective function of this problem is defined as summation of weighted time factor and detection error. The inputs of the islanding detection problem can be a large number of network parameters such as frequency, voltage, current, power, their sequence components or the rate of change. Consequently, the variables of the classification problem are the number and type of input parameters of the classification problem. The genetic algorithm is used to solve this optimal problem, and support vector machine is used for the classification. In order to assess the validity of the proposed method, different situations are considered for the operation of the grid, and in each case, different events are modeled. The results are compared with the methods of other papers, and the advantage of the suggested method is shown.

This paper proposes a mathematical model for the short-term security constrained operation problem in a radial Microgrid. The conventional security modeling in the transmission system is not appropriate for radial networks, where, a line outage creates an island and usually leads to the load shedding. This paper proposes a security model for the radial Microgrid using the consumers’ damage indices. In addition, it analyses the load control effects. It includes these contingency state constraints in the short-term scheduling: power balance in the island, allowed frequency deviation range, island’ s capability to continue the service and load sensitivity to the duration of the abnormal condition. Hourly scheduling of the resources, loads and power exchange with the main grid is performed with the aim of minimizing the load damage in the contingency states and maximizing the profit at this selected security level. The proposed model is applied to IEEE 123-bus test system. It reduces the load damage index at least 38% in comparison to the other operational strategies. The cost-benefit assessment is performed to analyze the proposed method in different viewpoints.