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Chaos in the APFM nonlinear adaptive filter
, Article Signal Processing ; Volume 89, Issue 5 , 2009 , Pages 697-702 ; 01651684 (ISSN) ; Haeri, M ; Sharif University of Technology
2009
Abstract
In this paper, we show that an amplitude phase frequency model (APFM) which is used as a nonlinear adaptive filter in signal processing can exhibit chaotic behavior. It is illustrated that both frequency and amplitude of the input components have effect on the filter characteristics which can lead the filter to behave chaotically for some values of these parameters. We have exploited different measures such as the Lyapunov exponents, bifurcation diagram, sensitivity to initial condition, and 0-1 test on time series to confirm our claim. Existence of the chaotic behavior in APFM does not conform to the prior conjectures about this filter. © 2008 Elsevier B.V. All rights reserved
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...
Autoregressive video modeling through 2D Wavelet Statistics
, Article Proceedings - 2010 6th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2010, 15 October 2010 through 17 October 2010 ; October , 2010 , Pages 272-275 ; 9780769542225 (ISBN) ; Ghaemmaghami, S ; Shirmohammadi, S ; Sharif University of Technology
2010
Abstract
We present an Autoregressive (AR) modeling method for video signal analysis based on 2D Wavelet Statistics. The video signal is assumed to be a combination of spatial feature time series that are temporally approximated by the AR model. The AR model yields a linear approximation to the temporal evolution of a stationary stochastic process. Generalized Gaussian Density (GGD) parameters, extracted from 2D wavelet transform subbands, are used as the spatial features. Wavelet transform efficiently resembles the Human Visual System (HVS) characteristics and captures more suitable features, as compared to color histogram features. The AR model describes each spatial feature vector as a linear...
Approaching complexity by stochastic methods: From biological systems to turbulence
, Article Physics Reports ; Volume 506, Issue 5 , 2011 , Pages 87-162 ; 03701573 (ISSN) ; Peinke, J ; Sahimi, M ; Reza Rahimi Tabar, M ; Sharif University of Technology
2011
Abstract
This review addresses a central question in the field of complex systems: given a fluctuating (in time or space), sequentially measured set of experimental data, how should one analyze the data, assess their underlying trends, and discover the characteristics of the fluctuations that generate the experimental traces? In recent years, significant progress has been made in addressing this question for a class of stochastic processes that can be modeled by Langevin equations, including additive as well as multiplicative fluctuations or noise. Important results have emerged from the analysis of temporal data for such diverse fields as neuroscience, cardiology, finance, economy, surface science,...
Application of ultrasonic wave technology as an asphaltene flocculation inhibition method
, Article Saint Petersburg 2012 - Geosciences: Making the Most of the Earth's Resources ; 2012 ; Amani, M ; Mousavi, M. R ; Ghazanfari, M. H ; Sharif University of Technology
Abstract
Based on series of crude oil rheological properties and asphaltene flocculation confocal microscopy analysis, Najafi et al., (2011) reported the existence of an optimum radiation time at which asphaltenic crude oils reach the minimum kinematic viscosity. Accordingly, they proposed the idea of asphaltene flocculation inhibition due to wave radiation.The present investigation is a continuous effort to provide more information about the process of flocculation inhibition. Confocal microscopy and rheological analyses are performed on different crude oils to prove the repeatability of the observed phenomena. The asphaltene content analysis was done based on IP143 procedure, which provides more...
A novel regression imputation framework for Tehran air pollution monitoring network using outputs from WRF and CAMx models
, Article Atmospheric Environment ; Volume 187 , 2018 , Pages 24-33 ; 13522310 (ISSN) ; Karimi, S ; Hosseini, V ; Yazgi, D ; Torbatian, S ; Sharif University of Technology
Elsevier Ltd
2018
Abstract
Missing or incomplete data in short or long intervals is a common problem in measuring air pollution. Severe issues may arise when dealing with missing data for time-series prediction schemes or mean analysis. This study aimed to develop a new regression imputation framework to impute missing values in the hourly air quality data set of Tehran and enhance the applicability of Tehran Air Pollution Forecasting System (TAPFS). The proposed framework was designed based on three types of features including measurements of other stations, WRF and CAMx physical models. In this framework, elastic net and neuro-fuzzy networks were efficiently combined in a two-layer structure. The framework was...
Anomalous fluctuations of vertical velocity of Earth and their possible implications for earthquakes
, Article Physical Review E - Statistical, Nonlinear, and Soft Matter Physics ; Volume 82, Issue 3 , September , 2010 ; 15393755 (ISSN) ; Ghasemi, F ; Matsumoto, T ; Gómez, J ; Sahimi, M ; Peinke, J ; Pacheco, A. F ; Rahimi Tabar, M. R ; Sharif University of Technology
2010
Abstract
High-quality measurements of seismic activities around the world provide a wealth of data and information that are relevant to understanding of when earthquakes may occur. If viewed as complex stochastic time series, such data may be analyzed by methods that provide deeper insights into their nature, hence leading to better understanding of the data and their possible implications for earthquakes. In this paper, we provide further evidence for our recent proposal for the existence of a transition in the shape of the probability density function (PDF) of the successive detrended increments of the stochastic fluctuations of Earth's vertical velocity Vz, collected by broadband stations before...
An intelligent ACO-SA approach for short term electricity load prediction
, Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 18 August 2010 through 21 August 2010 ; Volume 6216 LNAI , 2010 , Pages 623-633 ; 03029743 (ISSN) ; 9783642149313 (ISBN) ; Hadavandi, E ; Abbasian Naghneh, S ; Huang D. S ; Zhang X ; Sharif University of Technology
2010
Abstract
Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. On the other hand, electrical load prediction is one of the important concerns of power systems so development of intelligent prediction tools for performing accurate predictions is essential. This study presents an intelligent hybrid approach called ACO-SA by hybridization of Ant Colony Optimization (ACO) and Simulated Annealing (SA). The hybrid approach consists of two general stages. At the first stage time series inputs will be fed into ACO and it performs a global search to find a globally optimum solution....
A neural network-based model for wind farm output in probabilistic studies of power systems
, Article 21st Iranian Conference on Electrical Engineering, ICEE 2013 ; 2013 , 14-16 May ; 9781467356343 (ISBN) ; Abbaspour, A ; Fotuhi Firuzabad, M ; Moeini Aghtaie, M ; Sharif University of Technology
Abstract
The penetration of wind energy in power systems has been growing due to its interminable and mild environmental effects. The intrinsic attributes of this environmentally-friendly energy, i.e., the stochastic nature of wind farms generation, however, imposes various technical and financial challenges into power systems. So, developing an accurate wind farm modeling approach aimed at taking into account the wind generation intermittency can relieve many of these challenges. Therefore, this paper takes a step to an efficient wind farm modeling procedure employing an accurate as well as well-known Neural Network (NN)-based tool. The proposed approach is comprised of two main steps. The wind...
An artificial multi-channel model for generating abnormal electrocardiographic rhythms
, Article Computers in Cardiology 2008, CAR, Bologna, 14 September 2008 through 17 September 2008 ; Volume 35 , 2008 , Pages 773-776 ; 02766574 (ISSN); 1424437067 (ISBN); 9781424437061 (ISBN) ; Nemati, S ; Sameni, R ; Sharif University of Technology
2008
Abstract
We present generalizations of our previously published artificial models for generating multi-channel ECG so that the simulation of abnormal rhythms is possible. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are then specified either as new dipoles, or as perturbations of the existing dipole. Switching between normal and abnormal beat types is achieved using a hidden Markov model (HMM). Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes form beat-to-beat...
Analysis of cross correlations between well logs of hydrocarbon reservoirs
, Article Transport in Porous Media ; Volume 90, Issue 2 , 2011 , Pages 445-464 ; 01693913 (ISSN) ; Jafari, G. R ; Lai, Z. K ; Masihi, M ; Sahimi, M ; Sharif University of Technology
Abstract
We carry out a series of cross-correlation analysis of raw well-log data, in order to study the possible connection between natural gamma ray (GR) logs and other types of well logs, such as neutron porosity (NPHI), sonic transient time (denoted usually by DT), and bulk density (RHOB) of oil and gas reservoirs. Three distinct, but complementary, methods are used to analyze the cross correlations, namely, the multifractal detrended cross-correlation analysis (MF-DXA), the so-called Qcc(m) test in conjunction with the statistical test-the χ2(m) distribution-and the cross-wavelet transform (XWT) and wavelet coherency. The Qcc(m) test and MF-DXA are used to identify and quantify the strength of...
Analysis and data-driven reconstruction of bivariate jump-diffusion processes
, Article Physical Review E ; Volume 100, Issue 6 , 2019 ; 24700045 (ISSN) ; Heysel, J ; Lehnertz, K ; Rahimi Tabar, M. R ; Sharif University of Technology
American Physical Society
2019
Abstract
We introduce the bivariate jump-diffusion process, consisting of two-dimensional diffusion and two-dimensional jumps, that can be coupled to one another. We present a data-driven, nonparametric estimation procedure of higher-order (up to 8) Kramers-Moyal coefficients that allows one to reconstruct relevant aspects of the underlying jump-diffusion processes and to recover the underlying parameters. The procedure is validated with numerically integrated data using synthetic bivariate time series from continuous and discontinuous processes. We further evaluate the possibility of estimating the parameters of the jump-diffusion model via data-driven analyses of the higher-order Kramers-Moyal...
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...
A Langevin equation for the rates of currency exchange based on the Markov analysis
, Article Physica A: Statistical Mechanics and its Applications ; Volume 385, Issue 2 , 2007 , Pages 601-608 ; 03784371 (ISSN) ; Eskandari, Z ; Bahraminasab, A ; Jafari, G. R ; Ghasemi, F ; Sahimi, M ; Reza Rahimi Tabar, M ; Sharif University of Technology
2007
Abstract
We propose a method for analyzing the data for the rates of exchange of various currencies versus the U.S. dollar. The method analyzes the return time series of the data as a Markov process, and develops an effective equation which reconstructs it. We find that the Markov time scale, i.e., the time scale over which the data are Markov-correlated, is one day for the majority of the daily exchange rates that we analyze. We derive an effective Langevin equation to describe the fluctuations in the rates. The equation contains two quantities, D(1) and D(2), representing the drift and diffusion coefficients, respectively. We demonstrate how the two coefficients are estimated directly from the...
A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation
, Article Expert Systems with Applications ; Volume 36, Issue 8 , 2009 , Pages 11108-11117 ; 09574174 (ISSN) ; Saberi, M ; Gitiforouz, A ; Saberi, Z ; Sharif University of Technology
2009
Abstract
This paper presents a hybrid adaptive network based fuzzy inference system (ANFIS), computer simulation and time series algorithm to estimate and predict electricity consumption estimation. The difficulty with electricity consumption estimation modeling approach such as time series is the reason for proposing the hybrid approach of this study. The algorithm is ideal for uncertain, ambiguous and complex estimation and forecasting. Computer simulation is developed to generate random variables for monthly electricity consumption. Various structures of ANFIS are examined and the preferred model is selected for estimation by the proposed algorithm. Finally, the preferred ANFIS and time series...
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...
A genetic fuzzy expert system for stock price forecasting
, Article Proceedings - 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010, 10 August 2010 through 12 August 2010 ; Volume 1 , August , 2010 , Pages 41-44 ; 9781424459346 (ISBN) ; Shavandi, H ; Ghanbari, A ; Sharif University of Technology
2010
Abstract
Forecasting stock price time series is very important and challenging in the real world because they are affected by many highly interrelated economic, social, political and even psychological factors, and these factors interact with each other in a very complicated manner. This article presents an approach based on Genetic Fuzzy Systems (GFS) for constructing a stock price forecasting expert system. We use a GFS model with the ability of rule base extraction and data base tuning for next day stock price prediction to extract useful patterns of information with a descriptive rule induction approach. We evaluate capability of the proposed approach by applying it on stock price forecasting...
Accurate and novel recommendations: an algorithm based on popularity forecasting
, Article ACM Transactions on Intelligent Systems and Technology ; Vol. 5, issue. 4 , 2015 ; Jalili, M ; Sharif University of Technology
Abstract
Recommender systems are in the center of network science, and they are becoming increasingly important in individual businesses for providing efficient, personalized services and products to users. Previous research in the field of recommendation systems focused on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and novelty of recommendation lists as key characteristics of modern recommender systems. In many cases, novelty and precision do not go hand in hand, and the accuracy-novelty dilemma is one of the challenging problems in recommender systems, which needs efforts in making a...
A biologically plausible learning method for neurorobotic systems
, Article 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09, Antalya, 29 April 2009 through 2 May 2009 ; 2009 , Pages 128-131 ; 9781424420735 (ISBN) ; Vosoughi Vahdat, B ; National Institutes of Health, NIH; National Institute of Neurological Disorders and Stroke, NINDS; National Science Foundation, NSF ; Sharif University of Technology
2009
Abstract
This paper introduces an incremental local learning algorithm inspired by learning in neurobiological systems. This algorithm has no training phase and learns the world during operation, in a lifetime manner. It is a semi-supervised algorithm which combines soft competitive learning in input space and linear regression with recursive update in output space. This method is also robust to negative interference and compromises bias-variance dilemma. These qualities make the learning method a good nonlinear function approximator having possible applications in neuro-robotic systems. Some simulations illustrate the effectiveness of the proposed algorithm in function approximation, time-series...