• Computers and Concrete
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• v.32 no.4
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• pp.399-410
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• 2023

This study proposes a novel use of backpropagated Levenberg-Marquardt neural networks based on computational intelligence heuristics to comprehend the examination of hybrid nanoparticles on the flow of dusty liquid via stretched cylinder. A two-phase model is employed in the present work to describe the fluid flow. The use of desulphated nanoparticles of silver and molybdenum suspended in water as base fluid. The mathematical model represented in terms of partial differential equations, Implementing similarity transformationsis model is converted to ordinary differential equations for the analysis . By adjusting the particle mass concentration and curvature parameter, a unique technique is utilized to generate a dataset for the proposed Levenberg-Marquardt neural networks in various nanoparticle circumstances on the flow of dusty liquid via stretched cylinder. The intelligent solver Levenberg-Marquardt neural networks is trained, tested and verified to identify the nanoparticles on the flow of dusty liquid solution for various situations. The Levenberg-Marquardt neural networks approach is applied for the solution of the hybrid nanoparticles on the flow of dusty liquid via stretched cylinder model. It is validated by comparison with the standard solution, regression analysis, histograms, and absolute error analysis. Strong agreement between proposed results and reference solutions as well as accuracy provide an evidence of the framework's validity.

• Journal of information and communication convergence engineering
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• v.2 no.3
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• pp.177-181
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• 2004

In this paper, we suggest an Iris recognition system with an excellent recognition rate and confidence as an alternative biometric recognition technique that solves the limit in an existing individual discrimination. For its implementation, we extracted coefficients feature values with the wavelet transformation mainly used in the signal processing, and we used neural network to see a recognition rate. However, Scale Conjugate Gradient of nonlinear optimum method mainly used in neural network is not suitable to solve the optimum problem for its slow velocity of convergence. So we intended to enhance the recognition rate by using Levenberg-Marquardt Back-propagation which supplements existing Scale Conjugate Gradient for an implementation of the iris recognition system. We improved convergence velocity, efficiency, and stability by changing properly the size according to both convergence rate of solution and variation rate of variable vector with the implementation of an applied algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.8 no.5
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• pp.534-544
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• 1997

The permittivity distribution of a two-dimensional high-contrast object with large size, which leads to the global minimum of cost function, is reconstructed by iteratively using the hybrid algorithm of Levenberg-magquardt algorithm(LMA) plus Genetic Algorithm(GA). The scattered fields calculated in a cost function are expanded in angular spectral modes, of which only effective propagating modes are used. The definition of cost function based on the effective propagating modes enables us to formulate the minimum number of incident waves for the reconstruction of object. It is numerically shown that LMA has an advantage of fast convergence but can't reconstruct a high-contrast object with large size and GA can reconstruct a high-contrast object with large size but has an disadvantage of slow convergence, whereas an inverse scattering technique using the hybrid algorithm adopts only advantages of LMA and GA.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.2
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• pp.303-315
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• 1997

The theoretical model for the transient probe method is the modified Jaeger model which is used perfect line source theory. The transient probe technique has been developed for the simultaneous determination of thermal conductivity, diffusivity and volumetric heat capacity of liquids. The Levenberg-Marquardt iteration method is adapted to obtain thermal property within nonlinear range. Experimental results of liquids were found to agree well with recommended thermal property data.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.768-776
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• 2011

This paper considers a data fitting problem for rational function models using the LM (Levenberg-Marquardt) optimization method. Rational function models have various merits on representing a wide range of shapes and modeling complicated structures by polynomials of low degrees in both the numerator and denominator. However, rational functions are nonlinear in the parameter vector, thereby requiring nonlinear optimization methods to solve the fitting problem. In this paper, we propose a data fitting method for rational function models based on the LM algorithm which is renowned as an effective nonlinear optimization technique. Simulations show that the fitting results are robust against the measurement noises and uncertainties. The effectiveness of the proposed method is further demonstrated by the real application to a 3D depth camera calibration problem.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.657-664
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• 2001

It is possible to extract a 3D models from its multiple views using the self-calibration. Though it is possible to construct 3D models of objects from their multiple views, accuracy of 3D models depends on the fundamental matrix estimated between two views. In this paper, we show the fundamental matrix accuracy can be improved by taking a non-linear minimization technique. Furthermore, the corresponding points which are completely mismatches or have greater discrepancy errors in their locations, reduce the fundamental matrix accuracy. Thus, applying the Monte Carlo technique and the non-linear minimization Levenberg-Marquardt method to remove the outliers, we can estimate the fundamental matrix with the higher accuracy.

• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.45-57
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• 1998

The purpose of this paper is to develop a neural network model in order to forecast flood inflow into the reservoir that has the nature of uncertainty and nonlinearity. The model has the features of multi-layered structure and parallel multi-connections. To develop the model. backpropagation learning algorithm was used with the Momentum and Levenberg-Marquardt techniques. The former technique uses gradient descent method and the later uses gradient descent and Gauss-Newton method respectively to solve the problems of local minima and for the speed of convergency. Used data for learning are continuous fixed real values of input as well as output to emulate the real physical aspects. after learning process. a reservoir inflows forecasting model at flood period was constructed. The data for learning were used to calibrate the developed model and the results were very satisfactory. applicability of the model to the Chungju Mlultipurpose Reservoir proved the availability of the developed model.

• Journal of Industrial Technology
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• v.19
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• pp.127-133
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• 1999

This paper is a research about system identification which optimizes uncertain geothechnical properties from the data measured during geotechnical design and construction. Various numerical optimization algorithms of Simplex method, Powell method, Rosenbrock method and Levenberg-Marquardt method were applied to the excavation problem to determine which method showed the best results with respect to robustness of success in finding an optimal solution to within a certain accuracy and number of function evaluations. From the results of numerical analysis, all of four algorithms are converged to exact solution after satisfying the allowed criteria, and Levenberg-Marquardt's algorithms was identified to be the most efficient method in number of function evaluations. System identification was applied to geotechnical engineering problems, possibly being occurred in field, to verify its applicability : estimation of settlement due to self-weight consolidation in dredged and filled soil. For self-weight consolidational settlement of a dredged soil, a program of evaluating the constitutive relationship of effective stress-void ratio-permeability was developed by using the technique of system identification. Thus, consolidational characteristics of a dredged soil, having a very high initial void ratio, can be evaluated.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.5
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• pp.466-472
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• 2009

An acoustic leak monitoring system(ALMS) using acoustic emission(AE) technique was applied for leakage detection of nuclear power plant's pipeline which is operated in high temperature and pressure condition. Since this system only monitors the existence of leak using the root mean square(RMS) value of raw signal from AE sensor, the difficulty occurs when the characteristics of leak size and shape need to be evaluated. In this study, dual monitoring system using AE sensor and accelerometer was introduced in order to solve this problem. In addition, artificial neural network(ANN) with Levenberg.Marquardt(LM) training algorithm was also applied due to rapid training rate and gave the reliable classification performance. The input parameters of this ANN were extracted from varying signal received from experimental conditions such as the fluid pressure inside pipe, the shape and size of the leak area. Additional experiments were also carried out and with different objective which is to study the generation and characteristic of lamb and surface wave according to the pipe thickness.

• Tunnel and Underground Space
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• v.20 no.2
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• pp.97-104
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• 2010

The materials failure relation $\ddot{\Omega}=A{(\dot{\Omega})}^\alpha$ where $\Omega$ is a measurable quantity such as displacement and the dot superscript is the time derivative, may be used to analyze the accelerating creep of materials. Coefficients, A and $\alpha$, are determined by fitting given data sets. In this study, it is tried to predict the failure time of tunnel using the materials failure relation. Four fitting techniques of applying the materials failure relation are attempted to forecast a failure time. Log velocity versus log acceleration technique, log time versus log velocity technique, inverse velocity technique are based on the linear least squares fits and non-linear least squares technique utilizes the Levenberg-Marquardt algorithm. Since the log velocity versus log acceleration technique utilizes a logarithmic representation of the materials failure relation, it indicates the suitability of the materials failure relation applied to predict a failure time of tunnel. A linear correlation between log velocity and log acceleration appears satisfactory(R=0.84) and this represents that the materials failure relation is a suitable model for predicting a failure time of tunnel. Through comparing the real failure time of tunnel with the predicted failure times from four curve fittings, it is shown that the log time versus log velocity technique results in the best prediction.