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Open Access Article
1 - Coexisting Behaviors Analysis and Chaos-Based Secure Communication Scheme by a Novel High-Order Nonlinear Autonomous System
Javad Mostafaee Saleh Mobayen Behrouz Vaseghi Mohammad VahediThis paper constructs a new 6–D hyper–chaotic system with complex dynamic behaviors for se-cure communication scheme. We analyze the chaotic attractor, bifurcation diagram, equilibrium points, Poincare map, Lyapunov exponent behaviors, and Control parameter. MoreThis paper constructs a new 6–D hyper–chaotic system with complex dynamic behaviors for se-cure communication scheme. We analyze the chaotic attractor, bifurcation diagram, equilibrium points, Poincare map, Lyapunov exponent behaviors, and Control parameter. The more nonlinear the autonomous system is and the higher the parametric sensitivity it is, the more performative it will be and the more difficult it will be to decode. We will show that the designed system will have attractive and different behaviors due to very small changes in control parameters, which is a sign of the high sensitivity of the system. Then, with the construction of master-slave systems and the design of a new terminal sliding mode controller, the application of the hyper-chaotic system in synchronization and transmission of secure communications is shown. Finally, using the MATLAB simulation, the results are confirmed for the new hyper–chaotic system Manuscript profile -
Open Access Article
2 - Locating Fast Charging Stations for Plug-In Hybrid Electric Vehicles in Distribution Networks with Considering Load Uncertainties Using a New Multi-Agent Harmony Search Algorithm
Razieh Heidari Alimorad Khajehzadeh Mahdiyeh EslamiThe use of Plug-in Hybrid Electric Vehicles (PHEV) can be known as an efficient factor for reducing the pollution caused by fossil fuels. It is obvious that with increasing the number of these vehicles, charging stations are needed to be established on the network. Ther MoreThe use of Plug-in Hybrid Electric Vehicles (PHEV) can be known as an efficient factor for reducing the pollution caused by fossil fuels. It is obvious that with increasing the number of these vehicles, charging stations are needed to be established on the network. Therefore, in this paper, the problem of locating fast charging stations for Plug-in Hybrid Electric Vehicles using a new Multi-agent Harmony Search Algorithm is completely studied. According to the fact that the load uncertainties caused by charging hybrid electric vehicles are an effective and important factor to determine the number and also suitable locations of charging stations, the Poison and normal distributions are here used for considering uncertainties about the number of hybrid electric vehicles per hour and the charging demand for each hybrid electric vehicle, respectively. To study the problem in this paper, first, 10,000 different scenarios are made per hour and then, using Latin Hypercube, the number of the scenarios of each hour is dropped to 10. Finally, a new Multi-Agent Harmony Search Algorithm is applied to reduce network losses in different scenarios. The obtained results show that determining the number and the suitable locations for vehicle charging stations can greatly reduce the risk of overloading in the network caused by charging hybrid electric vehicles Manuscript profile -
Open Access Article
3 - Brain Tumor Detection Using Deep Transfer Learning Method
Alireza Fazelnia Hassan Masoumi Mohammad Fatehi Jasem JamaliAccurate brain tumor MR images detection plays an important role in diagnosis and treatment decision making. The machine learning methods for classification only uses low-level or high-level features, to tackle the problem of classifications using some handcrafted featu MoreAccurate brain tumor MR images detection plays an important role in diagnosis and treatment decision making. The machine learning methods for classification only uses low-level or high-level features, to tackle the problem of classifications using some handcrafted features. Development on deep learning, transfer learning and deep convolution neural networks (CNNs) has shown great progress and has succeeded in the image classification task. Deep learning is very powerful for feature representation. In this study, deep transfer learning method for features extraction and detection is used that it does not use any handcrafted features, and needs minimal preprocessing. Transfer learning is a method of transferring information during training and testing. In this study, features extraction from images with pre-trained CNN method, namely, GoogLeNet, VGGNet and AlexNet, for tumor detection is used. The accuracy of tumor detection is 99.84%. The results show that our method, shows the best accuracy for detections tumor Manuscript profile -
Open Access Article
4 - Switching Pattern for Transformer Cascaded Inverter in Dynamic Voltage Restorer Structure
Shirin Soltani Majid Dehghani Majid MoazzamiSensitive loads cannot tolerate voltage sags and swells in power distribution networks and lots of problems are engendered for them. Therefore, dif f e rent instruments are pla nn ed to compensate volt a ge swells and sags. Between them dynamic voltage restorer (DVR) h MoreSensitive loads cannot tolerate voltage sags and swells in power distribution networks and lots of problems are engendered for them. Therefore, dif f e rent instruments are pla nn ed to compensate volt a ge swells and sags. Between them dynamic voltage restorer (DVR) has found special import a n c e, because it operates better than others for this purpose and restores voltage to its initial value conveniently. A DVR is a power-electronic controller that can protect sensitive loads from disturbances in the supply system. The DVR is installed in series to the network. In this paper 9 level cascaded inverters with cas ca d ed transfor m e r s are used in DVR internal circuit, but the difference in magnetic flux in cascading transformers has adverse effects on the plan. Therefore, a new switching pattern is used Manuscript profile -
Open Access Article
5 - Deep Learning Method for Sleep Stages Classification by Time-Frequency Image
Mohammad Moradi Mohammad Fatehi Hassan Masoumi Mehdi TaghizadehClassification of sleep stages is an important method in diagnosing sleep problems. This is done by experts, based on visual inspection of bio-signals such as EEG, EOGs, ECG, EMG, etc. The deep learning method is one of the newest and most important methods for analyzin MoreClassification of sleep stages is an important method in diagnosing sleep problems. This is done by experts, based on visual inspection of bio-signals such as EEG, EOGs, ECG, EMG, etc. The deep learning method is one of the newest and most important methods for analyzing, separating, and detecting images, which is becoming more and more widespread. In this paper, for the first time, the deep learning method is used to extract the EEG signal time frequency image to classify sleep stages. Here, from the one channel of EEG signal, the time frequency image of the signal is extracted and then feature extraction using the deep learning method is done. Finally, without changing the nature of the signal, the sleep steps are detected with acceptable accuracy. In this article, for the first time, time-frequency image (TFI) was provided from the one channel of the EEG signal. Then, using the AlexNet convolutional neural network by the Wigner-Ville distribution method (ANWVD), using Deeper layers contain higher-level features were extracted, and finally, using the SVM classifier, the sleep steps were classified with acceptable accuracy. The accuracy 97.6% and the time of calculations 0.36s have been reached Manuscript profile -
Open Access Article
6 - A Wide-Range-Gain Switched-Coupled-Inductor Buck-Boost ZETA Converter
Mustafa Okati mahdiyeh Eslami Mehdi Jafari ShahbazadehIn the present work, an improved buck-boost converter with the possibility of flexible design is introduced to gain required voltage and easily adapt to any wide-range-gain application. Switched-coupled-inductors are employed as the key in this converter to achieve the MoreIn the present work, an improved buck-boost converter with the possibility of flexible design is introduced to gain required voltage and easily adapt to any wide-range-gain application. Switched-coupled-inductors are employed as the key in this converter to achieve the wide-range-gain buck-boost operation through adjusting their turn ratio. The set of coupled-inductors are integrated with the conventional ZETA converter to be used in place of buck-boost cell of its original circuit, which relies on ZETA converter. This converter increased the voltage gain degree of freedom to design a wide-range gain operation for any required application. In comparison with the conventional competitors, the proposed converter is required to use smaller passive components with lower voltage and current ratings for its semiconductors. All these features and claims are investigated theoretically and then, are evaluated experimentally by implementing a test rig with the rated power of 200W Manuscript profile
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