Levenburg-Marquardt Algorithm Research Articles

DRM-Based Colour Photometric Stereo Using Diffuse-Specular Separation for Non-Lambertian Surfaces.

This paper presents a photometric stereo (PS) method based on the dichromatic reflectance model (DRM) using colour images. The proposed method estimates surface orientations for surfaces with non-Lambertian reflectance using diffuse-specular separation and contains two steps. The first step, referred to as diffuse-specular separation, initialises surface orientations in a specular invariant colour subspace and further separates the diffuse and specular components in the RGB space. In the second step, the surface orientations are refined by first initialising specular parameters via solving a log-linear regression problem owing to the separation and then fitting the DRM using Levenburg-Marquardt algorithm. Since reliable information from diffuse reflection free from specularities is adopted in the initialisations, the proposed method is robust and feasible with less observations. At pixels where dense non-Lambertian reflectances appear, signals from specularities are exploited to refine the surface orientations and the additionally acquired specular parameters are potentially valuable for more applications, such as digital relighting. The effectiveness of the newly proposed surface normal refinement step was evaluated and the accuracy in estimating surface orientations was enhanced around on average by including this step. The proposed method was also proven effective in an experiment using synthetic input images comprised of twenty-four different reflectances of dielectric materals. A comparison with nine other PS methods on five representative datasets further prove the validity of the proposed method.

An empirical equation for the gas compressibility factor, Z

An empirical equation is presented for the gas compressibility factor, Z, as a function of reduced temperature and reduced pressure. The average error of the equation is 2.5%, median error is 1.3%, and the coefficient of determination (R2) is 0.98. The equation is presented in a manner ready for use in software code. Comparison is made to the traditional generalized compressibility factor chart. The equation was found using genetic programming for symbolic regression coupled with the Levenburg-Marquardt algorithm for fitting-parameter optimization.

Combination linear lines of position and neural network for mobile station location estimation

To enhance the effectiveness and accuracy of mobile station location estimation, author utilizes time of arrival measurements from three base stations and one angle of arrival information at the serving base station to locate mobile station in non-line-of-sight environments. This article makes use of linear lines of position, rather than circular lines of position, to give location estimation of the mobile station. It is much easier to solve two linear line equations rather than nonlinear circular ones. Artificial neural networks are widely used techniques in various areas due to overcoming the problem of exclusive and nonlinear relationships. The proposed algorithms employ the intersections of three linear lines of position and one angle of arrival line, based on Levenburg–Marquardt algorithm, to determine the mobile station location without requiring a priori information about the non-line-of-sight error. The simulation results show that the proposed algorithms can always provide much better location estimation than Taylor series algorithm, hybrid lines of position algorithm as well as the geometrical positioning methods for different levels of biased, unbiased, and distance-dependent non-line-of-sight errors.

Analysis of Liquid Viscosity by Image Processing Techniques

Objective: In this paper, a technique is proposed for measurement of viscosity using the principle of dispersion of incident light with variation of liquid viscosity. The objective of the proposed technique is to design a non contact technique to analyze the characteristics of dispersed image using image processing for the light incident on liquid whose viscosity is to be measured. Methods: A camera is used to capture the background image of the refracted image. This captured image is processed by techniques like thresholding, filtering, and histogram to arrive at a quantified relation between viscosity and histogram values. To establish the relation an artificial neural network model is designed. Proposed neural network is trained by Levenburg Marquardt Algorithm, once trained it is tested with the real time system conditions. Findings: Once the neural network is designed, tests are conducted. Several samples of liquid are used with varying viscosity. From the obtained results it is clear that the proposed technique is able to measure viscosity accurately with a root mean percentage of error 2.01%. Application: From the obtained results it is clear that the proposed viscosity measurement technique can be used for measurement of liquid viscosity, even in dynamic flow conditions.

Fast and accurate global motion estimation algorithm using pixel subsampling

Global motion generally describes the motion of a camera, although it may comprise motions of large objects. Global motions are often modeled by parametric transformations of two-dimensional images. The process of estimating the motions parameters is called global motion estimation (GME). GME is widely employed in many applications such as video coding, image stabilization and super-resolution. To estimate global motion parameters, the Levenburg–Marquardt algorithm (LMA) is typically used to minimize an objective function iteratively. Since the region of support for the global motion representation consists of the entire image frame, the minimization process tends to be very expensive computationally by involving all the pixels within an image frame. In order to significantly reduce the computational complexity of the LMA, we proposed to select only a small subset of the pixels for estimating the motion parameters, based on several subsampling patterns and their combinations. Simulation results demonstrated that the proposed method could speed up the conventional GME approach by over ten times, with only a very slight loss (less than 0.1 dB) in estimation accuracy. The proposed method was also found to outperform several state-of-the-art fast GME methods in terms of the speed/accuracy tradeoffs.

Survival kinetics of faecal bacterial indicators in spent broiler litter composting

The objective of this study was to deduce and analyse equations that best describe the behaviour of faecal bacterial indicators and two decomposition parameters during broiler litter composting. Mathematical models were fitted and the order of rate equations were identified. The Levenburg-Marquardt algorithm was used to fit nonlinear mathematical models to total coliforms (TC), faecal coliforms (FC), Escherichia coli (EC), faecal enterococci (FE), organic-C and volatile solids reduction, VS Red, by the least squares procedure. The rate equations showed that TC, FC and EC reductions were expressed by second-order decay kinetics. FE reduction followed first-order decay. Temperature dependency of decomposition rate was effectively verified by applying empirically derived rate equations. The governing mathematical models critically compare the inactivation kinetics of faecal indicators. TC, FC and EC were rapidly destroyed while FE was more resistant. Temperature elevation, organic-C and VS Red dynamics provide an accurate understanding of composting-induced decomposition of the broiler litter. The conservative performance of FE with respect to the other indicators has been established. Hence, FE presents better opportunities to encompass the totality of the composting process in terms of attainment of hygiene efficacy compared with EC.

Implementation of an artificial-neural-network-based real-time adaptive controller for an interior permanent-magnet motor drive

This paper presents the implementation of an artificial-neural-network (ANN)-based real-time adaptive controller for accurate speed control of an interior permanent-magnet synchronous motor (IPMSM) under system uncertainties. A field-oriented IPMSM model is used to decouple the flux and torque components of the motor dynamics. The initial estimation of coefficients of the proposed ANN speed controller is obtained by offline training method. Online training has been carried out to update the ANN under continuous mode of operation. Dynamic backpropagation with the Levenburg-Marquardt algorithm is utilized for online training purposes. The controller is implemented in real time using a digital-signal-processor-based hardware environment to prove the feasibility of the proposed method. The simulation and experimental results reveal that the control architecture adapts and generalizes its learning to a wide range of operating conditions and provides promising results under parameter variations and load changes.

Urban change detection based on an artificial neural network

A method based on an artificial neural network (ANN) was developed to detect newly urbanized areas depicted in satellite sensor images. The method uses two Landsat Thematic Mapper (TM) images of a region acquired on different dates as input and supervises the ANN to classify the image data into 'from-to' classes. Principal component analysis (PCA) was applied to extract the salient features and to reduce the dimensionality of the input data prior to the ANN-based change detection. The Levenburg-Marquardt algorithm was used to accelerate the ANN's convergence. Experimental results from a case study show the ANN-based method requires only modest training time but can be 20-30% more accurate than post-classification comparison. PCA not only reduced the computational cost but improved the change detection accuracy as well. The results suggest the practical value of ANN-based change detection.

Identification of passive knee joint and shank dynamics in paraplegics using quadriceps stimulation

The dynamics of the knee-joint-freely-swinging-lower-leg system of three sitting paraplegic patients was identified using a parameterized analytical model. Surface electrodes were used for stimulation. The output of the knee joint-lower leg system was the angular position, velocity and acceleration. A model structure was taken from the literature and its complexity minimized based on the experiments and offline identification. The resulting model structure facilitates possible recursive identification. Model parameters of the lower leg dynamics were estimated using a least-squares algorithm in combination with the Levenburg-Marquardt algorithm. The intersubject variability in parameter values for the knee joint damping and the gravitational component appeared to be small when normalized to the inertia of the lower leg. The identified model for the knee joint and shank dynamics accurately predicted passive lower leg movement and could be used to estimate the magnitude of active knee torque due to elicited quadriceps contraction.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Modeling the isovolumic relaxation period

The isovolumic relaxation period of the left ventricular pressure curve in man has been assumed to be best represented by an exponential decay. To determine which model most closely approximates the empiric pressure data of isovolumic relaxation in man, several models were compared. They included linear, exponential with a zero mmHg pressure asymptote, exponential with a variable asymptote, and second-to fifth-order polynomials. In addition, four different methods of computing parameters of isovolumic relaxation by the exponential model with a variable asymptote were tested. It was found that the isovolumic relaxation period approximates an exponential, that the theoretic asymptote is variable, and that the Levenburg-Marquardt algorithm can be used efficiently to model this period.