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Simultaneous use of an efficient routing protocol and mobile sink not only prevents rapid sensor energy depletion but also effectively improves the energy balance in wireless sensor networks. In this paper, a new data aggregation method is proposed called "Ant colony-based Routing and Petal-shaped Data Aggregation (ARPDA)", which includes clustering, Cluster Head (CH) selection, intra-cluster routing, determining polling points, and designing the sink node trajectory. In the proposed method, the network is divided by several virtual concentric circles that are equidistant from each other. The intersection point of these circles with the virtual lines passing through the origin of the network determines the probable polling points for the sink node. First, by efficiently clustering the sensor nodes and forming an intra-cluster routing tree based on the ant colony optimization algorithm, the sensor data is aggregated in CHs; then, by selecting the appropriate polling points and designing a petal-shaped path, the sink node periodically collects the aggregated data in CHs based on two linear and circular movements. Considering the sink movement in the CH selection process is another advantage of the proposed algorithm. Numerical results confirm the better performance of the proposed algorithm compared to the EDT algorithm. [ABSTRACT FROM AUTHOR]

In this paper, using a deep learning technique, the sensing characteristic of a refractive index (RI) sensor based on a multimode optical fiber speckle-gram has been extracted. The investigated sensor structure consists of a tapered optical fiber or microfiber (MF) induced on a standard multimode optical fiber (MMF). Due to the presence of evanescent waves around the MF, the guiding status of the end MMF is dependent on the external RI around the taper. Thereby, the propagating modes and resulting interference pattern or speckle-gram become sensitive to the changes in the taper environment. Herein, by analyzing and quantifying the speckle-gram modulation, the RI changes around the taper are characterized. To extract the sensing characteristic and improve its measurement accuracy, deep learning algorithms and specifically the ResNet deep neural network are used in the processing of the output speckle-grams. Using this algorithm, while the tapered fiber is immersed in different refractive index solutions in the range of 1.33 to 1.368, a set of output speckle images is captured for learning and then a number of them are used for measurement, and finally the accuracy of detecting the refractive index of the solutions RI has been achieved up to 95%. [ABSTRACT FROM AUTHOR]

Recently, studies of squirrel-cage induction machines validate the idea that these machines follow the double-cage induction model; however, the manufacturers categorize these machines as single-cage machines. There are many references that use the squirrel-model for squirrel-cage machines, then machine parameters increase from 5 to 7. In the laboratory, parameter determination is performed to estimate 5 parameters; however, 7 parameters have to be determined. Also, the DC test is not an exact test and is used for approximate estimation. Therefore, locked rotor test equations are changed and an overload test is added. After measurements in the laboratory according to the equations of this paper, a MATLAB program is prepared and 7 parameters are determined in a loop until reaching the desired error. This paper demonstrates that laboratory squirrel-cage machines have to be corrected using the 7-parameter model. Furthermore, this paper shows the necessity of new laboratory test equations for squirrel-cage machines. Then a numerical method is used to determine the machine parameters. [ABSTRACT FROM AUTHOR]

In this paper, a decentralized energy management system is presented for intelligent microgrids with the presence of distributed resources using reinforcement learning. Due to the unpredictable nature of renewable energy resources, the variability of load consumption, and the nonlinear model of batteries, the design of a microgrid energy management system is associated with many challenges. In addition, centralized control structures in large-scale systems increase computational volume and complexity in control algorithms. In this paper, a fully decentralized multi-agent structure for a microgrid energy management system is proposed and the Markov decision process is used to model the stochastic behavior of agents in the microgrid. Electrical and thermal distributed resources, batteries, and consumers are considered intelligent and independent agents. They have the learning ability to explore and exploit the environment in a fully decentralized manner and achieve their optimal policies. The proposed method for hourly microgrid management is model-independent and based on learning. The method maximizes the profits of all manufacturers, minimizes consumer costs, and reduces the dependence of the microgrid on the maingrid. Finally, using real data from renewable energy sources and consumers, the accuracy of the proposed method in the Iranian electricity market is simulated and verified. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel state estimation plan using Adaptive Neuro-Fuzzy Inference System (ANFIS). To increase the speed and accuracy of estimation, an individual ANFIS is used for each bus. To improve the accuracy of training, the grasshopper optimization algorithm (GOA) is used for the training and optimization of ANFIS parameters. The main advantage of GOA training of ANFIS parameters is the increase in speed and accuracy in estimating power system state variables. One of the main features of the proposed design is its ability to provide an appropriate state estimation when incomplete data is sent to the control center due to the disconnection of communication or the failure of the measurement devices. The recovery of the missed data is implemented by the Group Method of Data Handling (GMDH) neural network. The GMDH neural network is widely used due to its proper speed for function estimation and approximation. Incomplete information obtained from measurements to estimate the state is processed by the GMDH neural network to recover lost information. The output of this neural network, which is the retrieval of complete measurement information, is given to ANFIS to estimate the state of the power system as input. [ABSTRACT FROM AUTHOR]

According to statistics, over half a billion vehicles are moving on the roads worldwide. All vehicles have a license plate as their primary identifier, which is one of the most suitable tools for vehicle identification. Automatic License Plate Recognition (ALPR) can be effective in improving road security, reducing traffic congestion, enhancing transportation efficiency, preventing car theft, and more. Traditional methods proposed for license plate detection mainly relied on manual feature extraction and lacked could not be generalized to variable image components in different conditions. With recent advancements in deep learning, algorithms have emerged that can automatically extract high-level representations of images in addition to learning complex image structures. Therefore, in this paper, the high capacity of deep neural networks is utilized for learning license plate identifiers. The proposed model consists of two stages: license plate localization and plate identification. For localization, a combination of Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) is used in an encoder-decoder network. The proposed model is evaluated on two datasets, FZU Cars and Stanford Cars, and based on the experimental results, it outperforms baseline methods in terms of accuracy on both datasets. [ABSTRACT FROM AUTHOR]

This paper presents an online scheme of a wide-area-damping-controller (WADC) for controlling the inter-area oscillatory modes in interconnected power systems using wide-area power system stabilizers and measuring signals. The proposed adaptive controller includes two individual approaches WSSS for evaluating the signal participation factor selection and GPSS as a global power system stabilizer for damping inter-area oscillations. For this purpose, considering the concept of center of inertia over inter-area signals, generators with the highest interactions are determined as oscillating areas while based on evaluating signal damping ratio, proper participation factors are estimated in which GPSS with positive damping performance is developed. In real-time working mode, based on oscillatory signals gathered from WAMS data, different combinations of PSSs are evaluated in which the combinations with the best controllability on damping the oscillations are selected. The effectiveness of the proposed widearea damping controller scheme is evaluated on the IEEE test system and practical Iran national power grid with the potential of unstable inter-area oscillations. Results show the effectiveness of the proposed WADC approach for fast and secure damping of inter-area oscillation. [ABSTRACT FROM AUTHOR]

These days, one of the biggest challenges in the world is dealing with the outbreak of the Co vid 19 virus. Recently, new variants of this virus have been identified that have a mucli liigher rate of transmission. To effectively control and manage the spread of the disease, a clear understanding of its transmission dynamics and effective control techniques to reduce or inllibit the spread of the virus is necessary. Although vaccine production and distribution are currently underway. Non-Pharmacological Interventions (NPI) continue to be an important and fundamental strategy for controlling the spread of the virus in various countries around the world. In this paper. Covid 19 dynamics is modeled using four well-known categories (SEIR): Susceptible-Exposed-Infected-Recovered. Since the parameters of the model have uncertainty, a robust control method should be designed. In this paper, using fractional calculus and fuzzy logic, a robust fuzzy fractional-order sliding mode controller (FOFSMC) for Covid 19 dynamics is proposed, which aims to control the prevalence of the disease using NPI. The proposed method is implemented both on the integer and fractional order model. Finally, the performance of the proposed controller on the new variant of the Covid 19 virus with a faster disease transmission rate will be evaluated. [ABSTRACT FROM AUTHOR]

In this paper, the problem of improving the stability of the power system with DFIG and in the presence of SSSC using nonlinear method is discussed. The nonlinear controller is designed by the multi-input backstepping method with a sliding mode observer. This controller is applied simultaneously to the excitation system of synchronous generators and the rotor side converter in DFIG and sssc in a way that improves the stability of the power system compared to the linear and nonlinear methods described in this paper. The control coefficient matrices are adjusted using intelligent algorithms in such a way that the stability of the system is more optimized. Practical constraints on the system are considered in selecting the control inputs. The designed controller is robust to changes in the operating point and the location of the disturbance. The pei'formance of the designed controller in a 39-bus, 10 macliines NEW ENGLAND network including DFIG generators and in the presence of sssc is simulated and investigated using MATLAB software. [ABSTRACT FROM AUTHOR]

In the islanded microgrids, distributed energy sources are responsible for powersharing, and accurate power-sharing is essential among these microgrids. Due to the mismatch of the physical impedance of power lines, poor reactive power-sharing is observed. Therefore, in this paper, the use of virtual impedance in the form of two methods of virtual positive impedance and virtual negative impedance is proposed to improve the accuracy of reactive power-sharing in a conventional droop controlled islanded microgrid. In this paper, in addition to presenting a controller to properly adjust the virtual impedance, the concept of vir'tual negative impedance is used to correct the effective impedance of the power supply line and improve the common bus voltage, and increase tire output power of energy sources. To evaluate the proposed control methods, a test islanded microgrid is simulated inPSCAD/EMTDC software. The results show that both pr'oposed methods have a good effect on the proper reactive power-sharing irr the microgrid. In addition, the results show that the virtual negative iirrpedance method can improve the common bus voltage and increase the output power of the source by reducing the effective line impedance. [ABSTRACT FROM AUTHOR]

Low-frequency noise modeling and estimation is a critical issue in the design of analog and digital circuits, especially for short-channel CMOS technologies. The conventional noise models do not fit the experimental results adequately at frequencies close to DC for amplifiers and close to the carrier frequency for oscillators. Furthermore, measuring this noise necessitates the use of specialized and expensive equipment. In this paper, the bound of carrier trapping and releasing are extracted while examining the characteristics of low-frequency noise using the EMD method, and an innovative model based on carrier number fluctuations is proposed. The method introduces a low-cost measurement system for low=frequency noise. The performance accuracy of this measurement system and the proposed model is evaluated and compared with conventional noise modeling methods. The evaluation results demonstrate the success of the proposed measurement setup and model in estimating near-DC noise. [ABSTRACT FROM AUTHOR]

The commutation torque ripple adversely affects the performance of the six-phase inverter of the BLDC motor with trapezoidal back EMF and creates vibration and noise for industrial applications. In this paper, the motor model is obtained in non-commutation times and during the commutation period, and according to that, a suitable method to reduce the torque ripple, by equalizing the slope of the current disconnected from the motor and the slope of the current connected to the motor during commutation, is presented. At low speeds, torque ripple is reduced using predictive pulse width modulation technique. With this method, the duty cycle of the switch involved in the commutation is predicted and applied to the switch during the commutation intervals. At high speeds, this reduction is done using quasi z-source converter and selector circuit. The quasi z-source converter and the selector circuit increase the input voltage of the inverter during commutation intervals and increase its value to four times the back EMF voltage of the motor, thus reducing the torque ripple at high speeds. The theoretical and analytical results are verified using the simulations performed in the PLECS software. [ABSTRACT FROM AUTHOR]

In this paper, a three-level framework is proposed to determine the optimal scheduling of a Multi-Area Active Distribution Network in the presence of inter-area Soft Open Points (SOPs). In this framework, the uncertainty of renewable generation and forecasted demand is modeled using information gap decision theory in a risk-averse manner. Coordinated scheduling of Controllable Distributed Generators (CDGs) and SOPs, inter-area energy exchanges and energy trading with upstream network considering the uncertainties are the contribution of the presented method. To improve the computational efficiency and to achieve the optimal solution, the scheduling problem is modeled as a second-order conic programming in which the operational and security constraints of the network, CDG limitations, and operational constraints of SOPs are accurately modeled and the problem is solved by executing CPLEX solver in MATLAB environment. A case study on IEEE 33-bus test system showcases the superiority of the proposed model compared to meta-heuristic algorithms such as particle swarm optimization, genetic algorithm, and gravitational search algorithm. [ABSTRACT FROM AUTHOR]

Islanding is one of the important challenges in power networks in the presence of distributed generation which is considered an undesirable incident due to the possibility of damage to operators, network equipment, and consumers. Therefore, it is necessary to quickly detect islanding and make a decision regarding the connection status of local distributed generation units in the network. In this paper, a passive islanding detection method is proposed using adaptive threshold-based instantaneous frequency droop characteristic. The proposed threshold limit is dynamically changed depending on under-studied conditions to discriminate the island from load changes, capacitor bank switching, motor start-up, and short circuit types. Two medium voltage networks of Cigre and 34-bus IEEE are used to evaluate the proposed method. The simulations are performed in Digsilent software and the proposed method is implemented in MATLAB software. The simulation results indicate that the island is correctly detected with appropriate accuracy in a short period of time from other disturbances in the presence of wind, solar and diesel types of distributed generations. [ABSTRACT FROM AUTHOR]

Traditionally, photovoltaic power plants use Maximum Power Point Tracking (MPPT) algorithms to produce maximum electrical energy. However, with the increase of grid-installed photovoltaic power plants, power system operators have faced new challenges such as overload, overvoltage, and proper performance during grid voltage disturbances. As a result, network standards and codes have been updated in the direction of photovoltaic systems that are more controllable and compatible with the network. To provide various grid support functions, such as frequency response and voltage support, the Constant Power Generation (CPG) control algorithm is established by grid codes. For this purpose, MPPT algorithms have been replaced by flexible power point tracking (FPPT) algorithms. This paper proposes an algorithm to control photovoltaic systems based on a multi-level cascaded full-bridge (CHB) inverter to achieve constant power generation control by the FPPT method. The required power reference is distributed among the sub-modules of the CHB converter according to the available power of each sub-module in such a way that, as much as possible, each sub-module is loaded equally. Then, the FPPT algorithm with an adaptive voltage step is used to set the power of each submodule in its reference value. The voltage step is calculated adaptively based on the observed condition (transient or steady state) so that the tracking performance has fast dynamics. The effectiveness of the proposed algorithm is evaluated by simulating a 500-kW photovoltaic system directly connected to a 4.7 kV grid. [ABSTRACT FROM AUTHOR]

Magnetic Induction Tomography (MIT) is a promising modality for noninvasive imaging due to its contactless and nonionizing technology. In this imaging method, a primary magnetic field is applied by excitation coils to induce eddy currents in the material to be studied, and a secondary magnetic field is detected from these eddy currents using sensing coils. The image (spatial distribution of electrical conductivity) is then reconstructed using measurement data, the initial estimation of electrical conductivity, and the iterative solution of forward and inverse problems. The inverse problem can be solved using one-step linear, iterative nonlinear, and special methods. In general, the MIT inverse problem can be solved by Gauss-Newton iterative method with acceptable accuracy. In this paper, this algorithm is extended and the zoning technique is employed for the reduction of unknown coefficients. The simulation results obtained by the proposed method are compared with the real conductivity coefficients and the mean relative error rate is reduced to 24.22%. On the other hand, Gauss-Newton iterative method is extended for solving the inverse problem of the MIT, and sensitivity measurement matrices are extracted in different experimental and normalization conditions. [ABSTRACT FROM AUTHOR]

The presence of electric vehicle technology in distribution networks as controllable resources provides advantages such as voltage regulation, power peaking, and loss reduction. This technology has a positive effect on the performance of the distribution network, but the simultaneous presence of electric vehicles and distributed generation sources requires optimal planning because the lack of access to an application reduces the life of these technologies and can cause blackouts in the power network. Therefore, in this study, the dynamic reconfiguration of the distribution network in the simultaneous presence of distributed generation sources and electric vehicles is proposed. Also, the time-of-use mechanism has been proposed as one of the demand response applications to improve the power consumption of subscribers. The objective functions in this study include the reduction of energy loss, operational cost and energy not supplied as the objective function of reliability. In general, the optimization problem of distribution network reconfiguration is complex and non-convex. Also, considering the effect of distributed generation units and electric vehicles makes the problem more complicated than before. Therefore, finding a practical method to solve the optimization problem is one of the main challenges of this paper. For this purpose, a novel hybrid algorithm based on a combination of an improved particle swarm optimization-artificial bee colony is presented to overcome the complexities of the optimization problem. The proposed method has [ABSTRACT FROM AUTHOR]

Islanding is one of the important challenges in power networks in the presence of distributed generation which is considered an undesirable incident due to the possibility of damage to operators, network equipment, and consumers. Therefore, it is necessary to quickly detect islanding and make a decision regarding the connection status of local distributed generation units in the network. In this paper, a passive islanding detection method is proposed using adaptive threshold-based instantaneous frequency droop characteristic. The proposed threshold limit is dynamically changed depending on under-studied conditions to discriminate the island from load changes, capacitor bank switching, motor start-up, and short circuit types. Two medium voltage networks of Cigre and 34-bus IEEE are used to evaluate the proposed method. The simulations are performed in Digsilent software and the proposed method is implemented in MATLAB software. The simulation results indicate that the island is correctly detected with appropriate accuracy in a short period of time from other disturbances in the presence of wind, solar and diesel types of distributed generations. [ABSTRACT FROM AUTHOR]

Adequate training data is essential in all supervised learning methods, including deep learning and machine vision. One of the approaches used to increase the number of training examples in deep learning is the "data augmentation" method. This method involves rotation transformation, transitions, and cropping on training images, which leads to an increase in the number of samples, which are different from training data. In this paper, the "style transfer" algorithm is used to increase the number of training samples. The goal in style transfer is to apply the appearance or visual style of one image to another image. In this paper, this method is used to produce new training examples and as an application, the proposed method is applied to the problem of fire detection. Assuming that the training images recorded during the night are less than the samples taken during the day, by applying a style transfer method, the images of the day are converted into night images and added to the data set as training data. The test results show the efficiency of the proposed data augmentation method. On average, the correct detection rate has increased by 7%. [ABSTRACT FROM AUTHOR]

The 3D integrated circuit is emerged as a promising solution to integrate very largescale circuits on electronics chips. In such chips, several layers of silicon substrates are stacked which are separated by insulator interfaces. Interconnection between two layers is realized using Through Silicon Via (TSV). Fabrication of TSVs is challenging due to their large size and complex process. Consequently, the number of TSVs should be minimized in the circuit's implementation. The 3D implementation consists of three main steps: Partitioning, Placement, and Routing. In this paper, the first two steps are accomplished using the Simulated Annealing-based optimization approach wherein minimization of the number of TSVs and total wire length are considered the main objectives. In this paper, an improved version of the pathfinder method has been developed which would efficiently generate the necessary interconnections among circuit modules. The results of simulations on MCNC benchmark circuits show that the proposed method outperforms the previous state-of-the-art methods in all aspects. In comparison with FSA, the number of TSVs is reduced by 6.15%, and the algorithm's runtime is decreased by 27.79%. Moreover, in comparison with the hMETIS method, the number of TSVs is reduced by 9.78%, and the algorithm's runtime is decreased by 31.73% . [ABSTRACT FROM AUTHOR]

In today's transmission networks (TNs), the sudden failure of some generation units (GUs) from the generation cycle can cause some problems such as the imbalance between generation and demand-side (consumption) leading to increasing the cost of operating the power system. On the other hand, distributed generators (DGs) in distribution networks (DNs) can be a relatively good alternative to-the-disconnected GUs with their ability to react quickly. Therefore, robust cooperation-for integrated TN&DN is essential. However, it is impossible to solve the cooperation of TN&DN problem without considering the AC optimal power flow (ACOPF), the operators' independence, and the information privacy. Thus, in this paper, a linearized ACOPF model for the robust cooperation of TN&DN problem is presented along with an efficient hierarchical decentralized solution method to solve the robust cooperation of integrated TN&DN in which the information-privacy-is-maintained-for both network operators. In this paper, the numerical simulations are analyzed for standard IEEE 30/118-bus transmission and IEEE 33/69-bus distribution networks. Finally, the paper validates through simulations the accuracy and computational-efficiency-of the proposed robust optimization problem and hierarchical decentralized method. [ABSTRACT FROM AUTHOR]

In today's transmission networks (TNs), the sudden failure of some generation units (GUs) from the generation cycle can cause some problems such as the imbalance between generation and demand-side (consumption) leading to increasing the cost of operating the power system. On the other hand, distributed generators (DGs) in distribution networks (DNs) can be a relatively good alternative to-the-disconnected GUs with their ability to react quickly. Therefore, robust cooperation-for integrated TN&DN is essential. However, it is impossible to solve the cooperation of TN&DN problem without considering the AC optimal power flow (ACOPF), the operators' independence, and the information privacy. Thus, in this paper, a linearized ACOPF model for the robust cooperation of TN&DN problem is presented along with an efficient hierarchical decentralized solution method to solve the robust cooperation of integrated TN&DN in which the information-privacy-is-maintained-for both network operators. In this paper, the numerical simulations are analyzed for standard IEEE 30/118-bus transmission and IEEE 33/69-bus distribution networks. Finally, the paper validates through simulations the accuracy and computational-efficiency-of the proposed robust optimization problem and hierarchical decentralized method. [ABSTRACT FROM AUTHOR]

This paper uses the unique biometric features of fingerprints to generate random cryptographic keys. The main aspects of the security of the generated key include the privacy of the fingerprint and the randomness and complexity of the key generation algorithm. In the proposed method, first, the unique fingerprint features, which include Minutiae points, are extracted from the fingerprint image. Then, to increase the statistical properties and complexity of the algorithm, the Euclidean distance and the angle of all the points of Minutiae relative to each other are calculated and stored. In the next step, after normalizing to 8-bit numbers, these data are moved by permutation operations and combined. In the following, the proposed method is used to increase the level of security and the ability to be random from the non-linear operations of 8-bit S-boxes S0 and S1 used in the CLEFIA block cipher. Statistical analyzes performed on the generated keys show the acceptable random nature of the keys. Therefore, the proposed structure for generating a random key can be used in encrypting digital signals with large volumes of data such as image and sound. [ABSTRACT FROM AUTHOR]

The ever-increasing of renewable distributed generation sources in distribution networks, as well as increasing network size, have faced agent-based protection schemes with a heavy communicational load. Accordingly, despite the fast and reliable nature of multi-agent systems, they have the possibility of improper performance, especially in centralized protection systems. This paper presents an intelligent self-healing method that has the ability to replace common multi-agent systems during fault conditions. Therefore, protection tasks are performed in a single control level, without dependence on higher communicational levels, to clear the fault. Decentralized operation of this structure is provided by using intelligent electronic devices and distributed communications. In this way, the proposed scheme is described with high-speed peer-to-peer communication capability using the IEC-61850 GOOSE protocol. Then, a penetration-free algorithm, without the help of a central controller, is provided by using GOOSE message capabilities, to prevent any electricity interruption due to insufficient protection settings. Finally, by planning different scenarios and simulating a practical distribution network via ETAP software, the accuracy of the proposed algorithm has been proven. [ABSTRACT FROM AUTHOR]

In Network-on-Chip architecture, wired structure and multi-step communication increase consumption power and latency. Combining wired media for a regular transmission and high-bandwidth wireless media for multi-step communication is a way to reduce latency and consumption of power. Wireless nodes are prone to error in on-chip wireless networks due to their complexity and relatively high usage; they are also crowded due to their sharing between several nodes, but their job is to increase efficiency. However, the presence of wireless nodes in wireless networks on the chip increases the cost and area. Therefore, finding an optimal structure for communication between cores is necessary. In this paper, a new three-dimensional architecture for a Wireless Network on Chip is presented, which has two levels; depending on the location of the error in the second level, the wireless routers in the first level are assigned to the processing elements. The demand matrix is used to optimize different traffic patterns. The performance of 3D architecture has been compared under different traffic patterns. The obtained results show that the proposed structure has a relatively good performance and increases the network's reliability. [ABSTRACT FROM AUTHOR]

Multi-Input and Output Radar System (MIMO) is a new type of radar system that has been the subject of much research in recent years due to its many advantages. Meanwhile, the issue of target tracking in MIMO radar systems is of great importance, and providing an efficient solution for it remains an unresolved issue. In this paper, target tracking in MIMO radar systems using optimizing Kalman interactive filter is investigated. The proposed method in this research is the MIMO radar system for the simultaneous tracking of multiple targets. In the assumed system model, the interactive multiple model (IMM) based on Extended Kalman Filter (EKF) is used to understand the effective tracking of the target. Also, the optimization of the multi-objective tracking model in MIMO radar systems has been done by the proposed method using the particle swarm optimization (PSO) algorithm. This optimization algorithm estimates the fit of each studied response based on the number of energy resources and time consumed. The efficiency of the proposed method in a simulated environment has been evaluated and its performance in tracking multiple targets has been investigated in terms of different criteria. Based on the results of these experiments, the proposed method, in addition to reducing the amount of tracking error, can be effective in reducing energy consumption and reducing the sampling period. Thus, the proposed method will reduce resource consumption in the multi-objective tracking system. [ABSTRACT FROM AUTHOR]

Nowadays, using telecommunication systems and advanced measuring devices underlies cyberattacks on electrical grids. Bad data injection and failure to detect it on time, cause drastic damage to the network. This paper presents a new method for bad data detection (BDD) in state estimating when a cyber attacker manipulates the important measurements. Therefore, the new attack index is defined by simultaneously manipulating the network parameters and injecting incorrect data into the measured values. For this purpose, considering the masking and swamping effect, the diagnostic robust generalized potential (DRGP) algorithm detected and isolated high-leverage measurements installed in important locations from low-leverage measurements. Then, the state estimation process performs using low-leverage measurements. The Generalized Studentized Residual (GSR) algorithm detects bad data. With simultaneous manipulation of network parameters and measurement values, conventional BDD methods are unable to detect an attack. To evaluate the performance of the proposed method, they were implemented on the IEEE standard 14-bus network using MATLAB and Rstudio software. The simulation results show the ability of the proposed algorithm to detect a bad data attack. [ABSTRACT FROM AUTHOR]

Permanent Magnets (PM), which are one of the most vulnerable and important components of permanent magnet machines, are at risk of demagnetization for reasons such as overheating and corrosion. One of the most critical tasks of engineers in the industry is to prevent the machine's operation in the demagnetization condition and replace the machine's defective PMs as much as possible. Demagnetization causes linkage flux reduction, electromagnetic force changes, and machine heating. This paper presents a new approach for demagnetized PM diagnosis in a permanent magnet linear synchronous machine using wavelet packet transform. Due to the high sensitivity to the flux changes, the electromotive force signal was selected as a detection signal. By examining this method in different demagnetization cases, it is determined that this method has a proper performance. In this paper, the finite element software (Maxwell) and MATLAB were used to simulate the machine and analyze the data, respectively. [ABSTRACT FROM AUTHOR]

After extreme natural events like earthquakes, storms, and floods, the electrical distribution network may be isolated from the upstream grid. In such a situation, the electrical power of the upstream grid is lost and the available microgrids (MGs) are the only power sources. Since the power outputs of MGs are restricted, restoring all de-energized loads is impossible and restoring critical loads (CLs) becomes the most important concern of the distribution network operators. Therefore, in this paper, a new hour-by-hour restoration strategy for CLs restoration after a blackout is proposed. The desired objective functions include restored energy and switching operations. The proposed restoration strategy presents a restoration plan for eacli hour of the outage period. The efficiency of the proposed strategy is carried out on an IEEE 123-bus distribution network and the simulation results confii'm the superiority of the proposed hour-by-hour restoration strategy over the other conventional restoration methods. [ABSTRACT FROM AUTHOR]

In this paper, the Hunger Games Search (HGS) optimization algorithm is used to estimate the electrical parameters of the induction machine steady state model. Induction machine nameplate data is used as input to the proposed algorithm. The performance of the proposed method is confil'med by comparing the output characteristics obtained by estimating the motor parameters including torque, current and power factor in the steady state model of the induction machine with the values provided by the manufacturer. In addition, by evaluating and comparing the results of the proposed method with the results of previous research, it is shown that the proposed algorithm is a very effective and accurate method for the acceptable estimation of induction machine parameters. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-objective function for hourly optimization of microgrids performance through minimizing the operating costs, losses and, voltage deviation index. A fuzzy decision-maker is used in this study to select the best solution from the optimally set goals by the Pareto Front beam method. In this paper, the collections of microgrids are separated into several clusters and power transaction between clusters as well as between distribution system and MG clusters, with consideration for the uncertainty of renewable energy resources (RESs), is examined. An hourly robust energy management approach is presented for distribution systems under the penetration of renewable energy-based MGs. In addition to wind turbines and photovoltaics as RESs, the MGs are equipped with energy storage systems and micro-turbines. The uncertainty of renewable generation is demonstrated via the information gap decision theory (IGDT) technique. To validate the effectiveness of the proposed model, it is tested on a 94 bus distribution test system using the general algebraic modeling system (GAMS) software. The results show the prominence of MGs clustering in improving the techno-economic characteristics of the distribution system and indicate the important consequences of clustered microgrids in optimal power transaction and distribution system operation. [ABSTRACT FROM AUTHOR]

The stochastic nature of the active power generated by wind turbines (WTs) has posed new challenges to the reliability assessment, load flow analysis, and reconfiguration process of distribution systems penetrated with WTs. In this paper, a new optimization approach has been presented to find the best configuration of the distribution system along with optimal locations of WTs. This is aimed at maximizing the reliability of the system, while energy losses are minimized. To this end, based on the graph theory and the system topology, a simple technique is proposed for finding minimal cut sets that could be used for calculating reliability indices. Furthermore, the multiple flow directions created in the presence of WTs cause difficulties in calculating reliability indices and in performing load flow analysis. Accordingly, a new concept called the independent reliability zone is introduced to overcome these problems. In this paper, the multi-objective harmony search algorithm, due to its suitability for multi-objective problems, is employed as a solution for solving the proposed optimization problem. The effect of the proposed approach has been evaluated on both test and real systems. The results show that using the proposed approach, a more practical solution is achieved for low-cost and reliable 24-hour planning of the system in the presence of uncertainties in DGs. [ABSTRACT FROM AUTHOR]

The wireless body area network (WBAN) is one of the applications of the Internet of things (IoT) for healthcare, which monitors human physiological parameters. In this paper, a two-objective optimization problem is designed to maximize the sum-throughput and wireless transferred energy simultaneously. The designed one-tier network includes body surface sensors, an implantable sensor, and one coordinator. All of these sensors can harvest energy from the body and radio frequency (RF) signals. A temperature constraint is considered for the implanted sensor due to the sensitivity of internal tissues. Each sensor transmits physiological data to the coordinator in the uplink according to the scheduled time slots obtained by solving the optimization problem. At the beginning of each frame in the downlink, the coordinator transmits a pilot signal to transfer wireless energy and for channel estimation at the sensors. We have used a Recurrent Neural Network (RNN) architecture to predict one time step ahead of the channel. Then by interpolation, the channel gains of all time slots of a frame are estimated and the time scheduling of the sensor access to the channel is improved. The simulation results show that the objective function and time scheduling are improved by the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, to estimate the parameters of a three-phase induction motor, the current measured in one phase and Harris Hawk's optimization algorithm (HHO) as well as the recently introduced improved technique of this algorithm (IHHO) are used. This method includes estimating the electrical equivalent circuit parameters of a three-phase induction motor, including rotor and stator resistance, leakage inductance, magnetizing inductance, as well as mechanical parameters including the moment of inertia and friction coefficient. As three-phase induction motors are widely used in various industries, it is necessary to identify their parameters to implement adjustable speed control systems for such motors. Therefore, it is necessary to develop appropriate algorithms to accurately estimate the induction motor parameters. The main inspiration of the proposed method is the behavior of the Harris hawk group in nature. The objective function is defined as the calculation of the least mean squares of the error resulting from the numerical simulation of the dynamic equations of the motor and the measured value of the current of one motor's phase. The proposed method is applied to the dynamic model of a specific motor and the results of the proposed method are compared with other previously published methods. The obtained results demonstrate the remarkable capability of the proposed method and its rapid convergence. [ABSTRACT FROM AUTHOR]

Lack of coordination between the operation of capacitors and OLTC to voltage control is one of the basic problems in distribution networks, which can cause problems in the control function. In this paper, a new control method is proposed to create coordination between the operation of capacitors and OLTC. The criterion of this coordination is based on fuzzy logic to determine the status of transformer and capacitor function, which reduces unnecessary switching. In this method, the control function is considered with more accuracy such that the quantity of voltage and power factor are set within the range of 0.98 to 1.02 p.u and between 0.95 lag and lead, respectively. Another advantage of the proposed method is that it is automatic and eliminates human resources to adjust the position of the transformer and capacitors. Simulation results are done on the 14-bus network by using two software MATLAB and DIGSILENT, that these results show the accuracy of the proposed method in the desired performance in distribution networks. [ABSTRACT FROM AUTHOR]

In this paper, the problem of simultaneous stabilization of a collection of nonlinear systems is proposed by using the Takagi-Sugeno (TS) fuzzy modeling and synthesis approach. In practice, due to the uncertainty in the system model, failure modes, or different operating points, we often encounter the problem of simultaneous stabilization. The TS fuzzy model has been considered for the controller design of complex nonlinear systems as it provides a simple and effective concept. This problem refers to the stabilization of some nonlinear systems by utilizing a single nonlinear controller. Both state feedback and output feedback controllers are designed based on the PDC concept and by solving some linear matrix inequalities (LMIs). Moreover, to reduce the conservatisms related to using the common Lyapunov function and restrictive equality conditions in the design method, Finsler's lemma has been employed. Finally, three numerical examples are provided to show that the proposed controllers stabilize well-considered nonlinear systems. [ABSTRACT FROM AUTHOR]

In this paper, to solve the multi-objective problem of distribution feeder reconfiguration (DFR) in the presence of distributed generation (DG), the hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) has been proposed, which is a combination of particle optimization (PSO) and gravitational (GSA) optimization algorithm. In this field, the power losses and operating costs are the two most used objective functions in the literature. In addition to the mentioned objective functions, this paper also considers the optimal generation capacity of DG resources and energy not supplied (ENS), which is one of the basic reliability indexes of distribution networks. In this paper, the values of different objective functions are normalized by the fuzzy method, and also the Fuzzy decision-maker is used to determine the most optimal solution among the Pareto-optimal solutions. The proposed algorithm is implemented on IEEE 70-bus and 119-bus test systems. The simulation results show the efficiency of the proposed PSOGSA in improving the considered objective functions. The proposed method, by establishing a suitable fit between different objective functions has introduced a more efficient structure with lower losses and operating costs, as well as greater reliability, compared to other optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper deals with the dynamic economic and emission dispatch during a day as an important challenge in engineering. On the other hand, renewable energy provides an undeniable contribution to the energy supply. Therefore, to create an efficient model, the probability wind energy models have been proposed. In principle, this problem has several limitations and to bring it to reality, practical and nonlinear constraints such as power balance, ramp rate, prohibited zone, non-smooth cost function, and production constraints have been considered. Since these functions i.e. emission, cost, and wind models are conflicting in nature, to solve this problem, a multi-objective virus colony search algorithm (VCS) based on Pareto theory has been proposed. To improve the performance of the virus colony search algorithm, the chaos theory has been employed that eliminates the weakness of the standard algorithm, ie the speed of convergence and increased number of iterations of the algorithm to achieve the optimal solution. Chaos theory refers to the nature of complex systems with unpredictable behavior. Using the stochastic properties of chaos system, Chaos theory can improve the quality of population distribution in search space and enhance algorithm convergence function. The fuzzy decision function is also used to select the best solution from the set of solutions. The proposed model and method are applied to different systems and in some cases are compared with other methods in the articles. The results show an improvement in the performance of the proposed algorithm. The results also show that the presence of renewable resources has reduced production costs and thus increased network security. [ABSTRACT FROM AUTHOR]

Generative models try to obtain a probability distribution that is similar to that of observed data. Two different solutions have been proposed in this regard in recent years: one is to minimize the divergence (distance) between the two distributions by maximizing the variational lower bound, and the other is to implicitly reduce the distance between the two distributions through adversarial processes. One of the problems in generative adversarial networks (GANs) is the mode collapse. Mode collapse is a phenomenon in which, for various inputs, the generative model generates low variety or similar images. This paper tries to provide a solution to the mode collapse problem proposing a novel method called variational generative adversarial networks (VGANs). This method exploits variational autoencoders to initialize GANs. In other words, in addition to maximizing the variational lower bound, it also implicitly reduces the distance between the two distributions. Experimental results show that this method can deal with the mode collapse problem better than the state-of-the-art. Moreover, in the qualitative analysis, according to a survey of 136 people on the authenticity of the generated images, the proposed method can generate images more similar to real ones. [ABSTRACT FROM AUTHOR]

As social networks grow, they become more and more complex, and analyzing them becomes complicated. One way to reduce this complexity is to divide the network into subnets, which are also called communities. Dividing social networks into desirable communities can help analysts and experts to understand the behavior and function of the networks. Community detection in networks is a challenging topic in network science and various methods have been proposed for that. Modularity maximization is one of the state-of-the-art methods suggested for community detection. Modularity maximization is an NP-hard problem meaning that no polynomial-time algorithm exists that could solve the problem optimally unless P=NP. A group of approaches that could solve such problems is approximate algorithms. Identifying the influential nodes has many important applications in social networks. This technique could also be used in community detection. To maximize modularity, in this paper, we propose approximate algorithms based on identifying the influential nodes and their influence domain. We used the concept of scale-free networks to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods of community detection algorithms. [ABSTRACT FROM AUTHOR]

The Josephson junction is a device consisting of two superconducting electrodes connected by a weak junction such as a thin insulation coating. The Josephson junction has chaotic behavior parameters not desirable in high-frequency applications. In this paper, a model predictive control approach based on an ant colony optimization algorithm is proposed to synchronize two Josephson junction models with different parameters. Here, the Josephson junction is described with a nonlinear model, and the synchronization is obtained using the slave-master technique. For this purpose, an appropriate objective function is defined to assess the particles within the state space. This objective function minimizes simultaneously the tracking error, control effort, and control smoothness. The dynamic optimization problem is solved using an ant colony optimization algorithm. Numerical simulations are conducted to assess the efficiency of the proposed algorithm. Also, a Monte Carlo evaluation is achieved to compute the statistic performance of the suggested controller. In addition, sensitivity analysis to changes in the number of ants and the number of iteration of the inner loop of the algorithm was performed. The results show that the controller is significantly sensitive to reducing the number of iteration of the inner loop. [ABSTRACT FROM AUTHOR]

The review of the literature survey of transient stability constrained optimal power flow (TSCOPF) shows that only mechanical input power is used as a key parameter in the energy sensitivity approaches while generator's voltage changes are not considered. Using AC-OPF, this paper considers the effect of generator voltage changes in the calculation of energy sensitivity. To evaluate the transient stability, a new index from the family of corrected hybrid techniques is proposed and applied. It is shown that the change of the proposed index against the mentioned key parameters is linear. Thus, to reduce the computational burden of the approach, this linear property is considered to define the transient stability constraint. The proposed framework is implemented on the New England 39-bus test network. A comparison of the obtained results with those in the literature survey shows that using the proposed approach, the operation cost is decreased from 61799.68 ($/h) to 60927.86 ($/h) which achieves 1.41% saving in the fuel cost while securing the network against considered contingency. [ABSTRACT FROM AUTHOR]

The choice of a mathematical model of eye movement has had a significant impact on scientific research. This research resulted in a controlling robotic arm that could follow the movement of the human eye and thoroughly examine the structure of the human eye. Exploring the previous models, a model equivalent to a robot with two degrees of freedom is presented. Then, by designing and simulating an optimal linear square controller on the desired system, we could model an eye robot. The purpose of tracking is to target the system, which is shown by simulating this controller. This paper describes how to design a fuzzy controller for the system by presenting the simulation results. Finally, the performance of the two control methods in non-zero initial conditions, rapid eye movements, and uncertainty in the model system constructions are compared. The simulation results show that despite the system control provided by both control methods, the fuzzy control method has a better performance for tracking fast eye movements. This method is resistant to modeling uncertainties by using the qualitative behavior of the system instead of its exact mathematical model. Also, the smaller volume of simulation of control inputs in the fuzzy controller will significantly reduce costs by using this controller. [ABSTRACT FROM AUTHOR]

The Josephson junction is a device consisting of two superconducting electrodes connected by a weak junction such as a thin insulation coating. The Josephson junction has chaotic behavior parameters not desirable in high-frequency applications. In this paper, a model predictive control approach based on an ant colony optimization algorithm is proposed to synchronize two Josephson junction models with different parameters. Here, the Josephson junction is described with a nonlinear model, and the synchronization is obtained using the slave-master technique. For this purpose, an appropriate objective function is defined to assess the particles within the state space. This objective function minimizes simultaneously the tracking error, control effort, and control smoothness. The dynamic optimization problem is solved using an ant colony optimization algorithm. Numerical simulations are conducted to assess the efficiency of the proposed algorithm. Also, a Monte Carlo evaluation is achieved to compute the statistic performance of the suggested controller. In addition, sensitivity analysis to changes in the number of ants and the number of iteration of the inner loop of the algorithm was performed. The results show that the controller is significantly sensitive to reducing the number of iteration of the inner loop. [ABSTRACT FROM AUTHOR]

The review of the literature survey of transient stability constrained optimal power flow (TSCOPF) shows that only mechanical input power is used as a key parameter in the energy sensitivity approaches while generator's voltage changes are not considered. Using AC-OPF, this paper considers the effect of generator voltage changes in the calculation of energy sensitivity. To evaluate the transient stability, a new index from the family of corrected hybrid techniques is proposed and applied. It is shown that the change of the proposed index against the mentioned key parameters is linear. Thus, to reduce the computational burden of the approach, this linear property is considered to define the transient stability constraint. The proposed framework is implemented on the New England 39-bus test network. A comparison of the obtained results with those in the literature survey shows that using the proposed approach, the operation cost is decreased from 61799.68 ($/h) to 60927.86 ($/h) which achieves 1.41% saving in the fuel cost while securing the network against considered contingency. [ABSTRACT FROM AUTHOR]

This paper deals with the dynamic economic and emission dispatch during a day as an important challenge in engineering. On the other hand, renewable energy provides an undeniable contribution to the energy supply. Therefore, to create an efficient model, the probability wind energy models have been proposed. In principle, this problem has several limitations and to bring it to reality, practical and nonlinear constraints such as power balance, ramp rate, prohibited zone, non-smooth cost function, and production constraints have been considered. Since these functions i.e. emission, cost, and wind models are conflicting in nature, to solve this problem, a multi-objective virus colony search algorithm (VCS) based on Pareto theory has been proposed. To improve the performance of the virus colony search algorithm, the chaos theory has been employed that eliminates the weakness of the standard algorithm, ie the speed of convergence and increased number of iterations of the algorithm to achieve the optimal solution. Chaos theory refers to the nature of complex systems with unpredictable behavior. Using the stochastic properties of chaos system, Chaos theory can improve the quality of population distribution in search space and enhance algorithm convergence function. The fuzzy decision function is also used to select the best solution from the set of solutions. The proposed model and method are applied to different systems and in some cases are compared with other methods in the articles. The results show an improvement in the performance of the proposed algorithm. The results also show that the presence of renewable resources has reduced production costs and thus increased network security. [ABSTRACT FROM AUTHOR]

As social networks grow, they become more and more complex, and analyzing them becomes complicated. One way to reduce this complexity is to divide the network into subnets, which are also called communities. Dividing social networks into desirable communities can help analysts and experts to understand the behavior and function of the networks. Community detection in networks is a challenging topic in network science and various methods have been proposed for that. Modularity maximization is one of the state-of-the-art methods suggested for community detection. Modularity maximization is an NP-hard problem meaning that no polynomial-time algorithm exists that could solve the problem optimally unless P=NP. A group of approaches that could solve such problems is approximate algorithms. Identifying the influential nodes has many important applications in social networks. This technique could also be used in community detection. To maximize modularity, in this paper, we propose approximate algorithms based on identifying the influential nodes and their influence domain. We used the concept of scale-free networks to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods of community detection algorithms. [ABSTRACT FROM AUTHOR]

Generative models try to obtain a probability distribution that is similar to that of observed data. Two different solutions have been proposed in this regard in recent years: one is to minimize the divergence (distance) between the two distributions by maximizing the variational lower bound, and the other is to implicitly reduce the distance between the two distributions through adversarial processes. One of the problems in generative adversarial networks (GANs) is the mode collapse. Mode collapse is a phenomenon in which, for various inputs, the generative model generates low variety or similar images. This paper tries to provide a solution to the mode collapse problem proposing a novel method called variational generative adversarial networks (VGANs). This method exploits variational autoencoders to initialize GANs. In other words, in addition to maximizing the variational lower bound, it also implicitly reduces the distance between the two distributions. Experimental results show that this method can deal with the mode collapse problem better than the state-of-the-art. Moreover, in the qualitative analysis, according to a survey of 136 people on the authenticity of the generated images, the proposed method can generate images more similar to real ones. [ABSTRACT FROM AUTHOR]

The choice of a mathematical model of eye movement has had a significant impact on scientific research. This research resulted in a controlling robotic arm that could follow the movement of the human eye and thoroughly examine the structure of the human eye. Exploring the previous models, a model equivalent to a robot with two degrees of freedom is presented. Then, by designing and simulating an optimal linear square controller on the desired system, we could model an eye robot. The purpose of tracking is to target the system, which is shown by simulating this controller. This paper describes how to design a fuzzy controller for the system by presenting the simulation results. Finally, the performance of the two control methods in non-zero initial conditions, rapid eye movements, and uncertainty in the model system constructions are compared. The simulation results show that despite the system control provided by both control methods, the fuzzy control method has a better performance for tracking fast eye movements. This method is resistant to modeling uncertainties by using the qualitative behavior of the system instead of its exact mathematical model. Also, the smaller volume of simulation of control inputs in the fuzzy controller will significantly reduce costs by using this controller. [ABSTRACT FROM AUTHOR]

Residential load forecasting plays an important role in management and planning of modern smart grids. Accurate residential load forecasting is needed in planning to keep demand and supply balanced. This paper presents a mid-term residential load forecasting method based on feature selection to solve the linear regression problem. In this way, for feature selection to perform the regression, the neighborhood component analysis method is used. For this purpose, an optimization problem is designed, and the problem is solved using the LBFGS algorithm. The AMPds2 dataset is used to implement the proposed method, and the results were compared with the results of the other six forecasting methods. Comparisons were made through mean squared error, root mean squared error, and mean absolute percentage error. The simulation results confirm the effectiveness of the proposed method for accurate residential load forecasting. [ABSTRACT FROM AUTHOR]