In this paper, based on the vector control of axial field flux switching permanent magnet (AFFSPM) motor, an optimized field-weakening control method of AFFSPM motor is proposed. A new AFFSPM motor with 12 stator slots (S) and 19 rotor poles (P) is taken as the object to simulate and optimize the flux-weakening speed control. The AFFSPM motor adopts constant torque control with the maximum torque per ampere below the base speed, which reduces motor losses, improves the efficiency of the inverter and adopts constant power and sub-regional speed control above the rated speed. By combining the cross-axis current and direct-axis current in the flux weakening control method, the power factor of the AFFSPM motor can be improved and speed range can be extended. By considering the speed fluctuation in field weakening control, and the fuzzy self-tuning PI control method is proposed to improve the performance of the AFFSPM motor field weakening control. To verify the feasibility of proposed control method, Co-Simulation is used. Finally, the control algorithm of the drive system is implemented in a prototype of AFFSPM motor.
To improve the performance of the fault-tolerant-hybrid excitation axial field flux-switching (FT-HEAFFS) motor and attain the minimum copper loss, a fault-tolerant control method based on the model predictive control algorithm is proposed. Considering a 6 stator slots/13- rotor poles FT-HEAFFS machine as the control object, under the open circuit failure of single-phase winding, the minimum cropper loss fault-tolerant method based on the model predictive torque control (MPTC) and direct torque control are studied and analyzed, respectively. The feasibility and effectiveness of the proposed fault-tolerant control method are verified. The research results showed that both methods could make the speed, torque and stator flux-linkage almost unchanged, ensuring the stable operation of the system. Compared with direct torque control, the model predictive flux control had smaller flux-linkage ripple before and after the open circuit failure.
Different surface impedance models are applied to circular nano-wires at terahertz and optical frequencies and the accuracy of these surface impedance boundary conditions (SIBCs) is studied. The simplest form of SIBC defines a local relation between the tangential electric and magnetic equivalent surface currents at each point on the boundary. This definition is very dependent on the constituent material of the wire and its radius. The generalized IBC (GIBC) improves the accuracy of the local definition by considering the curvature of the surface at each observation point. On the other hand, the operator definition of the surface impedance presented in the SIGO method (surface impedance generating operator), is an exact field theoretical approach that determines the relation between equivalent electric and magnetic surface currents. Moreover, this method is suitable for parallel processing. For the special case of circular wires, the SIGO operator is derived. To validate the SIBC models, the results are compared with the SIGO. In spite of its extreme simplicity, it is observed that the accuracy of SIBC models is limited at optical and terahertz frequencies. It is also shown that some forms of SIBCs presented in the literature for nano-wires can be considered as special cases of SIGO formulation.
In this paper, two different methods of human activity recognition based on video signals are introduced. The first method explores the effectiveness of combining feature descriptors obtained by local descriptors and artificial neural network classifier. It is used in the traditional approach and the local descriptors extract interest points or local patches from the videos, and the feature vectors are later constructed based on the intrests, and eventually feature vectors are used as the input of a two-layer feed-forward artificial neural network (ANN). Experimental results show that using the HOG3D descriptor with ANN gives the best performance. On the other hand, deep learning architectures have attracted much consideration for automatic feature extraction in the last years, so an improved 3D convolutional neural network architecture is also designed as the second method. They are implemented and compared with state-of-the-art approaches on two data sets. The results exhibit that method 1 is superior when the shortage of sample data is the main restriction. It respectively achieves recognition accuracies of 97.8% and 99.8% for the Weizmann and KTH action data sets. In addition, method 2 is considerable for its automatic features extraction, and achieves an acceptable result with lots of original training data. As a result, it gets recognition accuracy of 92% for the KTH data set while this value is drastically reduced for the Weizmann data set.
This study has developed a three axis capacitive accelerometer, in which part of the acceleration calculating capacitor is installed in Z direction in spring so that the sensitivity in the said direction can be increased. While observing the positive characteristics of the previously designed accelerometers, the suggested accelerometer has compensated for their shortcomings by increasing both sensitivity and pull-in voltage. Besides, spring torsion is reduced, rigidity calculation is easier, and the nonlinear behaviors of the springs are less. As such, without increasing the total occupancy level of the sensor, the surface area of the capacitive planes for measuring acceleration in z direction increases, thus increasing in the sensitivity in the z direction while observing all the merits of the previous accelerometers. In designing the suggested accelerometer, factors like rise time, overshoot, settling time, and peak time are also calculated. Therefore, the features of the designed accelerometer are also extracted from the perspective of a second-order system. Designed accelerometer involves an operating frequency up to 20 kHz as well as a dynamic range to 1000 g. The sensitivity of the three axis accelerometer is 4 fF/g in z axis direction. Besides, the sensitivity in x and y directions is 9 fF/g.
Nowadays, renewables are the first choice option for a modern power system generation scenario. It is due to their high attraction, especially environmental attraction, cost aspects and also availability in almost all over the world. Wind and solar sources are now competitive with conventional sources and command a high percentage of investments in renewable power. The main challenge of using these cheap and clean energies is their output power uncertainty, and their variability may lead to wind/solar power curtailment, or load shedding caused by insufficient spinning and fast reserve. Energy storage systems integrated with renewable energies are a common solution for this challenge. However, they impose extra cost to planning, and operation costs need a suitable economic study for the best location and size of these systems. In this paper, a flexibility based approach is used to show the role of Battery Energy Storage System (BESS) in the wind/load curtailment reduction. This approach can lead to a suitable economic routine to determine BESS size based on economic trade-off between BESS fixed, variable costs and wind/load curtailment costs. First, the BESS flexibility index is introduced and the suitable State of Charge (SoC) control is presented to use for Dynamic Economic Load Dispatch (DELD) solution based on the wind/load curtailment reduction. The simulation results show the efficient dependency between system flexibility improved by BESS integration, and the wind/load curtailment reduction.
This paper considers robust control of uncertain linear neutral systems with multiple state and state derivative delays. With equivalent descriptor representation, the stabilization problem is extended to more general class of neutral-type uncertain linear systems with discrete and distributed delays. The parametric uncertainties are time varying and unknown but norm bounded. Two delay-dependent/independent approaches are proposed to design robust controllers for a class of uncertain linear neutral systems with parametric uncertainty, discrete and distributed multiple delays. Using a presented descriptor model and an appropriate Lyapunov functional, sufficient conditions for closed loop stability are given in terms of linear matrix inequalities (LMIs). Solving the LMI problems, a robust memoryless state feedback is designed for all admissible uncertainties. The results depend on the size and varying rate of the delays. Two examples are provided to show the effectiveness of the proposed strategy.
For carrying out their missions, many satellites use closed-loop attitude control. For closing the control loop, they use several sensors, such as sun sensors, magnetometer, and star tracker. Sun sensors are not operational during the eclipse; therefore, one of the observed vectors is lost. For this reason, attitude determination in eclipse can be a challenging issue for control engineers. This paper presents a novel idea for producing a new generation of sensors that can measure the induced electric field vector not only in eclipse but also in the whole orbit. This electric field comes from the high velocity of the spacecraft in the magnetic field of the Earth. This vector is always perpendicular to the magnetic field; thus, it is never aligned with the magnetic vector and never causes singularity and accuracy decreasing. The induced electric field is measured by three RC circuits that are actuated by sinusoidal voltage. The dielectrics of the capacitors are made of ferroelectric materials; therefore, the induced voltage affects the permittivity and voltage of the capacitor. By measuring and calibrating this effect in three perpendicular axes can measure the three components of the electric field vector. The theory of the proposed sensor has been developed, and simulation studies validate the results.
Nowadays the control and stability of DG systems are important topics that researchers in both academia and industry. Small and large signal analyses for stability studies on various systems have been done in papers and books. In this paper, first models of an inverter-based Distributed Generation (DG) subsystems are created. After the linearization, if required, the small-signal stability analysis of the DG which is controlled with a voltage and frequency control scheme based on the model predictive control (MPC) that has been used previously, is established. In this control scheme, load currents at the point of common coupling (PCC) of the DG are considered as disturbances and used as feed-forward signals. This technique enhances the performance of the DG control system in transient and steady-state conditions for a wide range of loads. The stability of the DG system under various loads (such as one phase load as imbalanced load, rectifier load as nonlinear load and induction motor load as dynamic load) is demonstrated by the eigenvalues trajectory. The sensitivity analysis and robustness assessment of the control scheme are also conducted and discussed. For more performance consideration, the DG system is simulated with MATLAB/SIMULINK software, implemented in the lab and later the suitable performance of the system is demonstrated by the simulation and experimental studies.
In this paper, a new active lossless snubber circuit is proposed which provides soft switching condition for the traditional pulse width modulation (PWM) fly-back converter. This active lossless snubber circuit creates Zero Voltage Switching (ZVS) condition for the main switch, while Zero Current Switching (ZCS) condition is achieved for the auxiliary switch. Moreover, based on soft-switching condition, diode reverse recovery problem is omitted that leads to reduction of switching losses and increased efficiency. Also, the voltage stress of the auxiliary switch is clamped to the input voltage level which leads to its low capacitive turn ON loss. Furthermore, the presented active lossless snubber circuit provides soft-switching condition independent of load condition. The main and auxiliary switches do not need isolated gate driver, since their source pins are connected to the input ground. In this manuscript, the different operating modes are explained in details and also, a comprehensive design procedure is presented. Furthermore, loss breakdown for converter elements is offered at full load. The simulation results of the proposed converter using PSPICE software are shown for 155V input, 24 V, 120 W output and 100 kHz switching frequency to justify the theoretical analysis. The proposed converter has a high efficiency of 94.08% at full load.
Distribution Network Reconfiguration (DNR) is an important challenge in the operation of distribution networks which may be influenced by factors such as Wind Turbine Generators (WTG). In this paper, a novel policy is implemented to solve the DNR problem in presence of WTGs. The objectives of proposed DNR policy are minimization of active power losses, total electrical energy costs, and total emissions of the network. To solve the problem, an improved version of Honey Bee Mating Optimization (IHBMO) algorithm is implemented. Moreover, a stochastic scenario-based model is considered to meet the uncertainty of WTGs and loads. The bases of the proposed stochastic model are generation of stochastic scenarios using the roulette wheel mechanism, and a scenario reduction technique to decrease the computation burden of the problem. For each scenario, a multi-objective mechanism is employed to save non-dominated solutions extracted by IHBMO. A decision-making procedure based on fuzzy clustering technique is used to rank the obtained non-dominated solutions according to the decision-maker preferences. Finally, an 84-bus distribution test network is considered to evaluate the feasibility and effectiveness of the proposed method. Obtained results show that the proposed method can be a very promising potential method for solving the stochastic multi-objective reconfiguration problem in distribution systems.
This paper introduces a new symmetric single-phase 13-Level Flying Capacitor Inverter (13-LFCI) structure using a two dc sources and three capacitors. The proposed single-phase 13-level inverter has the ability to increase the output voltage with fewer semiconductor components compared to the state-of-the-art structures. The Phase Disposition Sinusoidal Pulse Width Modulation (PDSPWM) method is utilized to produce switching pulses. Using this modulation scheme facilitate the 13-LFCI with self-balancing capability of voltage of capacitors. The optimal capacitors are designed for minimum voltage drop of capacitors in the different loads. The 13-LFCI is also capable of transferring reactive power through R-L loads without any limitations. Furthermore, the inverter can properly generate 5-level, 7-Level, 9-level, 11-level, and 13-level output voltage for different applications with change of modulation index. Moreover, a comparison with state-of-the-art 13-level inverters is provided in terms of the number active and passive components, boosting ability, reactive power support and voltage conversion ratio in order to show the advantages of the proposed structure. In addition, the theoretically losses is calculated to show the efficiency of proposed topology in various powers. The performance of the converter is illustrated through simulations for different operating conditions for resistive and resistive-inductive loads. Eventually, to confirm different aspects and implementation of proposed structure, the proposed 13-LFCI has been approved in MATLAB/SIMULINK software, and the simulation results achieved are considered utilizing a laboratory prototype.
In deregulated electricity markets, the electricity consumer should distribute his required electricity optimally between different markets including spots markets with instantaneous price and bilateral contract markets. The present study is aimed to design a model for selecting the optimal electricity market portfolio, so the purchase costs can be minimized by considering a risk level. For this purpose, an optimization approach based on random planning was proposed to minimize costs and reduce power supply risk. Conditional value at risk was used as an appropriate and well-known factor for reducing unfavorable situations in decision-making under uncertain conditions. For simulations, the real information of Iran in 2018 was used as much as possible. Due to the small number of industrial subscribers, the whole population was studied. A genetic algorithm has been used to solve this optimization problem. In addition, MATLAB software was used for implementing the proposed model. The efficiency of the proposed model was proved by analyzing different sensitivities and the best components of the risk-averse decision-making purchasing portfolio in β=5 included from the energy exchange, then from the energy pool, and finally from bilateral contracts.
Coronavirus disease 2019 (COVID-19) is a rapidly spreading disease that has infected millions of people worldwide. One of the essential steps to prevent spreading COVID-19 is an effective screening of infected individuals. In addition to clinical tests like reverse transcription-polymerase chain reaction (RT-PCR), medical imaging techniques such as computed tomography (CT) can be used as a rapid technique to detect and evaluate patients infected by COVID-19. Conventionally, CT-based COVID-19 detection is done by an expert radiologist. In this paper, we detect COVID-19 subject-by-subject in addition to detecting it slice-by-slice. We present a deep learning Convolutional Neural Network (CNN) model that we have developed to detect chest CT images with COVID-19 lesions. Then, based on the fact that in an infected individual, more than one slice is involved, we determine and apply the best threshold to detect COVID-19 positive patients. We collect 5,225 CT images from 130 COVID-19 positive patients and 4,955 CT images from 130 healthy subjects. We use 3,684 CT images with COVID-19 lesions and their corresponding slices from the healthy control subjects to build our model. We use 5-fold-cross-validation to evaluate the model, in which each fold contains 26 patients and 26 healthy subjects. We obtain a sensitivity of 91.5%±6.8%, a specificity of 94.6%±3.4%, an accuracy of 93.0%±3.9%, a precision of 94.5%±3.5%, and an F1-Score of 0.93±0.04.
The interdependency between power and natural gas is so tight, especially where natural gas extraction is economical. Therefore, co-expansion planning is imperative for having efficient systems with minimum cost. In this paper, multistage stochastic co-expansion planning power and natural gas systems is presented. Natural gas load flow (NGLF) is modeled with the Weymouth equation, a non-linear and non-convex problem. In order to overcome the non-convexity of the problem, mixed-integer second-order cone programming (MISOCP) is utilized to solve NGLF. Furthermore, linepack constraints are added to exploit the natural gas stored in the pipeline for co-expansion planning, mainly at the transmission level where voluminous pipelines are used and linepack is noticeable. Natural gas storage is considered in the model to alleviate operational and investment costs. Decreasing the investment and operational costs of co-expansion planning is the objective of the model. Investment decisions can be taken more than once so that investment costs can be divided into the whole planning horizon to avoid an enormous budget at the beginning of the planning horizon. Power and natural gas load growth are taken into account as long-term uncertainties. The proposed model is applied in a real case of southwestern Iran. The results determine that by implementing the proposed model, the investment and operational costs decrease 6.3% and 14%, respectively.
Most of the studies on phenotype differences, including some diseases, are based on studying some specific positions in the genome called Single Nucleotide Polymorphism (SNP). Some SNPs alone and some by interacting with others, play an important role in any phenotype or specific disease. Various models, including the regression models, are designed and implemented for the prediction of these diseases. In this paper, three penalized logistic models including Ridge, Lasso and Elastic Net (EN), are used to predict the risk of a specific disease, while overcoming the limitation of the classic logistic regression on high-dimensional SNP datasets. The models are implemented on 10000 samples of the SNP datasets of OWKIN-Inserm Institute, which contains 18124 SNPs. Among these three, the Lasso model with minimizer lambda indicate higher accuracy (73.73%) and AUC (83.54%). The model is also less complex, since it eliminates less related features as much as possible and keeps only the most informative. Additionally, getting better results with Lasso indicates that multicollinearity is either not existence between variables or is low and can be neglected.
The automatic modulation recognition of the received signal is very attractive in both military and civilian applications. In recent years, deep learning techniques have received much attention since their excellent performance in signal, audio, image and video processing. This paper examines the feasibility of using deep learning algorithms on automatic recognition of the received radio signals' modulation schemes. Modulation recognition has been performed on eight digital modulation types with a signal-to-noise ratio (SNR) from -20dB to 20dB. First, a vanilla neural network is used to classify the type of modulation. Then, convolutional neural networks (CNN) and recurrent neural networks are applied for modulation recognition. These neural networks are widely used in image and signal processing applications. This is followed by designing the other architectures, including densely connected neural network (DenseNet), inception network, recurrent neural network (RNN), long-short term memory network (LSTM), and convolutional long short-term memory deep neural network (CLDNN) for modulation recognition problem, and their results are presented. During this investigation, a basic model is initially considered for each architecture, and then the network performance is studied by adjusting its parameters. The simulation results show that the proposed modified CLDNN model can provide an accuracy of 98% in high SNRs.
In this work, an aptamer-based electrochemical nanobiosensor has been developed for early detection of prostate cancer. Prostate-specific antigen (PSA) is the most common marker of prostate cancer, and this study aimes to detect this biomarker through electrochemical nanobiosensor-based aptamer, using nanostructures Graphene Oxide/graphitic Carbon Nitride/Gold nanoparticles (GO/g-C3N4/Au NPs). The aptamer chains are stabilized on the surface of a glassy carbon electrode (GCE) by Reduced Graphene Oxide, graphitic Carbon Nitride, and Gold nanoparticles (rGO/g-C3N4/Au NPs). To ensure the correct operation of the aptamer, a selectivity analysis was taken between five substances, and an electrochemical biosensor designed with good stability and high selectivity, diagnosis the desired analyte (PSA) compared to other materials. For characterization of aptasensor Electrochemical, CV, SQW and, EIS tests were performed to investigate the features of the synthesized nanoparticles, XRD, FTIR, SEM, TEM tests were carried out, and the results indicated that the used nanoparticles were well synthesized. The limit of detection (LOD) is 1.67 pg.ml-1 in hexafrrocyanide ([Fe(CN)6]-3/-4) media, this limit of detection is much lower and demonstrates the high ability of the nanobiosensor in early detection of PSA. The designed biosensor needs a short time (about 30 min) to detect the PSA as a symptom of prostate cancer.
In this paper, a speckle noise suppression algorithm based on the 2D Gaussian filter is addressed, which employs entropy to estimate the filter's variance effectively. Speckle noise is an inherent characteristic of coherent imaging systems which degrades the quality of resulting images. Gaussian filter is a traditional approach for speckle denoising; however, estimating its optimum variance is still a challenge. Many algorithms have been developed to estimate the optimum variance but they suffer from the type of noise or a predetermined variance. Our proposed method demonstrates an improved 2D Gaussian filter since it estimates the optimum variance of the filter in the context of differential entropy between the noisy and filtered images under different p-norms. This optimum variance is directly estimated from the speckle noise level of image and it differs for different types of noise and images. The optimization problem is numerically solved, and also, the value of the norm order is appropriately determined. The blind estimation of norm order is also accomplished based on the level of noise variance. Finally, the proposed method's performance is appraised, utilizing both standard and real ultrasound (US) images. The quality of filtered images is assessed through the qualitative and quantitative simulations in terms of peak signal to noise ratio (PSNR), correlation coefficient (CoC), structural similarity (SSIM), and equivalent number of looks (ENL). The experimental results reveal the proposed method's proficiency in contradiction to state-of-the-art despeckling methods through the capability of strong speckle noise removal and preserving the edges and local features.
Learning and reconstruction-based methods are the two main approaches to the solve single image super resolution (SISR) problem. In this paper, to exploit the advantages of both learning based and reconstruction based approaches, we propose a new SISR framework by combining them, which can effectively utilize their benefits. The external directional dictionaries (EDD) are learned from external high quality images. Additionally, we embeded the nonlocal means (NLM) filter and an isotropic total variation (TV) scheme in the reconstruction based method. We suggest a new supervised clustering scheme via curvelet based direction extraction method (CCDE) to learn the external directional dictionaries from candidate patches with sharp edges. Each input patch is coded by all the EDD. Each of the reconstructed patches under different EDD is applied with a weighted penalty to characterize the given input patch. To disclose new details, the local smoothness and nonlocal self-similarity priors are added on the recovered patch by TV scheme and NLM filter. Extensive experimental results validate the effectiveness and robustness of the proposed method comparing with the state-of- the-art algorithms in SISR methods. Our proposed schemes can retrieve more fine structures and obtain superior results than the competing methods with the scaling factors of 2 and 3.
