Search for: perceptron
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    Prediction of shear strength parameters of hydrocarbon contaminated sand based on machine learning methods

    , Article Georisk ; 2020 Rezaee, M ; Mojtahedi, S. F. F ; Taherabadi, E ; Soleymani, K ; Pejman, M ; Sharif University of Technology
    Taylor and Francis Ltd  2020
    The objective of this paper is to predict the effect of hydrocarbon contamination on the shear strength parameters of sand by using various machine learning platforms. Multilayer perceptron, support vector machine, random forest, gradient boosting method, and multi-output support vector machine were methods used to predict the hydrocarbon contamination impacts on the internal friction angle and cohesion of contaminated sand. Random forest exhibited the best results for cohesion, whereas, for the friction angle, the gradient boosting method outperformed other approaches. Moreover, the multi-output support vector machine yielded better results than those pertaining to a single support vector... 

    Detecting and estimating the time of a single-step change in nonlinear profiles using artificial neural networks

    , Article International Journal of Systems Assurance Engineering and Management ; 2021 ; 09756809 (ISSN) Ghazizadeh, A ; Sarani, M ; Hamid, M ; Ghasemkhani, A ; Sharif University of Technology
    Springer  2021
    This effort attempts to study the change point problem in the area of non-linear profiles. A method based on Artificial Neural Networks (ANN) is proposed for estimating the real time of a single step change. The feature vector of the proposed Multi-Layer Perceptron (MLP) is based on Z and control chart statistics for nonlinear profiles. The merits of the proposed estimator are evaluated through simulation experiments. The results show that the estimator provides an accurate estimate of the single step change point in non-linear profiles in the selected case problem. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of... 

    The simulation of microbial enhanced oil recovery by using a two-layer perceptron neural network

    , Article Petroleum Science and Technology ; Vol. 32, Issue. 22 , 2014 , pp. 2700-2707 ; ISSN: 10916466 Morshedi, S ; Torkaman, M ; Sedaghat, M. H ; Ghazanfari M.H ; Sharif University of Technology
    The authors simulated a reservoir by using two-layer perceptron. Indeed a model was developed to simulate the increase in oil recovery caused by bacteria injection into an oil reservoir. This model was affected by reservoir temperature and amount of water injected into the reservoir for enhancing oil recovery. Comparing experimental and simulation results and also the erratic trend of data show that the neural networks have modeled this system properly. Considering the effects of nonlinear factors and their erratic and unknown impacts on recovered oil, the perceptron neural network can develop a proper model for oil recovery factor in various conditions. The neural networks have not been... 

    A genetic optimization algorithm and perceptron learning rules for a bi-criteria parallel machine scheduling

    , Article Journal of the Chinese Institute of Industrial Engineers ; Volume 29, Issue 3 , 2012 , Pages 206-218 ; 10170669 (ISSN) Fazlollahtabar, H ; Hassanzadeh, R ; Mahdavi, I ; Mahdavi Amiri, N ; Sharif University of Technology
    This work considers scheduling problems minding the setup and removal times of jobs rather than processing times. For some production systems, setup times and removal times are so important to be considered independent of processing times. In general, jobs are performed according to the automatic machine processing in production systems, and the processing times are considered to be constant regardless of the process sequence. As the human factor can influence the setup and removal times, when the setup process is repetitive the setup times decreases. This fact is considered as learning effect in scheduling literature. In this study, a bi-criteria m-identical parallel machines scheduling... 

    Short term load forecasting of Iran national power system using artificial neural network

    , Article 2001 IEEE Porto Power Tech Conference, Porto, 10 September 2001 through 13 September 2001 ; Volume 3 , 2001 , Pages 361-365 ; 0780371399 (ISBN); 9780780371392 (ISBN) Barghinia, S ; Ansarimehr, P ; Habibi, H ; Vafadar, N ; Sharif University of Technology
    One of the most important requirements for the operation and planning activities of an electrical utility is the prediction of load for the next hour to several days out, known as short term load forecasting (STLF). This paper presents STLF of Iran national power system (INPS) using artificial neural network (ANN). The developed program is based on a four-layered perceptron ANN building block. The optimum inputs were selected for the ANN considering historical data of the INPS. Instead of conventional back propagation (BP) methods, Levenberg-Marquardt BP (LMBP) method has been used for the ANN training to increase the speed of convergence. A data analyzer and a temperature forecaster are... 

    Prediction of CO2 loading capacity of chemical absorbents using a multi-layer perceptron neural network

    , Article Fluid Phase Equilibria ; Volume 354 , September , 2013 , Pages 6-11 ; 03783812 (ISSN) Bastani, D ; Hamzehie, M. E ; Davardoost, F ; Mazinani, S ; Poorbashiri, A ; Sharif University of Technology
    A feed forward multi-layer perceptron neural network was developed to predict carbon dioxide loading capacity of chemical absorbents over wide ranges of temperature, pressure, and concentration based on the molecular weight of solution. To verify the suggested artificial neural network (ANN), regression analysis was conducted on the estimated and experimental values of CO2 solubility in various aqueous solutions. Furthermore, a comparison was performed between results of the proposed neural network and experimental data that were not previously used for network training, as well as a set of data for binary solutions. Comparison between the proposed multi-layer perceptron (MLP) network and... 

    A complexity-based approach in image compression using neural networks

    , Article World Academy of Science, Engineering and Technology ; Volume 35 , 2009 , Pages 684-694 ; 2010376X (ISSN) Veisi, H ; Jamzad, M ; Sharif University of Technology
    In this paper we present an adaptive method for image compression that is based on complexity level of the image. The basic compressor/de-compressor structure of this method is a multilayer perceptron artificial neural network. In adaptive approach different Back-Propagation artificial neural networks are used as compressor and de-compressor and this is done by dividing the image into blocks, computing the complexity of each block and then selecting one network for each block according to its complexity value. Three complexity measure methods, called Entropy, Activity and Pattern-based are used to determine the level of complexity in image blocks and their ability in complexity estimation... 

    Long-Term Water Demand Forecasting for the Tehran City under Uncertainties

    , M.Sc. Thesis Sharif University of Technology Miraki, Ghasem (Author) ; Abrishamchi, Ahmad (Supervisor)
    Forecasting model of water consumption amounts could be used in order to manage water resources for future condition of city. In this thesis, a model for forecasting water demand for Tehran has been presented by evaluating regression models and intelligent models. In this study, uncertainties which are connected to climate and population changes are taken into account. The considered variables include minimum, maximum and medium temperature, precipitation and solar radiation. Considering objectives of this thesis and various forecasting methods and their advantages and regional conditions of Tehran, in addition to regression analysis, perceptron neural network, probabilistic neural network... 

    Remote Sensing of Hyperspectral Images for Detection Surface Mines

    , M.Sc. Thesis Sharif University of Technology Motahari Kelarestaghi, Alireza (Author) ; Amini, Arash (Supervisor)
    Hyperspectral unmixing (HU) is a method used to estimate the fractional abundances corresponding to endmembers in each of the mixed pixels in the hyperspectral remote sensing image. In recent times, deep learning has been recognized as an effective technique for hyperspectral image classification. In this thesis, an end-to-end HU method is proposed based on the convolutional neural network (CNN) and multi-layer perceptron (MLP). which consists of two steps: the first stage extracts features from the input data along with the inverse learning of the spectral library matrix in the hyperspectral image where columns represent the pure spectral of endmembers and The second stage is to estimate... 

    Implementation of Optical Character Recognition with Deep Learning

    , M.Sc. Thesis Sharif University of Technology Samangouei, Mohammad (Author) ; Bagheri Shouraki, Saeed (Supervisor)
    Optical character recognition (OCR) method has been used in converting printed text into editable text. OCR is very usefuland popular method in various applications. Accuracy of OCRn can be dependent on text preprocessing and segmentation algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, complex background of image etc. and Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations and a deep belief network (DBN) is a probabilistic ,... 

    Deep Learning for Speech Recognition

    , M.Sc. Thesis Sharif University of Technology Azadi Yazdi, Saman (Author) ; Sameti, Hossein (Supervisor)
    Speech recognition is one of the first goals of speech processing. Our goal in this thesis is to use deep learning for speech recognition. In recent years little improvement of speech recognition accuracies are reported. Deep learning is a new learning algorithm that results in improvement in many machine learning tasks. Following improvements reported in speech recognition in English language by deep learning, in this thesis we tried to improve accuracy over common and new recognition methods for Persian language.
    First the overall structure of a typical speech recognition system is introduced. For this purpose, the modules of a speech recognition system are introduced. Deep multilayer... 

    Two new methods for DNA splice site prediction based on neuro-fuzzy network and clustering

    , Article Neural Computing and Applications ; Volume 23, Issue SUPPL1 , 2013 , Pages 407-414 ; 09410643 (ISSN) Moghimi, F ; Manzuri Shalmani, M. T ; Khaki Sedigh, A ; Kia, M ; Sharif University of Technology
    Nowadays, genetic disorders, like cancer and birth defects, are a great threat to human life. Since the first noticing of these types of diseases, many efforts have been made and researches performed in order to recognize them and find a cure for them. These disorders affect genes and they appear as abnormal traits in a genetic organism. In order to recognize abnormal genes, we need to predict splice sites in a DNA signal; then, we can process the genetic codes between two continuous splice sites and analyze the trait that it represents. In addition to abnormal genes and their consequent disorders, we can also identify other normal human traits like physical and mental features. So the... 

    The prediction of the density of undersaturated crude oil using multilayer feed-forward back-propagation perceptron

    , Article Petroleum Science and Technology ; Volume 30, Issue 1 , 2011 , Pages 89-99 ; 10916466 (ISSN) Rostami, H ; Shahkarami, A ; Azin, R ; Sharif University of Technology
    Crude oil density is an important thermodynamic property in simulation processes and design of equipment. Using laboratory methods to measure crude oil density is costly and time consuming; thus, predicting the density of crude oil using modeling is cost-effective. In this article, we develop a neural network-based model to predict the density of undersaturated crude oil. We compare our results with previous works and show that our method outperforms them  

    Removing undesired effects of mass/inertia on transparency using artificial neural networks in a haptic mechanism

    , Article ICCAS 2010 - International Conference on Control, Automation and Systems, 27 October 2010 through 30 October 2010, Gyeonggi-do ; 2010 , Pages 156-161 ; 9781424474530 (ISBN) Khodabakhsh, M ; Boroushaki, M ; Vossoughi, G ; Sharif University of Technology
    In this paper, Artificial Neural Networks (ANN) has been used to identify the dynamics of robots used in haptic and master slave devices in order to improve transparency. In haptic and master slave devices, transparency depends on some factors such as robot's mass and inertia, gravitational forces and friction [1]. In such systems, mass and inertia of the robot has an undesirable effect on the system outputs, which should be neutralized for improved transparency. The main purpose of this paper introducting a method to neutralize the undesirable effects of mass and inertia of the robot. A recurrent multilayer perceptron (RMLP) is used in a way that the inputs and outputs of the neural network... 

    Reduced complexity enhancement of steganalysis of LSB-matching image steganography

    , Article 7th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA-2009, Rabat, 10 May 2009 through 13 May 2009 ; 2009 , Pages 1013-1017 ; 9781424438068 (ISBN) Malekmohamadi, H ; Ghaemmaghami, S ; Sharif University of Technology
    We propose a method for steganalysis of still, grayscale images using a novel set of features that are extracted from images. This feature set employs the Gabor filter coefficients to train a multi-layer perceptron neural network and a support vector machine classifier. We show that incorporation of the Gabor filter coefficients to the feature sets of images could have a significant role in discrimination between clean and altered images. Experimental results show that the proposed method outperforms previous methods, introduced for steganalysis of LSB-matching image steganography, in terms of both discrimination accuracy and feature set dimensionality. © 2009 IEEE  

    Application of multilayer perceptron network for unsteady three dimensional aerodynamic load prediction

    , Article 25th AIAA Applied Aerodynamics Conference, 2007, Miami, FL, 25 June 2007 through 28 June 2007 ; Volume 2 , 2007 , Pages 1197-1202 ; 10485953 (ISSN) ; 1563478986 (ISBN); 9781563478987 (ISBN) Gholamrezaei, M ; Soltani, M. R ; Ghorbanian, K ; Amiralaei, M. R ; Sharif University of Technology
    Surface pressure measurements were conducted for a pitch oscillation wing in a subsonic closed circuit wind tunnel. Experimental results have been used to train a multilayer perceptron network to foresee the effect of modification of oscillation amplitude and reduced frequency. Consistent results are obtained both for the training data as well as generalization to other amplitudes and reduced frequencies. This work indicates that artificial neural networks can reliably predict aerodynamic coefficients and forecast the effects of oscillation amplitude as well as reduced frequency on the wind turbine blade performance. Moreover, this study introduces a new tool for the designers to have enough... 

    Application of neural networks and state space averaging to a DC/DC PWM converter in sliding mode operation

    , Article IECON Proceedings (Industrial Electronics Conference) ; Volume 1 , 2000 , Pages 172-177 Mahdavi, J ; Nasiri, M. R ; Agah, A ; Sharif University of Technology
    IEEE Computer Society  2000
    A novel output feedback neural controller is presented for the implementation of sliding mode control of DC/DC converters. The controller, which consists of a multilayer perceptron, has been trained so as to be robust for large variations of system parameters and state variables. Fast dynamic behavior is the other main advantage of the proposed controller, which allows realization of all the beneficial features of sliding mode control technique. Other advantages of the controller are simplicity and low cost. Computer simulations are carried out to investigate the effectiveness of the controller in voltage regulation for a relatively complex topology such as a Cuk converter. Simulation... 

    HPASC – OPCC bi-surface Shear Strength Prediction Model Using Deep Learning

    , M.Sc. Thesis Sharif University of Technology Khademi, Pooria (Author) ; Toufigh, Vahab (Supervisor)
    Selecting a suitable material is crucial for repairing the old concrete structures and joining precast panels of bridges, especially the bond strength between the substrate concrete and the overlay material. In this regard, this research focused on high-performance alkali-activated slag concrete (HPASC) as a new concrete used as an overlay on ordinary Portland cement concrete (OPCC) as a block of old concrete. Approximately four hundred bi-surface shear (BSS) tests were performed to evaluate the interface properties of OPCC and HPASC. HPASC specimens were designed with different NaOH molarity, silica fume (SF) content, steel fiber content, age of repair material, and proportion of grooved... 

    Cerebrovascular Attack Detection Using Artificial Intelligent Neural Network

    , M.Sc. Thesis Sharif University of Technology Bagheri, Mahdi (Author) ; Bagheri Shouraki, Saeed (Supervisor) ; Haj Sadeghi, Khosrow (Co-Advisor)
    Cerebrovascular Attack has been ranked the second or third of top 10 death causes in Taiwan. It has caused about 13,000 deaths every year since 1986. Once Cerebrovascular Attack (CVA) occurs, it not only leads to the huge cost of medical care, but even death. All developed countries in the world put CVA prevention and treatment in high priority. However, it is necessary to build a detective model to enhance the accurate diagnosis of CVA. From this detective model, CVA classification rules were extracted and used to improve the diagnosis and detection of CVA. This study acquired 2449 valid samples from this CVA prevention and treatment program, and adopted three classification algorithms,... 

    Intelligent Diagnosis of Cardiovascular Disease using ECG Signals

    , M.Sc. Thesis Sharif University of Technology Baghdadi, Fatemeh (Author) ; Haj Sadeghi, Khosrow (Supervisor)
    Cardiovascular diseases (CVDs) have ranked first cause of deaths globally. In 2016, about 17.7 million people died from CVDs representing 31% of all world deaths. So, early intelligent detection of cardiovascular disease could help to save many lives in worldwide. There are several methods to analyze heart activity and to detect any abnormalities including Electrocardiogram, Stress test, Echocardiography, cardiac catheterization and coronary angiography.Among all methods, Electrocardiogram (ECG) is the most common and convenient type where it measures heart electrical activity and records it as a series of pulses. Analyzing these pulses would provide useful information about normal and...