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On the control of unknown continuous time chaotic systems by applying takens embedding theory
, Article Chaos, Solitons and Fractals ; Volume 109 , April , 2018 , Pages 53-57 ; 09600779 (ISSN) ; Salarieh, H ; Hajiloo, R ; Sharif University of Technology
Elsevier Ltd
2018
Abstract
In this paper, a new approach to control continuous time chaotic systems with an unknown governing equation and limitation on the measurement of states, has been investigated. In many chaotic systems, disability to measure all of the states is a usual limitation, like in some economical, biological and many other engineering systems. Takens showed that a chaotic attractor has an astonishing feature in which it can embed to a mathematically similar attractor by using time series of one of the states. The new embedded attractor saves much information from the original attractor. This phenomenon has been deployed to present a new way to control continuous time chaotic systems, when only one of...
Comparative study of application of different supervised learning methods in forecasting future states of NPPs operating parameters
, Article Annals of Nuclear Energy ; Volume 132 , 2019 , Pages 87-99 ; 03064549 (ISSN) ; Sharif University of Technology
Elsevier Ltd
2019
Abstract
In this paper, some important operating parameters of nuclear power plants (NPPs) transients are forecasted using different supervised learning methods including feed-forward back propagation (FFBP) neural networks such as cascade feed-forward neural network (CFFNN), statistical methods such as support vector regression (SVR), and localized networks such as radial basis network (RBN). Different learning algorithms, including gradient descent (GD), gradient descent with momentum (GDM), scaled conjugate gradient (SCG), Levenberg-Marquardt (LM), and Bayesian regularization (BR) are used in CFFNN method. SVR method is used with different kernel functions including Gaussian, polynomial, and...
On the existence of proper stochastic Markov models for statistical reconstruction and prediction of chaotic time series
, Article Chaos, Solitons and Fractals ; Volume 123 , 2019 , Pages 373-382 ; 09600779 (ISSN) ; Salarieh, H ; Alasty, A ; Sharif University of Technology
Elsevier Ltd
2019
Abstract
In this paper, the problem of statistical reconstruction and prediction of chaotic systems with unknown governing equations using stochastic Markov models is investigated. Using the time series of only one measurable state, an algorithm is proposed to design any orders of Markov models and the approach is state transition matrix extraction. Using this modeling, two goals are followed: first, using the time series, statistical reconstruction is performed through which the probability density and conditional probability density functions are reconstructed; and second, prediction is performed. For this problem, some estimators are required and here the maximum likelihood and the conditional...
Network-based direction of movement prediction in financial markets
, Article Engineering Applications of Artificial Intelligence ; Volume 88 , February , 2020 ; Haratizadeh, S ; Shouraki, S. B ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
Market prediction has been an important research problem for decades. Having better predictive models that are both more accurate and faster has been attractive for both researchers and traders. Among many approaches, semi-supervised graph-based prediction has been used as a solution in recent researches. Based on this approach, we present two prediction models. In the first model, a new network structure is introduced that can capture more information about markets’ direction of movements compared to the previous state of the art methods. Based on this novel network, a new algorithm for semi-supervised label propagation is designed that is able to prediction the direction of movement faster...
Stockwell transform of time-series of fMRI data for diagnoses of attention deficit hyperactive disorder
, Article Applied Soft Computing Journal ; Volume 86 , 2020 ; Kalbkhani, H ; Ghasemzadeh, P ; Shayesteh, M. G ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
Attention deficit hyperactivity disorder (ADHD) is a common brain disorder among children. It presents various symptoms, hence, utilizing the information obtained from functional magnetic resonance imaging (fMRI) time-series data can be useful. Finding functional connections in typically developed control (TDC) and ADHD patients can be helpful in classification. The aim of this paper is to present a multifold method for the study of fMRI data to diagnose ADHD patients. In the proposed method, first, by applying the Stockwell transform (ST), we obtain detailed information about the time-series of the region of interests (ROIs) in the time and frequency domains. ST provides information about...
Classification of sleep stages based on LSTAR model
, Article Applied Soft Computing Journal ; Volume 75 , 2019 , Pages 523-536 ; 15684946 (ISSN) ; Kalbkhani, H ; Sartipi, S ; Shayesteh, M. G ; Sharif University of Technology
Elsevier Ltd
2019
Abstract
Sleep study is very important in the health since sleep disorders affect the productivity of individuals. One of the important topics in sleep research is the classification of sleep stages using the electroencephalogram (EEG) signal. Electrical activities of brain are measured by EEG signal in the laboratory. In real-world environments, EEG signal is also used in portable monitoring devices to analyze sleep. In this study, we propose an efficient method for classification of sleep stages. EEG signals are examined by a new model from autoregressive (AR) family, namely logistic smooth transition autoregressive (LSTAR) to study sleep process. In contrast to the AR model, LSTAR is a non-linear...
Pattern recognition in financial surveillance with the ARMA-GARCH time series model using support vector machine
, Article Expert Systems with Applications ; Volume 182 , 2021 ; 09574174 (ISSN) ; Akhavan Niaki, S. T ; Sharif University of Technology
Elsevier Ltd
2021
Abstract
As the intersection of finance and statistics, financial surveillance is a new interdisciplinary field of research. In this field, statistical process control methods are applied to monitor financial indices. The final aim is to detect out-of-control conditions and trigger a signal as soon as possible. These early signals can help practitioners in making on-time decisions. In this paper, a new method based on a support vector machine is proposed to detect upward and downward shifts with step and trend patterns in auto-correlated financial processes. These processes are modeled by the autoregressive moving average (ARMA) and generalized autoregressive conditional heteroskedasticity (GARCH)...
Deep long short-term memory (LSTM) networks for ultrasonic-based distributed damage assessment in concrete
, Article Cement and Concrete Research ; Volume 162 , 2022 ; 00088846 (ISSN) ; Toufigh, V ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
This paper presented a comprehensive study on developing a deep learning approach for ultrasonic-based distributed damage assessment in concrete. In particular, two architectures of long short-term memory (LSTM) networks were proposed: (1) a classification model to evaluate the concrete's damage stage; (2) a regression model to predict the concrete's absorbed energy ratio. Two input configurations were considered and compared for both architectures: (1) the input was a single signal; (2) the inputs were four signals from four sides of the specimen. A comprehensive experimental study was designed and conducted on ground granulated blast furnace slag-based geopolymer concrete, providing a...
Impact of mobility on COVID-19 spread – A time series analysis
, Article Transportation Research Interdisciplinary Perspectives ; Volume 13 , 2022 ; 25901982 (ISSN) ; Aminpour, N ; Ahmadian, M. A ; Samimi, A ; Saidi, S ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
In this paper, we investigate the impact of mobility on the spread of COVID-19 in Tehran, Iran. We have performed a time series analysis between the indicators of public transit use and inter-city trips on the number of infected people. Our results showed a significant relationship between the number of infected people and mobility variables with both short-term and long-term lags. The long-term effect of mobility showed to have a consistent lag correlation with the weekly number of new COVID-19 positive cases. In our statistical analysis, we also investigated key non-transportation variables. For instance, the mandatory use of masks in public transit resulted in observing a 10% decrease in...
A high-accuracy hybrid method for short-term wind power forecasting
, Article Energy ; Volume 238 , 2022 ; 03605442 (ISSN) ; Ehsan, M ; Soleymani, S ; Mohammadnezhad Shourkaei, H ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
In this article, a high-accuracy hybrid approach for short-term wind power forecasting is proposed using historical data of wind farm and Numerical Weather Prediction (NWP) data. The power forecasting is carried out in three stages: wind direction forecasting, wind speed forecasting, and wind power forecasting. In all three phases, the same hybrid method is used, and the only difference is in the input data set. The main steps of the proposed method are constituted of outlier detection, decomposition of time series using wavelet transform, effective feature selection and prediction of each time series decomposed using Multilayer Perceptron (MLP) neural network. The combination of automatic...
ECG fiducial point extraction using switching Kalman filter
, Article Computer Methods and Programs in Biomedicine ; Volume 157 , 2018 , Pages 129-136 ; 01692607 (ISSN) ; Montazeri Ghahjaverestan, N ; Shamsollahi, M. B ; Jutten, C ; Sharif University of Technology
Elsevier Ireland Ltd
2018
Abstract
In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regressive models. In the proposed method, a discrete state variable called “switch” is considered that affects only the observation equations. We denote a mode as a specific observation equation and switch changes between 7 modes and corresponds to different segments of an ECG beat. At each time instant, the probability of each mode is calculated and...
OmpF, a nucleotide-sensing nanoprobe, computational evaluation of single channel activities
, Article Physica A: Statistical Mechanics and its Applications ; Volume 457 , 2016 , Pages 215-224 ; 03784371 (ISSN) ; Mobasheri, H ; Nikouee, A ; Ejtehadi, M. R ; Sharif University of Technology
Elsevier B.V
2016
Abstract
The results of highthroughput practical single channel experiments should be formulated and validated by signal analysis approaches to increase the recognition precision of translocating molecules. For this purpose, the activities of the single nano-pore forming protein, OmpF, in the presence of nucleotides were recorded in real time by the voltage clamp technique and used as a means for nucleotide recognition. The results were analyzed based on the permutation entropy of current Time Series (TS), fractality, autocorrelation, structure function, spectral density, and peak fraction to recognize each nucleotide, based on its signature effect on the conductance, gating frequency and voltage...
Chaos control in delayed phase space constructed by the Takens embedding theory
, Article Communications in Nonlinear Science and Numerical Simulation ; Volume 54 , 2018 , Pages 453-465 ; 10075704 (ISSN) ; Salarieh, H ; Alasty, A ; Sharif University of Technology
Elsevier B.V
2018
Abstract
In this paper, the problem of chaos control in discrete-time chaotic systems with unknown governing equations and limited measurable states is investigated. Using the time-series of only one measurable state, an algorithm is proposed to stabilize unstable fixed points. The approach consists of three steps: first, using Takens embedding theory, a delayed phase space preserving the topological characteristics of the unknown system is reconstructed. Second, a dynamic model is identified by recursive least squares method to estimate the time-series data in the delayed phase space. Finally, based on the reconstructed model, an appropriate linear delayed feedback controller is obtained for...
Multifractal detrended fluctuation analysis of continuous neural time series in primate visual cortex
, Article Journal of Neuroscience Methods ; Volume 312 , 2019 , Pages 84-92 ; 01650270 (ISSN) ; Bahadorian, M ; Doostmohammadi, J ; Davoodnia, V ; Khodadadian, S ; Lashgari, R ; Sharif University of Technology
Elsevier B.V
2019
Abstract
Background: Local field potential (LFP) recordings have become an important tool to study the activity of populations of neurons. The functional activity of LFPs is usually compared with the activity of neighboring single spike neurons with sampling rates much higher than those of the continuous field potential channel (5 kHz). However, comparison of these signals generated with the lower sampling rate technique is important. New method: In this study, we provide an analysis of extracellular field potential time series using the sophisticated nonlinear multifractal detrended fluctuation analysis (MF-DFA). Using the MF-DFA, we demonstrate that the integral of the singularity spectrum is a...
An access and inference control model for time series databases
, Article Future Generation Computer Systems ; Volume 92 , 2019 , Pages 93-108 ; 0167739X (ISSN) ; Amini, M ; Sharif University of Technology
Elsevier B.V
2019
Abstract
Today, many applications produce and use time series data. The data of this type may contain sensitive information. So they should be protected against unauthorized accesses. In this paper, security issues of time series data are identified and an access and inference control model for satisfying the identified security requirements is proposed. Using this model, administrators can define authorization rules based on various time-based granularities (e.g. day or month) and apply value-based constraints over the accessed times series data. Furthermore, they can define policy rules over the composition of multiple time-series other than the base time-series data. Detecting and resolving...
Modeling the accuracy of traffic crash prediction models
, Article IATSS Research ; Volume 46, Issue 3 , 2022 , Pages 345-352 ; 03861112 (ISSN) ; Keshavarz, S ; Pazari, P ; Safahieh, N ; Samimi, A ; Sharif University of Technology
Elsevier B.V
2022
Abstract
Crash forecasting enables safety planners to take appropriate actions before casualty or loss occurs. Identifying and analyzing the attributes influencing forecasting accuracy is of great importance in road crash forecasting. This study aims to model the forecasting accuracy of 31 provinces using their macroeconomic variables and road traffic indicators. Iran's road crashes throughout 2011–2018 are calibrated and cross-validated using the Holt-Winters (HW) forecasting method. The sensitivity of crash forecast reliability is studied by a regression model. The results suggested that the root mean square error (RMSE) of crash prediction increased among the provinces with higher and more variant...
Seasonal fractal-scaling of floods in two U.S. water resources regions
, Article Journal of Hydrology ; Volume 540 , 2016 , Pages 232-239 ; 00221694 (ISSN) ; Tayefeh Rezakhani, A ; Shamsai, A ; Sharif University of Technology
Elsevier
2016
Abstract
Understanding the behavior and estimating the magnitude of floods with specific recurrence intervals are important tasks for various applications such as flood protection strategies. Fractal analysis has proven useful in characterization of flood frequency behavior. We employ a systematic fractal approach which enables dividing streamflow data into different behavior regimes and, in particular, identifying flood regimes. Since seasonality is a key factor in flood-formation scenarios, we incorporate this concept in our analysis through generating two separate streamflow data sets for summer and winter, and next performing associated fractal analysis on each. To illustrate our approach and see...
Characterization of complex behaviors of TCP/RED computer networks based on nonlinear time series analysis methods
, Article Physica D: Nonlinear Phenomena ; Volume 233, Issue 2 , 2007 , Pages 138-150 ; 01672789 (ISSN) ; Haeri, M ; Choobkar, S ; Jannesari, F ; Sharif University of Technology
Elsevier
2007
Abstract
Packet-level observations are representative of the high sensitivity of TCP/RED computer network behavior with respect to network/RED parameter variations. That is, while we do not have any control on network parameters, mis-choosing of the RED parameters results in complex non-periodic oscillations in the router queue length that may damage the Quality of Service requirements. Characterizing the nature of such behaviors, however, helps the network designers to modify the RED design method in order to achieve better overall performance. In this paper, we first investigate the effect of variations in different RED parameters on the network behavior and then seek for the origin of such complex...
Searching for variable stars in the cores of five metal-rich globular clusters using EMCCD observations
, Article Astronomy and Astrophysics ; Volume 573 , January , 2015 ; 00046361 (ISSN) ; Bramich, D. M ; Figuera Jaimes, R ; Jørgensen, U. G ; Kains, N ; Arellano Ferro, A ; Alsubai, K. A ; Bozza, V ; Calchi Novati, S ; Ciceri, S ; D'Ago, G ; Dominik, M ; Galianni, P ; Gu, S. H ; Harpsøe, K. B. W ; Haugbølle, T ; Hinse, T. C ; Hundertmark, M ; Juncher, D ; Korhonen, H ; Liebig, C ; Mancini, L ; Popovas, A ; Rabus, M ; Rahvar, S ; Scarpetta, G ; Schmidt, R. W ; Snodgrass, C ; Southworth, J ; Starkey, D ; Street, R. A ; Surdej, J ; Wang, X. B ; Wertz, O ; Sharif University of Technology
EDP Sciences
2015
Abstract
Aims. In this paper, we present the analysis of time-series observations from 2013 and 2014 of five metal-rich ([Fe/H] > -1) globular clusters: NGC 6388, NGC 6441, NGC 6528, NGC 6638, and NGC 6652. The data have been used to perform a census of the variable stars in the central parts of these clusters. Methods. The observations were made with the electron-multiplying charge-couple device (EMCCD) camera at the Danish 1.54m Telescope at La Silla, Chile, and they were analysed using difference image analysis to obtain high-precision light curves of the variable stars. Results. It was possible to identify and classify all of the previously known or suspected variable stars in the central regions...
Predicting the competitive position of extended gates: The case of inland customs zones
, Article European Journal of Transport and Infrastructure Research ; Volume 18, Issue 4 , 2018 , Pages 433-456 ; 15677141 (ISSN) ; Saffarzadeh, M ; Tavasszy, L ; Fatemi Ardestani, S. F ; Sharif University of Technology
Editorial Board EJTIR
2018
Abstract
The extended gate concept aims to reduce the pressure on international ports by postponing administrative processes from these border gates to inland terminals. At present, this approach is used mainly in the container transport industry in European and Asian ports. In this paper we study an extended gate concept, where inland customs services are made available from all entry points of a country. Our aim is to predict the portion of the current flow through border gates that is diverted to these inland customs zones. We propose a time-series gravity models to predict these changes and estimate the parameters of this model using publicly available data for different cargo groups. The focus...