• Journal of the Korean Society of Propulsion Engineers
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• v.11 no.5
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• pp.1-13
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• 2007

Scramjet engine is one of the most promising propulsion systems for future transport. For the ground test with T4 shock tunnel, model scramjet engine is designed. Design flight Mach number is 7.6 and flight altitude is 30km. Engine intake is designed by Levenberg-Marquardt optimization method and Korkegi relation. Furthermore, cowl cut out region is installed by the rule of Kantrowitz limit. Inside the combustor, cavity type flame holder is installed. Cavity is designed by Rayleigh line relation and PSR model. Numerical analysis is performed for the design confirm.

• 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.28 no.B
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• pp.143-148
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• 2008

A real-time face detection is to find human faces robustly under the cluttered background free from the effect of occlusion by other objects or various lightening conditions. We propose a face detection system for real-time applications using wavelet decomposition method based on Gabor features. Firstly, skin candidate regions are extracted from the given image by skin color filtering and projection method. Then Gabor-feature based template matching is performed to choose face cadidate from the skin candidate regions. The chosen face candidate region is transformed into 2-level wavelet decomposition images, from which feature vectors are extracted for classification. Based on the extracted feature vectors, the face candidate region is finally classified into either face or nonface class by the Levenberg-Marguardt back-propagation neural network.

• International Journal of Automotive Technology
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• v.3 no.2
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• pp.71-78
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• 2002

A new control method far vision-based autonomous vehicle is proposed to determine navigation direction by analyzing lane information from a camera and to navigate a vehicle. In this paper, characteristic featured data points are extracted from lane images using a lane recognition algorithm. Then the vehicle is controlled using new Levenberg-Marquardt neural network algorithm. To verify the usefulness of the algorithm, another algorithm, which utilizes the geometric relation of a camera and vehicle, is introduced. The second one involves transformation from an image coordinate to a vehicle coordinate, then steering is determined from Ackermann angle. The steering scheme using Ackermann angle is heavily depends on the correct geometric data of a vehicle and a camera. Meanwhile, the proposed neural network algorithm does not need geometric relations and it depends on the driving style of human driver. The proposed method is superior than other referenced neural network algorithms such as conjugate gradient method or gradient decent one in autonomous lateral control .

• 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 Korean Society of Coastal and Ocean Engineers
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• v.16 no.3
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• pp.152-161
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• 2004

Double-peak normal distribution function was suggested as the probability density function of the tidal elevation data in Korean coastal zone. Frequency distribution analysis was carried out using hourly tidal elevation data of the ten tidal gauging stations, i.e., Incheon, Kunsan, Mokpo, Cheju, Yeosu, Masan, Gadeokdo, Pusan, Pohang, and Sokcho which were served through the Internet Homepage by the National Ocean Research Institute. Based on the RMS error and $R^2$ value comparison analysis, it was found that this suggested function as the probability density function of the tidal elevation data was found to be more appropriate than the normal distribution function. The parameters of the double-peak function were estimated optimally using Levenberg-Marquardt method which was modified from the Newton method. The estimated parameters were highly correlated with the non-tidal constants of the tidal gauging stations.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.1
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• pp.121-131
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• 2015

Passive emitter localization is preferred to use a small number of receivers as possible for the efficiency of strategic management in the field of modern electronic warfare support. Accurate emitter localization can be expected when utilizing continuous measurable parameters and a appropriate combination of theirs. For this reason, we compare CRLB (Cramer-Rao lower bound) of two moving platform with various measurable parameters to choose a appropriate combination of parameters for a better localization performance. And we propose the passive emitter localization method based on Levenberg-Marquardt algorithm with combined TDOA/FDOA and DOA to achieve better accuracy of emitter localization which is located on the ground and stationary. In addition, we present a method for determining the initial emitter position for LM algorithm's input to avoid the divergence of estimation and local minimum.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.12
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• pp.1124-1131
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• 2011

In this paper, image reconstruction algorithm for breast cancer detection using MT(Microwave Tomography) was investigated. The breast cancer detection system under development uses 16 transmit/receive antennas. The signal waveform was a sinusoidal wave at 900 MHz. To solve the 2D inverse scattering problem, we used the 2D FDTD (Finite Difference Time Domain) method for forward calculation and LMA(Levenberg-Marquardt Algorithm) for optimization. The result of the image reconstruction using the numerical phantom by MRI(Magnetic Resonance Imaging) obtained from real patient of breast cancer showed that we can detect the position of the tumor accurately.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.297-301
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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 propose constructing feature models using invariant vector of geometry. Secondly, we propose a two-stage calculation method to improve accuracy and convergence by using homography and LM method. In the experiment, we compare and analyze the proposed method with existing method to demonstrate the superiority of the proposed algorithms.