• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.77-85
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• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.6
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• pp.939-945
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• 2000

A cylindrical object buried in the lossy half space is reconstructed from the measured scattered fields above the lossy half space. The position, the size and the medium parameters i.e. relative dielectric constants and conductivity of the buried object as well as the medium parameters of the background lossy half space are obtained from the scattered fields by using the iterative inversion method and the optimization hybrid algorithm combining the genetic algorithm and the Levenberg-Marquardt algorithm. Illposedness of the inversion due to the measurement errors in the scattered fields are regularized by filtering out the evanescent modes in the spatial frequency spectrum domain.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.135-145
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• 1995

A new algorithm was given to successively fit the multiexponential function/nonlinear function to data by a weighted least squares method, using Gauss-Newton, Marquardt, gradient and DUD methods for convergence. This study also considers the problem of linear-nonlimear weighted least squares estimation which is based upon the usual Taylor's formula process.

• Journal of Energy Engineering
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• v.10 no.3
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• pp.183-187
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• 2001

Impedance imaging for binary mixture is a kind of nonlinear inverse problem, which is usually solved iteratively by the Newton-Raphson method. Then, the ill-posedness of Hessian matrix often requires the use of a regularization method to stabilize the solution. In this study, the Levenberg-Marquredt regularization method is introduced for the binary-mixture system with various resistivity contrasts (1:2∼1:1000). Several mixture distribution are tested and the results show that the Newton-Raphson iteration combined with the Levenberg-Marquardt regularization can reconstruct reasonably good images.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.687-690
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• 2000

Lately, many studies have been progressed for the protection human's lives and property as holding in check accidents happened by human's carelessness or mistakes. One part of these is the development of an autonomouse vehicle. General control method of vision-based autonomous vehicle system is to determine the navigation direction by analyzing lane images from a camera, and to navigate using proper control algorithm. In this paper, characteristic points are abstracted from lane images using lane recognition algorithm with sobel operator. And then the vehicle is controlled using two proposed auto-steering algorithms. Two steering control algorithms are introduced in this paper. First method is to use the geometric relation of a camera. After transforming from an image coordinate to a vehicle coordinate, a steering angle is calculated using Ackermann angle. Second one is using a neural network algorithm. It doesn't need to use the geometric relation of a camera and is easy to apply a steering algorithm. In addition, It is a nearest algorithm for the driving style of human driver. Proposed controller is a multilayer neural network using Levenberg-Marquardt backpropagation learning algorithm which was estimated much better than other methods, i.e. Conjugate Gradient or Gradient Decent ones.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.210-214
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• 2003

This paper presents a residence time distribution (RTD) measurement method using a radioisotope tracer and the estimation method of RTD model parameters to analyze a submerged bio-reactor. The mathematical RTD models have been investigated to represent the flow behavior and the existence of stagnant regions in the reactor. Knowing the parameters of the RTD model is important for understanding the mixing characteristics of a reactor The radioisotope tracer experiment was carried out by injecting a radioisotope tracer as a pulse into the inlet of the reactor and recording the change of its concentration at the outlet of the reactor to obtain the experimental RTD response. The parameter estimation was performed by the Levenberg-Marquardt optimization algorithm. The proposed scheme allowed the parameter estimation of RTD model suggested by Adler-Hovorka with very low deviations. The estimation procedure is shown to lead to accurate estimation of the RTD parameters and to a good agreement between experimental and simulated response.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.101-106
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• 2006

The geoidal undulations are needed for determining the orthometric heights from the Global Positioning System GPS-derived ellipsoidal heights. There are several methods for geoidal undulation determination. The paper presents a method employing a simple architecture Two Layer Multiquadric-Biharmonic Artificial Neural Network (TLMB-ANN) to approximate an area of 4200 square kilometres quasigeoid surface with GPS-levelling data. Hardy’s Multiquadric-Biharmonic functions is used as the hidden layer neurons’ activation function and Levenberg-Marquardt algorithm is used to train the artificial neural network. In numerical examples five surfaces were compared: the gravimetric geometry hybrid quasigeoid, Support Vector Machine (SVM) model, Hybrid Fuzzy Neural Network (HFNN) model, Traditional Three Layer Artificial Neural Network (ANN) with tanh activation function and TLMB-ANN surface approximation. The effectiveness of TLMB-ANN surface approximation depends on the number of control points. If the number of well-distributed control points is sufficiently large, the results are similar with those obtained by gravity and geometry hybrid method. Importantly, TLMB-ANN surface approximation model possesses good extrapolation performance with high precision.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.505-515
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• 2000

THe purpose of this study is to forecast of runoff hydrographs according to rainfall event in a stream. The neural network theory as a hydrologic blackbox model is used to solve hydrological problems. The Back-Propagation(BP) algorithm by the Levenberg-Marquardt(LM) techniques and Radial Basis Function(RBF) network in Neural Network(NN) models are used. Runoff hydrograph is forecasted in Bocheongstream basin which is a IHP the representative basin. The possibility of a simulation for runoff hydrographs about unlearned stations is considered. The results show that NN models are performed to effective learning for rainfall-runoff process of hydrologic system which involves a complexity and nonliner relationships. The RBF networks consist of 2 learning steps. The first step is an unsupervised learning in hidden layer and the next step is a supervised learning in output layer. Therefore, the RBF networks could provide rather time saved in the learning step than the BP algorithm. The peak discharge both BP algorithm and RBF network model in the estimation of an unlearned are a is trended to observed values.

• Journal of Internet Computing and Services
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• v.6 no.6
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• pp.187-197
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• 2005

In this paper, we propose a method to estimate camera motion parameter based on invariant point features, Typically, feature information of image has drawbacks, it is variable to camera viewpoint, and therefore information quantity increases after time, The LM(Levenberg-Marquardt) method using nonlinear minimum square evaluation for camera extrinsic parameter estimation also has a weak point, which has different iteration number for approaching the minimal point according to the initial values and convergence time increases if the process run into a local minimum, In order to complement these shortfalls, we, first proposed constructing feature models using invariant vector of geometry, Secondly, we proposed a two-stage calculation method to improve accuracy and convergence by using 2D homography and LM method, In the experiment, we compared and analyzed the proposed method with existing method to demonstrate the superiority of the proposed algorithms.

• 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.