فهرست مطالب

Journal of Artificial Intelligence in Electrical Engineering
Volume:10 Issue: 39, Autumn 2021

  • تاریخ انتشار: 1401/09/29
  • تعداد عناوین: 6
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  • Masoud Samaei, Maghsoud Jahani Pages 1-22

     In the last decade, overcoming the detrimental effects of non-recyclable materials is became a global concern. Polyethylene-Terephthalate (PET) is one of the non-recyclable materials used to produce liquid containers. The use of such materials in soil improvement has acquired importance. This study developed a tree-based predictive model for shear strength improvement caused by PET elements. To predict shear strength, a series of parametric studies led to the development of four models, i.e., DT, RF, XGB, and AdaBoost. These parametric studies aimed to determine which hyperparameters are the best for tree-based models. In spite of the fact that DT and RF are among the most powerful prediction models, XGBoost and AdaBoost offer better results. Due to their ability to learn from mistakes, they are more robust than DTs and RFs, which are semi-stochastic. According to the AdaBoost and XGBoost models, the AdaBoost model with R2train=0.99 and R2test=0.98 performed better when compared with the XGBoost model.

    Keywords: Polyethylene-Terephthalate, AdaBoost, XGBoost models
  • Razieh Keshavarzian Pages 23-30

    Compressed sensing (CS) is a new and promising framework for simultaneous sampling and compression of signals at sub-Nyquist rates. Under certain conditions, the signal can be reconstructed exactly from a small set of measurements via solving an optimization problem. In order to make this possible, compressed sensing is based on two principles of sparsity and incoherence. Compressed sensing takes advantage of the fact that most signals in nature are sparse or compressible, which means that when expressed in a suitable basis called as sparsifying basis, they will have a sparse representation. In the CS, the sparse signal is sampled by a non-adaptive linear sampling matrix. Then, based on the limited measurements obtained from the sampling matrix and using a non-linear algorithm, the original signal is reconstructed. The sparse signal reconstruction problem in the CS is an optimization problem that various algorithms have been proposed to solve it. The compressed sensing has a great application potential and can be used in a wide range of applications. Recently, deep learning has been used to solve the CS problem and its medical applications. In this paper, the generalities of compressed sensing are presented and CS reconstruction algorithms are reviewed. Also, the application of CS in magnetic resonance imaging (MRI) are investigated.

    Keywords: Compressed sensing, Reconstruction algorithm, Sampling matrix, Sparsity
  • Ghiyam Eslami Pages 31-38

    In this paper, it is discussed how Zadeh’s extension principle (ZEP) can be used for uncertainty analysis of a system. For this end, basic concepts of the fuzzy mathematics including fuzzy sets, fuzzy numbers and ZEP are briefly presented. A comparison made among the results obtained by the sensitivity analysis, ZEP and Monte Carlo (MC) methods. It is shown that ZEP gives the same outputs as the MC method and is in full agreement with the concept of “uncertainty”. The sensitivity analysis result is not the same as the uncertainty analysis and, often results in smaller range for the output parameters.

    Keywords: Uncertainty, Sensitivity, Fuzzy mathematics, Zadeh’s extension principle, Monte Carlo method
  • Behnam Kazempour Pages 39-45

    An electro-optic tunable single and multi-channel optical filter based on one-dimensional defective photonic crystal (1DDPC) structure is proposed. A couple of externally tunable defects in arrangement of (AB)5D1(BA)D2(BA)5, where A and B are dielectric materials, D1 and D2 are the tunable defects are used. The defects are composed of the ferroelectric LiNbO3 crystals and two pairs of thin Ag layers to make the voltage connections. With this arrangement it is possible to apply different external biases that facilitate the tunability and even more adjustability of the channel frequencies. Depending on the thickness of defect layers, a single or multi resonant peak can be induced inside the photonic band gap which can be employed to filter channels. About 37 nm (46 nm) of blue shift for 300 V and 37 nm (37 nm) of red shift for -300 V biases are observed without (and with) loss incorporation. An important and notable effect that happened was the dispersion loss of the structure due to metal layers is compensated by the negative biases. Our proposed structure can be good candidate to design an externally tunable optical filter and a voltage sensor with potential applications in all-optical signal processing and information communications fields.

    Keywords: Photonic crystal, tunable optical filter, ferroelectric
  • MohammadEsmaeil akbari, alireza mangouri, Sajjad Atazadeh, sahand akbari Pages 46-51

    With the advent of digital systems and significant progress in all fields of industry experts inthe field about the spread and use associated with this sector have fallen so every dayinteresting events and far-fetched in this field. In this article we will discuss the first to introducedigital systems and the features they'll say,weneed to investigate the use of digital systems inthe field of protection and safety particularly smart locks will pay and in the end, we'reintroducing intelligent system design much needed in this area to solve it also criticized thesystem and the strengths and weaknesses ofwill say it.

    Keywords: Intelligent systems, intelligent lock, auto lock, door locks, building protection, security
  • Tohid malekzadeh Dilmaghani Pages 52-61

    The coordinates of the stations along with their velocity field and determination of the strain field are the most important parameters in determining the surface deformation of the shell. Preliminary estimation of the Earth's crust velocity field, especially in seismic areas and near faults, can provide valuable information on the geodynamic structure as well as how faults operate. Today, this is done by geodynamic network stations. Lack of sufficient number of stations around active faults and tectonic zones is one of the main problems in estimating velocity and strain in these sensitive areas. This factor can cause many problems in studying the mechanism of active and tectonic faults in the relevant areas.Different solutions can be offered to solve such a problem. Paying attention to the reliability of the solution, its accuracy and efficiency, how to do it and most importantly the discussion of time and cost can be important and fundamental factors in this work. Therefore, the main focus of this project is to provide a method with high reliability in results, low cost and high execution speed. Using different interpolation methods such as multilayer artificial neural network (MLP-ANN) or accurate statistical and mathematical methods such as kriging, collocation and polynomial methods can achieve velocity and strain field, especially in areas Be sensitive and responsive. The purpose of this paper is to use modern and accurate methods to estimate and determine the velocity field and displacement field as well as strain tensor parameters in 3D. Artificial neural network (ANN) method with particle mass optimization training (PSO) algorithm for spatial estimation of crustal velocity changes in Iran has been studied. GPS measurements of Central Alborz network stations have been used to evaluate the method.

    Keywords: Artificial Neural Network, Speed Field, GPS Observations, Central Alborz