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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...
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...
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...
Forecasting smoothed non-stationary time series using genetic algorithms
, Article International Journal of Modern Physics C ; Volume 18, Issue 6 , 2007 , Pages 1071-1086 ; 01291831 (ISSN) ; Rahmani, B ; Norouzzadeh, M. S ; Sharif University of Technology
2007
Abstract
We introduce kernel smoothing method to extract the global trend of a time series and remove short time scales variations and fluctuations from it. A multifractal detrended fluctuation analysis (MF-DFA) shows that the multifractality nature of TEPIX returns time series is due to both fatness of the probability density function of returns and long range correlations between them. MF-DFA results help us to understand how genetic algorithm and kernel smoothing methods act. Then we utilize a recently developed genetic algorithm for carrying out successful forecasts of the trend in financial time series and deriving a functional form of Tehran price index (TEPIX) that best approximates the time...
Project completion time in dynamic PERT networks with generating projects
, Article Scientia Iranica ; Volume 14, Issue 1 , 2007 , Pages 56-63 ; 10263098 (ISSN) ; Modarres, M ; Sharif University of Technology
Sharif University of Technology
2007
Abstract
In this paper, an analytical method is developed to compute the project completion time distribution in a dynamic PERT network, where the activity durations are exponentially distributed random variables. The projects are generated according to a renewal process and share the same facilities. Thus, these projects cannot be analyzed independently. The authors' approach is to transform this dynamic PERT network into a stochastic network and, then, to obtain the project completion time distribution by constructing a proper continuous-time Markov chain. This dynamic PERT network is represented as a network of queues, where the service times represent the durations of the corresponding activities...
On the resolution of existing discontinuities in the dynamic responses of an Euler-Bernoulli beam subjected to the moving mass
, Article 8th Biennial ASME Conference on Engineering Systems Design and Analysis, ESDA2006, Torino, 4 July 2006 through 7 July 2006 ; Volume 2006 , 2006 ; 0791837793 (ISBN); 9780791837795 (ISBN) ; Saeedi, K ; Sharif University of Technology
American Society of Mechanical Engineers
2006
Abstract
The dynamic response of a one-dimensional distributed parameter system subjected to a moving mass with constant speed is investigated. An Euler-Bernoulli beam with the uniform cross-section and finite length with specified boundary support conditions is assumed. In this paper, rather a new method based on the time dependent series expansion for calculating the bending moment and the shear force due to motion of the mass is suggested. Governing differential equations of the motion are derived and solved. The accuracy of the numerical results primarily is verified and further the rapid convergence of this new technique was illustrated over other existing methods. Finally, it is shown that a...
Developing a conjunctive nonlinear model for inflow prediction using wavelet transforms and artificial neural networks: A case study of Dez reservoir dam, Iran
, Article Operations Management 2006, Sacramento, CA, 14 August 2006 through 16 August 2006 ; Volume 2006 , 2006 , Pages 69-78 ; 0784408750 (ISBN); 9780784408759 (ISBN) ; Abrishamchi, A ; Khodaei, H ; Sharif University of Technology
2006
Abstract
Dez reservoir dam is one of the famous dams in Khuzestan province in southwestern part of Iran. Operation of Dez reservoir dam and it's strategic position has an important role regard to providing country's net power, it's long precedence in operation, Dam dimension, multidisciplinary uses and existence of essential land use in downstream. Thus inflow forecasting could have a great role in effective operation of dam, flood non-structural and risk management by applying an effective flood warning. The more accurate the forecasting of inflow to reservoir will be results the less peak outflow and less risk to downstream. This paper presents a new Conjunctive nonlinear model using Wavelet...
Seizure detection in EEG signals: a comparison of different approaches
, Article 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, New York, NY, 30 August 2006 through 3 September 2006 ; 2006 , Pages 6724-6727 ; 05891019 (ISSN); 1424400325 (ISBN); 9781424400324 (ISBN) ; Maghsoudi, A ; Shamsollahi, M. B ; Sharif University of Technology
2006
Abstract
In this paper, the performance of traditional variance-based method for detection of epileptic seizures in EEG signals are compared with various methods based on nonlinear time series analysis, entropies, logistic regression, discrete wavelet transform and time frequency distributions. We noted that variance-based method in compare to the mentioned methods had the best result (100%) applied on the same database. © 2006 IEEE
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)...
Time series analysis framework for forecasting the construction labor costs
, Article KSCE Journal of Civil Engineering ; Volume 25, Issue 8 , 2021 , Pages 2809-2823 ; 12267988 (ISSN) ; Gholipour, Y ; Kashani, H ; Sharif University of Technology
Springer Verlag
2021
Abstract
This manuscript presents a framework to develop vector error correction (VEC) models applicable to forecasting the short- and long-run movements of the average hourly earnings of construction labor, which is an essential predictor of the construction labor costs. These models characterize the relationship between average hourly earnings and a set of explanatory variables. The framework is applied to develop VEC forecasting models for the average hourly earnings of construction labor in the USA based on the identified variables that govern its movements, such as Global Energy Price Index, Gross Domestic Product, and Personal Consumption Expenditures. More than 150 candidate VEC models were...
Dynamic time warping-based features with class-specific joint importance maps for action recognition using kinect depth sensor
, Article IEEE Sensors Journal ; Volume 21, Issue 7 , 2021 , Pages 9300-9313 ; 1530437X (ISSN) ; Hosseini, S ; Rezaei Dastjerdehei, M. R ; Tabejamaat, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2021
Abstract
This paper proposes a novel 3D action recognition technique that uses time-series information extracted from depth image sequences for use in systems of human daily activity monitoring. To this end, each action is represented as a multi-dimensional time series, where each dimension represents the position variation of one skeleton joint over time. The time series is then mapped onto a vector space using Dynamic Time Warping (DTW) distance. Furthermore, to employ the correlation-distinctiveness relationship of the sequences in recognition, this vector space is remapped onto a discriminative space using the regularized Fisher method, where final decisions about the actions are made. Unlike...
fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease
, Article Signal, Image and Video Processing ; Volume 15, Issue 4 , 2021 , Pages 715-723 ; 18631703 (ISSN) ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2021
Abstract
Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear...
fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease
, Article Signal, Image and Video Processing ; Volume 15, Issue 4 , 2021 , Pages 715-723 ; 18631703 (ISSN) ; Fatemizadeh, E ; Motie-Nasrabadi, A ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2021
Abstract
Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear...
Dimensional characterization of anesthesia dynamic in reconstructed embedding space
, Article 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07, Lyon, 23 August 2007 through 26 August 2007 ; 2007 , Pages 6483-6486 ; 05891019 (ISSN) ; 1424407885 (ISBN); 9781424407880 (ISBN) ; Rabiee, H. R ; Hashemi, M. R ; Ghanbari, M ; Sharif University of Technology
2007
Abstract
The depth of anesthesia quantification has been one of the most research interests in the field of EEG signal processing and nonlinear dynamical analysis has emerged as a novel method for the study of complex systems in the past few decades. In this investigation we use the concept of nonlinear time series analysis techniques to reconstruct the attractor of anesthesia from EEG signal which have been obtained from different hypnotic states during surgery to give a characterization of the dimensional complexity of EEG by Correlation Dimension estimation. The dimension of the anesthesia strange attractor can be thought of as a measure of the degrees of freedom or the 'complexity' of the...
Short-term prediction of air pollution using TD-CMAC neural network model
, Article Soft Computing with Industrial Applications - International Symposium on Soft Computing for Industry, ISSCI - Sixth Biannual World Automation Congress, WAC 2004, Sevilla, 28 June 2004 through 1 July 2004 ; 2004 , Pages 357-362 ; 1889335231 (ISBN) ; Teshnehlab, M ; Abbaspour, M ; Setayeshi, S ; Sharif University of Technology
2004
Abstract
This paper presents a new model to short-term prediction of air pollution using a new structure is based on the intelligent neural networks. A new structure known as Time Delay Cerebellar Model Arithmetic Computer (TD-CMAC), an extension to the CMAC, it requires fewer memory sizes. The new model is demonstrated and validated with three primary air pollutants known as carbon monoxide (CO), sulfur dioxide (SO2), and nitrogen dioxide (NO 2). The simulation results for the half an hour ahead-prediction of the air pollutant data set show that the suggested new model is suitable for our purpose
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...
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...