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Nowadays, sleep deprivation is a pervasive problem that affects human physical and mental health. In this research, the effects of sleep deprivation on brain function and its diagnosis have been studied using electroencephalogram (EEG) signals recorded from 30 subjects after complete sleep and one day of sleep deprivation with open and closed eyes. Linear features like signal power and nonlinear features consisting of Shannon, Renyi, sample, and permutation entropies were extracted from signals. We used the PCA algorithm and Wilcoxon feature ranking method to extract the superior features and employed SVM, KNN, and a Decision tree to detect sleep-deprived cases. Brain maps of extracted features were plotted using the sLORETA algorithm to investigate the effects of sleep deprivation. Based on the results, the decision tree classifier with 100 superior selected features of Wilcoxon achieved the best performance with accuracy and precision of 99.0% and 99.8%, respectively. Also, comparing the results of linear and nonlinear features reveals the impressive role of the nonlinear features in the classification problem of this work. The maps of the features revealed noticeable changes in the level of attention, concentration, decision-making, and visual and movement activities.

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Currently, renewable energy is rapidly developing across the world in response to technical, economic, and environmental developments, as well as political and social initiatives. Moreover, the excessive penetration of distributed generation (DG) systems into electrical networks may lead to various problems and operational limit violations, such as over and under voltages, excessive line losses, overloading of transformers and feeders, protection failure and high harmonic distortion levels exceeding the limits of international standards. These problems occur when the system exceeds its Hosting Capacity (HC) limit. The HC is a transactive approach that provides a way for the distribution network to be integrated with different types of energy systems.
Distributed Generation (DG) sources are one of the important componeents of novel distribution systems, among which renewable and clean energy sources have received more attention due to the important role of these sources in reducing greenhouse pollutants.
The use of renewable sources such as wind and photovoltaic sources is expanding day by day with the increasing demand for electric energy supply. However, the limitations in the amount of the penetration of DG resources are one of the main challenges in the development of the use of these resources.
This paper is looking for a method to improve the HC of the distribution network from DG sources by using reactive power compensation and the reconfiguration of distribution systems. The results of the simulation show the advantages of using the proposed method in increasing the HC and as a result the development of the use of renewable resources.
 

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In an islanded microgrid, power electronic converters are used to exchange power, and these converters have very low inertia, thus compromising the frequency stability of the microgrid. Virtual inertia control is used to improve the frequency stability of an islanded microgrid. The derivative control technique is usually used to implement virtual inertia control in the microgrid. Factors such as disturbance and uncertainty of parameters of the islanded microgrid compromise the performance of virtual inertia control and may cause system frequency instability. Therefore, the virtual inertia control structure, a complementary controller is needed that can weaken the effect of disturbance on the microgrid as much as possible and be resistant to the uncertainty of parameters of the microgrid. In this paper, a robust control method is used in a virtual inertia control structure that uses system output feedback. The proposed method is expressed based on linear matrix inequality and is proved based on the Lyapunov criterion. Among the advantages of the proposed method is the attenuation of disturbance, resistance to the uncertainty of parameters of the microgrid, and increasing the degree of freedom to control the system in this method. The results of the proposed method to improve the performance of virtual inertia control in several different scenarios by considering the uncertainty of parameters of the two-zone microgrid and disturbances on the microgrid are compared with several methods and the effectiveness of the proposed method in terms of improving frequency stability is shown.

 

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The control of unmanned aerial vehicles is a challenging problem due to their lightweight and intense coupling between longitudinal and lateral motion. Considering this issue, in this article, an automatic landing system for a fixed-wing unmanned aircraft exposed to wind disturbances and parametric uncertainties is designed using the backstepping algorithm and the disturbance observer-based sliding mode control. Two controllers are designed based on the backstepping algorithm and sliding mode control to stabilize the attitude angles. The longitudinal speed controller uses the sliding mode technique to maintain the total speed relative to the ground at a constant desired value in all landing phases. A nonlinear disturbance-observer is considered in the sliding mode controller structure to estimate wind disturbance and parametric uncertainty. The new robust automatic landing system is software implemented, and its performance is investigated by several numerical simulations; Lateral deviation relative to the runway is eliminated while the unmanned aerial vehicle maintains its desired trajectory slope angle in all phases of the landing at the desired value. Therefore, the results of numerical simulations prove that the new control structure is stable and robust against different initial conditions, different types of wind disturbances (wind shear and discrete gust), and parametric uncertainty.

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In order to reduce the cost of electric power generation and also reduce greenhouse gas emissions in ships with electric propulsion system, renewable energy resources and energy storage systems are used along with thermal units. Therefore, in this paper, a stochastic mixed integer linear model has been suggested for optimal management of electrical energy of a ship with electric propulsion system, energy storage, heat generators and renewable solar resources with the aim of minimizing the cost of electricity production and determining the optimal ship speed. In this paper, the Monte Carlo simulation method is used to model the uncertainty in predicting the solar power and the ship's electric load. The proposed model is implemented and analyzed in GAMS optimization software. The simulation results show the efficiency of the proposed model and the introduced energy management strategy.
 

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Extracting maximum power, especially with partial shading conditions, is one of the most critical issues in using a photovoltaic system. Under partial shading conditions, the power-voltage characteristic of photovoltaic arrays has several local maximum points. A maximum power point tracking method for photovoltaic systems should enable fast and accurate tracking of the global maximum during partial shading conditions to minimize power losses and steady-state fluctuations. This research presents an algorithm for tracking the maximum power point in a photovoltaic system under partial shading conditions using the gray wolf optimization technique. The gray wolf algorithm is a new optimization method that overcomes limitations such as poor tracking, steady-state fluctuations, and undesirable transients in perturb and observe and particle swarm optimization techniques. The proposed algorithm based on the gray wolf optimization algorithm is implemented on a photovoltaic system in MATLAB software to prove its efficiency. The performance of the proposed design is compared with two maximum power point tracking techniques based on cuckoo search and particle swarm optimization. The simulation results show that the performance of the proposed maximum power point tracking technique is superior to the compared designs in terms of speed and steady-state stability of the response, so that it reduces the values of maximum overshoot, settling time, and sustained fluctuations up to 40.91%, 66.67% and 59.1% respectively.
 

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Disturbance and uncertaities exist in industrial systems and greatly affect the performance and stability of these systems. The robotic manipulator is one the most widely used devices in the industry that is highly affected by various disturbances. Hence establishing a proper control algorithm to estimate and eliminate disturbances seems crucial. Since the robotic manipulator is a highly nonlinear system, we need to design a nonlinear disturbance observer. In this thesis a nonlinear disturbance observer is proposed to estimate the constant and oscillatory disturbances in the studied system. On the other hand, since proportional-derivative controllers (PD) are widely used in industrial systems, so in this thesis, a suitable proportional derivative controller will be designed. This controller is not capable of dealing with disturbances and uncertainties, so a new supervisory controller structure has been proposed to estimate disturbances and stabilize the system. The core of proposed controller uses a new sliding model controller. Finally, some comparisions with PD and super twisting sliding mode controllers have been performed in several cases and the numerical results show the advantages of the proposed controller.

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Insufficient training data is one of the main challenges of utilizing deep Convolutional Neural Networks (CNNs) for Environmental Sound Classification (ESC). As a promising solution, Transfer Learning (TL) has addressed this issue by adapting a network pre-trained on a large-scale dataset to the target task. In this paper, we demonstrate that not all neurons/kernels of every layer in CNN networks are equally utilized to process the inputs of different classes, but there is a specific subgroups of neurons/kernels in every layer that play the key role in classification of every output class. Based on this observation and due to similarities that exist between feature spaces of some source and target classes, we propose to concentrate the fine-tuning process only on those neurons/kernels that do need changes and have the greatest impact on misclassifying target data. To identify these neurons/kernels, we pose a nested optimization problem for which we propose an effective evolutionary approach as solution.  Compared to the conventional fine-tuning approach, our proposed method achieves absolute improvements of about 1.9% and 2.3% in accuracy on ESC-50 and DCASE-17, respectively; remarkable improvements produced not by adding augmented data but with a more efficient utilization of knowledge stored in the pre-trained network. It is noteworthy that the computation time overhead of the proposed evolutionary method is rather small (about one third of the time required to train the model from scratch.

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The issue of frequency control is very important in the power system. The presence of wind turbine in the power system makes frequency control challenging. In order to improve the frequency control of the power system in the presence of wind turbine, in this paper, a new control method is designed. In this method, the coordinated control of load-frequency control (LFC) system and superconducting magnetic energy storage (SMES) has been discussed using PD-FOPID cascade controller. The PD member in this type of controller responds to the frequency changes of the power system faster and also the FOPID member has a favorable performance against the uncertainty of the system parameters and disturbances. In this paper, the problem of owl search algorithm is solved. Considering that the owl search algorithm may get stuck in the local optimum. In this paper, solutions are presented to solve this problem of the owl search algorithm, which is called the developed owl search algorithm, and in order to improve the performance of the PD-FOPID controller, the developed owl search algorithm is used to optimally adjust its parameters. . The proposed control method with several methods including: Load frequency control (LFC) and superconducting magnetic energy storage (SMES) based on the robust controller, LFC and SMES based on the MSA-PID controller, LFC based on the MSA-PID controller with SMES and LFC based on the MSA-PID controller without SMES has been compared in four scenarios and the results show the superiority of the proposed method over the other mentioned methods. Is. The proposed method is resistant to load disturbances, disturbances caused by wind turbines, and uncertainty related to system parameters.

 

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In this paper, formation control based on the virtual structure for the non-holonomic mobile robot system with two models of certain and uncertain kinematic equations is discussed. First, the formation equations of a certain model are calculated and then it is proved that it is possible to create a geometric shape and maintain that state by using the sliding model control theory for any two moving mobile robots. Then, after deriving the formation equations of the uncertain model, a sliding model controller is designed that is able to control the uncertain model provided that the uncertainty range of the kinematic equation is present. For each design, the stability of the system is guaranteed using the Lyapunov stability theorem. Finally, in order to compare the performance of the designed controllers, a pre-designed back-stepping controller is introduced and the results will be presented in the form of simulations. The simulation results show the effective performance of the designed controllers.

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Usually, constrained lateral acceleration would have undesirable effects on the stability and performance of a guidance system. The composite nonlinear feedback (CNF) can be effectively used to improve the transient response of the closed-loop system in the presence of the constrained input. In this way, guidance law consists of an extra nonlinear term besides the conventional linear one. As a result, such a term adjusts the qualitative characteristics of the transient response. Meanwhile, the nonlinear term is a function of the rate of line-of-sight (LOS) angle which is not activated at origin and infinity. Thus it would be effective only in a specified region. In this paper, proportional navigation is employed for the linear term of the CNF-based guidance law. Therefore, a guidance algorithm is developed for tracking problems using the CNF idea. Applying the proposed guidance method, the closed-loop stability is analytically proved via the well-known Lyapunov stability theory. The suggested approach is simulated in a numerical example. Then the results are compared with an existing technique. As expected, guaranteeing closed-loop stability, in contrast to a similar method, the addressing scheme considerably improves the performance and transient response of the guidance system in the presence of lateral acceleration limitations.
 

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This paper presents a new online scheme of estimating power system transient voltage instability of interconnected synchronous generators using intelligent Bayesian theory based on wide area signals from WAMS data. For this purpose, by using online measurement of the system oscillatory signals gathered from WAMS technology and developing them as a series of input-output pairs data, the system dynamic category (stable/unstable) are achieved and used for Bayesian training. The proposed scheme is an online non-model-based technique with the ability of estimating binary decisions among the power system dynamic signals. In the case of evaluating effectiveness of the developed algorithm, by using an IEEE 39 test system, different fault events with the potential of transient voltage instabilities are investigated. In this case, considering two sampling data at pre-fault and post-fault occurrence moments, the system dynamic statues are estimated. Results present ability of the proposed scheme for fast and secure estimations of the system transient voltage instability.

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Satellites attitude control based on their mission always is one of the main challenges in attitude control. Actually, in a certain class of satellites when satellites placed in a predetermined attitude or axis, sometimes an oscillating or chaotic behavior occurs, therefore, various models have been presented to analyze of this class. In this paper, H∞  Robust output feedback control under saturation is designed for this class of satellites that are also under external disturbance. The main point in this paper actually is design of the static output feedback (SOF) controller in the mentioned conditions in the form of solving a linear matrix inequality (LMI) problem. Then, in the same way, the stable region (B_∈) based on Lyapunov's theorem is obtained, which finally enlarges the region of attraction (ROA) for this control system. The proposed method was implemented on the presented model and the results showed that in addition to the appropriate speed of the system states in convergence, the control signal did not enter the saturation area and the system has become stable with the least cost and energy. The procedure design of output feedback controller is specified in the form of pseudo-code.
 

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This paper presents a new method of the adaptive non-singular Second-order Terminal Sliding Mode (SOTSM) control for the fast and finite time stabilization of Cyber-Physical Systems (CPSs) in the simultaneous presence of parametric uncertainties, unwanted disturbances and actuator cyber-attacks. By utilizing the presented non-linear manifold and sliding surface, the reaching mode is deleted and the entire system’s robust performance is improved. The proposed online adaptive laws deal with parametric uncertainties, unwanted disturbances and cyber-attacks, so that there is no need to identify their upper bounds. The designed adaptive non-singular SOTSM control method guarantees the robust performance of the system in the mentioned conditions along with fast and smooth response, high accuracy and flexibility, without transient fluctuations and chattering, as well as proper convergence in finite time. The numerical simulation results show the effectiveness and success of the adaptive non-singular second-order terminal sliding mode control method in comparison with the results of adaptive integral sliding mode control, traditional sliding mode control and state feedback control.
 

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In spectrum sharing among different users, it is very essential to deal with the jammers in the network. This work is more important when the jammer is intelligent and capable of learning the communication patterns between users. In this paper, a spectrum sharing method has been suggested to deal with the jammers of the network. The proposed method is based on multi agent reinforcement learning. In the first stage, the interfered sub-bands are obtained by the base station. In the next stage, deep reinforcement learning is designed to offer each user a safe and appropriate spectrum. For this purpose, the effect of the jammer in the sub-band is obtained from the correlation between the transmitted pilot and the received feedback in each frequency interval. Then, the discount factor of the reinforcement learning algorithm is adjusted adaptively based on the correlation value. In this way, the sub-bands that are less affected by the jammer are assigned to the users. The proposed method has been evaluated in different scenarios and the results indicate that the sum rate of network converges to the desired level faster and the proposed method shows resistance against the jammer.

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Estimating the state of charge of lithium- ion batteries is of great importance not only for optimal energy management, but also for ensuring safe operation, preventing charging and discharging, and as a result reducing the life of the battery. However, this parameter cannot be measured directly from the battery terminals. Therefore, there is a need to estimate it. In this paper an improved auxiliary marginal particle filter is presented to estimate the state of charge of lithium-ion batteries. In the proposed method, unlike the particle filter, sampling is done on the marginal distribution and the sampling dimensions do not increase with the passage of time. In addition, genetic operators and M-H algorithm have been used in the proposed method to increase diversity among particles. The use of genetic operators and the M-H algorithm causes the resampled particles to asymptotically approximate the samples from the posterior probability density function of the true state and increases the compatibility. The performance of the proposed method for estimating the state of charge of the battery has been compared with the estimation of the state of charge based on the developed particle filter and traceless particle filter. The results show the effective performance of the proposed method in comparison with other methods. The proposed method to obtain the same estimation accuracy as the particle filter requires far fewer particles and the amount of calculations is low. The root mean square error in the proposed method with different particles is close to 0.007, while in other methods, the root mean square error increases with the decrease of particles. 

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Thrust vector control is a special method to change the attitude and position of flying objects, which can only be applied in some missions. These systems require feedback control and lead to better maneuverability. In this paper, a finite time adaptive sliding model controller is presented for controlling the thrust vector of a flying object. The first-order sliding model method requires information about the upper bound of system uncertainties and also this method causes chattering in the control signal. The standard adaptive sliding model method has solved the problem of the need for the uncertainty bound and also reduces the chattering range. But this method does not guarantee finite time stability. In this article, the finite time type of adaptive sliding model is used to control the thrust vector. This method guarantees finite time stability without the need for upper bound information of system uncertainties, and in it, the convergence time of the tracking error and estimation depending on the initial conditions can be calculated. The performance of the proposed thrust vector control system has been investigated by computer simulation and its efficiency is shown in comparison with other methods.
 

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In this article, an efficient method to minimize energy losses is presented. The proposed method uses intermittent load conditions over a future time interval instead of an instantaneous network condition. This method obtains the optimal condition during the given period according to the current value of the condition. A given time interval is divided into many smaller subintervals. By increasing the number of subintervals or load profiles, the dimensions of the problem increase, for which an optimal value must be obtained. In this method, the variables are divided into the group of continuous and discrete control variables. While only continuous control variables are allowed to change in each sub-interval, continuous and discrete variables are set at the beginning of each time interval. This problem is solved by using the GBD general bend decomposition method. Using this method, the load conditions for each subinterval in the NLP subproblem are solved. Then, the results of the NLP subproblem are used in the main subproblem. As shown in the simulation results, the proposed method not only improves the voltage profile but also reduces the total energy wasted in the desired period.

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Overcoming the problem of transmission lines or a region of the power system congestion is one of the important issues facing the operators. In the meantime, in addition to choosing the appropriate method to relief transmission line congestion, the process should be implemented with the aim of minimizing the imposed costs. In this article, in order to optimally control the transmission lines congestion, an integrated method including management of the production and demand side of the systems along with the use of the flexibility of multi-carrier energy systems (electricity, gas and heat) is presented. The congestion alleviation technique is analyzed in the two modes of independent operation of the power system and integrated operation of multi-carrier systems, and their results have been compared. Also, taking into consideration the scenario of transmission line congestion with the simultaneous outage of a power generation unit, the effect of energy storage in solving this problem has also been studied. The simulation of scenarios and the evaluation of the effectiveness of the proposed method have been implemented in a multi-carrier energy system including the IEEE 39 bus power system in combination with the Belgian 20-node gas network and several energy hubs. The evaluation of the obtained results shows a significant reduction in the congestion management costs in the mode of integrated operation with the rescheduling of generation and consumption compared to the independent operation of the power system. In addition, it was proved that the properly storage and discharge of all types of energy can be successful in order to optimize (at least 10%) the imposed costs.