Signal Processing and Renewable Energy
Volume:3 Issue: 4, Autumn 2019

In this paper, unbalanced, nonlinear and asymmetric ohmic-inductive three-phase load are supplied by the NPC inverter based on the model named Model Predictive Control (MPC). The MPC is designed for the compensator. The basic principles of MPC as well as MPC model are described in this paper. The design of the proposed controller along with the MPC control steps for controlling the power converter and modeling the power converter are provided to determine all possible switching conditions. Also, the cost function that describes the optimal behavior of the system is formulated. Further, discrete models are defined when future behavior predicts controlled variables. Then the switching modes of the NPC inverter are presented and the control scheme is described based on the control schema for the MPCs with the purpose of power converters and drives, variables prediction and cost function definition. The system performance is evaluated based on the proposed method in various loads including symmetric load and symmetric reference flow, unbalanced load, and symmetric phase voltage. The simulation results indicate the optimal performance of the proposed method in supplying three-phase load demand with optimal quality so that the current distortion is low and the inverter output voltage is also multi-level. In addition, considering asymmetric ohmic-inductive loadand step variation in load, the harmonic distortion of the flow is 0.5% in a phase and the output voltage of inverter is also extracted multilevel. The best advantage of the proposed approach compared to SVM methods is controlling without the use of 3D Space Vector. This makes it easier to compute and implement easier than the 3D-SVM.

The purpose of this study is to predict catching diabetes in patients having the β-thalassemia. Here, an intelligent system predicts risk of catching diabetes in patients having major thalassemia and intermedia, using multilayer perceptron according to the thalassemia dataset, ZAFAR. In this work, the clinical characteristics of 255 patients, having β-thalassemia, have been studied in two groups of diabetic and non- diabetic patients. Research data includes gender, age, parental family relationship, type of thalassemia, the spleen, gall bladder and liver condition, the age of blood transfusion, blood transfusion intervals, the number of received blood units in each period, the condition of iron overload, the age of start iron chelation therapy, the average of heart T2 rates, the liver T2 and LIC, the number of years having MRI, serum ferritin level, glucose and the number of years the patient was observe. The accuracy of artificial neural network in diagnosing the patients having thalassemia and exposing diabetes would be 80.78% according to the collected dataset. The accuracy rate of the system is 89.48%, using this intelligent system and doing pre-processing. This system has a desirable performance in predicting catching diabetes in patients having β-thalassemia. The best mean square error in this model is 0.07 which results in reducing learning time and increasing the system accuracy. Based on the obtained results from this system, there are two important factors in catching diabetes by the patients having β-thalassemia; first the number of years passed by patient with the high serum ferritin level and second is the glucose level rate in previous years long. The purpose of this study is to predict catching diabetes in patients having the β-thalassemia. Here, an intelligent system predicts risk of catching diabetes in patients having major thalassemia and intermedia, using multilayer perceptron according to the thalassemia dataset, ZAFAR. In this work, the clinical characteristics of 255 patients, having β-thalassemia, have been studied in two groups of diabetic and non- diabetic patients. Research data includes gender, age, parental family relationship, type of thalassemia, the spleen, gall bladder and liver condition, the age of blood transfusion, blood transfusion intervals, the number of received blood units in each period, the condition of iron overload, the age of start iron chelation therapy, the average of heart T2 rates, the liver T2 and LIC, the number of years having MRI, serum ferritin level, glucose and the number of years the patient was observe. The accuracy of artificial neural network in diagnosing the patients having thalassemia and exposing diabetes would be 80.78% according to the collected dataset. The accuracy rate of the system is 89.48%, using this intelligent system and doing pre-processing. This system has a desirable performance in predicting catching diabetes in patients having β-thalassemia. The best mean square error in this model is 0.07 which results in reducing learning time and increasing the system accuracy. Based on the obtained results from this system, there are two important factors in catching diabetes by the patients having β-thalassemia; first the number of years passed by patient with the high serum ferritin level and second is the glucose level rate in previous years long. The purpose of this study is to predict catching diabetes in patients having the β-thalassemia. Here, an intelligent system predicts risk of catching diabetes in patients having major thalassemia and intermedia, using multilayer perceptron according to the thalassemia dataset, ZAFAR. In this work, the clinical characteristics of 255 patients, having β-thalassemia, have been studied in two groups of diabetic and non- diabetic patients. Research data includes gender, age, parental family relationship, type of thalassemia, the spleen, gall bladder and liver condition, the age of blood transfusion, blood transfusion intervals, the number of received blood units in each period, the condition of iron overload, the age of start iron chelation therapy, the average of heart T2 rates, the liver T2 and LIC, the number of years having MRI, serum ferritin level, glucose and the number of years the patient was observe. The accuracy of artificial neural network in diagnosing the patients having thalassemia and exposing diabetes would be 80.78% according to the collected dataset. The accuracy rate of the system is 89.48%, using this intelligent system and doing pre-processing. This system has a desirable performance in predicting catching diabetes in patients having β-thalassemia. The best mean square error in this model is 0.07 which results in reducing learning time and increasing the system accuracy. Based on the obtained results from this system, there are two important factors in catching diabetes by the patients having β-thalassemia; first the number of years passed by patient with the high serum ferritin level and second is the glucose level rate in previous years long.

Autism, also called autism spectrum disorder (ASD), is a complicated condition that includes problems with communication and behavior. It can involve a wide range of symptoms and skills. ASD can be a minor problem or a disability that needs full-time care in a special facility. People with autism have trouble with communication. They have trouble understanding what other people think and feel. This makes it hard for them to express themselves, either with words or through gestures, facial expressions, and touch. According to the Centers for Disease Control, autism affects an estimated 1 in 59 children today. Indicators of autism usually appear by age 2 or 3. Some associated development delays can appear even earlier, and often, it can be diagnosed as early as 18 months. Research shows that early intervention leads to positive outcomes later in life for people with autism. In this paper, we describe an Autism detection algorithm that runs over electroencephalography (EEG) signals. Because this technique comprises different parameters that significantly affect the detection performance, we will use genetic algorithms (GAs) to optimize these parameters to improve the detection accuracy. And in the end, the results have been compared statistically by the T-test. In this paper, we describe the GA setup. EEG signals of 20 children with Autism and 20 healthy children aged 6 to 12 years have been obtained. The results have been compared. Lower correlation levels between resources of the left hemisphere of the brain especially C3 channels region in autistic children compared with healthy subjects have been observed. Also, the average energy of theta frequency band in C3 and F3 channels for children with autism was lower than that in healthy people and this criterion was higher in the gamma frequency band.

In this paper, behavior of MV cross-linked polyethylene insulation and homogenization of its electric field against the current flow, resulting from lightning stroke to arrest, is studied. In addition, it is shown that using two conductors with different structures field distribution can be homogenized and smoothed in the cable. A theoretical model is proposed for field distribution in the cable and the proposed cable. There is only a radial field (along x) inside a coaxial cable which its external conductor is grounded and varies with distance. That is, Schwager coefficient inside the coaxial cable is a function of this radius, and purpose of this study is to increase insulation breakdown voltage. For instance, if Schwager coefficient reaches 1, no insulation is required, and cable has a maximum breakdown voltage between middle conductor and external conductor. Therefore, by increasing Schwager coefficient, less insulation can be used. By reaching a Schwager coefficient of 58%, field inside the cable becomes homogenized and uniform; insulation can be used for the remained 42%. Now, if this insulation is made of refractory material, cable would provide high insulation strength. Among other advantages of refractory material, high melting point can be mentioned as a result of which they become pasty under critical and boundary conditions. One of the best insulations is XLPE or polyethylene with cross connections between its layers (cross-linked). In this study, critical and boundary condition for coaxial cable are applying lightning stroke of 1.2/50µsec. That is, wave front is 1.2ms and half wave front is 50µs. This current induces a voltage in devices including descending conductor between the aerial terminal of the arrester and ground connection of the arrester which might be several million volts. After obtaining field equations in the studied cable, characteristics of waveforms and creation of these waveforms are described in Simulink.

The purpose of this research is to design and validate the Business Intelligence Model (BIM) based on the ambidexterity approach. The research method is descriptive (non-experimental) and correlation research project is a structural equation type with the least squares. Participants in this study were employees of the Social Security Organization in Tehran. The participants in this study were the managers of the houses of municipality district of Tehran. In this research, the number of members of the statistical population is formed by 600 managers of the houses of municipality district of Tehran. Based on the Cochran formula, 234 managers of the houses of Tehran municipalities were selected as the statistical sample. In this research, a multi-stage cluster sampling method was used. To measure the variables of the research, a researcher-made questionnaire was prepared and adjusted. Research findings indicate that exploration management and organizational culture have a positive and significant effect on entrepreneurship orientation. Also, probe management and organizational culture have a positive and significant effect on flexibility. Further, the effect of entrepreneurial orientation and flexibility on exploratory intelligence is considerable as well as the impact of entrepreneurial orientation and flexibility on utilitarian intelligence.t. Having said that, however, the moderating role of technology absorption capacity in relation to entrepreneurial orientation and flexibility on exploratory intelligence and utilitarian intelligence is the subject that worth to be highlighted.

The data-driven empirical mode decomposition (EMD) method is designed to analyze the non-stationary signals like biomedical signals originating from nonlinear biological systems.  EMD analysis produces a local complete separation of the input signal in fast and slow oscillations along with the time-frequency localization. EMD extracts the amplitude and frequency modulated (AM–FM) functions, i.e. the intrinsic mode functions (IMFs), that have been widely used for biomedical signal de-noising, de-trending, feature extraction, compression, and identification. To overcome the problems of EMD, like mode mixing, new generations of EMD have been proposed and applied for biomedical signal analysis. Besides, the bidimensional EMD (BEMD) was introduced and improved for image processing. BEMD and its modified versions have been widely used for medical image de-noising, de-speckling, segmentation, registration, fusion, compression, and classification. In this paper, a review of notable studies in the biomedical signal and image processing based on EMD or BEMD method and their modified versions were considered. The studies on using EMD and its modified versions for mono-dimensional and bidimensional(image) signal processing showed the capabilities of the improved EMD and BEMD methods on biomedical signal and image processing.