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    Chaos Control in Continuous Time Systems Using Delayed Phase Space Constructed by Takens’ Embedding Theory

    , M.Sc. Thesis Sharif University of Technology Kaveh, Hojjat (Author) ; Salarieh, Hassan (Supervisor)
    This research has dedicated to study the control of chaos when the system dynamics is unknown and there are some limitations on measuring states. There are many chaotic systems with these features occurring in many biological, economical and mechanical systems. The usual chaos control methods do not have the ability to present a systematic control method for these kinds of systems. To fulfill these strict conditions we have employed Takens embedding theory which guarantees the preservation of topological characteristics of the chaotic attractor under an embedding named "Takens transformation". Takens transformation just needs time series of one of the measurable states. This transformation... 

    Using Time Series and Planning Uncertain Demand in Supply Chain with System Dynamics Approach

    , M.Sc. Thesis Sharif University of Technology Rezaei, Davoud (Author) ; Kianfar, Farhad (Supervisor)
    In the field of supply chain, management constantly seeks to adopt strategies for realizing its targeted objectives. The supply chain, therefore, includes integrated production-distribution processes with all effective parts where proper management requires a comprehensive and dynamic approach. It is necessary to meet required items at required time. This becomes more important with the supply systems providing items. On the other hand, the difficulty of supply chain management, decisions, and policy-making can be increased by a wide range of conditions such as changes in demand. Thus, a comprehensive approach is needed to take the present dynamics into account. The current paper develops a... 

    Optimization of Support Vector Regression Parameters Using Firefly Algorithm

    , M.Sc. Thesis Sharif University of Technology Ghanbari, Mohammad Reza (Author) ; Mahdavi-Amiri, Nezameddin (Supervisor)
    Support vector regression (SVR) in the field of machine learning attracted much attention because of its attractive features and high efficiency for high-dimensional and nonlinear data. Although support vector regression has shown to be very effective for prediction problems, it is necessary to adjust the parameters contained therein to obtain the desired output with error rates. In the past, this was done manually, by trial and error. Over time and by development of optimization algorithms, one of the newest methods to solve such problems is the meta-heuristic optimization algorithms. Therefore, in this thesis, we use the firefly optimization algorithm, which is a population-based... 

    Activation Detection in fMRI Using Nonlinear Time Series Analysis

    , M.Sc. Thesis Sharif University of Technology Taalimi, Ali (Author) ; Fatemizadeh, Emadeddin (Supervisor)
    Functional Magnetic Resonance Imaging (fMRI) is a recently developed neuroimaging technique with capacity to map neural activity with high spatial precision. To locate active brain areas, the method utilizes local blood oxygenation changes which are reflected as small intensity changes in a special type of MR images. The ability to non-invasively map brain functions provides new opportunities to unravel the mysteries and advance the understanding of the human brain, as well as to perform pre-surgical examinations in order to optimize surgical interventions. To obtain these goals the analysis of fMRI is the first condition which should be met. First methods were linear and assumed the... 

    Study of Statistical Behavior of Chaotic Maps and Design of Stochastic Models for Reconstruction and Prediction of Behavioral Patterns of Chaotic Systems

    , M.Sc. Thesis Sharif University of Technology Jokar, Meysam (Author) ; Salarieh, Hassan (Supervisor) ; Alasty, Aria (Supervisor)
    Chaotic time series analysis, study of statistical behavior of chaotic maps and eventually an attempt to reconstruction and prediction of dynamical and statistical properties of output data of chaotic systems using stochastic models such as Markov models and autoregressive-moving average models are the main purposes of the present research. Examples of chaotic time series abound in the output of economics, engineering systems, the natural sciences (especially geophysics and meteorology) and social sciences. An intrinsic feature of an output time series of a dynamic system is that, adjacent observations are dependent. Time series analysis is concerned with techniques for the analysis of this... 

    Change Point Estimation for Multistage Processes

    , Ph.D. Dissertation Sharif University of Technology Davoodi, Mehdi (Author) ; Akhavan Niaki, Taghi (Supervisor)
    Knowing the time of change would narrow the search to find and identify the variables disturbing a process. Having this information, an appropriate corrective action could be implemented and valuable time could be saved. Multistage processes that are often observed in current manufacturing processes must be monitored to assure quality products. The change-point detection of such processes has not been proposes investigated yet. Thus, this dissertation proposes maximum likelihood step-change estimators of two kinds of these processes. First, a multistage process with variable quality characteristics is considered and formulated by the first-order auto-regressive model. For the location... 

    Improved Supply Chain Management Performance by Applying Hybrid Forecast Method

    , M.Sc. Thesis Sharif University of Technology Shiri, Davood (Author) ; Hajji, Alireza (Supervisor)
    In today’s competitive world, using an efficient forecast method is necessity for companies. To now, many forecast methods have been developed, but many of them have not an expected efficiency. In this research, we develop a new hybrid forecast method with application of forecasting retails demand. The hybrid method is the combination of ARIMA method and neural networks. To test the efficiency of the method we use the 96 weeks data of plastic containers demand. We also comprise the hybrid method with other forecast methods including naïve method, ARIMA method and neural network method by applying root mean square error and mean absolute percentage error indexes. In the case of plastic... 

    Forecasting P/E Ratio by Decomposing into Constituent Factros

    , M.Sc. Thesis Sharif University of Technology Lotfi, Ali (Author) ; Zamani, Shiva (Supervisor) ; Abdoh Tabrizi, Hossein (Supervisor)
    P/E ratio is studied in four levels in this study:
    1)Macroeconomics level
    2)Capital market level
    3)Industry level
    4)Company level
    The first level studies effects of macroeconomics variables on P/E ratio. At this level we use variables such as economic growth, inflation, exchange rate, and etc.The next level uses capital market variables such as market volume, and IPO information.The third level that we study in this research is industry level. Stocks of an industry generally behave similar, because they have common advantages and disadvantages, thus industry is an effective factor on P/E ratio.The last level studies financial statements and internal features of a... 

    The Application of Deep Learning Models in Estimating the Energy of Residential Buildings

    , M.Sc. Thesis Sharif University of Technology Mohammadzadeh, Mohammad (Author) ; Rafiee, Majid (Supervisor) ; Shavandi, Hassan (Co-Supervisor)
    Electricity consumption has increased dramatically in recent decades, and this increase has severely affected electricity distribution. Therefore, forecasting electricity demand can provide a precondition for distributors. Predicting power consumption requires many parameters to be considered.In this research, machine learning, and deep learning methods such as recursive neural networks, long short-term memory networks, etc., as well as the ARIMA model will be used. These models have been tested on the London Smart Measurement Database. In order to evaluate the capability of the models in forecasting electricity consumption, each has been used to predict the electricity consumption of a... 

    Analysis and Prediction of Cryptocurrency Prices Using Time Series Analysis and Machine Learning

    , M.Sc. Thesis Sharif University of Technology Hashemian, Farid (Author) ; Akhavan Niaki, Taghi (Supervisor)
    Over the past few decades, with the exponential increase in data volume, scientists and researchers have tried to discover relationships and algorithms for productivity and find useful information from this amount of data in various fields. Their efforts in data analysis have led to the development of algorithms in the big data field. The result of researchers' working in multiple fields has come to aid the people of science and technology. Among the most important of these areas, we can mention the health and medical sectors, financial sectors, services, manufacturing sectors, etc. The purpose of this study is to enter the financial industry and use data mining tools. One of the newest and... 

    House Value Forecasting Based on Time Series

    , M.Sc. Thesis Sharif University of Technology Ahmadi, Shahrzad (Author) ; Shavandi, Hassan (Supervisor) ; Khedmati, Majid (Supervisor)
    Making money and maintaining the value of assets has always been one of the most important concerns of people. Real estate is one of the essential human needs, but it is also considered an investment tool for individuals. In addition to individuals in a family, various groups and organizations such as policymakers, analysts, banks and financial institutions, taxpayers, and real estate investors are directly or indirectly affected by the dynamic characteristic of the housing market. Therefore, forecasting the exact amount of housing value in the future is very important. Factors that can improve this forecasting's accuracy include considering the relationship between housing value and... 

    Real Time Trend Forecasting of Noisy Signal Using Deep Recurrent LSTM Network

    , M.Sc. Thesis Sharif University of Technology Aghaee, Arman (Author) ; Vosoughi Vahdat, Bijan (Supervisor)
    Artificial neural networks are mathematical models inspired by the nervous system and brain. The types and applications of these networks are very widespread nowadays, and it seems that they can be used to track the signals well and estimate the data of the next. In this research, we try to present a model that can predict the future of the trend of noisy signals that have unpredictable behavior, or in other words, chaotic signals. Such research is also widely used in the medical sciences, including the diagnosis of epileptic seizures or heart attacks. In this research, a study with high volatility financial data has been done as an example on this issue and the proposed model tries to be... 

    A Machine Learning and Time-Frequency Domain Combined Approach for Improving Stock Portfolio Management

    , Ph.D. Dissertation Sharif University of Technology Dezhkam, Arsalan (Author) ; Manzuri, Mohammad Taghi (Supervisor)
    Price prediction in financial markets is an exciting problem for a vast majority of groups and people; however, investment portfolio managers and owners are always looking for holistic predic-tion approaches and tools having high functional accurate metrics. Strictly speaking, players in fi-nancial markets are always in search of methods and toolboxes since they need to overcome the un-certainty of their buy, sell, or hold decisions in order to reduce the investment risk. In this research, we have tried to deal with the stock price prediction problem as an asset pricing problem and find a novel approach to push forward the state-of-the-art of the problem based on the fundamental pric-ing... 

    Crop Classification using Sentinel-Image Timeseries and Deep Learning

    , M.Sc. Thesis Sharif University of Technology Ghafourian Akbarzadeh, Mahnoosh (Author) ; Manzuri, Mohammad Taghi (Supervisor)
    Crop classification is one of the most important applications of remote sensing in agriculture. Knowing what crops are on the farm is invaluable both on a micro and macro scale. For example, this information can be used to design and imple- ment agricultural policies, product management and ensure food security. Also, this information can be used as a prerequisite for implementing other programs at the farm scale, such as monitoring and detecting anomalies during the crop growth cycle. Most of the studies in this field are focused on the optical data of the Sentinel-2 satel- lite, but the optical data are vulnerable to atmospheric conditions, and on the other hand, there is valuable... 

    Accurate and novel recommendations: an algorithm based on popularity forecasting

    , Article ACM Transactions on Intelligent Systems and Technology ; Vol. 5, issue. 4 , 2015 Javari, A ; Jalili, M ; Sharif University of Technology
    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... 

    Development of a robust identifier for NPPs transients combining ARIMA model and ebp algorithm

    , Article IEEE Transactions on Nuclear Science ; Vol. 61, issue. 4 , August , 2014 , p. 2383-2391 Moshkbar-Bakhshayesh, K ; Ghofrani, M. B ; Sharif University of Technology
    This study introduces a novel identification method for recognition of nuclear power plants (NPPs) transients by combining the autoregressive integrated moving-average (ARIMA) model and the neural network with error back-propagation (EBP) learning algorithm. The proposed method consists of three steps. First, an EBP based identifier is adopted to distinguish the plant normal states from the faulty ones. In the second step, ARIMA models use integrated (I) process to convert non-stationary data of the selected variables into stationary ones. Subsequently, ARIMA processes, including autoregressive (AR), moving-average (MA), or autoregressive moving-average (ARMA) are used to forecast time... 

    Exploring self-organized criticality conditions in Iran bulk power system with disturbance times series

    , Article Scientia Iranica ; Vol. 21, issue. 6 , 2014 , p. 2264-2272 ; 10263098 Karimi, E ; Ebrahimi, A ; Fotuhi-Firuzabad, M ; Sharif University of Technology
    Ubiquitous power-law as a fingerprint of Self-Organized Criticality (SOC) is used for describing catastrophic events in different fields. In this paper, by investigating the prerequisites of SOC, we show that SOC-like dynamics drive a correlation among disturbances in Iranian bulk power systems. The existence of power-law regions in probability distribution is discussed for empirical data using maximum likelihood estimation. To verify the results, long time correlation is evaluated in terms of Hurst exponents, by means of statistical analysis of time series, including Rescaled Range (R/S) and Scaled Windowed Variance (SWV) analysis. Also, sensitivity analysis showed that for correct... 

    Estimating the parameters of globular cluster M 30 (NGC 7099) from time-series photometry

    , Article Astronomy and Astrophysics ; Volume 555 , 2013 ; 00046361 (ISSN) Kains, N ; Bramich, D. M ; Arellano Ferro, A ; Figuera Jaimes, R ; Jørgensen, U. G ; Giridhar, S ; Penny, M. T ; Alsubai, K. A ; Andersen, J. M ; Bozza, V ; Browne, P ; Burgdorf, M ; Calchi Novati, S ; Damerdji, Y ; Diehl, C ; Dodds, P ; Dominik, M ; Elyiv, A ; Fang, X. S ; Giannini, E ; Gu, S. H ; Hardis, S ; Harpsøe, K ; Hinse, T. C ; Hornstrup, A ; Hundertmark, M ; Jessen Hansen, J ; Juncher, D ; Kerins, E ; Kjeldsen, H ; Korhonen, H ; Liebig, C ; Lund, M. N ; Lundkvist, M ; Mancini, L ; Martin, R ; Mathiasen, M ; Rabus, M ; Rahvar, S ; Ricci, D ; Sahu, K ; Scarpetta, G ; Skottfelt, J ; Snodgrass, C ; Southworth, J ; Surdej, J ; Tregloan Reed, J ; Vilela, C ; Wertz, O ; Williams, A
    Aims. We present the analysis of 26 nights of V and I time-series observations from 2011 and 2012 of the globular cluster M 30 (NGC 7099). We used our data to search for variable stars in this cluster and refine the periods of known variables; we then used our variable star light curves to derive values for the cluster's parameters. Methods. We used difference image analysis to reduce our data to obtain high-precision light curves of variable stars. We then estimated the cluster parameters by performing a Fourier decomposition of the light curves of RR Lyrae stars for which a good period estimate was possible. We also derived an estimate for the age of the cluster by fitting theoretical... 

    Early detection of apnea-bradycardia episodes in preterm infants based on coupled hidden Markov model

    , Article IEEE International Symposium on Signal Processing and Information Technology, IEEE ISSPIT 2013 ; 2013 , Pages 243-248 Masoudi, S ; Montazeri, N ; Shamsollahi, M. B ; Ge, D ; Beuchee, A ; Pladys, P ; Hernandez, A. I ; Sharif University of Technology
    IEEE Computer Society  2013
    The incidence of apnea-bradycardia episodes in preterm infants may lead to neurological disorders. Prediction and detection of these episodes are an important task in healthcare systems. In this paper, a coupled hidden Markov model (CHMM) based method is applied to detect apnea-bradycardia episodes. This model is evaluated and compared with two other methods based on hidden Markov model (HMM) and hidden semi-Markov model (HSMM). Evaluation and comparison are performed on a dataset of 233 apnea-bradycardia episodes which have been manually annotated. Observations are composed of RR-interval time series and QRS duration time series. The performance of each method was evaluated in terms of... 

    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...