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    Inference of gene regulatory networks by extended Kalman filtering using gene expression time seriesdata

    , Article BIOINFORMATICS 2012 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms ; 2012 , Pages 150-155 ; 9789898425904 (ISBN) Fouladi, R ; Fatemizadeh, E ; Arab, S. S ; Sharif University of Technology
    In this paper, the Extended Kalman filtering (EKF) approach has been used to infer gene regulatory networks using time-series gene expression data. Gene expression values are considered stochastic processes and the gene regulatory network, a dynamical nonlinear stochastic model. Using these values and a modified Kalman filtering approach, the model's parameters and consequently the interactions amongst genes are predicted. In this paper, each gene-gene interaction is modeled using a linear term, a nonlinear one, and a constant term. The linear and nonlinear term coefficients are included in the state vector together with the gene expressions' true values. Through the extended Kalman... 

    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 Najafi, I ; Amani, M ; Mousavi, M. R ; Ghazanfari, M. H ; Sharif University of Technology
    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... 

    DNE: A method for extracting cascaded diffusion networks from social networks

    , Article Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011, 9 October 2011 through 11 October 2011 ; October , 2011 , Pages 41-48 ; 9780769545783 (ISBN) Eslami, M ; Rabiee, H. R ; Salehi, M ; Sharif University of Technology
    The spread of information cascades over social networks forms the diffusion networks. The latent structure of diffusion networks makes the problem of extracting diffusion links difficult. As observing the sources of information is not usually possible, the only available prior knowledge is the infection times of individuals. We confront these challenges by proposing a new method called DNE to extract the diffusion networks by using the time-series data. We model the diffusion process on information networks as a Markov random walk process and develop an algorithm to discover the most probable diffusion links. We validate our model on both synthetic and real data and show the low dependency... 

    Dynamic model for market-based capacity investment decision considering stochastic characteristic of wind power

    , Article Renewable Energy ; Volume 36, Issue 8 , August , 2011 , Pages 2205-2219 ; 09601481 (ISSN) Hasani Marzooni, M ; Hosseini, S. H ; Sharif University of Technology
    This paper proposes a decentralized market-based model for long-term capacity investment decisions in a liberalized electricity market with significant wind power generation. In such an environment, investment and construction decisions are based on price signal feedbacks and imperfect foresight of future conditions in electricity market. System dynamics concepts are used to model structural characteristics of power market such as, long-term firms' behavior and relationships between variables, feedbacks and time delays. For conventional generation units, short-term price feedback for generation dispatching of forward market is implemented as well as long-term price expectation for... 

    Discrete Fourier Transform based approach to forecast monthly peak load

    , Article Asia-Pacific Power and Energy Engineering Conference, APPEEC ; 2011 ; 21574839 (ISSN) ; 9781424462551 (ISBN) Beiraghi, M ; Ranjbar, A. M ; IEEE Power and Energy Society (PES); Chinese Society for Electrical Engineering (CSEE); State Grid Corporation of China; China Southern Power Grid; Wuhan University ; Sharif University of Technology
    This paper presents a new method in order to predict the monthly electricity peak load of a country based on the prediction of Discrete Fourier Transform (DFT) of monthly peak electricity demand variation using the ARIMA methodology. For validation, the result of this method was used to predict monthly peak load variation of the recent two years in Iranian national grid. The primary goal of this article is to show the application and implementation of Discrete Fourier Transform to predict monthly variation of electricity peak load in national electric power systems. Furthermore, it is elaborated to demonstrate the benefits and shortcomings of DFT approach comparing to the commonly used... 

    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) Skottfelt, J ; 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
    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... 

    Long-term prediction of solar and geomagnetic activity daily time series using singular spectrum analysis and fuzzy descriptor models

    , Article Earth, Planets and Space ; Volume 61, Issue 9 , 2009 , Pages 1089-1101 ; 13438832 (ISSN) Mirmomeni, M ; Kamaliha, E ; Shafiee, M ; Lucas, C ; Sharif University of Technology
    Of the various conditions that affect space weather, Sun-driven phenomena are the most dominant. Cyclic solar activity has a significant effect on the Earth, its climate, satellites, and space missions. In recent years, space weather hazards have become a major area of investigation, especially due to the advent of satellite technology. As such, the design of reliable alerting and warning systems is of utmost importance, and international collaboration is needed to develop accurate short-term and long-term prediction methodologies. Several methods have been proposed and implemented for the prediction of solar and geomagnetic activity indices, but problems in predicting the exact time and... 

    Strong short-term non-linearity of solar irradiance fluctuations

    , Article Solar Energy ; Volume 144 , 2017 , Pages 1-9 ; 0038092X (ISSN) Madanchi, A ; Absalan, M ; Lohmann, G ; Anvari, M ; Rahimi Tabar, M. R ; Sharif University of Technology
    Elsevier Ltd  2017
    We investigate short-term non-linearity of solar irradiance fluctuations using the multifractal detrended fluctuation analysis (MFDFA). The MFDFA shows that time series of solar irradiance have a long range correlation function with a multifractal behavior. We apply this method to solar irradiance time series from several regions around the world with resolutions of seconds and minutes. The obtained generalized Hurst and Renyi exponents h(q) and τ(q) suggest the non-linear and non-stationary essence of measured irradiance time series. Also, we analyze shuffled, random phase, and rank-wised surrogated data to reveal the nature of the multifractality and conclude that linear and non-linear... 

    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) Davoudi, H ; Vosoughi Vahdat, B ; National Institutes of Health, NIH; National Institute of Neurological Disorders and Stroke, NINDS; National Science Foundation, NSF ; Sharif University of Technology
    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... 

    Sparsness embedding in bending of space and time; a case study on unsupervised 3D action recognition

    , Article Journal of Visual Communication and Image Representation ; Volume 66 , January , 2020 Mohammadzade, H ; Tabejamaat, M ; Sharif University of Technology
    Academic Press Inc  2020
    Human action recognition from skeletal data is one of the most popular topics in computer vision which has been widely studied in the literature, occasionally with some very promising results. However, being supervised, most of the existing methods suffer from two major drawbacks; (1) too much reliance on massive labeled data and (2) high sensitivity to outliers, which in turn hinder their applications in such real-world scenarios as recognizing long-term and complex movements. In this paper, we propose a novel unsupervised 3D action recognition method called Sparseness Embedding in which the spatiotemporal representation of action sequences is nonlinearly projected into an unwarped feature... 

    Network-based direction of movement prediction in financial markets

    , Article Engineering Applications of Artificial Intelligence ; Volume 88 , February , 2020 Kia, A. N ; Haratizadeh, S ; Shouraki, S. B ; Sharif University of Technology
    Elsevier Ltd  2020
    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... 

    Minimizing the uncertainties of seismological-geotechnical source parameters using a genetic algorithm approach

    , Article 9th International Conference on Computational Structures Technology, CST 2008, Athens, 2 September 2008 through 5 September 2008 ; Volume 88 , 2008 ; 17593433 (ISSN); 9781905088232 (ISBN) Nicknam, A ; Abbasnia, R ; Bozorgnasab, M ; Eslamian, Y ; Nicknam, A ; Sharif University of Technology
    Civil-Comp Press  2008
    The main purpose of this article is to estimate the seismological source parameters of the December 26, 2003, Bam earthquake Mw6.5 (Iran). The selected station is far away from the causative fault so that the synthesized ground motion would not be influenced by near source problems such as directivity effects. The well known Empirical Green's Functions (EGF) is used to synthesize the three components of main shock. The Kostrov slip model describing the entire rupture process was incorporated in the model. A generic algorithm (GA) technique is proposed for minimizing the differences between the synthesized time series and those of observed data. The estimated time series were validated by... 

    Project completion time in dynamic PERT networks with generating projects

    , Article Scientia Iranica ; Volume 14, Issue 1 , 2007 , Pages 56-63 ; 10263098 (ISSN) Azaron, A ; Modarres, M ; Sharif University of Technology
    Sharif University of Technology  2007
    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... 

    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) Faghih, S. A. M ; Gholipour, Y ; Kashani, H ; Sharif University of Technology
    Springer Verlag  2021
    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... 

    Designing a multivariate-multistage quality control system using artificial neural networks

    , Article International Journal of Production Research ; Volume 47, Issue 1 , 2009 , Pages 251-271 ; 00207543 (ISSN) Akhavan Niaki, T ; Davoodi, M ; Sharif University of Technology
    In most real-world manufacturing systems, the production of goods comprises several autocorrelated stages and the quality characteristics of the goods at each stage are correlated random variables. This paper addresses the problem of monitoring a multivariate-multistage manufacturing process and diagnoses the possible causes of out-of-control signals. To achieve this purpose using multivariate time series models, first a model for the autocorrelated data coming from multivariate-multistage processes is developed. Then, a single neural network is designed, trained and employed to control and classify mean shifts in quality characteristics of all stages. In-control and out-of-control average... 

    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) Moshkbar Bakhshayesh, K ; Sharif University of Technology
    Elsevier Ltd  2019
    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... 

    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) Fayyaz, Z ; Bahadorian, M ; Doostmohammadi, J ; Davoodnia, V ; Khodadadian, S ; Lashgari, R ; Sharif University of Technology
    Elsevier B.V  2019
    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... 

    Nonlinear dynamic modeling of surface defects in rolling element bearing systems

    , Article Journal of Sound and Vibration ; Volume 319, Issue 3-5 , 2009 , Pages 1150-1174 ; 0022460X (ISSN) Rafsanjani, A ; Abbasion, S ; Farshidianfar, A ; Moeenfard, H ; Sharif University of Technology
    In this paper an analytical model is proposed to study the nonlinear dynamic behavior of rolling element bearing systems including surface defects. Various surface defects due to local imperfections on raceways and rolling elements are introduced to the proposed model. The contact force of each rolling element described according to nonlinear Hertzian contact deformation and the effect of internal radial clearance has been taken into account. Mathematical expressions were derived for inner race, outer race and rolling element local defects. To overcome the strong nonlinearity of the governing equations of motion, a modified Newmark time integration technique was used to solve the equations... 

    Processing the Local Field Potential Signals in Comparison to Neighboring Simple and Complex Neurons of Primary Visual Cortex

    , M.Sc. Thesis Sharif University of Technology Eftekhar, Morteza (Author) ; Lashgari, Reza (Supervisor)
    In neural systems of living organism, moreover than differences in anatomic structure of cells, there is also differences in physiological functions of analogous cells.Specification and categorization of neurons based on physiological functions is one of objectives of neuroscience. Study of cognitive behaviors and systematic study of neural system, modeling and practical applications in neural prosthesis design are some of applications of categorizing neural cells. Neural signals can be studied by Spike rate of a single neuron activity or Local Field Potential (LFP) of a finite number of neurons. In previous studies neurons of first visual cortex are divided into two groups of simple and... 

    The Effect of Temporal Alignment in 3D Action Recognition Using Recurrent Neural Network

    , M.Sc. Thesis Sharif University of Technology Akyash, Mohammad Hossein (Author) ; Behroozi, Hamid (Supervisor) ; Mohammadzadeh, Hoda (Co-Supervisor)
    Action recognition has a lot of applications in everyday human life. In the past, the researchers concentrated on using RGB frames, but since the advent of 3-dimensional sensors such as Kinect, 3D action recognition drew researchers' attention. Kinect can extract the joints of the body in action as time series. One of the main challenges of action recognition is that different individuals perform an action with various styles and speeds. Hence, the conventional methods such as calculating Euclidean distance seem inappropriate for this task. One solution is to use the techniques such as DTW, which aims to temporal aligning of the sequences. The DTW is not a metric distance; hence, in this...