Order Linear Model Research Articles

An extended Levinson-Durbin algorithm and its application in mixed excitation linear prediction

Ten order all-pole model is used in the 2400 bit/second mixed excitation linear prediction to describe human vocal tract. Traditional Levinson-Durbin algorithm is one of the methods to solve the Yule-Walker equations conducted by the ten order linear prediction model. Taking the iteration step of traditional Levinson-Durbin algorithm as 1, an extended algorithm with any positive integer iteration step which is no larger than the order of Teoplitz matrix is proposed. The extended algorithm considers interaction between the adjacent subtracts. A hybrid algorithm of the extended algorithm and traditional algorithm has been applied to solve the 2400 bit/second mixed excitation linear prediction under some conditions. The perceptual evaluation of speech quality mean opinion score of nasal syllable is improved in some degree.

Fuzzy-based Gain Adaptive Scheme for Set-Point Modulated Model Reference Adaptive Controller

In model reference adaptive controller (MRACs), the adaptive gain of the controller is varied according to the process dynamic variation as it is directly related with the system stability. In MRAC, there is no provision of an automatic selection of adaptive gain and adaptation rate. To get rid of this problem and for the automatic selection of adaptive gain, a fuzzy-based scheme is presented in this article. In the proposed fuzzy-based technique, the controller output gain is illustrated as the function of input process parameters, which is continuously amended for any process parameter variations. A set-point modulation scheme is also incorporated to tackle the undesired process parameter variations and to improve performance indices of the process under control. The performance of the proposed set-point modulated fuzzy-based model reference adaptive controller (SFMRAC) is demonstrated on different second order linear, marginally stable and nonlinear models. The scheme is also explored on a real-time twin arm overhead crane for transport of material without pendulation.

Identification-based controller design using cloud model for course-keeping of ships in waves

Course-keeping plays an important role in ensuring navigation safety of ships. This contribution proposed new approaches about system identification and controller design to facilitate the main goal which is to design a controller for course-keeping of ships based on the identified ship plant and the cloud model. The investigated plant as the control model was the first order linear Nomoto model. In order to estimate the parameters of this model, the support vector machines (SVM) optimized by artificial bee colony algorithm (ABC) was applied in combination with simulated data including the rudder and heading angles generated by the dynamic model of a Mariner class cargo ship. Based on the identified linear Nomoto model of Mariner class cargo ship, the cloud model was then applied to design the controller for course-keeping. To well demonstrate the feasibility and effectiveness of the proposed controller, a fuzzy logic PID controller, and a PID controller were also considered as comparison mechanisms. Aiming at validating the robustness of the proposed cloud model-based controller, it was used to compensate for the critical environmental disturbances induced by waves. Finally, simulation results indicate the desirable performance of ABC on optimizing parameters in SVM. Comparison with PID and fuzzy logic PID controllers demonstrates that the cloud model-based controller presents slightly preferable performance on course-keeping. This is due to that the flexible and intuitive modification of the digital characteristics of the cloud model and the structure of cloud inference engines makes the cloud model efficient to satisfy the required mapping between inputs and outputs for a controller.

Simplified performance correlation of an indirect evaporative cooling system: Development and validation

At present, there is an increasing interest in indirect evaporative coolers (IEC), which are an effective alternative to conventional cooling systems due to their high efficiency. In this study a new simplified first order linear regression model of an IEC system has been developed. The model is based on experimental data and results of the proposed correlation have been compared with ones of a phenomenological model and with further measurements collected in significant operating conditions (data center, desiccant evaporative cooling cycle and residential application). It is shown that deviations of wet bulb effectiveness are limited to 3.4% for the simplified model and 2.1% for the detailed one. Therefore, due to its good accuracy and low computational load, the simplified model could be suitable to be implemented in simulation tools.

Virtual Unmodeled Dynamics Modeling for Nonlinear Multivariable Adaptive Control With Decoupling Design

For a class of complex industrial processes with nonlinear, strongly coupled multivariable properties, a new multivariable decoupling design framework which based on the concepts of virtual unmodeled dynamics (VUD) and lower order linear models is proposed in this paper. First, a self-tuning multivariable decoupling controller is constructed based on a lower order model. Then based on the compensator of the VUD, a nonlinear multivariable decoupling controller is designed, where a decomposition estimation algorithm is employed for modeling the VUD. In our proposed scheme, it solves the problem that the current input signal is embedded in the VUD and the true input data vector used by the learner model is difficult to be obtained in time. The linear and nonlinear decoupling controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features. Finally, the stability and convergence of the proposed algorithm is analyzed. Experimental tests on a heavily coupled nonlinear twin-tank system are carried out to demonstrate the effectiveness and the practicability of the proposed method.

Frequency estimation of a sinusoidal signal with time-varying amplitude and phase

Abstract This paper is devoted to frequency estimation of a non-stationary sinusoidal signal. The amplitude is supposed to be a known function within a constant factor, the phase should be known. Example of such problem statement is sensorless angular velocity estimation for permanent magnet synchronous motors. On the first step by reparametrization, a third order linear regression model is obtained. On the next step, an estimation algorithm is constructed based on a standard gradient approach. The frequency estimate can be computed from one of the model parameters using inverse trigonometric functions. To improve estimates quality for noisy measurements we propose a new identification method, which can be tuned to attenuate the noise influence. It is shown that the frequency estimation error converges to zero exponentially fast. The described algorithm does not require measuring or calculating derivatives of the input signal. The efficiency of the proposed approach is demonstrated through the set of numerical simulations.

Discriminating between first and second order linear and nonlinear models for optimality

In this paper, an examination of the relationship between a response variable and several explanatory variables was considered for first and second order regression models (with and without interaction). To achieve this, the behaviour of the controllable variables (i.e., reaction time, reaction temperature and moisture content) against response variable (drying rate of bush mango seeds) was examined using ordinary least square method with the aid of Microsoft Excel and Minitab 16. Furthermore, the comparison of the fitted models, using model adequacy criteria procedure and optimality criterion technique was also done. This was to determine the most suitable model that best predicts optimal response variable for given settings of the controllable variables. The result showed that the second order regression model with interaction was the most suitable model, and a new operating region in which a process or product may be improved was identified using optimising multivariable function. This research recommends the extreme points and the identified optimal value for production process.

Discriminating between first and second order linear and nonlinear models for optimality

In this paper, an examination of the relationship between a response variable and several explanatory variables was considered for first and second order regression models (with and without interaction). To achieve this, the behaviour of the controllable variables (i.e., reaction time, reaction temperature and moisture content) against response variable (drying rate of bush mango seeds) was examined using ordinary least square method with the aid of Microsoft Excel and Minitab 16. Furthermore, the comparison of the fitted models, using model adequacy criteria procedure and optimality criterion technique was also done. This was to determine the most suitable model that best predicts optimal response variable for given settings of the controllable variables. The result showed that the second order regression model with interaction was the most suitable model, and a new operating region in which a process or product may be improved was identified using optimising multivariable function. This research recommends the extreme points and the identified optimal value for production process.

Forecasting energy futures volatility based on the unbiased extreme value volatility estimator

This paper uses the opening, high, low, and closing prices of five energy futures to estimate and model volatility based on the unbiased extreme value volatility estimator (the Add RS estimator). The statistical and distributional properties of the logarithm of the Add RS estimator support the use of the appropriate order linear long-memory Gaussian model (ARFIMA-Add RS model) to model the Add RS estimator. The out-of-sample evaluation analysis indicates that the volatility forecasts based on the ARFIMA-Add RS model provide superior forecasts of realised volatility of energy futures in relation to the alternative models from the GARCH family. This suggests that the ARFIMA-Add RS model can be a viable candidate for generating more accurate forecasts of realised volatility for energy futures. The study has important implications for governments, policy makers, oil importers, and oil traders.

Identification-based simplified model of large container ships using support vector machines and artificial bee colony algorithm

The 6 degrees of freedom (DOF) model with a high degree of complexity for capturing ship dynamics is generally able to track the nonlinear and coupling dynamics of ships. However, the 6 DOF model makes challenges in estimating model coefficients and designing the model-based control. Therefore, simplified ship dynamic models within allowed accuracy are essential. This paper simplified the 6 DOF nonlinear dynamic model of ships into two decoupled models including the speed model and the steering model through reasonable assumptions. Those models were tested through maneuvering simulations of a container ship with a 4 DOF dynamic model. Support vector machines (SVM) optimized by the artificial bee colony algorithm (ABC) was used to identify parameters of speed and steering models by analyzing the rudder angle, propeller shaft speed, surge and sway velocities, and yaw rate from simulated data extracted from a series of maneuvers made by the container ship. Comparisons with the first order linear and nonlinear Nomoto models show that the simplified nonlinear steering model can capture more complicated dynamics and performs better. Additionally, comparisons among three different parameter identification methods demonstrate similar identification results but the different performance involving the applicability and effectiveness. SVM optimized by ABC is relatively convenient and effective for parameter identification of ship simplified dynamic models.

Nonparametric modelling of VO2 response to exercise.

This paper investigates the modelling of oxygen consumption (VO2) response to jogging exercise on treadmill. Unlike most of the previous methods, which often use simple parametric models, e.g., first order linear time invariant model, this study applied a nonparametric kernel based regularised method to estimate VO2 to address the ill-conditioned modelling problem and achieve accurate estimation. In particular, it is worthy to be noted that the selection of kernels will affect the results for different modelling scenarios. Therefore, in this research, both radial basis kernel and stable spline kernel were selected for testing. In order to select the favourable kernel for this system, a simulation related to VO2-jogging speed was carried out. The results of simulation indicated that spline kernel can achieve higher accuracy comparing to radial basis function kernel. Experimentally, the kernel based estimation method and spline kernel were tested using six participants. From the results, an average impulse response is obtained. It showed the VO2 estimation, based on the average finite impulse response, is fitted well to the six observations collected from the participants.

Frequency estimation of a sinusoidal signal with time-varying amplitude

This paper is devoted to frequency estimation of a sinusoidal signal with a time-varying amplitude. The amplitude can take zero values, but should be known and bounded. The first order linear regression model is obtained. The estimation algorithm has been constructed basing on standard gradient approach. It is shown that the frequency estimation error converges to zero exponentially fast. The described algorithm does not require measuring or calculating derivatives of the input signal. The efficiency of the proposed approach is demonstrated through a set of numerical simulations.

Application of Kalman filter to determination of coal liquefaction mechanisms using discrete time models

Seven different liquefaction mechanisms that consist of reversible and irreversible steps are suggested for a Turkish lignite (Tunçbilek) in tetralin with the use of microwave heating. Compliance of the proposed mechanisms are determined by forming first order linear Discrete-time models with the experimental data and reaction rate constants are determined with the program written in MATLAB with the use of the Kalman filter. The most appropriate suggested model is determined by looking at the sum of the experimental data and squared differences of the values calculated with the model. The magnitude of the reaction rate constants is examined for the models that best fit. The obtained results show that majority of the oil is formed directly from coal. Furthermore, whereas coal:oil reaction step has maximum reaction rate constant, coal:asphaltene and coal:preasphaltene reaction steps have minimum rate constant. Models containing parallel and reversible steps are seen to provide a better fit to the experimental data. The best fit was obtained with model that has both reversible (between asphaltene:coal, asphaltene:oil, and asphaltene:preasphaltene) and irreversible (coal:oil, coal:preasphaltene and preasphaltene:oil) reaction steps. The model also evidenced that the reversible reactions are critical on the liquefaction of Tunçbilek coal.

Restricted affine motion compensation and estimation in video coding with particle filtering and importance sampling: a multi-resolution approach

In this paper, we propose a multi-resolution affine block-based tracker for motion estimation and compensation, compatible with existing video coding standards such as H.264 and HEVC. We propose three modifications to traditional motion compensation techniques in video coding standards such as H.264 and HEVC. First, we replace traditional search methods with an efficient particle filtering-based method, which incorporates information from both spatial and temporal continuity. Second, we use a higher order linear model in place of the traditional translation motion model in these standards to efficiently represent complex motions such as rotation and zoom. Third, we propose a multi-resolution framework that enables efficient parameter estimation. Results of extensive experimentation show reduced residual energy and better Peak Signal-to-Noise Ratio (PSNR, hereafter) as compared to H.264/HEVC for instance, especially in regions of complex motion such as zooming and rotation.

System identification application using Hammerstein model

Generally, memoryless polynomial nonlinear model for nonlinear part and finite impulse response (FIR) model or infinite impulse response model for linear part are preferred in Hammerstein models in literature. In this paper, system identification applications of Hammerstein model that is cascade of nonlinear second order volterra and linear FIR model are studied. Recursive least square algorithm is used to identify the proposed Hammerstein model parameters. Furthermore, the results are compared to identify the success of proposed Hammerstein model and different types of models.

A new data-driven model for post-transplant antibody dynamics in high risk kidney transplantation

The dynamics of donor specific human leukocyte antigen antibodies during early stage after kidney transplantation are of great clinical interest as these antibodies are considered to be associated with short and long term clinical outcomes. The limited number of antibody time series and their diverse patterns have made the task of modelling difficult. Focusing on one typical post-transplant dynamic pattern with rapid falls and stable settling levels, a novel data-driven model has been developed for the first time. A variational Bayesian inference method has been applied to select the best model and learn its parameters for 39 time series from two groups of graft recipients, i.e. patients with and without acute antibody-mediated rejection (AMR) episodes. Linear and nonlinear dynamic models of different order were attempted to fit the time series, and the third order linear model provided the best description of the common features in both groups. Both deterministic and stochastic parameters are found to be significantly different in the AMR and no-AMR groups showing that the time series in the AMR group have significantly higher frequency of oscillations and faster dissipation rates. This research may potentially lead to better understanding of the immunological mechanisms involved in kidney transplantation.

Time domain analysis of the weighted distributed order rheological model

This paper presents the fundamental solution and relevant properties of the weighted distributed order rheological model in the time domain. Based on the construction of distributed order damper and the idea of distributed order element networks, this paper studies the weighted distributed order operator of the rheological model, a generalization of distributed order linear rheological model. The inverse Laplace transform on weighted distributed order operators of rheological model has been obtained by cutting the complex plane and computing the complex path integral along the Hankel path, which leads to the asymptotic property and boundary discussions. The relaxation response to weighted distributed order rheological model is analyzed, and it is closely related to many physical phenomena. A number of novel characteristics of weighted distributed order rheological model, such as power-law decay and intermediate phenomenon, have been discovered as well. And meanwhile several illustrated examples play important role in validating these results.

Less Daily Computer Use is Related to Smaller Hippocampal Volumes in Cognitively Intact Elderly.

Background: Computer use is becoming a common activity in the daily life of older individuals and declines over time in those with mild cognitive impairment (MCI). The relationship between daily computer use (DCU) and imaging markers of neurodegeneration is unknown.Objective:The objective of this study was to examine the relationship between average DCU and volumetric markers of neurodegeneration on brain MRI.Methods: Cognitively intact volunteers enrolled in the Intelligent Systems for Assessing Aging Change study underwent MRI. Total in-home computer use per day was calculated using mouse movement detection and averaged over a one-month period surrounding the MRI. Spearman’s rank order correlation (univariate analysis) and linear regression models (multivariate analysis) examined hippocampal, gray matter (GM), white matter hyperintensity (WMH), and ventricular cerebral spinal fluid (vCSF) volumes in relation to DCU. A voxel-based morphometry analysis identified relationships between regional GM density and DCU.Results: Twenty-seven cognitively intact participants used their computer for 51.3 minutes per day on average. Less DCU was associated with smaller hippocampal volumes (r = 0.48, p = 0.01), but not total GM, WMH, or vCSF volumes. After adjusting for age, education, and gender, less DCU remained associated with smaller hippocampal volume (p = 0.01). Voxel-wise analysis demonstrated that less daily computer use was associated with decreased GM density in the bilateral hippocampi and temporal lobes.Conclusions: Less daily computer use is associated with smaller brain volume in regions that are integral to memory function and known to be involved early with Alzheimer’s pathology and conversion to dementia. Continuous monitoring of daily computer use may detect signs of preclinical neurodegeneration in older individuals at risk for dementia.

Energy optimal depth control for long range underwater vehicles with applications to a hybrid underwater glider

The efficient control of a vehicle's depth is a ubiquitous need of underwater vehicles for long term deployments. Using standard hydrodynamic drag and lift formulations for a hybrid autonomous underwater glider (AUG) the performance penalty for long range underwater vehicles with a non zero buoyant force is shown to be significant. To this end, an energy optimal depth controller design methodology for a long range autonomous underwater vehicle is presented with applications to a propeller driven hybrid AUG during level flight. The method makes use of a reduced order linear model that has been validated from field data. The standard state space model is augmented with the state integral matrix and rewritten to the state error representation. The resulting representation is well suited to the computation of energy optimal gains for a linear quadratic regulator. Field demonstrations of the standard pitching depth controller and the energy optimal depth controller using the computed gains shows the reduced order model to be sufficient for the purpose of the controller design, improving on the standard pitching depth controller response, transport efficiency and ease of tuning.

The Design of Glass Fiber//Epoxy Composite Pipes by the Implementation of the Full Factorial Experimental Design

In the present work, the attempt was made to assess the applicability of the full factorial experimental design in predicting the hoop tensile strength of glass fiber/ epoxy resin composite pipes by using of a split disk specimens. Split disk tension tests, provide reasonably accurate information with regard to the apparent tensile strength of composite pipe.In the study we used a number of composite pipes with different fiber orientation, fiber tension and velocity of the winding. The composite pipes were made by using of filament winding technology includes winding of resin impregnated fibers into a tool and hardening of the wound structure. The preparation of the composite experimental samples was conducted in accordance with the 23 full factorial experimental design. The winding speed of the composites was taken to be the first factor, the second was the fiber tension and the third winding angle. The first factor low and high levels were set at 5,21 m/min and 21 m/min, respec­tively, for the second factor – at 64N and 110N, respectively, and for the third factor – at 100 and 900. To approxi­mate the response i.e. the hoop tensile strength of the composite pipes within the study domain (5,25 – 21) m/min x (64-110)N x (10 – 90)0, the first order linear model with the interaction was used. The influence of each individual factor to the response function was established, as well as the influence of the interaction of the two and three factors. We found out that the estimated first-degree regression equation with the interaction gave a very good approximation of the experimental results of the hoop tensile strength of composites within the study domain.