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Obtaining the optimal control low for nonlinear systems is one of the most active subjects in the control theory. Solutions become more complicate in the presence of physical constraints, especially input constrains. In this paper, a new method has been developed to design a constrained nonlinear controller. In this method, the performance index is developed based on the predictive approach. Then, an optimization problem is solved to obtain the optimal control law in the presence of input constraints. Two constrained optimization methods have been used in this paper. The KKT-based method provides an analytical solution for obtaining the control law. The other method is based on the genetic algorithm (GA) which is considered a numerical optimization technique. These methods are implemented on an example and the results are compared

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3D surface compression is a selected method for reduction of required memory and effective triangular mesh data transfer in networks with low bandwidth. In this paper, an improved technique for compression of triangular surfaces is introduced. It is based on centroidal Voronoi tessellation with density function. By choosing appropriate density function with curvature feature and Lloyd relaxation, vertex density in compressed mesh tends toward details in rough surfaces and prevents vertex redundancy and non-necessary vertex concentration in smoother surface areas. In post-processing step, non-linear Nelder-Mead optimization is applied for better vertex localization and reducing compression error in the simplified mesh. Our proposed method is compared with classic and modern techniques in recent studies. Implementation results show improvements in compression and accuracy of our method compared with available techniques.

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In this study, a dual-mode Delta Sigma modulator for both ‎WLAN and GSM standards has been designed. In order to reduce the power consumption and to ‎optimize the chip area, a reconfigurable structure, which can be separately tuned for these two different ‎applications, is used. The proposed modulator is based on a multi-bit VCO instead of a conventional flash ‎quantizer. Due to the possibility of lower voltage application and lower power consumption, the VCO-‎based Delta Sigma modulator can be a good choice especially for sub-100nm technology, where the ‎performance of the analog circuits becomes limited, and conversely superior switching rates can be ‎achieved. The idea of VCO-based Delta Sigma converter has been recently developed, however the multi-‎mode reconfigurable structure is proposed and analyzed for the first time in this research. The proposed ‎modulator is dedicated to operate with the two major WLAN and GSM standards. It is designed at ‎system level in general, but the VCO part is implemented at transistor level using 180nm CMOS ‎technology. The simulations’ results show a saving of at least 86% of power consumption in the ‎VCO part, in the GSM standard compared to WLAN.‎

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Steady-state and transient response of the active power filter based on instantaneous power theory compensation method is dependent on the accurately and quickly extract the DC component of load active power. In this paper, for improving the response of this method, the numerical filter based on recursive least squares with variable forgetting factor in order to separation of DC component of load active power is proposed. Unlike conventional low-pass filters, the feature of designed numerical filter is independent of load harmonic components, quick and precision of the answer. Also, using wavelet transform the performance of active power filter under the non-ideal voltage condition is improved. Finally, the second order delta-sigma modulation is used to generate the switching pulses. Simulation results on MATLAB / SIMULINK software shows that the proposed method is properly.

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In this paper, a new nonlinear model for magnetorheological (MR) damper based on the bouc-wen model is identified. This model including the hysteresis loop is linear with respect to the input current. This advantage makes it easy to use the proposed model in semi-active suspension systems as the inverse model. For better comparison, a polynomial model developed in the prevalent papers is also designed. The performance evaluation of two models shows that the proposed bouc-wen model has higher accuracy in the smaller velocity area. At the rest of the paper, an LQR controller designed for calculation of the active suspension force is integrated with the two identified inverse models. The simulation studies are carried out for active and semi-active suspension systems on a standard road. The results show that the simplified nonlinear bouc-wen model has better performance in controlling the semi active suspension system compared with the other models. In high amplitude velocities of the damper, the performance of two models is close to each other.

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The development of renewable energy sources, distributed generation, energy storage and nonlinear controllable loads in modern distribution networks has led to the consideration of the state estimation in intelligent and active distribution networks. The performance of the Energy Management Central's distribution network depends on the results of the state estimation. In this paper, two-stage estimation with the network reduction process is proposed. The number of distribution network meter is low, so obtaining accurate initial information from network conditions improves performance state estimation. The initial state estimation with the network reduction process to obtain accurate initial data is performed. The initial data are used as a measure to improve the performance of secondary estimation. This method solves the problem of scarcity of accurate measurements and improves the accuracy of the state estimation in distribution network. The results of the proposed methodology are demonstrated on the 69 nodes of IEEE  standard distribution network.

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3D human pose estimation is one of the most significant tasks in computer vision with wide range of applications. The works for estimating human pose initialized from 2D skeletal estimation from multiple data and has proceeded toward 3D skeletal estimation from minimum input information. In this paper, 3D human pose estimation from a single RGB image is investigated. The proposed work is considered as the ones which firstly estimate 2D pose and then lift the estimated 2D configuration to 3D space. Since most of the errors in this attitude are originated by inaccurate 2D pose inference, we have proposed a method for predicting more accurate 2D poses to obtain 3D poses with less errors. The proposed approach for estimating 2D pose has leveraged deep learning along with the information of the edge map. In other words, we have made use of edge features, which are hand-designed features, in order to guide the deep neural network in training and in learning the features in accordance with the defined objective. Experimental results have demonstrated less errors in 2D and consequently 3D pose estimation in Human3.6M and HumanEva-I benchmarks.

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Wind powers are very unstable in voltage fluctuations, especially in short circuit error and sharp and sudden voltage drops, which one of its main reasons is the use of induction generators in these power plants and thus need to reactive power and high magnetizing current. To improve the ride_through voltage from WECS in error conditions and damping the oscillations of the induction generator rotor, a UPFC is used the controller FOPID is used in UPFC controllers for the first time. Since FOPID has two parameters more than IOPID, it has recently attracted much attention (needing more work and research motivation) as it gives more flexibility to a control system designing and a better opportunity to adjust the system dynamics, especially, if a system is to be controlled be a fractional system. The investigations indicate that controller FOPID adjusted by presented PSO algorithm, show improved dynamic performance than traditional PID and feedback controller in a wide range of operating conditions.

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Human facial generation of example image is used as a requirement for biometric applications for the purpose of identifying individuals. In this paper, face generation consists of three main steps. In the first step, detection of significant lines and edges of the example image are carried out using nonlinear grayscale morphology. Then, hair areas are identified from the face of sample. The final step combines images from previous steps. Similarity and matching between synthesized face sketch and artistic sketch are compared with two methods of extracting features, Principle Component Analysis and Linear Discriminant Analysis, and time of the process is calculated. The experiments on the pair of CUHK database images show that the proposed method compared with state of the art methods such as: Eigen transformation, LLE, and MRF, has no computational complexity and creates a person's face with good quality and much less time. Matching of synthesized face sketch of the proposed method is achieved with a maximum value of 90% when Linear Discriminant Analysis is used to extract feature. The proposed method is also resistant to background effects and brightness of example images.

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The particle swarm optimizer (PSO) is a population-based metaheuristic optimization method that can be applied to a wide range of problems but it has the drawbacks like it easily falls into local optima and suffers from slow convergence in the later stages. In order to solve these problems, improved PSO (IPSO) variants, have been proposed. To bring about a balance between the exploration and exploitation characteristics of PSO, this paper introduces computationally fast and efficient IPSO algorithms based on a novel class of exponential learning factors (ELF-PSO). This class contains time-varying exponential learning factors (TELF), random exponential learning factors (RELF), self-adjusting exponential learning factors (SELF) and linear-exponential learning factors (LELF) strategies. Experiment is performed and compared with a set of well-known constant, random, time-varying and adaptive learning factors strategies on a suite of nonlinear benchmark functions. The experimental results and statistical analysis prove that ELF-PSO algorithms are able to solve a wide range of difficult nonlinear optimization problems efficiently. Also these results show that the proposed methods outperform other algorithms in most cases.

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In this paper, a fluidic biosensor with possibility to fabricate by Micro-Electro-Mechanical Systems (MEMS) technology is proposed for biomedical mass detection and lab-on-chip applications. This is designed by electromechanical coupling of harmonic micromechanical resonators with harmonic springers as a mechanical resonator array. It can disperse mechanical wave along the array by electrostatic method using interdigitate capacitors as actuators and sensors. It has some vital advantages like: low cost fabrication method, low fluidic interference damping effect, and high sensitivity with large absorbent area. In order to estimate the sensitivity of the proposed biosensor against the mass perturbation, the measurability of capacitance changes and fluidic interference damping effect, the stimulated analysis is conducted by COMSOL. It results, a suitable sensitivity and possibility to measure the biosensor outputs by available electronic instrumentations.

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This paper presents a new circuit to configure power amplifier (PA) for return-to-zero on-off-keying (RZ-OOK) transmitters. The proposed PA works as a multimode structure with configurable data rate and output power. The programmable data rate function is achieved by duty cycle adjustment of input data and producing input RZ-data by a simple circuit, which leads to a linear scale of data rate with power consumption. This implies that any desired level of output power can be transmitted with different power consumption according to the power budget. The RZ-data is also utilized to perform the output power reconfiguration. The PA represents data rate of 0.3Mb/s to 3Mb/s and it can deliver output power level from -23dBm to 0dBm. During data rate adjustment, power consumption varies from 0.099mW to 0.99mW when the output power is 0dBm. Also, PA consumes 0.07mW to 0.99mW at the output power tuning range with a data rate of 3Mb/s.

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Sign language recognition systems help deaf people to access various media. In this paper, the Leap Motion Controller (LMC) and the image of the hand are exploited for sign language recognition. The LMC provides 3D position of the hand joints. The first set of features are extracted from the data provided by the LMC. When the hand is not located in vertical view of the LMC or when the hand posed like a fist, the precise position of the hand joints is not recognizable. The second feature extracted from hand image helps most hand gestures be recognized precisely. The second feature includes histogram of oriented gradients and the distance of the hand contour form the center of the hand. Also, a dataset composed of variant American sign language gestures is created which includes 64000 samples. In recognition stage, random forest is applied which is a good option for large datasets. The experimental results show that the proposed method performs better than similar methods.
 

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In this paper, a real-time interactive high resolution soft tissue modeling is implemented that enriches a coarse model in a data-driven approach to produce a fine model. As a preprocess step, a set of corresponding coarse and fine models are simulated for the database. In the test step, by using a regressor, the coarse model in the test set is compared to the coarse models in the training set and the blending weights are assigned to the training coarse models. These weights are used for approximating the fine model as a linear combination of the corresponding fine models in the train set. To decrease the computational complexity, assuming that applying a force on the tissue results in a local deformation, a feature extraction algorithm is proposed that considers the displacements of the contact node and its neighbor nodes and ignores the rest. This results in a low dimensional feature vector and decreases the computational complexity. In order to compute the blending weights, a nonlinear regressor with Gaussian kernel is leveraged. To eliminate the artefacts resulting from negative weights, a nonnegative least square algorithm is used for regression. Simulation results of applying the proposed method on two soft tissue models are investigated regarding the reconstruction accuracy, computational complexity and running time.

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Nowadays, various kind of smart phones and 3D software are produced, which require large memory space. However, large number of vertices and faces in 3D models not only decrease the speed of sending and receiving of data but also can make problem in systems with low memory space. In this paper, an anisotropic re-meshing of 3D models is proposed. In this algorithm, the Nyquist theorem is employed for sampling from each selected segment of the mesh, locally. Then, the re-triangulation algorithm is applied to the selecte samples to construct the simplified mesh. In order to construct a high quality mesh from the remeshed model, a non linear subdivision is employed. The achieved results show that the algorithm can reduce the number of vertices and faces beside preserving details of model. The proposed method is also compared to the state-of-the-art algorithms are used in simplification studies, the outcomes illustrate the ability of the proposed method in producing high quality models.
 

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Since the introduction of the first silicon solar cell, there have been steady improvements in its performance parameters such as light trapping, solar absorption, cell efficiency and manufacturing costs. In thin silicon cells, some of the light photons that are not absorbed by the semiconductor are always lose in various ways. The diffraction grating causes the photons to travel a longer light path due to the collision with this structure, which increases the length of the light path of the photons and cell absorption, that thus improving cell efficiency. In each of the mentioned structures, optimal materials and geometric properties have been used to achieve maximum efficiency of silicon cells. Intelligent optimization methods have been used to find the optimal geometric parameters for the structure. In choosing search methods from the two algorithms particle swarm optimization and genetics and creating a combination of the both, the positive feature of both algorithms was used to achieve the best answer. This combination has produced very positive results, which thereby, 23.293 efficiencies and 35.41 mA/cm2 short circuit current were obtained.

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Face plays an important role in visual communication. By looking at the face, it can be automatically extracted many non-verbal messages, such as identity, intention, and emotion. In computer vision, localization of the key points of the face is usually a key step for automatic extraction of face information, and many facial analysis techniques are built on the precise recognition of these embossed. Facial landmark detection and alignment in images with occlusion is a very important and challenging task in many visual and image processing tasks. In this paper, a comprehensive method for initialization and alignment of facial landmark through training of local binary features (LBP) and histogram orientated gradient (HOG) and a facial landmark detection method using robust cascade pose regression, which are specified as pixel difference features of landmarks, is introduced. At first, by analyzing the correlation of the local binary pattern histogram (LBP) and then by using histogram orientated gradient, the features of the training images are obtained. For the test image using these features the instructional images are estimated as optimal guide points. In the test stage, according to initialization of the image, the selection of the appropriate feature for the image is used to speed up the process, which means the number of steps to be chosen for each image is better. A strong cascade mode regression is then used to adjust the face, and a local principle is applied to learn the features of the guide points. The local principle helps to learn a set of highly distinctive binary features for the face guide points independently; these local binary features are used to jointly learn the cascade mode regression for the final output. The results show that the initialization used in this work has increased the accuracy of the estimation in the cascade state regression and has obtained better results than the random initialization.

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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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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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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.