Search results for: polynomial regression.
884 Churn Prediction: Does Technology Matter?
Authors: John Hadden, Ashutosh Tiwari, Rajkumar Roy, Dymitr Ruta
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The aim of this paper is to identify the most suitable model for churn prediction based on three different techniques. The paper identifies the variables that affect churn in reverence of customer complaints data and provides a comparative analysis of neural networks, regression trees and regression in their capabilities of predicting customer churn.Keywords: Churn, Decision Trees, Neural Networks, Regression.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3284883 Cryptographic Attack on Lucas Based Cryptosystems Using Chinese Remainder Theorem
Authors: Tze Jin Wong, Lee Feng Koo, Pang Hung Yiu
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Lenstra’s attack uses Chinese remainder theorem as a tool and requires a faulty signature to be successful. This paper reports on the security responses of fourth and sixth order Lucas based (LUC4,6) cryptosystem under the Lenstra’s attack as compared to the other two Lucas based cryptosystems such as LUC and LUC3 cryptosystems. All the Lucas based cryptosystems were exposed mathematically to the Lenstra’s attack using Chinese Remainder Theorem and Dickson polynomial. Result shows that the possibility for successful Lenstra’s attack is less against LUC4,6 cryptosystem than LUC3 and LUC cryptosystems. Current study concludes that LUC4,6 cryptosystem is more secure than LUC and LUC3 cryptosystems in sustaining against Lenstra’s attack.Keywords: Lucas sequence, Dickson Polynomial, faulty signature, corresponding signature, congruence.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 720882 Categorical Data Modeling: Logistic Regression Software
Authors: Abdellatif Tchantchane
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A Matlab based software for logistic regression is developed to enhance the process of teaching quantitative topics and assist researchers with analyzing wide area of applications where categorical data is involved. The software offers an option of performing stepwise logistic regression to select the most significant predictors. The software includes a feature to detect influential observations in data, and investigates the effect of dropping or misclassifying an observation on a predictor variable. The input data may consist either as a set of individual responses (yes/no) with the predictor variables or as grouped records summarizing various categories for each unique set of predictor variables' values. Graphical displays are used to output various statistical results and to assess the goodness of fit of the logistic regression model. The software recognizes possible convergence constraints when present in data, and the user is notified accordingly.
Keywords: Logistic regression, Matlab, Categorical data, Influential observation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1874881 Research on the Problems of Housing Prices in Qingdao from a Macro Perspective
Authors: Liu Zhiyuan, Sun Zongdi, Liu Zhiyuan, Sun Zongdi
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Qingdao is a seaside city. Taking into account the characteristics of Qingdao, this article established a multiple linear regression model to analyze the impact of macroeconomic factors on housing prices. We used stepwise regression method to make multiple linear regression analysis, and made statistical analysis of F test values and T test values. According to the analysis results, the model is continuously optimized. Finally, this article obtained the multiple linear regression equation and the influencing factors, and the reliability of the model was verified by F test and T test.
Keywords: Housing prices, multiple linear regression model, macroeconomic factors, Qingdao City.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1168880 Advanced ILQ Control for Buck-Converter viaTwo-Degrees of Freedom Servo-System
Authors: Sidshchadhaa Aumted, Shuhei Shiina, Hiroshi Takami
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In this paper, we propose an advanced ILQ control for the buck-converter via two-degrees of freedom servo-system. Our presented strategy is based on Inverse Linear Quadratic (ILQ) servo-system controller without solving Riccati-s equation directly. The optimal controller of the current and voltage control system is designed. The stability and robust control are analyzed. A conscious and persistent effort has been made to improve ILQ control via two-degrees of freedom guarantees the optimal gains on the basis of polynomial pole assignment, which our results of the proposed strategy shows that the advanced ILQ control can be controlled independently the step response and the disturbance response by appending a feed-forward compensator.
Keywords: Optimal voltage control, inverse LQ design method, second order polynomial, two-degrees of freedom.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1970879 Estimating Regression Parameters in Linear Regression Model with a Censored Response Variable
Authors: Jesus Orbe, Vicente Nunez-Anton
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In this work we study the effect of several covariates X on a censored response variable T with unknown probability distribution. In this context, most of the studies in the literature can be located in two possible general classes of regression models: models that study the effect the covariates have on the hazard function; and models that study the effect the covariates have on the censored response variable. Proposals in this paper are in the second class of models and, more specifically, on least squares based model approach. Thus, using the bootstrap estimate of the bias, we try to improve the estimation of the regression parameters by reducing their bias, for small sample sizes. Simulation results presented in the paper show that, for reasonable sample sizes and censoring levels, the bias is always smaller for the new proposals.
Keywords: Censored response variable, regression, bias.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1464878 The Analogue of a Property of Pisot Numbers in Fields of Formal Power Series
Authors: Wiem Gadri
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This study delves into the intriguing properties of Pisot and Salem numbers within the framework of formal Laurent series over finite fields, a domain where these numbers’ spectral characteristics, Λm(β) and lm(β), have yet to be fully explored. Utilizing a methodological approach that combines algebraic number theory with the analysis of power series, we extend the foundational work of Erdos, Joo, and Komornik to this setting. Our research uncovers bounds for lm(β), revealing how these depend on the degree of the minimal polynomial of β and thus offering a characterization of Pisot and Salem formal power series. The findings significantly contribute to our understanding of these numbers, highlighting their distribution and properties in the context of formal power series. This investigation not only bridges number theory with formal power series analysis but also sets the stage for further interdisciplinary research in these areas.
Keywords: Pisot numbers, Salem numbers, Formal power series, Minimal polynomial degree.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 114877 Adjusted Ratio and Regression Type Estimators for Estimation of Population Mean when some Observations are missing
Authors: Nuanpan Nangsue
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Ratio and regression type estimators have been used by previous authors to estimate a population mean for the principal variable from samples in which both auxiliary x and principal y variable data are available. However, missing data are a common problem in statistical analyses with real data. Ratio and regression type estimators have also been used for imputing values of missing y data. In this paper, six new ratio and regression type estimators are proposed for imputing values for any missing y data and estimating a population mean for y from samples with missing x and/or y data. A simulation study has been conducted to compare the six ratio and regression type estimators with a previous estimator of Rueda. Two population sizes N = 1,000 and 5,000 have been considered with sample sizes of 10% and 30% and with correlation coefficients between population variables X and Y of 0.5 and 0.8. In the simulations, 10 and 40 percent of sample y values and 10 and 40 percent of sample x values were randomly designated as missing. The new ratio and regression type estimators give similar mean absolute percentage errors that are smaller than the Rueda estimator for all cases. The new estimators give a large reduction in errors for the case of 40% missing y values and sampling fraction of 30%.
Keywords: Auxiliary variable, missing data, ratio and regression type estimators.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1721876 Friction Behavior of Wood-Plastic Composites against Uncoated Cemented Carbide
Authors: A. Vilutis, V. Jankauskas
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The paper presents the results of the investigation of the dry sliding friction of wood-plastic composites (WPCs) against tungsten carbide-cobalt (WC-Co) hard alloy. The dependence of the dynamic coefficient of friction on the main influencing factors (vertical load, temperature, and sliding distance) was investigated by evaluating their mutual interaction. Multiple regression analysis showed a high polynomial dependence (adjusted R2 > 0.98). The resistance of the composite to thermo-mechanical effects determines how temperature and force factors affect the magnitude of the coefficient of friction. WPC-B composite has the lowest friction and highest resistance compared to WPC-A, while composite and cemented carbide materials wear the least. Energy Dispersive Spectroscopy (EDS), based on elemental composition, provided important insights into the friction process.
Keywords: Friction, composite, carbide, temperature.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 55875 A Technique for Improving the Performance of Median Smoothers at the Corners Characterized by Low Order Polynomials
Authors: E. Srinivasan, D. Ebenezer
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Median filters with larger windows offer greater smoothing and are more robust than the median filters of smaller windows. However, the larger median smoothers (the median filters with the larger windows) fail to track low order polynomial trends in the signals. Due to this, constant regions are produced at the signal corners, leading to the loss of fine details. In this paper, an algorithm, which combines the ability of the 3-point median smoother in preserving the low order polynomial trends and the superior noise filtering characteristics of the larger median smoother, is introduced. The proposed algorithm (called the combiner algorithm in this paper) is evaluated for its performance on a test image corrupted with different types of noise and the results obtained are included.
Keywords: Image filtering, detail preservation, median filters, nonlinear filters, order statistics filtering, Rank order filtering.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1365874 A Contribution to the Polynomial Eigen Problem
Authors: Malika Yaici, Kamel Hariche, Tim Clarke
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The relationship between eigenstructure (eigenvalues and eigenvectors) and latent structure (latent roots and latent vectors) is established. In control theory eigenstructure is associated with the state space description of a dynamic multi-variable system and a latent structure is associated with its matrix fraction description. Beginning with block controller and block observer state space forms and moving on to any general state space form, we develop the identities that relate eigenvectors and latent vectors in either direction. Numerical examples illustrate this result. A brief discussion of the potential of these identities in linear control system design follows. Additionally, we present a consequent result: a quick and easy method to solve the polynomial eigenvalue problem for regular matrix polynomials.
Keywords: Eigenvalues/Eigenvectors, Latent values/vectors, Matrix fraction description, State space description.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1878873 Quality of Service Evaluation using a Combination of Fuzzy C-Means and Regression Model
Authors: Aboagela Dogman, Reza Saatchi, Samir Al-Khayatt
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In this study, a network quality of service (QoS) evaluation system was proposed. The system used a combination of fuzzy C-means (FCM) and regression model to analyse and assess the QoS in a simulated network. Network QoS parameters of multimedia applications were intelligently analysed by FCM clustering algorithm. The QoS parameters for each FCM cluster centre were then inputted to a regression model in order to quantify the overall QoS. The proposed QoS evaluation system provided valuable information about the network-s QoS patterns and based on this information, the overall network-s QoS was effectively quantified.Keywords: Fuzzy C-means; regression model, network quality of service
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1708872 Defect Cause Modeling with Decision Tree and Regression Analysis
Authors: B. Bakır, İ. Batmaz, F. A. Güntürkün, İ. A. İpekçi, G. Köksal, N. E. Özdemirel
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The main aim of this study is to identify the most influential variables that cause defects on the items produced by a casting company located in Turkey. To this end, one of the items produced by the company with high defective percentage rates is selected. Two approaches-the regression analysis and decision treesare used to model the relationship between process parameters and defect types. Although logistic regression models failed, decision tree model gives meaningful results. Based on these results, it can be claimed that the decision tree approach is a promising technique for determining the most important process variables.Keywords: Casting industry, decision tree algorithm C5.0, logistic regression, quality improvement.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2510871 Performance Analysis of Adaptive LMS Filter through Regression Analysis using SystemC
Authors: Hyeong-Geon Lee, Jae-Young Park, Suk-ki Lee, Jong-Tae Kim
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The LMS adaptive filter has several parameters which can affect their performance. From among these parameters, most papers handle the step size parameter for controlling the performance. In this paper, we approach three parameters: step-size, filter tap-size and filter form. The regression analysis is used for defining the relation between parameters and performance of LMS adaptive filter with using the system level simulation results. The results present that all parameters have performance trends in each own particular form, which can be estimated from equations drawn by regression analysis.
Keywords: System level model, adaptive LMS FIR filter, regression analysis, systemC.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2787870 Density Estimation using Generalized Linear Model and a Linear Combination of Gaussians
Authors: Aly Farag, Ayman El-Baz, Refaat Mohamed
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In this paper we present a novel approach for density estimation. The proposed approach is based on using the logistic regression model to get initial density estimation for the given empirical density. The empirical data does not exactly follow the logistic regression model, so, there will be a deviation between the empirical density and the density estimated using logistic regression model. This deviation may be positive and/or negative. In this paper we use a linear combination of Gaussian (LCG) with positive and negative components as a model for this deviation. Also, we will use the expectation maximization (EM) algorithm to estimate the parameters of LCG. Experiments on real images demonstrate the accuracy of our approach.
Keywords: Logistic regression model, Expectationmaximization, Segmentation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1725869 Multiple Regression based Graphical Modeling for Images
Authors: Pavan S., Sridhar G., Sridhar V.
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Super resolution is one of the commonly referred inference problems in computer vision. In the case of images, this problem is generally addressed using a graphical model framework wherein each node represents a portion of the image and the edges between the nodes represent the statistical dependencies. However, the large dimensionality of images along with the large number of possible states for a node makes the inference problem computationally intractable. In this paper, we propose a representation wherein each node can be represented as acombination of multiple regression functions. The proposed approach achieves a tradeoff between the computational complexity and inference accuracy by varying the number of regression functions for a node.
Keywords: Belief propagation, Graphical model, Regression, Super resolution.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1535868 Design of Digital IIR filters with the Advantages of Model Order Reduction Technique
Authors: K.Ramesh, A.Nirmalkumar, G.Gurusamy
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In this paper, a new model order reduction phenomenon is introduced at the design stage of linear phase digital IIR filter. The complexity of a system can be reduced by adopting the model order reduction method in their design. In this paper a mixed method of model order reduction is proposed for linear IIR filter. The proposed method employs the advantages of factor division technique to derive the reduced order denominator polynomial and the reduced order numerator is obtained based on the resultant denominator polynomial. The order reduction technique is used to reduce the delay units at the design stage of IIR filter. The validity of the proposed method is illustrated with design example in frequency domain and stability is also examined with help of nyquist plot.Keywords: Error index (J), Factor division method, IIR filter, Nyquist plot, Order reduction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1773867 An Attack on the Lucas Based El-Gamal Cryptosystem in the Elliptic Curve Group Over Finite Field Using Greater Common Divisor
Authors: Lee Feng Koo, Tze Jin Wong, Pang Hung Yiu, Nik Mohd Asri Nik Long
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Greater common divisor (GCD) attack is an attack that relies on the polynomial structure of the cryptosystem. This attack required two plaintexts differ from a fixed number and encrypted under same modulus. This paper reports a security reaction of Lucas Based El-Gamal Cryptosystem in the Elliptic Curve group over finite field under GCD attack. Lucas Based El-Gamal Cryptosystem in the Elliptic Curve group over finite field was exposed mathematically to the GCD attack using GCD and Dickson polynomial. The result shows that the cryptanalyst is able to get the plaintext without decryption by using GCD attack. Thus, the study concluded that it is highly perilous when two plaintexts have a slight difference from a fixed number in the same Elliptic curve group over finite field.
Keywords: Decryption, encryption, elliptic curve, greater common divisor.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 690866 Monomial Form Approach to Rectangular Surface Modeling
Authors: Taweechai Nuntawisuttiwong, Natasha Dejdumrong
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Geometric modeling plays an important role in the constructions and manufacturing of curve, surface and solid modeling. Their algorithms are critically important not only in the automobile, ship and aircraft manufacturing business, but are also absolutely necessary in a wide variety of modern applications, e.g., robotics, optimization, computer vision, data analytics and visualization. The calculation and display of geometric objects can be accomplished by these six techniques: Polynomial basis, Recursive, Iterative, Coefficient matrix, Polar form approach and Pyramidal algorithms. In this research, the coefficient matrix (simply called monomial form approach) will be used to model polynomial rectangular patches, i.e., Said-Ball, Wang-Ball, DP, Dejdumrong and NB1 surfaces. Some examples of the monomial forms for these surface modeling are illustrated in many aspects, e.g., construction, derivatives, model transformation, degree elevation and degress reduction.Keywords: Monomial form, rectangular surfaces, CAGD curves, monomial matrix applications.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 690865 Estimation of Time -Varying Linear Regression with Unknown Time -Volatility via Continuous Generalization of the Akaike Information Criterion
Authors: Elena Ezhova, Vadim Mottl, Olga Krasotkina
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The problem of estimating time-varying regression is inevitably concerned with the necessity to choose the appropriate level of model volatility - ranging from the full stationarity of instant regression models to their absolute independence of each other. In the stationary case the number of regression coefficients to be estimated equals that of regressors, whereas the absence of any smoothness assumptions augments the dimension of the unknown vector by the factor of the time-series length. The Akaike Information Criterion is a commonly adopted means of adjusting a model to the given data set within a succession of nested parametric model classes, but its crucial restriction is that the classes are rigidly defined by the growing integer-valued dimension of the unknown vector. To make the Kullback information maximization principle underlying the classical AIC applicable to the problem of time-varying regression estimation, we extend it onto a wider class of data models in which the dimension of the parameter is fixed, but the freedom of its values is softly constrained by a family of continuously nested a priori probability distributions.Keywords: Time varying regression, time-volatility of regression coefficients, Akaike Information Criterion (AIC), Kullback information maximization principle.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1523864 Comparison of Neural Network and Logistic Regression Methods to Predict Xerostomia after Radiotherapy
Authors: Hui-Min Ting, Tsair-Fwu Lee, Ming-Yuan Cho, Pei-Ju Chao, Chun-Ming Chang, Long-Chang Chen, Fu-Min Fang
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To evaluate the ability to predict xerostomia after radiotherapy, we constructed and compared neural network and logistic regression models. In this study, 61 patients who completed a questionnaire about their quality of life (QoL) before and after a full course of radiation therapy were included. Based on this questionnaire, some statistical data about the condition of the patients’ salivary glands were obtained, and these subjects were included as the inputs of the neural network and logistic regression models in order to predict the probability of xerostomia. Seven variables were then selected from the statistical data according to Cramer’s V and point-biserial correlation values and were trained by each model to obtain the respective outputs which were 0.88 and 0.89 for AUC, 9.20 and 7.65 for SSE, and 13.7% and 19.0% for MAPE, respectively. These parameters demonstrate that both neural network and logistic regression methods are effective for predicting conditions of parotid glands.
Keywords: NPC, ANN, logistic regression, xerostomia.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1625863 Bioprocess Optimization Based On Relevance Vector Regression Models and Evolutionary Programming Technique
Authors: R. Simutis, V. Galvanauskas, D. Levisauskas, J. Repsyte
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This paper proposes a bioprocess optimization procedure based on Relevance Vector Regression models and evolutionary programming technique. Relevance Vector Regression scheme allows developing a compact and stable data-based process model avoiding time-consuming modeling expenses. The model building and process optimization procedure could be done in a half-automated way and repeated after every new cultivation run. The proposed technique was tested in a simulated mammalian cell cultivation process. The obtained results are promising and could be attractive for optimization of industrial bioprocesses.
Keywords: Bioprocess optimization, Evolutionary programming, Relevance Vector Regression.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2182862 Performance Analysis of Proprietary and Non-Proprietary Tools for Regression Testing Using Genetic Algorithm
Authors: K. Hema Shankari, R. Thirumalaiselvi, N. V. Balasubramanian
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The present paper addresses to the research in the area of regression testing with emphasis on automated tools as well as prioritization of test cases. The uniqueness of regression testing and its cyclic nature is pointed out. The difference in approach between industry, with business model as basis, and academia, with focus on data mining, is highlighted. Test Metrics are discussed as a prelude to our formula for prioritization; a case study is further discussed to illustrate this methodology. An industrial case study is also described in the paper, where the number of test cases is so large that they have to be grouped as Test Suites. In such situations, a genetic algorithm proposed by us can be used to reconfigure these Test Suites in each cycle of regression testing. The comparison is made between a proprietary tool and an open source tool using the above-mentioned metrics. Our approach is clarified through several tables.Keywords: APFD metric, genetic algorithm, regression testing, RFT tool, test case prioritization, selenium tool.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 906861 A Fuzzy Nonlinear Regression Model for Interval Type-2 Fuzzy Sets
Authors: O. Poleshchuk, E.Komarov
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This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions.
Keywords: Interval type-2 fuzzy sets, fuzzy regression, weighted interval.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2206860 An Adaptive Hand-Talking System for the Hearing Impaired
Authors: Zhou Yu, Jiang Feng
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An adaptive Chinese hand-talking system is presented in this paper. By analyzing the 3 data collecting strategies for new users, the adaptation framework including supervised and unsupervised adaptation methods is proposed. For supervised adaptation, affinity propagation (AP) is used to extract exemplar subsets, and enhanced maximum a posteriori / vector field smoothing (eMAP/VFS) is proposed to pool the adaptation data among different models. For unsupervised adaptation, polynomial segment models (PSMs) are used to help hidden Markov models (HMMs) to accurately label the unlabeled data, then the "labeled" data together with signerindependent models are inputted to MAP algorithm to generate signer-adapted models. Experimental results show that the proposed framework can execute both supervised adaptation with small amount of labeled data and unsupervised adaptation with large amount of unlabeled data to tailor the original models, and both achieve improvements on the performance of recognition rate.Keywords: sign language recognition, signer adaptation, eMAP/VFS, polynomial segment model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1749859 Improved Neutron Leakage Treatment on Nodal Expansion Method for PWR Reactors
Authors: Antonio Carlos Marques Alvim, Fernando Carvalho da Silva, Aquilino Senra Martinez
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For a quick and accurate calculation of spatial neutron distribution in nuclear power reactors 3D nodal codes are usually used aiming at solving the neutron diffusion equation for a given reactor core geometry and material composition. These codes use a second order polynomial to represent the transverse leakage term. In this work, a nodal method based on the well known nodal expansion method (NEM), developed at COPPE, making use of this polynomial expansion was modified to treat the transverse leakage term for the external surfaces of peripheral reflector nodes. The proposed method was implemented into a computational system which, besides solving the diffusion equation, also solves the burnup equations governing the gradual changes in material compositions of the core due to fuel depletion. Results confirm the effectiveness of this modified treatment of peripheral nodes for practical purposes in PWR reactors.Keywords: Transverse leakage, nodal expansion method, power density, PWR reactors
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2021858 Lagrange and Multilevel Wavelet-Galerkin with Polynomial Time Basis for Heat Equation
Authors: Watcharakorn Thongchuay, Puntip Toghaw, Montri Maleewong
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The Wavelet-Galerkin finite element method for solving the one-dimensional heat equation is presented in this work. Two types of basis functions which are the Lagrange and multi-level wavelet bases are employed to derive the full form of matrix system. We consider both linear and quadratic bases in the Galerkin method. Time derivative is approximated by polynomial time basis that provides easily extend the order of approximation in time space. Our numerical results show that the rate of convergences for the linear Lagrange and the linear wavelet bases are the same and in order 2 while the rate of convergences for the quadratic Lagrange and the quadratic wavelet bases are approximately in order 4. It also reveals that the wavelet basis provides an easy treatment to improve numerical resolutions that can be done by increasing just its desired levels in the multilevel construction process.Keywords: Galerkin finite element method, Heat equation , Lagrange basis function, Wavelet basis function.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1721857 Forecast of the Small Wind Turbines Sales with Replacement Purchases and with or without Account of Price Changes
Authors: V. Churkin, M. Lopatin
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The purpose of the paper is to estimate the US small wind turbines market potential and forecast the small wind turbines sales in the US. The forecasting method is based on the application of the Bass model and the generalized Bass model of innovations diffusion under replacement purchases. In the work an exponential distribution is used for modeling of replacement purchases. Only one parameter of such distribution is determined by average lifetime of small wind turbines. The identification of the model parameters is based on nonlinear regression analysis on the basis of the annual sales statistics which has been published by the American Wind Energy Association (AWEA) since 2001 up to 2012. The estimation of the US average market potential of small wind turbines (for adoption purchases) without account of price changes is 57080 (confidence interval from 49294 to 64866 at P = 0.95) under average lifetime of wind turbines 15 years, and 62402 (confidence interval from 54154 to 70648 at P = 0.95) under average lifetime of wind turbines 20 years. In the first case the explained variance is 90,7%, while in the second - 91,8%. The effect of the wind turbines price changes on their sales was estimated using generalized Bass model. This required a price forecast. To do this, the polynomial regression function, which is based on the Berkeley Lab statistics, was used. The estimation of the US average market potential of small wind turbines (for adoption purchases) in that case is 42542 (confidence interval from 32863 to 52221 at P = 0.95) under average lifetime of wind turbines 15 years, and 47426 (confidence interval from 36092 to 58760 at P = 0.95) under average lifetime of wind turbines 20 years. In the first case the explained variance is 95,3%, while in the second – 95,3%.Keywords: Bass model, generalized Bass model, replacement purchases, sales forecasting of innovations, statistics of sales of small wind turbines in the United States.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1873856 Design and Implementation of Reed Solomon Encoder on FPGA
Authors: Amandeep Singh, Mandeep Kaur
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Error correcting codes are used for detection and correction of errors in digital communication system. Error correcting coding is based on appending of redundancy to the information message according to a prescribed algorithm. Reed Solomon codes are part of channel coding and withstand the effect of noise, interference and fading. Galois field arithmetic is used for encoding and decoding reed Solomon codes. Galois field multipliers and linear feedback shift registers are used for encoding the information data block. The design of Reed Solomon encoder is complex because of use of LFSR and Galois field arithmetic. The purpose of this paper is to design and implement Reed Solomon (255, 239) encoder with optimized and lesser number of Galois Field multipliers. Symmetric generator polynomial is used to reduce the number of GF multipliers. To increase the capability toward error correction, convolution interleaving will be used with RS encoder. The Design will be implemented on Xilinx FPGA Spartan II.
Keywords: Galois Field, Generator polynomial, LFSR, Reed Solomon.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4833855 Predicting Bridge Pier Scour Depth with SVM
Authors: Arun Goel
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Prediction of maximum local scour is necessary for the safety and economical design of the bridges. A number of equations have been developed over the years to predict local scour depth using laboratory data and a few pier equations have also been proposed using field data. Most of these equations are empirical in nature as indicated by the past publications. In this paper attempts have been made to compute local depth of scour around bridge pier in dimensional and non-dimensional form by using linear regression, simple regression and SVM (Poly & Rbf) techniques along with few conventional empirical equations. The outcome of this study suggests that the SVM (Poly & Rbf) based modeling can be employed as an alternate to linear regression, simple regression and the conventional empirical equations in predicting scour depth of bridge piers. The results of present study on the basis of non-dimensional form of bridge pier scour indicate the improvement in the performance of SVM (Poly & Rbf) in comparison to dimensional form of scour.Keywords: Modeling, pier scour, regression, prediction, SVM (Poly & Rbf kernels).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1533