• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.7
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• pp.557-566
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• 2018

In this paper, a vision-based relative navigation system is addressed for autonomous aerial refueling. In the air-to-air refueling, it is assumed that the tanker has the drogue, and the receiver has the probe. To obtain the relative information from the drogue, a vision-based imaging technology by infra-red camera is applied. In this process, the relative information is obtained by using Gaussian Least Squares Differential Correction (GLSDC), and Levenberg-Marquadt(LM), where the drouge geometric information calculated through image processing is used. These two approaches proposed in this paper are analyzed through numerical simulations.

• Advances in Computational Design
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• v.2 no.4
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• pp.283-291
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• 2017

High frequency full bridge series resonant inverters have become increasingly popular among power supply designers. One of the most important parameter for a High Frequency Full Bridge Series Resonant Inverter is optimal coil design. The optimal coil designing procedure is not a easy task. This paper deals with the New Approach to Optimal Design Procedure for a Real-time High Frequency Full Bridge Series Resonant Inverter in Induction Heating Equipment devices. A new design to experimental modelling of the physical properties and a practical power input simulation process for the non-sinusoidal input waveform is accepted. The design sensitivity analysis with Levenberg-Marquardt technique is used for the optimal design process. The proposed technique is applied to an Induction Heating Equipment devices model and the result is verified by real-time experiment. The main advantages of this design technique is to achieve more accurate temperature control with a huge amount of power saving.

• Journal of Astronomy and Space Sciences
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• v.31 no.2
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• pp.121-130
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• 2014

Essential ideas, successes, and difficulties of Areal Density Analysis (ADA) for color-magnitude diagrams (CMD's) of resolved stellar populations are examined, with explanation of various algorithms and strategies for optimal performance. A CMD-generation program computes theoretical datasets with simulated observational error and a solution program inverts the problem by the method of Differential Corrections (DC) so as to compute parameter values from observed magnitudes and colors, with standard error estimates and correlation coefficients. ADA promises not only impersonal results, but also significant saving of labor, especially where a given dataset is analyzed with several evolution models. Observational errors and multiple star systems, along with various single star characteristics and phenomena, are modeled directly via the Functional Statistics Algorithm (FSA). Unlike Monte Carlo, FSA is not dependent on a random number generator. Discussions include difficulties and overall requirements, such as need for fast evolutionary computation and realization of goals within machine memory limits. Degradation of results due to influence of pixelization on derivatives, Initial Mass Function (IMF) quantization, IMF steepness, low Areal Densities ($\mathcal{A}$), and large variation in $\mathcal{A}$ are reduced or eliminated through a variety of schemes that are explained sufficiently for general application. The Levenberg-Marquardt and MMS algorithms for improvement of solution convergence are contained within the DC program. An example of convergence, which typically is very good, is shown in tabular form. A number of theoretical and practical solution issues are discussed, as are prospects for further development.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.59-68
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• 1994

The role of the department of statistics in Korea is discussed. There are more than 70 statistics departments in the universities throughout the country. However, we can hardly find that statisticians and statistical service organizations who are responsible for, for example, identifying the statistical services needed by clients in our society. In developed countries, statistics is now widely employed in all areas of societies including industry and government. Statistics and statisticians also have vital internal roles in production, research, marketing, and support functions of the modern corporation as stated by Marquardt(1987). Professors in the department of statistics are responsible for solving the problems with the discipline of statistics and with statisticians. In this article some of those problems are explored.

• Transactions of Materials Processing
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• v.27 no.4
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• pp.227-235
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• 2018

The flow stresses have been identified prior to a numerical simulation for predicting a deformation of materials using the experimental or analytical analysis. Recently, the flow stress models considering the temperature effect have been developed to reduce the number of experiments. Artificial neural network can provide a simple procedure for solving a problem from the analytical models. The objective of this paper is the prediction of flow stress on the fiber metal laminate using the artificial neural network. First, the training data were obtained by conducting the uniaxial tensile tests at the various temperature conditions. After, the artificial neural network has been trained by Levenberg-Marquardt method. The numerical results of the trained model were compared with the analytical models predicted at the previous study. It is noted that the artificial neural network can predict flow stress effectively as compared with the previously-proposed analytical models.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.457-467
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• 2010

The settlement prediction is very important to preloading method for a construction site on a soft ground. At the design stage, however, it is hard to predict the settlement exactly due to limitations of the site survey. Most of the settlement prediction is performed by a regression settlement curve based on the field data during a construction. In Korea, hyperbolic method has been most commonly used to align the settlement curve with the field data, because of its simplicity and many application cases. The results from hyperbolic method, however, may be differed by data selections or data fitting methods. In this study, the analyses using hyperbolic method were performed about the field data of $\bigcirc\bigcirc$ site in Pusan. Two data fitting methods, using an axis transformation or an alternative method, were applied with the various data group. If data was used only after the ground water level being stabilized, fitting results using both methods were in good agreement with the measured data. Without the information about the ground water level, the alternative method gives better results with the field data than the method using an axis transformation.

• ETRI Journal
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• v.22 no.1
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• pp.38-46
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• 2000

The extraction of model parameters for embedded passive components is crucial for designing and characterizing the performance of multichip module (MCM) substrates. In this paper, a method for optimizing the extraction of these parameters using genetic algorithms is presented. The results of this method are compared with optimization using the Levenberg-Marquardt (LM) algorithm used in the HSPICE circuit modeling tool. A set of integrated resistor structures are fabricated, and their scattering parameters are measured for a range of frequencies from 45 MHz to 5 GHz. Optimal equivalent circuit models for these structures are derived from the s-parameter measurements using each algorithm. Predicted s-parameters for the optimized equivalent circuit are then obtained from HSPICE. The difference between the measured and predicted s-parameters in the frequency range of interest is used as a measure of the accuracy of the two optimization algorithms. It is determined that the LM method is extremely dependent upon the initial starting point of the parameter search and is thus prone to become trapped in local minima. This drawback is alleviated and the accuracy of the parameter values obtained is improved using genetic algorithms.

• Archives of Plastic Surgery
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• v.33 no.2
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• pp.168-174
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• 2006

For over two thousand years, many artists and scientists have tried to understand or quantify the form of the perfect, ideal, or the most beautiful face both in art and in vivo(life). However, this mathematical relationship has been consistently and repeatedly reported to be present in beautiful things. This particular relationship is referred to as the golden ratio. It is a mathematical ratio of 1.618 : 1 that seems to appear recurrently in beautiful things in nature as well as in other things that are seen as beautiful. Dr. Marquardt made the facial golden mask that contains and includes all of the 1-dimensional and 2-dimensional geometric golden element formed from the golden ratio. The purpose of this study is to evaluate the usefulness of the golden facial mask. In our cases(n=40), the authors applied the facial golden mask to the preoperative and postoperative photographs, and scored each photograph. Compared with the average scores of the facial mask applied photographs and none applied photographs using non-parametric test, statistical significance was not checked (p > 0.05). It means that the facial golden mask can be used for facial analysis. The facial golden mask is easy to apply, cheap and relatively objective. So, the authors introduce the one of useful facial analyses.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.6
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• pp.738-748
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• 2005

Considering pollution source is transferred by discharge, it is very important to analyze the correlation between discharge and water quality. And temperature also influent to the water quality. In this paper, it is used water quality data that was measured DO (Dissolved Oxygen), TOC (Total Organic Carbon), TN (Total Nitrogen), TP (Total Phosphorus) at Dalchun real time monitoring stations in Namhan river. These characteristics were analyzed with the water quality of rainy and nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water quality forecasting models were applied. LMNN (Levenberg-Marquardt Neural Network), MDNN (MoDular Neural Network), and ANFIS (Adaptive Neuro-Fuzzy Inference System) models have achieved the highest overall accuracy of TOC data. LMNN and MDNN model which are applied for DO, TN, TP forecasting shows better results than ANFIS. MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. If some data has periodical properties, it seems effective using qualitative data to forecast.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.2
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• pp.30-37
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• 2012

Pneumatic Active Engine Mount(PAEM) with open-loop control system has been developed to reduce the transmission of the idle-shake vibration induced by engine effectively and economically. A solenoid valve installed between PAEM and vacuum tank is on-off switched by the Pulse Width Modulate(PWM) control signal to decrease the dynamic stiffness of the engine mount. This paper presents the methodology to identify the optimal values of control parameters of a PAEM, i.e, turn-on timing and duty ratio of PWM signal for 6 different idle driving conditions. A scanning algorithm was first applied to the vehicle test to obtain the approximate optimal control parameters minimizing the vibration at front seat rail and at steering wheel. Then the PAEM system identification was fulfilled to find accurate optimal control parameters by using multi-layer neural networks of Levenberg-Marquardt algorithm with vehicle test data.