Search results for: linear predictive coding

Authors: Chanhoe Kang, Changhyun Lee, Seock-Hee Jun, Yeong-Tae Oh

Abstract:

Offshore floating structure under the various environmental conditions maintains a fixed position by mooring system. Environmental conditions, vessel motions and mooring loads are applied to mooring lines as the dynamic tension. Because global responses of mooring system in deep water are specified as wave frequency and low frequency response, they should be calculated from the time-domain analysis due to non-linear dynamic characteristics. To take into account all mooring loads, environmental conditions, added mass and damping terms at each time step, a lot of computation time and capacities are required. Thus, under the premise that reliable fatigue damage could be derived through reasonable analysis method, it is necessary to reduce the analysis cases through the sensitivity studies and appropriate assumptions. In this paper, effects in fatigue are studied for spread mooring system connected with oil FPSO which is positioned in deep water of West Africa offshore. The target FPSO with two Mbbls storage has 16 spread mooring lines (4 bundles x 4 lines). The various sensitivity studies are performed for environmental loads, type of responses, vessel offsets, mooring position, loading conditions and riser behavior. Each parameter applied to the sensitivity studies is investigated from the effects of fatigue damage through fatigue analysis. Based on the sensitivity studies, the following results are presented: Wave loads are more dominant in terms of fatigue than other environment conditions. Wave frequency response causes the higher fatigue damage than low frequency response. The larger vessel offset increases the mean tension and so it results in the increased fatigue damage. The external line of each bundle shows the highest fatigue damage by the governed vessel pitch motion due to swell wave conditions. Among three kinds of loading conditions, ballast condition has the highest fatigue damage due to higher tension. The riser damping occurred by riser behavior tends to reduce the fatigue damage. The various analysis results obtained from these sensitivity studies can be used for a simplified fatigue analysis of spread mooring line as the reference.

Keywords: Mooring system, fatigue analysis, time domain, non-linear dynamic characteristics.

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Authors: Norfifah Bachok, Norihan Md. Arifin

Abstract:

In this paper we use classical linear stability theory to investigate the effects of uniform internal heat generation on the onset of Marangoni convection in a horizontal layer of fluid heated from below. We use a analytical technique to obtain the close form analytical expression for the onset of Marangoni convection when the lower boundary is conducting with free-slip condition. We show that the effect of increasing the internal heat generation is always to destabilize the layer.

Keywords: Marangoni convection, heat generation, free-slip

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Authors: Zhizhong Jiang, Craig Comstock

Abstract:

The past decade has seen enormous growth in the amount of software produced. However, given the ever increasing complexity of the software being developed and the concomitant rise in the typical project size, managers are becoming increasingly aware of the importance of issues that influence the productivity levels of the project teams involved. By analyzing the latest release of ISBSG data repository, we report on the factors found to significantly influence the productivity among which average team size and language type are the two most essential ones. Building on this we present an original model for evaluating the potential productivity during the project planning stage.

Keywords: ISBSG, Linear Model, Productivity, SoftwareEngineering.

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Authors: Liping Jing, Michael K. Ng, Tieyong Zeng

Abstract:

Microarray data profiles gene expression on a whole genome scale, therefore, it provides a good way to study associations between gene expression and occurrence or progression of cancer. More and more researchers realized that microarray data is helpful to predict cancer sample. However, the high dimension of gene expressions is much larger than the sample size, which makes this task very difficult. Therefore, how to identify the significant genes causing cancer becomes emergency and also a hot and hard research topic. Many feature selection algorithms have been proposed in the past focusing on improving cancer predictive accuracy at the expense of ignoring the correlations between the features. In this work, a novel framework (named by SGS) is presented for stable gene selection and efficient cancer prediction . The proposed framework first performs clustering algorithm to find the gene groups where genes in each group have higher correlation coefficient, and then selects the significant genes in each group with Bayesian Lasso and important gene groups with group Lasso, and finally builds prediction model based on the shrinkage gene space with efficient classification algorithm (such as, SVM, 1NN, Regression and etc.). Experiment results on real world data show that the proposed framework often outperforms the existing feature selection and prediction methods, say SAM, IG and Lasso-type prediction model.

Keywords: Gene Selection, Cancer Prediction, Lasso, Clustering, Classification.

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Authors: Adnene Arbi, Chaouki Aouiti, Abderrahmane Touati

Abstract:

In this paper, we consider the uniform asymptotic stability, global asymptotic stability and global exponential stability of the equilibrium point of discrete Hopfield neural networks with delays. Some new stability criteria for system are derived by using the Lyapunov functional method and the linear matrix inequality approach, for estimating the upper bound of Lyapunov functional derivative.

Keywords: Hopfield neural networks, uniform asymptotic stability, global asymptotic stability, exponential stability.

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Authors: F. Abdul Majeed, H. Karki, Y. Abdel Magid, M. Karkoub

Abstract:

This paper analyses the non linear properties exhibited by a drill string system under various un balanced mass conditions. The drill string is affected by continuous friction in the form of drill bit and well bore hole interactions. This paper proves the origin of limit cycling and increase of non linearity with increase in speed of the drilling in the presence of friction. The spectrum of the frequency response is also studied to detect the presence of vibration abnormalities arising during the drilling process.

Keywords: Drill strings, Nonlinear, Spectrum analysis, Unbalanced mass

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Authors: Y. Kumru, K. Enhos, H. Köymen

Abstract:

In this paper, we report the experimental results on using complementary Golay coded signals at 7.5 MHz to detect breast microcalcifications of 50 µm size. Simulations using complementary Golay coded signals show perfect consistence with the experimental results, confirming the improved signal to noise ratio for complementary Golay coded signals. For improving the success on detecting the microcalcifications, orthogonal complementary Golay sequences having cross-correlation for minimum interference are used as coded signals and compared to tone burst pulse of equal energy in terms of resolution under weak signal conditions. The measurements are conducted using an experimental ultrasound research scanner, Digital Phased Array System (DiPhAS) having 256 channels, a phased array transducer with 7.5 MHz center frequency and the results obtained through experiments are validated by Field-II simulation software. In addition, to investigate the superiority of coded signals in terms of resolution, multipurpose tissue equivalent phantom containing series of monofilament nylon targets, 240 µm in diameter, and cyst-like objects with attenuation of 0.5 dB/[MHz x cm] is used in the experiments. We obtained ultrasound images of monofilament nylon targets for the evaluation of resolution. Simulation and experimental results show that it is possible to differentiate closely positioned small targets with increased success by using coded excitation in very weak signal conditions.

Keywords: Coded excitation, complementary Golay codes, DiPhAS, medical ultrasound.

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Authors: Daniela Matei, Radu Matei

Abstract:

In this paper a class of analog algorithms based on the concept of Cellular Neural Network (CNN) is applied in some processing operations of some important medical images, namely retina images, for detecting various symptoms connected with diabetic retinopathy. Some specific processing tasks like morphological operations, linear filtering and thresholding are proposed, the corresponding template values are given and simulations on real retina images are provided.

Keywords: Diabetic retinopathy, pathology detection, cellular neural networks, analog algorithms.

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Authors: Swarniv Chandra, Basudev Ghosh

Abstract:

Nonlinear solitary structures of electron plasma waves have been investigated by using nonlinear quantum fluid equations for electrons with an arbitrary temperature. It is shown that the electron degeneracy parameter has significant effects on the linear and nonlinear properties of electron plasma waves. Depending on its value both compressive and rarefactive solitons can be excited in the model plasma under consideration.

Keywords: Electron Plasma Waves, Finite Temperature Model, Modulational Instability, Quantum Plasma, Solitary structure

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Authors: Subir Kundu, Sukhendra Singh, Sumedha Ojha, Kanika Kundu

Abstract:

The continuous decline of petroleum and natural gas reserves and non linear rise of oil price has brought about a realisation of the need for a change in our perpetual dependence on the fossil fuel. A day to day increased consumption of crude and petroleum products has made a considerable impact on our foreign exchange reserves. Hence, an alternate resource for the conversion of energy (both liquid and gas) is essential for the substitution of conventional fuels. Biomass is the alternate solution for the present scenario. Biomass can be converted into both liquid as well as gaseous fuels and other feedstocks for the industries.

Keywords: Bioenergy, Biomass conversion, Biorefining, Efficient utilisation of night soil.

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Authors: Young-Seok Choi

Abstract:

We present a new framework of the data-reusing (DR) adaptive algorithms by incorporating a constraint on noise, referred to as a noise constraint. The motivation behind this work is that the use of the statistical knowledge of the channel noise can contribute toward improving the convergence performance of an adaptive filter in identifying a noisy linear finite impulse response (FIR) channel. By incorporating the noise constraint into the cost function of the DR adaptive algorithms, the noise constrained DR (NC-DR) adaptive algorithms are derived. Experimental results clearly indicate their superior performance over the conventional DR ones.

Keywords: Adaptive filter, data-reusing, least-mean square (LMS), affine projection (AP), noise constraint.

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Authors: Akbar H. Borzabadi, Omid S. Fard

Abstract:

In this paper we introduce an approach via optimization methods to find approximate solutions for nonlinear Fredholm integral equations of the first kind. To this purpose, we consider two stages of approximation. First we convert the integral equation to a moment problem and then we modify the new problem to two classes of optimization problems, non-constraint optimization problems and optimal control problems. Finally numerical examples is proposed.

Keywords: Fredholm integral equation, Optimization method, Optimal control, Nonlinear and linear programming

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Authors: A. Giniatoulline

Abstract:

A nonlinear model of the mathematical fluid dynamics which describes the motion of an incompressible viscous rotating fluid in a homogeneous gravitational field is considered. The model is a generalization of the known Navier-Stokes system with the addition of the Coriolis parameter and the equations for changeable density. An explicit algorithm for the solution is constructed, and the proof of the existence and uniqueness theorems for the strong solution of the nonlinear problem is given. For the linear case, the localization and the structure of the spectrum of inner waves are also investigated.

Keywords: Galerkin method, Navier-Stokes equations, nonlinear partial differential equations, Sobolev spaces, stratified fluid.

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Authors: Hong Li, Shou-ming Zhong, Hou-biao Li

Abstract:

In this paper, some brief sufficient conditions for the stability of FO-LTI systems dαx(t) dtα = Ax(t) with the fractional order are investigated when the matrix A and the fractional order α are uncertain or both α and A are uncertain, respectively. In addition, we also relate the stability of a fractional-order system with order 0 < α ≤ 1 to the stability of its equivalent fractional-order system with order 1 ≤ β < 2, the relationship between α and β is presented. Finally, a numeric experiment is given to demonstrate the effectiveness of our results.

Keywords: Interval fractional-order systems, linear matrix inequality (LMI), asymptotical stability.

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Authors: Chao Yuan, Bao Guang Tian

Abstract:

A relative efficiency is defined as Ridge Estimate in the general linear model. The relative efficiency is based on the Mean square error. In this paper, we put forward a parameter of Ridge Estimate and discussions are made on the relative efficiency between the ridge estimation and the General Ridge Estimate. Eventually, this paper proves that the estimation is better than the general ridge estimate, which is based on the MSE.

Keywords: Ridge estimate, generalized ridge estimate, MSE, relative efficiency.

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Authors: Ming-Hui Lee, Shou-Jen Hsu

Abstract:

This study presents an active vibration control technique to reduce the earthquake responses of a retained structural system. The proposed technique is a synthesis of the adaptive input estimation method (AIEM) and linear quadratic Gaussian (LQG) controller. The AIEM can estimate an unknown system input online. The LQG controller offers optimal control forces to suppress wall-structural system vibration. The numerical results show robust performance in the active vibration control technique.

Keywords: Active vibration control, AIEM, LQG, Optimal control

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Authors: M. Farahnakian, M.R. Razfar, S. Elhami-Joosheghan

Abstract:

This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, depth of cut and engagement) leading to minimum surface roughness in face milling of high silicon stainless steel by coupling neural network (NN) and Electromagnetism-like Algorithm (EM). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and Electromagnetism-like optimization method are exploited in an integrated manner. To this end, numerous experiments on this stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness is created by using a back propogation neural network, then the optimization problem was solved by using EM optimization. Additional experiments were performed to validate optimum surface roughness value predicted by EM algorithm. It is clearly seen that a good agreement is observed between the predicted values by EM coupled with feed forward neural network and experimental measurements. The obtained results show that the EM algorithm coupled with back propogation neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.

Keywords: cutting parameters, face milling, surface roughness, artificial neural network, Electromagnetism-like algorithm,

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Authors: Mahmoud E. Mohamed, Ahmed F. Shalash, Hanan A. Kamal

Abstract:

In communication systems, frequency jump is a serious problem caused by the oscillators used. Kalman filters are used to detect that jump, despite the tradeoff between the noise level and the speed of the detection. In this paper, an improvement is introduced in the Kalman filter, through a nonlinear change in the bandwidth of the filter. Simulation results show a considerable improvement in the filter speed with a very low noise level. Additionally, the effect on the response to false alarms is also presented and false alarm rate show improvement.

Keywords: Kalman Filter, Innovation, False Detection.

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Authors: Ricardo C. Silva, Luiza A. P. Cantao, Akebo Yamakami

Abstract:

Based on the fuzzy set theory this work develops two adaptations of iterative methods that solve mathematical programming problems with uncertainties in the objective function and in the set of constraints. The first one uses the approach proposed by Zimmermann to fuzzy linear programming problems as a basis and the second one obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. We outline similarities between the two iterative methods studied. Selected examples from the literature are presented to validate the efficiency of the methods addressed.

Keywords: Fuzzy Theory, Nonlinear Optimization, Fuzzy Mathematics Programming.

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Authors: Rajneesh Rani, Renu Dhir, G. S. Lehal

Abstract:

Script identification is one of the challenging steps in the development of optical character recognition system for bilingual or multilingual documents. In this paper an attempt is made for identification of English numerals at word level from Punjabi documents by using Gabor features. The support vector machine (SVM) classifier with five fold cross validation is used to classify the word images. The results obtained are quite encouraging. Average accuracy with RBF kernel, Polynomial and Linear Kernel functions comes out to be greater than 99%.

Keywords: Script identification, gabor features, support vector machines.

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Authors: Nga-Viet Nguyen, Georgy Shevlyakov, Vladimir Shin

Abstract:

To solve the problem of multisensor data fusion under non-Gaussian channel noise. The advanced M-estimates are known to be robust solution while trading off some accuracy. In order to improve the estimation accuracy while still maintaining the equivalent robustness, a two-stage robust fusion algorithm is proposed using preliminary rejection of outliers then an optimal linear fusion. The numerical experiments show that the proposed algorithm is equivalent to the M-estimates in the case of uncorrelated local estimates and significantly outperforms the M-estimates when local estimates are correlated.

Keywords: Data fusion, estimation, robustness, M-estimates.

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Authors: Rachid Zentar, Mokrane Bala, Pascal Boustingorry

Abstract:

The prediction of the rheological behavior of concrete is at the center of current concerns of the concrete industry for different reasons. The shortage of good quality standard materials combined with variable properties of available materials imposes to improve existing models to take into account these variations at the design stage of concrete. The main reasons for improving the predictive models are, of course, saving time and cost at the design stage as well as to optimize concrete performances. In this study, we will highlight the different properties of the granular mixtures that affect the rheological properties of concrete. Our objective is to identify the intrinsic parameters of the aggregates which make it possible to predict the yield stress of concrete. The work was done using two typologies of grains: crushed and rolled aggregates. The experimental results have shown that the rheology of concrete is improved by increasing the packing density of the granular mixture using rolled aggregates. The experimental program realized allowed to model the yield stress of concrete by a modified model of Chateau et al. through a dimensionless parameter following Krieger-Dougherty law. The modelling confirms that the yield stress of concrete depends not only on the properties of cement paste but also on the packing density of the granular skeleton and the shape of grains.

Keywords: Crushed aggregates, intrinsic viscosity, packing density, rolled aggregates, slump, yield stress of concrete.

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Authors: Subir Kundu, Sukhendra Singh, Sumedha Ojha, Kanika Kundu

Abstract:

The continuous decline of petroleum and natural gas reserves and non linear rise of oil price has brought about a realisation of the need for a change in our perpetual dependence on the fossil fuel. A day to day increased consumption of crude and petroleum products has made a considerable impact on our foreign exchange reserves. Hence, an alternate resource for the conversion of energy (both liquid and gas) is essential for the substitution of conventional fuels. Biomass is the alternate solution for the present scenario. Biomass can be converted into both liquid as well as gaseous fuels and other feedstocks for the industries.

Keywords: Bioenergy, Biomass conversion, Biorefining, Efficient utilisation of night soil.

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Authors: Jožef Ritonja

Abstract:

The biochemical industry is one of the most important modern industries. Biochemical reactors are crucial devices of the biochemical industry. The essential bioprocess carried out in bioreactors is the fermentation process. A thorough insight into the fermentation process and the knowledge how to control it are essential for effective use of bioreactors to produce high quality and quantitatively enough products. The development of the control system starts with the determination of a mathematical model that describes the steady state and dynamic properties of the controlled plant satisfactorily, and is suitable for the development of the control system. The paper analyses the fermentation process in bioreactors thoroughly, using existing mathematical models. Most existing mathematical models do not allow the design of a control system for controlling the fermentation process in batch bioreactors. Due to this, a mathematical model was developed and presented that allows the development of a control system for batch bioreactors. Based on the developed mathematical model, a control system was designed to ensure optimal response of the biochemical quantities in the fermentation process. Due to the time-varying and non-linear nature of the controlled plant, the conventional control system with a proportional-integral-differential controller with constant parameters does not provide the desired transient response. The improved adaptive control system was proposed to improve the dynamics of the fermentation. The use of the adaptive control is suggested because the parameters’ variations of the fermentation process are very slow. The developed control system was tested to produce dairy products in the laboratory bioreactor. A carbon dioxide concentration was chosen as the controlled variable. The carbon dioxide concentration correlates well with the other, for the quality of the fermentation process in significant quantities. The level of the carbon dioxide concentration gives important information about the fermentation process. The obtained results showed that the designed control system provides minimum error between reference and actual values of carbon dioxide concentration during a transient response and in a steady state. The recommended control system makes reference signal tracking much more efficient than the currently used conventional control systems which are based on linear control theory. The proposed control system represents a very effective solution for the improvement of the milk fermentation process.

Keywords: Bioprocess engineering, biochemical reactor, fermentation process, modeling, adaptive control.

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Authors: Randhir Singh Baghel, Govind Prasad Sahu

Abstract:

An individual seasonality index is used to study the seasonality of rainfall over India. The seasonality indicator is examined for two time periods: early (1901-1970) and recent (1971-2015). In some regions of India throughout the recent time (1971-2015), trend analysis using linear regression during these two periods reveals a downward trend in the seasonality index (i.e., decreasing values of the index), which implies shorter dry spells resulting in more consistent rainfall throughout the year.

Keywords: Individual seasonality index, rainfall distribution, seasonality index, climate.

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Authors: Shishen Xie

Abstract:

In this article two algorithms, one based on variation iteration method and the other on Adomian's decomposition method, are developed to find the numerical solution of an initial value problem involving the non linear integro differantial equation where R is a nonlinear operator that contains partial derivatives with respect to x. Special cases of the integro-differential equation are solved using the algorithms. The numerical solutions are compared with analytical solutions. The results show that these two methods are efficient and accurate with only two or three iterations

Keywords: variation iteration method, decomposition method, nonlinear integro-differential equations

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Authors: Farhan Ismaeel Dakheel, Raoof Smko, K. Negrat, Abdelsalam Almarimi

Abstract:

In this paper we used data mining techniques to identify outlier patients who are using large amount of drugs over a long period of time. Any healthcare or health insurance system should deal with the quantities of drugs utilized by chronic diseases patients. In Kingdom of Bahrain, about 20% of health budget is spent on medications. For the managers of healthcare systems, there is no enough information about the ways of drug utilization by chronic diseases patients, is there any misuse or is there outliers patients. In this work, which has been done in cooperation with information department in the Bahrain Defence Force hospital; we select the data for Cardiac patients in the period starting from 1/1/2008 to December 31/12/2008 to be the data for the model in this paper. We used three techniques for finding the drug utilization for cardiac patients. First we applied a clustering technique, followed by measuring of clustering validity, and finally we applied a decision tree as classification algorithm. The clustering results is divided into three clusters according to the drug utilization, for 1603 patients, who received 15,806 prescriptions during this period can be partitioned into three groups, where 23 patients (2.59%) who received 1316 prescriptions (8.32%) are classified to be outliers. The classification algorithm shows that the use of average drug utilization and the age, and the gender of the patient can be considered to be the main predictive factors in the induced model.

Keywords: Data Mining, Clustering, Classification, Drug Utilization..

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Authors: Vinicius Binotte, Guilherme Baldo, Christiane Goulart, Carlos Valadão, Eliete Caldeira, Teodiano Bastos

Abstract:

Socially assistive robotic has become increasingly active and it is present in therapies of people affected for several neurobehavioral conditions, such as Autism Spectrum Disorder (ASD). In fact, robots have played a significant role for positive interaction with children with ASD, by stimulating their social and cognitive skills. This work introduces a mobile socially-assistive robot, which was built for interaction with children with ASD, using non-linear control techniques for this interaction.

Keywords: Socially assistive robotics, mobile robot, autonomous control, autism.

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Authors: Haifeng Wang, Haili Zhang

Abstract:

Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: Computational social science, movie preference, machine learning, SVM.

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Authors: Mohammad R. Jalali, Mohammad M. Jalali

Abstract:

The earliest theories of sloshing waves and solitary waves based on potential theory idealisations and irrotational flow have been extended to be applicable to more realistic domains. To this end, the computational fluid dynamics (CFD) methods are widely used. Three-dimensional CFD methods such as Navier-Stokes solvers with volume of fluid treatment of the free surface and Navier-Stokes solvers with mappings of the free surface inherently impose high computational expense; therefore, considerable effort has gone into developing depth-averaged approaches. Examples of such approaches include Green–Naghdi (GN) equations. In Cartesian system, GN velocity profile depends on horizontal directions, x-direction and y-direction. The effect of vertical direction (z-direction) is also taken into consideration by applying weighting function in approximation. GN theory considers the effect of vertical acceleration and the consequent non-hydrostatic pressure. Moreover, in GN theory, the flow is rotational. The present study illustrates the application of GN equations to propagation of sloshing waves and solitary waves. For this purpose, GN equations solver is verified for the benchmark tests of Gaussian hump sloshing and solitary wave propagation in shallow basins. Analysis of the free surface sloshing of even harmonic components of an initial Gaussian hump demonstrates that the GN model gives predictions in satisfactory agreement with the linear analytical solutions. Discrepancies between the GN predictions and the linear analytical solutions arise from the effect of wave nonlinearities arising from the wave amplitude itself and wave-wave interactions. Numerically predicted solitary wave propagation indicates that the GN model produces simulations in good agreement with the analytical solution of the linearised wave theory. Comparison between the GN model numerical prediction and the result from perturbation analysis confirms that nonlinear interaction between solitary wave and a solid wall is satisfactorilly modelled. Moreover, solitary wave propagation at an angle to the x-axis and the interaction of solitary waves with each other are conducted to validate the developed model.

Keywords: Even harmonic components of sloshing waves, Green–Naghdi equations, nonlinearity, solitary waves.

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