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This paper proposes an integrated energy management system for grid-connected microgrids, taking into account the demand response programs, fossil fuel-based generators, renewable energy sources and energy storage systems. In the proposed approach, the constraints of the problem are considered jointly in the model of the energy management systems and are used for microgrid energy management planning and economic dispatch. One of the innovations of this paper is to use the Internet of Things (IoT) platform to adjust the maximum ramp rate of production units in microgrid due to the limitations of production capacity. Since the system considered, models the general state of the internet communication of objects without the need to access the communication channel, so that the energy of consumers should be minimized as second objective function.in this platform, whenever one of the objects has a message to send, it sends it without the need to reserve a resource and schedule. The IoT can establish a good relationship between power producers in a way that reduces operating costs by exchanging data. Optimization of energy consumption in the hybrid power grid studied in this paper shows that the use of IoT platform can reduce the transmission line losses in addition to operating costs. The output results of using data in the IoT context and comparing it with the traditional mode represent the superiority of the proposed approach.

Multi-Megawatt wind turbines have long, slender and heavy blades that can undergo extremely wind loadings. Aeroelastic stability of wind turbine blades is of great importance in both power production and load carrying capacity of structure. This paper investigates the aeroelastic stability of wind turbine blades modeled as thin walled composite box beam, utilizing unsteady incompressible aerodynamics. The structural model incorporates a number of non-classical effects such as transverse shear, warping inhibition, non-uniform torsional model and rotary inertia. The unsteady incompressible aerodynamics based on Wagner’s function is used to determine the aerodynamic loads. Governing differential equations of motion are obtained using Hamilton’s principle and solved using extended Galerkin’s method. The results obtained in this paper, related to clarification of the effects of angular velocity and wind speed on the aeroelastic instability boundaries of the thin-walled composite beams. The obtained results are expected to be useful toward obtaining better predictions of the aeroelastic behavior of composite rotating blades.

The purpose of this paper is to introduce some new classes of rings and modules that are closely related to the classes of almost Dedekind domains and almost Dedekind modules. We introduce the concepts of $\phi$-almost Dedekind rings and $\Phi$-almost Dedekind modules and study some properties of this classes. In this paper we get some equivalent conditions for $\phi$-almost Dedekind rings and $\Phi$-almost Dedekind modules and obtain the relationship between $\phi$-almost Dedekind rings and $\Phi$-almost Dedekind modules.

Online scientific communities are bases that publish books, journals, and scientific papers, and help promote knowledge. The researchers use search engines to find the given information including scientific papers, an expert to collaborate with, and the publication venue, but in many cases due to search by keywords and lack of attention to the content, they do not achieve the desired results at the early stages. Online scientific communities can increase the system efficiency to respond to their users utilizing a customized search. In this paper, using a dataset including bibliographic information of user’s publication, the publication venues, and other published papers provided as a way to find an expert in a particular context where experts are recommended to a user according to his records and preferences. In this way, a user request to find an expert is presented with keywords that represent a certain expertise and the system output will be a certain number of ranked suggestions for a specific user. Each suggestion is the name of an expert who has been identified appropriate to collaborate with the user. In evaluation using IEEE database, the proposed method reached an accuracy of 71.50 percent that seems to be an acceptable result.

Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the MAP estimator depends on the proposed model for noise-free wavelet coefficients. Thus in the wavelet based image denoising, selecting a proper model for wavelet coefficients is very important. In this paper, we model wavelet coefficients in each sub-band by heavy-tail distributions that are from scale mixture of normal distribution family. The parameters of distributions are estimated adaptively to model the correlation between the coefficient amplitudes, so the intra-scale dependency of wavelet coefficients is also considered. The denoising results confirm the effectiveness of the proposed method.

Frequency control is one of the key parts for the arrangement of the performance of a microgrid (MG) system. Theoretically, model-based controllers may be the ideal control mechanisms; however, they are highly sensitive to model uncertainties and have difficulty with preserving robustness. The presence of serious disturbances, the increasing number of MG, varying voltage supplies of MGs, and both independent operations of MGs and their interaction with the main grid makes the design of model-based frequency controllers for MGs become inherently challenging and problematic. This paper proposes an approach that takes advantage of interval Type II fuzzy logic for modeling an MG system in the process of its robust H∞ frequency control. Specifically, the main contribution of this paper is that the parameters of the MG system are modeled by interval Type-II fuzzy system (IT2FS), and simultaneously MG deals with perturbation using H∞ index to control its frequency. The performance of the microgrid equipped with the proposed modeling and controller is then compared with the other controllers such as H2 and μ-synthesis during changes in the microgrid parameters and occurring perturbations. The comparison shows the superiority and effectiveness of the proposed approach in terms of robustness against uncertainties in the modeling parameters and perturbations.

As the energy demand is growing and fossil fuel based energy resources are getting depleted, there has been an increased focus on the use of renewable energy resources. Wind energy is a highly suitable energy resource that can be harvested for the purpose. This research deals with the design and fabrication of novel designs as a mean to harness wind energy using a ‘Savonius’ Turbine. It is generally employed to harvest the low to very low wind speed potentials. The paper introduces a novel concept about a Ducted Savonius wind turbine where power generation can increase more than 10 folds. The paper provides experimental validation of the effect of using a converging ducted structure with a single stage and double stage configurations of a Savonius wind turbine. The paper also compares the turbine’s performance with and without endplates and compares Single Stage and Double Stage Turbine. The experiment results show that around 15% increase in tangential blade velocity in single stage rotor when end plates are used. The results of the study proves that power coefficient increases with the addition of a converging ducted structure with the Savonius wind turbine, and also with the use of endplates.

The paper deals with falling film heat transfer across horizontal copper tubes at different tube surface geometries, mass flow rates, heat fluxes and weight percentage of salt in water salt solution at atmospheric pressure. The falling film heat transfer coefficient is significantly affected by heat flux, film Reynolds number and water salt solution for three types of augmented tubes viz. spiral, splined and smooth. This paper considers the influence of operating parameters on heat transfer coefficient using Fuzzy-Topsis applications. The experimental results reveal that falling film heat transfer has been greatly enhanced in case of spiral tube when compared with splined and smooth tubes. The spiral tube shows significant heat transfer performance than other two tubes for a given heat flux and Reynolds number as heat flux increases surface temperature also increases and increment in surface temperature of smooth tube is greater than spiral and spline tube for given heat flux and Reynolds number. As mass flow rate increases, surface temperature of all three tubes decreases but for a given heat flux and Reynolds number smooth tube has more surface temperature than other two tubes.

Phishing is one of the luring techniques used to exploit personal information. A phishing webpage detection system (PWDS) extracts features to determine whether it is a phishing webpage or not. Selecting appropriate features improves the performance of PWDS. Performance criteria are detection accuracy and system response time. The major time consumed by PWDS arises from feature extraction that is considered as feature cost in this paper. Here, two novel features are proposed. They use semantic similarity measure to determine the relationship between the content and the URL of a page. Since suggested features don't apply third-party services such as search engines result, the features extraction time decreases dramatically. Login form pre-filer is utilized to reduce unnecessary calculations and false positive rate. In this paper, a cost-based feature selection is presented as the most effective feature. The selected features are employed in the suggested PWDS. Extreme learning machine algorithm is used to classify webpages. The experimental results demonstrate that suggested PWDS achieves high accuracy of 97.6% and short average detection time of 120.07 milliseconds.

Cement rotary kiln is the main part of cement production process that have always attracted many researchers’ attention. But this complex nonlinear system has not been modeled efficiently which can make an appropriate performance specially in noisy condition. In this paper Takagi-Sugeno neuro-fuzzy system (TSNFS) is used for identification of cement rotary kiln, and gradient descent (GD) algorithm is applied for tuning the parameters of antecedent and consequent parts of fuzzy rules. In addition, the optimal inputs of the system are selected by genetic algorithm (GA) to achieve less complexity in fuzzy system. The data related to Saveh White Cement (SWC) factory is used in simulations. The Results demonstrate that the proposed identifier has a better performance in comparison with neural and fuzzy models have presented earlier for the same data. Furthermore, in this paper TSNFS is evaluated in noisy condition which had not been worked out before in related researches. Simulations show that this model has a proper performance in different noisy condition.

This paper, presents an adapted serious game for rating social ability in children with autism spectrum disorder (ASD). The required measurements are obtained by challenges of the proposed serious game. The proposed serious game uses reinforcement learning concepts for being adaptive. It is based on fuzzy logic to evaluate the social ability level of the children with ASD. The game adapts itself to the level of the autistic patient by reducing or increasing the challenges in the game via an intelligent agent during the play time. This task is accomplished by making more elements and reshaping them to a variety of real world shapes and redesigning their motions and speed. If autistic patient's communication level grows during the playtime, the challenges of game may become harder to make a dynamic procedure for evaluation. At each step or state, using fuzzy logic, the level of the player is estimated based on some attributes such as average of the distances between the fixed points gazed by the player, or number of the correct answers selected by the player divided by the number of the questioned objects. This paper offers the usage of dynamic AI difficulty system proposing a concept to enhance the conversation skills in autistic children. The proposed game is tested by participating of 3 autistic children. Each of them played the game in 5 turns. The results displays that the method is useful in the long-term.

The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine. The final classification decision has been made based on the small portion of data called support vectors. So, existence of the atypical observations in the aforementioned points, will result in deviation from the correct decision. Thus, the idea of Debruyne’s “outlier map” is employed in this paper to identify the outlying points in the SVM classification problem. However, due to the computational reasons such as convenience and rapidity, a robust Mahalanobis distance based on the minimum covariance determinant estimator is utilized. This method has a good compatibility by the data with low dimensional structure. In addition to the classification accuracy, the margin width is used as the criterion for the performance assessment. The larger margin is more desired, due to the higher generalization ability. It should be noted that, by omission of the detected outliers using the suggested outlier map the generalization ability and accuracy of SVM are increased. This leads to the conclusion that the proposed method is very efficient in identifying the outliers. The capability of recognizing the outlying and misclassified observations for this new version of outlier map has been retained similar to the older version, which is tested on the simulated and real world data.

In this paper, a high-performance optimal fractional emotional intelligent controller for an Automatic Voltage Regulator (AVR) in power system using Cuckoo optimization algorithm (COA) is proposed. AVR is the main controller within the excitation system that preserves the terminal voltage of a synchronous generator at a specified level. The proposed control strategy is based on brain emotional learning, which is a self-tuning controller so-called brain emotional learning based intelligent controller (BELBIC) and is based on sensory inputs and emotional cues. The major contribution of the paper is that to use the merits of fractional order PID (FOPID) controllers, a FOPID controller is employed to formulate stimulant input (SI) signal. This is a distinct advantage over published papers in the literature that a PID controller used to generate SI. Furthermore, another remarkable feature of the proposed approach is that it is a model-free controller. The proposed control strategy can be a promising controller in terms of simplicity of design, ease of implementation and less time-consuming. In addition, in order to enhance the performance of the proposed controller, its parameters are tuned by COA. In order to design BELBIC controller for AVR system a multi-objective optimization problem including overshoot, settling time, rise time and steady-state error is formulated. Simulation studies confirm that the proposed controller compared to classical PID and FOPID controllers introduced in the literature shows superior performance regarding model uncertainties. Having applied the proposed controller, the rise time and settling time are improved 47% and 57%, respectively.

Search-based optimization methods have been used for software engineering activities such as software testing. In the field of software testing, search-based test data generation refers to application of meta-heuristic optimization methods to generate test data that cover the code space of a program. Automatic test data generation that can cover all the paths of software is known as a major challenge. The paper establishes a new cost function for automatic test data generation, which can traverse the non-iterative paths of software control flow graphs. This function is later compared with similar cost functions proposed in other articles. The results indicate the superior performance of the proposed function. Still another innovation in this paper is the application of the Imperialist Competitive Algorithm in automatic test data generation along with the proposed cost function. Automatic test data generation is implemented through the Imperialist Competitive Algorithm as well as the Genetic and Particle Swarm Optimization Algorithms for three software programs with different search space sizes. The algorithms are compared with each other in terms of convergence speed, computational time, and local search. Test data generated by the proposed method has achieved better results than other algorithms in finding the number of non-iterative paths, the convergence speed and computational time with growing the searching space of the software's control flow graph.

Developing optimal flocking control procedure is an essential problem in mobile sensor networks (MSNs). Furthermore, finding the parameters such that the sensors can reach to the target in an appropriate time is an important issue. This paper offers an optimization approach based on metaheuristic methods for flocking control in MSNs to follow a target. We develop a non-differentiable optimization technique based on the gravitational search algorithm (GSA). Finding flocking parameters using swarm behaviors is the main contributing of this paper to minimize the cost function. The cost function displays the average of Euclidean distance of the center of mass (COM) away from the moving target. One of the benefits of using GSA is its application in multiple targets tracking with satisfying results. Simulation results indicate that this scheme outperforms existing ones and demonstrate the ability of this approach in comparison with the previous methods.

Information security and Intrusion Detection System (IDS) plays a critical role in the Internet. IDS is an essential tool for detecting different kinds of attacks in a network and maintaining data integrity, confidentiality and system availability against possible threats. In this paper, a hybrid approach towards achieving high performance is proposed. In fact, the important goal of this paper is generating an efficient training dataset. To exploit the strength of clustering and feature selection, an intensive focus on intrusion detection combines the two, so the proposed method is using these techniques too. At first, a new training dataset is created by K-Medoids clustering and Selecting Feature using SVM method. After that, Naïve Bayes classifier is used for evaluating. The proposed method is compared with another mentioned hybrid algorithm and also 10-fold cross validation. Experimental results based on KDD CUP’99 dataset show that the proposed method has better accuracy, detection rate and also false alarm rate than others.

The aim of image restoration is to obtain a higher quality desired image from a degraded image. In this strategy, an image inpainting method fills the degraded or lost area of the image by appropriate information. This is performed in such a way so that the obtained image is undistinguishable for a casual person who is unfamiliar with the original image. In this paper, different images are degraded by two procedures; one is to blur and to add noise to the original image, and the other one is to lose a percentage of the pixels belonging to the original image. Then, the degraded image is restored by the proposed method and also two state-of-art methods. For image restoration, it is required to use optimization methods. In this paper, we use a linear restoration method based on the total variation regularizer. The variable of optimization problem is split, and the new optimization problem is solved by using Lagrangian augmented method. The experimental results show that the proposed method is faster, and the restored images have higher quality compared to the other methods.

Plagiarism which is defined as “the wrongful appropriation of other writers’ or authors’ works and ideas without citing or informing them” poses a major challenge to knowledge spread publication. Plagiarism has been placed in four categories of direct, paraphrasing (rewriting), translation, and combinatory. This paper addresses translational plagiarism which is sometimes referred to as cross-lingual plagiarism. In cross-lingual translation, writers meld a translation with their own words and ideas. Based on monolingual plagiarism detection methods, this paper ultimately intends to find a way to detect cross-lingual plagiarism. A framework called Multi-Lingual Plagiarism Detection (MLPD) has been presented for cross-lingual plagiarism analysis with ultimate objective of detection of plagiarism cases. English is the reference language and Persian materials are back translated using translation tools. The data for assessment of MLPD were obtained from English-Persian Mizan parallel corpus. Apache’s Solr was also applied to record the creep of the documents and their indexation. The accuracy mean of the proposed method revealed to be 98.82% when employing highly accurate translation tools which indicate the high accuracy of the proposed method. Also, Google translation service showed the accuracy mean to be 56.9%. These tests demonstrate that improved translation tools enhance the accuracy of the proposed method.

In this paper a novel direct adaptive fuzzy system is proposed to control flexible-joints robot including actuator dynamics. The design includes two interior loops: the inner loop controls the motor position using proposed approach while the outer loop controls the joint angle of the robot using a PID control law. One novelty of this paper is the use of a PSO algorithm for optimizing the control design parameters to achieve a desired performance. It is worthy of note that to form control law by considering practical considerations just the available feedbacks are used. It is beneficial for industrial applications wherethe real-time computation is costly. The proposed control approach has a fast response with a good tracking performance under the well-behaved control efforts. The stability is guaranteed in the presence of both structured and unstructured uncertainties. As a result, all system states are remained bounded. Simulation results on a two-link flexible-joint robot show the efficiency of the proposed scheme.

In recent years, significant research efforts have been devoted to the optimal planning of power systems. Substation Expansion Planning (SEP) as a sub-system of power system planning consists of finding the most economical solution with the optimal location and size of future substations and/or feeders to meet the future load demand. The large number of design variables and combination of discrete and continuous variables make the substation expansion planning a very challenging problem. So far, various methods have been presented to solve such a complicated problem. Since the Bacterial Foraging Optimization Algorithm (BFOA) yield to proper results in power system studies, and it has not been applied to SEP in sub-transmission voltage level problems yet, this paper develops a new BFO-based method to solve the Sub-Transmission Substation Expansion Planning (STSEP) problem. The technique discussed in this paper uses BFOA to simultaneously optimize the sizes and locations of both the existing and new installed substations and feeders by considering reliability constraints. To clarify the capabilities of the presented method, two test systems (a typical network and a real ones) are considered, and the results of applying GA and BFOA on these networks are compared. The simulation results demonstrate that the BFOA has the potential to find more optimal results than the other algorithm under the same conditions. Also, the fast convergence, consideration of real-world networks limitations as problem constraints, and the simplicity in applying it to real networks are the main features of the proposed method.

This paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. The uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. The contribution of this paper is presenting a stability analysis for neuro-fuzzy speed control of induction motors. The online training of the neuro-fuzzy systems is based on the Lyapunov stability analysis and the reconstruction errors of the neuro-fuzzy systems are compensated in order to guarantee the asymptotic convergence of the speed tracking error. Moreover, to improve the control system performance and reduce the chattering, a PI structure is used to produce the input of the neuro-fuzzy systems. Finally, simulation results verify high performance characteristics and robustness of the proposed control system against plant parameter variation, external load and input voltage disturbance.

This paper presents an application of design of experiments techniques to determine the optimized parameters of artificial neural network (ANN), which are used to estimate force from Electromyogram (sEMG) signals. The accuracy of ANN model is highly dependent on the network parameters settings. There are plenty of algorithms that are used to obtain the optimal ANN setting. However, to the best of our knowledge they did not use regression analysis to model the effect of each parameter as well as present the percent contribution and significance level of the ANN parameters for force estimation. In this paper, sEMG experimental data are collected and the ANN parameters based on an orthogonal array design table are regulated to train the ANN. Taguchi help us to find the optimal parameters settings. Next, analysis of variance (ANOVA) technique is used to obtain significance level as well as contribution percentage of each parameter to optimize ANN’s modeling in human force estimation. The results indicated that design of experiments is a promising solution to estimate the human force from sEMG signals.

Technology assessment and selection has a substantial impact on organizations procedures in regards to technology transfer. Technological decisions are usually made by a group of experts, and whereby integrity of these viewpoints to a single decision can be quite complex. Today, operational databases and data warehouses exist to manage and organize data with specific features and henceforth, the need for a decision-aid approach is essential. The process of developing data warehouses involves time consuming steps, complex queries, slow query response rates and limited functions, which is also true for operational databases. In this regards, Fuzzy multi-criteria procedures in choosing efficient data sources (data warehouse and traditional relational databases) based on organization requirements is addressed in this paper. In proposing an appropriate selection framework the paper compares a Triangular Fuzzy Numbers (TFN) based framework and Fuzzy Analytical Hierarchy Process (AHP), based on data sources models, business logic, data access, storage and security. Results show that two procedures rank data sources in a similar manner and due to the accurate decision-making.

This paper proposes a discrete-time repetitive optimal control of electrically driven robotic manipulators using an uncertainty estimator. The proposed control method can be used for performing repetitive motion, which covers many industrial applications of robotic manipulators. This kind of control law is in the class of torque-based control in which the joint torques are generated by permanent magnet dc motors in the current mode. The motor current is regulated using a proportional-integral controller. The novelty of this paper is a modification in using the discrete-time linear quadratic control for the robot manipulator, which is a nonlinear uncertain system. For this purpose, a novel discrete linear time-variant model is introduced for the robotic system. Then, a time-delay uncertainty estimator is added to the discrete-time linear quadratic control to compensate the nonlinearity and uncertainty associated with the model. The proposed control approach is verified by stability analysis. Simulation results show the superiority of the proposed discrete-time repetitive optimal control over the discrete-time linear quadratic control.

This paper proposes a method to solve multi-objective problems using improved Particle Swarm Optimization. We propose leader particles which guide other particles inside the problem domain. Two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. The first one is based on the mean of the m optimal particles and the second one is based on appointing a leader particle for any n founded particles. We used an intensity criterion to delete the particles in both techniques. The proposed techniques were evaluated based on three standard tests in multi-objective evolutionary optimization problems. The evaluation criterion in this paper is the number of particles in the optimal-Pareto set, error, and uniformity. The results show that the proposed method searches more number of optimal particles with higher intensity and less error in comparison with basic MOPSO and SIGMA and CMPSO and NSGA-II and microGA and PAES and can be used as proper techniques to solve multi-objective optimization problems.

The main challenge of a search engine is ranking web documents to provide the best response to a user`s query. Despite the huge number of the extracted results for user`s query, only a small number of the first results are examined by users; therefore, the insertion of the related results in the first ranks is of great importance. In this paper, a ranking algorithm based on the reinforcement learning and user`s feedback called RL3F are considered. In the proposed algorithm, the ranking system has been considered to be the agent of learning system and selecting documents to display to the user is as the agents’ action. The reinforcement signal in the system is calculated according to a user`s clicks on documents. Action-value values of the proposed algorithm are computed for each feature. In each learning cycle, the documents are sorted out for the next query, and according to the document in the ranked list, documents are selected at random to show the user. Learning process continues until the training is completed. LETOR3 benchmark is used to evaluate the proposed method. Evaluation results indicated that the proposed method is more effective than other methods mentioned for comparison in this paper. The superiority of the proposed algorithm is using several features of document and user`s feedback simultaneously.

The harmonic in distribution systems becomes an important problem due to an increase in nonlinear loads. This paper presents a new approach based on a graph algorithm for optimum placement of passive harmonic filters in a multi-bus system, which suffers from harmonic current sources. The objective of this paper is to minimize the network loss, the cost of the filter and the total harmonic distortion of voltage, and also enhances voltage profile at each bus effectively. Four types of sub-graph have been used for search space of optimization. The method handles standard capacitor sizes in planning filters and associated costs. In this paper, objective function is not differential but eases solving process. The IEEE 30 bus test system is used for the placement of passive filter. The simulation has been done to show applicability of the proposed method. Simulation results prove that the method is effective and suitable for the passive filter planning in a power system.

In this paper we propose a new method for classification of subjects into schizophrenia and control groups using functional magnetic resonance imaging (fMRI) data. In the preprocessing step, the number of fMRI time points is reduced using principal component analysis (PCA). Then, independent component analysis (ICA) is used for further data analysis. It estimates independent components (ICs) of PCA results. For feature extraction, local binary patterns (LBP) technique is applied on the ICs. It transforms the ICs into spatial histograms of LBP values. For feature selection, genetic algorithm (GA) is used to obtain a set of features with large discrimination power. In the next step of feature selection, linear discriminant analysis (LDA) is applied to further extract features that maximize the ratio of between-class and within-class variability. Finally, a test subject is classified into schizophrenia or control group using a Euclidean distance based classifier and a majority vote method. In this paper, a leave-one-out cross validation method is used for performance evaluation. Experimental results prove that the proposed method has an acceptable accuracy.

The focus of this paper is to consider the compressed sensing problem. It is stated that the compressed sensing theory, under certain conditions, helps relax the Nyquist sampling theory and takes smaller samples. One of the important tasks in this theory is to carefully design measurement matrix (sampling operator). Most existing methods in the literature attempt to optimize a randomly initialized matrix with the aim of decreasing the amount of required measurements. However, these approaches mainly lead to sophisticated structure of measurement matrix which makes it very difficult to implement. In this paper we propose an intermediate structure for the measurement matrix based on random sampling. The main advantage of block-based proposed technique is simplicity and yet achieving acceptable performance obtained through using conventional techniques. The experimental results clearly confirm that in spite of simplicity of the proposed approach it can be competitive to the existing methods in terms of reconstruction quality. It also outperforms existing methods in terms of computation time.

Global warming and prices of energy carriers within political conflicts between different nations, are some of the problems for traditional energy production and economic dispatch. In traditional generation systems, about 25 percentage of energy is wasted, and the presence of Distributed Energy Resources (DERs) such as Photovoltaic, Wind Turbine and wind farms, Fuel Cell, and the Combined Heat and Power can reduce fuel consumption, pollution, transmission losses, and increase the microgrid productivity. In this paper, a complete energy management framework in a microgrid is proposed by considering the load distribution constraints using Improved Shuffled Frog Leaping Algorithm (ISFLA) algorithm, in which it determines the exact share of energy production or consumption for different units. The proposed scheme is used to select the best arrangement of DERs in the power grid, which the output of which is to determine the number and optimal location of DERs in the several bus-bars of the grid. Then, the Independent System Operator (ISO) determines the quantity of energy exchange and consumption by considering the load distribution constraints. Boilers and CHPs have also been used to maintain the balance between the production of thermal power by energy sources and thermal demands. In addition, the Demand Response Program has been used with the aim of smoothing the load curve and reducing the operating costs. Finally, the proposed method is implemented and simulated on the IEEE 69 and 118 bus systems using MATLAB, which comparing the output results with existing algorithms, shows the superiority of the proposed method.

Trombe walls and solar chimneys have been widely used by the construction industry for many years to heat buildings. In this study, the heat conductance of a Trombe wall was simulated and studied. The equations related to energy and momentum were solved numerically by use of the technique of control volume. The equations were solved simultaneously using the Simple algorithm. At first, a base case was defined and simulated. A sensitivity analysis study was then performed to investigate the parameters affecting the performance of the wall. Based on the results, an optimized geometry was suggested which maximized the performance of the Trombe wall. In addition, the effect of the presence of the fins on the surface of the absorber wall was studied. In order to obtain the best geometry, the fins were assumed to have different shapes but a constant area. The results showed that the Trombe wall with rectangular fins demonstrated the best performance compared to the other fin geometries studied in this paper. The presence of rectangular fins can increase the room temperature by 1.24% compared to other fin geometries.

This paper aims to analyze and identify the most appropriate strategies to deploy solar energy in Iran. For this purpose, in a comprehensive framework of strategy development, the internal and external factors of general and specific environment were determined. Then, four different strategies, including aggressive (strong expansion and development), conservative (maintaining, internal protection), competitive (maintaining, external protection), and defensive strategies (withdrawal, reduction of costs, ceasing on investment, joining another corporation), were considered using internal-external and SWOT (Strengths, Weaknesses, Opportunities, and Threats) matrices. According to the numerical results of the internal and external factor evaluations, which are 2.74 and 3.06 respectively, the aggressive strategies would be acceptable for Iran. However, since the location of these values in the internal-external matrix is close to the conservative cell, the effective strategy for the present condition of our country is a combination of aggressive and conservative strategies.

The use of ducted wind turbines is developing and various scientists in their studies investigate the performance, economic analysis, and energy production by these types of turbines at a lower cost. In this paper, the ratio of wind speed increment related to free stream wind speed and turbulence rate in a pre-designed duct used for a horizontal three-blade wind turbine was evaluated using a hot-wire anemometer sensor and data analysis methods. The duct installed in the University of Tehran Aerospace Faculty wind tunnel and flow characterization was performed by using CTA apparatus to measure and evaluate the wind flow turbulence in the throat section of the duct, where the wind turbine was installed. Wind speed analysis was done at different speed of the wind tunnel test section and shown that in the throat section of the duct the wind speed increased with a constant slope and in more analysis, it was found the wind speed in the duct throat can be increased to 2.5 up to 3 times of free stream flow speed at a different wind speed of wind tunnel test section. From spectral analysis, it was found that only a few peaks are included in the extracted frequency that shown low turbulence inside the duct it can be concluded that the flow disturbances will not have a significant impact on the performance of the wind turbine placed inside the duct throat.

Doubly-fed induction generators (DFIG) have been widely used in wind turbines installed in the last decades. These generators are prone to some faults that could deteriorate their performance and even lead to their outage from the network. Stator inter-turn short-circuits (SITSC) and high resistance connections (HRC) in the stator are two major types of faults that cause electrical asymmetry in the stator circuit. Yet, SITSC are more noticeable and require immediate scrutiny. Hence, if an HRC can be distinguished from a SITSC fault, the immediate outage of the WT can be avoided in the case of an HRC. In this paper, both types of faults are studied and compared, being their detection performed using appropriate fault indices obtained from the stator current, rotor current, and rotor modulating voltage signals, all available in the control system of the DFIG. Several fault severity indices are proposed for a better evaluation of the fault extension, and the discrimination between SITSC and HRC is discussed. The performance of the defined fault indices is verified using a magnetic equivalent circuit model of the DFIG and an experimental setup with the DFIG running at several operating conditions.

Electricity generation through renewable energy sources such as wind energy has been growing in recent years due to several reasons including free and infinite resources as well as their considerable impact on the reduction of fossil fuels consumptions as well as CO2 emissions. This paper aims to assess the impact of grid-connected large-scale wind farms in a region located in Iran, on the reduction of natural gas as well as gasoil fuel consumptions in heat-cycle power plants and their related CO2 emissions as a practical case study. The wind farms under study comprise about 51% of the total grid connected capacity of wind power generation in Iran by the end of March 2021. The total energy yielded by the studied wind farms are first extracted over a two-year period from April 2019 to March 2021 based on a detailed practical data and then, its impact is investigated on the reduction of natural gas and gasoil consumptions in a real heat-cycle power plant due to its practical fuel intake data. Finally, the reduction of CO2 emission is calculated as the result of reduction in the natural gas and gasoil consumptions of the considered heat-cycle power plant. The results of this practical case study well demonstrate the effective role of wind farms energy yields on the reduction of fossil fuels consumption in heat-cycle power plants and thus, the significant reduction of CO2 emission as one of the most crucial aspects of decarbonization and fossil fuel phase out plans.

Utilizing distributed generation (DG) units in power system has positive impacts such as: reduction active and reactive power loss, reduce load curtailment, increasing system reliability and reducing the need of installing the new power plant. Wind turbine (WT) is a type of DGs. Employing demand side management in a residential, industrial and commercial loads could highlight the role of consumers in managing the total power and increasing the efficiency of system. In this paper the impacts of utilizing WT in improving technical constraints of the reconfigurable distribution system has been evaluated. The Monte Carlo based power flow equation is implemented to the presented scheduling problem. Simulations are done on IEEE 33 bus reconfigurable distribution system

Today, the use of renewable energy sources is increasing day by day. The essential advantages of wind energy are that it is clean, low cost, and unlimited. In this paper, the wind energy potential of provinces of the Marmara region in Turkey was evaluated by multi-criteria decision-making (MCDM) methods. In the study, TOPSIS and PROMETHEE methods were used for analysis criteria weights were determined by two different approaches. In the first approach, the criteria weights were taken equally. In the second approach, the criteria were weighted using the AHP method. When the methods were applied by taking the criteria weights equally, Balıkesir and Çanakkale were determined as wind priority provinces in potential, while Kocaeli and Sakarya took the last rank. After the criteria weights were determined via AHP when TOPSIS and PROMETHEE methods were applied, Balıkesir ranked first, and Kocaeli ranked last. Spearman's Correlation Coefficient determined the level and direction of the relationship between the rankings obtained from TOPSIS and the PROMETHEE method. When the methods were applied, the value of “0.636” indicated that the relationship between the rankings was “positive” and “moderate”. When the criteria were weighted with AHP and the methods were applied, the correlation coefficient was obtained as “0.909”. This value indicated a “positive” and “very high” level of relationship. It was determined that the ranking results obtained when the methods were applied after the criterion weights were calculated with AHP were more supportive of each other.

Large amplitude inter-well oscillations in bi-stable energy harvesters made them a proper energy harvesting choice due to high energy generation. However, the co-existence of the chaotic attractor in these harvesters could essentially decrease their efficiency. In this paper, an algorithm for detecting chaos in bi-stable energy harvesters based on a data-gathering algorithm and estimating the largest Lyapunov exponent is investigated. First, a simple model of axially loaded non-linear energy harvesters is derived. This model is derived using the Euler-Bernoulli beam theory and the Assumed Mode method considering the Von-Karman non-linear strain-displacement equation. The harvester's numerical simulation results are used to test the algorithm's efficiency and accuracy in identifying chaotic response. The results showed the algorithm's success in detecting chaos in such systems with minimum possible calculation cost. The effect of noise on the algorithm's performance has been investigated, and the results showed the excellent robustness of the algorithm to noise. It can diagnose the harvester's chaotic or harmonic behavior with noise-contaminated data, with 10 percent noise density. The comparison between this algorithm and Wolf's method showed relatively less computation time, up to 80 percent, to detect chaos with reasonable accuracy.

Due to traditional energy resources problems, scientists looked for a new concept to face these problems. The Solar chimney is one of the promising concepts in renewable energy technology that needs performance enhancement, the proposed study aims at assessing the remarkable advances in the understanding of solar chimney power plant (SCPP) performance investigation through extensive studies with different focuses on several aspects of SCPP technology. In this scientific review paper solar chimneys based on the historical review, enhancements, working principle, components and effective factors, advantages and disadvantages.

In this paper, a novel switching table (ST) of the twelve sectors direct power command (DPC) strategy of doubly-fed induction generator (DFIG) based dual rotor wind power (DRWP) is proposed using two-level hysteresis controllers for reactive and active power and feedforward neural networks (FNNs) algorithms. This intelligent technique was used to replace the conventional ST in order to reduce rotor flux ripple, active power ripple, total harmonic distortion (THD) of stator voltage, torque and reactive power undulations. The simulation and modeling of the proposed strategy were carried out in Matlab software. The DFIG is tested in association with a DRWP systems. The simulation results show that the DPC with FNN controller (DPC-FNN) reduced the THD value of stator voltage, rotor flux undulation, active/reactive power undulation, and electromagnetic torque ripple compared to conventional DPC strategy. It was found that the current waveform becomes purely sinusoidal with a reduction in the THD rate to 0.64%.

In urban open spaces, especially in large cities with warm climates, users are experiencing high thermal loads, which causes thermal discomfort. Thermal comfort in open spaces can be improved; by shading. The problem is shading and protecting open spaces from stresses caused by overheating of the earth's surface and environment. The importance of shade and reducing radiation in achieving thermal comfort in open urban spaces is to increase human presence, create climate change, increase comfort conditions. Recognizing factors that create shadows, such as canopies and their characteristics, can create a favorable space to enjoy the capabilities of outdoor space. New membranes have many characteristics of nomadic tents, and, due to creating shade and natural ventilation, are very suitable for areas with hot climates. Introducing an optimal model and dimensions of a lightweight membrane canopy can create outdoor thermal comfort and increase the efficiency of outdoor spaces. In this paper, library, field, and simulation studies have been used. According to field studies, the presence of membrane canopy can cause temperature differences up to 7.8 C. The simulation results with Ansys, ENVI-met, and Ladybug showed that the membrane canopy cools the space below and prevents overheating. Between four canopy models, the saddle canopy is suitable with a 40.63% impact on the environment and creates cooler space under the canopy. Therefore, a lightweight saddle membrane canopy with dimensions of 5*5 m^2 and a useful height of 3 meters is introduced as a suitable model of membrane canopy for the hot climate of Semnan.

Recently, a class of low-density parity-check codes based on affine permutation matrices, called APM-LDPC codes, have been considered which have some advantages than quasi-cyclic (QC) LDPC codes in terms of minimum-distance, cycle distribution, and error-rate performance. Moreover, some explicit constructions for exponent matrices of conventional APM-LDPC codes with girth at least 6 have been investigated. In this paper, a class of 4-cycle free APM-LDPC codes is constructed by a new explicit method such that the constructed codes have better cycle distributions rather than the recently proposed APM codes with girth 6. As simulation results show, the constructed codes outperform PEG and random-like LDPC codes with the same rates and lengths.

This paper is a continuation of recent researches concerning generalization of injectivity of acts over moniods, namely, C-injectivity and InD-injectivity. We introduce new kinds of injectivity, such as, LC-injectivity and CQ-injectivity. Classifications of monoids by properties of these kinds of injective acts are presented. It is proved that a monoid S is completely (cyclic) injective if and only if it is completely quasi (CQ-) injective. Some results on quasi-injective acts are proved. Also new characterizations for right hereditary monoids and right PP-monoids are given.

The distance signless Laplacian spectral radius of a connected graph $G$ is the largest eigenvalue of the distance signless Laplacian matrix of $G$, defined as $D^{Q}(G)=Tr(G)+D(G)$, where $D(G)$ is the distance matrix of $G$ and $Tr(G)$ is the diagonal matrix of vertex transmissions of $G$. In this paper, we determine some new upper and lower bounds on the distance signless Laplacian spectral radius of $G$ and characterize the extremal graphs attaining these bounds.

Let (R,m) be a Noetherian local ring, M be a finitely generated R-module of dimension n and a be an ideal of R. In this paper, generalizing the main results of Dibaei and Jafari [3] and Rezaei [8], we will show that if T is a subset of AsshR M, then there exists an ideal a of R such that AttR Hna (M)=T. As an application, we give some relationships between top local cohomology modules and top formal local cohomology modules.

The distinguishing number $D(G)$ of a graph $G$ is the least integer $d$ such that $G$ has an vertex labeling with $d$ labels that is preserved only by a trivial automorphism. The minimum size of a label class in such a labeling of $G$ with $D(G) = d$ is called the cost of $d$-distinguishing $G$ and is denoted by $\rho_d(G)$. A set of vertices $S\subseteq V(G)$ is a determining set for $G$ if every automorphism of $G$ is uniquely determined by its action on $S$. The determining number of $G$, ${\rm Det}(G)$, is the minimum cardinality of determining sets of $G$. In this paper we compute the cost and the determining number for the friendship graphs and corona product of two graphs.

In this paper, we define the notion of regular prime monomorphism for $S$-posets over a pomonoid $S$ and investigate some categorical properties including products, coproducts and pullbacks. We study $\mathcal{M}$-injectivity in the category of $S$-posets where $\mathcal{M}$ is the class of regular prime monomorphisms and show that the Skornjakov criterion fails for the regular prime injectivity. Considering a weaker form of such kind of injectivity, we obtain some classifications for pomonoids.

This paper develops an energy management approach for a multi-microgrid (MMG) taking into account multiple objectives involving plug-in electric vehicle (PEV), photovoltaic (PV) power, and a distribution static compensator (DSTATCOM) to improve power provision sharing. In the proposed approach, there is a pool of fuzzy microgrids granules that they compete with each other to prolong their lives while monitored and evaluated by the specific fuzzy sets. In addition, based on the hourly reconfiguration of microgrids (MGs), granules learn to dispatch cost-effective resources. To promote interactive service, a well-defined, multi-objective approach is derived from fuzzy granulation analysis to improve power quality in MMGs. A combination of the meta-heuristic approach of genetic algorithm (GA) and particle swarm optimization (PSO) eliminates the computational difficulty of the nonlinearity and uncertainty analysis of the system and improves the precision of the results. The proposed approach is successfully applied to a 69-bus MMG test with results reported in terms of stored energy improvement, daily voltage profile improvement, MMG operations, and cost reduction.

This paper presents a new method for regression model prediction in an uncertain environment. In practical engineering problems, in order to develop regression or ANN model for making predictions, the average of set of repeated observed values are introduced to the model as an input variable. Therefore, the estimated response of the process is also the average of a set of output values where the variation around the mean is not determinate. However, to provide unbiased and precise estimations, the predictions are required to be correct on average and the spread of date be specified. To address this issue, we proposed a method based on the fuzzy inference system, and genetic and linear programming algorithms. We consider the crisp inputs and the symmetrical triangular fuzzy output. The proposed algorithm is applied to fit the fuzzy regression model. In addition, we apply a simulation example and a practical example in the field of machining process to assess the performance of the proposed method in dealing with practical problems in which the output variables have the nature of uncertainty and impression. Finally, we compare the performance of the suggested method with other methods. Based on the examples, the proposed method is verified for prediction. The results show that the proposed method reduces the error values to a minimum level and is more accurate than the Linear Programming (LP) and fuzzy weights with linear programming (FWLP) methods.

The Transactions in web data often consist of quantitative data, suggesting that fuzzy set theory can be used to represent such data. The time spent by users on each web page is one type of web data, was regarded as a trapezoidal membership function (TMF) and can be used to evaluate user browsing behavior. The quality of mining fuzzy association rules depends on membership functions and since the membership functions of each web page are different from those of other web pages, so automatic finding the number and position of TMF is significant. In this paper, a different reinforcement-based optimization approach called LA-OMF was proposed to find both the number and positions of TMFs for fuzzy association rules. In the proposed algorithm, the centers and spreads of TMFs were considered as parameters of the search space, and a new representation using learning automata (LA) was proposed to optimize these parameters. The performance of the proposed approach was evaluated and the results were compared with the results of other algorithms on a real dataset. Experiments on datasets with different sizes confirmed that the proposed LA-OMF improved the efficiency of mining fuzzy association rules by extracting optimized membership functions.