• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.200-208
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• 2003

This paper proposes a separation and feature extraction of image signals using the independent component analysis(ICA) based on neural networks of efficient learning rule. The proposed learning rule is a hybrid fixed-point(FP) algorithm based on secant method and momentum. Secant method is applied to improve the performance by simplifying the 1st-order derivative computation for optimizing the objective function, which is to minimize the mutual informations of the independent components. The momentum is applied for high-speed convergence by restraining the oscillation in the process of converging to the optimal solution. The proposed algorithm has been applied to the composite images generated by random mixing matrix from the 10 images of $512\times512$-pixel. The simulation results show that the proposed algorithm has better performances of the separation speed and rate than those using the FP algorithm based on Newton and secant method. The proposed algorithm has been also applied to extract the features using a 3 set of 10,000 image patches from the 10 fingerprints of $256\times256$-pixel and the front and the rear paper money of $480\times225$-pixel, respectively, The simulation results show that the proposed algorithm has also better extraction speed than those using the another methods. Especially, the 160 basis vectors(features) of $16\times16$-pixel show the local features which have the characteristics of spatial frequency and oriented edges in the images.

• Geophysics and Geophysical Exploration
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• v.7 no.3
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• pp.207-212
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• 2004

This article reviews recent developments in three-dimensional (3-D) magntotelluric (MT) imaging. The inversion of MT data is fundamentally ill-posed, and therefore the resultant solution is non-unique. A regularizing scheme must be involved to reduce the non-uniqueness while retaining certain a priori information in the solution. The standard approach to nonlinear inversion in geophysis has been the Gauss-Newton method, which solves a sequence of linearized inverse problems. When running to convergence, the algorithm minimizes an objective function over the space of models and in the sense produces an optimal solution of the inverse problem. The general usefulness of iterative, linearized inversion algorithms, however is greatly limited in 3-D MT applications by the requirement of computing the Jacobian(partial derivative, sensitivity) matrix of the forward problem. The difficulty may be relaxed using conjugate gradients(CG) methods. A linear CG technique is used to solve each step of Gauss-Newton iterations incompletely, while the method of nonlinear CG is applied directly to the minimization of the objective function. These CG techniques replace computation of jacobian matrix and solution of a large linear system with computations equivalent to only three forward problems per inversion iteration. Consequently, the algorithms are efficient in computational speed and memory requirement, making 3-D inversion feasible.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.1
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• pp.83-93
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• 2008

The topographic effect is one of the most important component in the solution of the geodetic boundary value problem (geodetic BVP). Therefore, topographic effect should be considered properly for developing the precise geoid model, especially for the area where contains many mountains like Korea. The selection of gravity reduction method in the context of the precise geoid determination depends on the magnitude of its indirect effect, the smoothness and magnitude of the reduced gravity anomalies, and their related geophysical interpretation. In this study, Korean digital elevation model with 100m resolution was constructed and topographic effect was calculated by three reduction methods as like Helmert condensation method and RTM method and Airy-isostatic reduction method. Through the analysis of computation results, we can find that RTM reduction method is the best optimal method and the results shows that gravity anomaly and indirect effect of geoidal height are $0.660{\pm}13.009mGal$, $-0.004{\pm}0.131m$ respectively and it is the most gentle slow of the three methods. Through this study, it was found that the RTM method is better suitable for calculating topographic effect precisely in context of precise geoid determination in Korea than other reduction methods.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.47-59
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• 2020

In this paper, we present the longitudinal Markov binary regression model with t-link function when its transition order is known or unknown. It is assumed that logit or probit models are considered in binary regression models. Here, t-link function can be used for more flexibility instead of the probit model since the t distribution approaches to normal distribution as the degree of freedom goes to infinity. A Markov regression model is considered because of the longitudinal data of each individual data set. We propose Bayesian method to determine the transition order of Markov regression model. In particular, we use the deviance information criterion (DIC) (Spiegelhalter et al., 2002) of possible models in order to determine the transition order of the Markov binary regression model if the transition order is known; however, we compute and compare their posterior probabilities if unknown. In order to overcome the complicated Bayesian computation, our proposed model is reconstructed by the ideas of Albert and Chib (1993), Kuo and Mallick (1998), and Erkanli et al. (2001). Our proposed method is applied to the simulated data and real data examined by Sommer et al. (1984). Markov chain Monte Carlo methods to determine the optimal model are used assuming that the transition order of the Markov regression model are known or unknown. Gelman and Rubin's method (1992) is also employed to check the convergence of the Metropolis Hastings algorithm.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.9
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• pp.862-869
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• 2010

Numerical analysis was done to evaluate the gas flow distribution in small scale SCR system which has $2.4{\times}2.4{\times}3.1\;m^3$ in volume and 25,300 Sm3/hr in flue gas flow capacity. Various types of baffles proposed for controlling the flow uniformity were evaluated by the CFD analysis to find the optimal geometry of the baffle in the SCR system. By installing baffles in the SCR system, the RMS (%) value was raised up to 6.2% compared with the baffle-uninstalled state. The effect of baffle thicknesses on the RMS (%) value was not shown within 0 and 8 mm in thickness, but the RMS (%) value was raised by 2.5% in 10 mm of baffles thickness, which causes the unstability in flow. By comparison between the shape of baffles, it is known that the lattice type baffle has better performance in controlling the flow uniformity than the circular truncated cone type baffle or mixer type baffle. RMS (%) values have more that 10% difference according to the shape of baffle type.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.60-68
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• 2014

In modern electronic warfare systems, a demand on the more accurate estimation method based on TDOA and FDOA has been increased. TDOA/FDOA localization consists of two-stage procedures; the extraction of information from signals, and the estimation of emitter location. CAF(complex ambiguity function) is known as a basic method in the extraction stage. However, when we extract TDOA and FDOA information from VHF(very high frequency) communication signals, conventional CAF algorithms may not work within a permitted time because of much computation. Therefore, in this paper, an improved sequential estimation algorithm based on CAF is proposed for effective calculation of extracting TDOA and FDOA estimates in terms of computational complexity. The proposed method is compared with the conventional CAF-based algorithms through simulation. In addition, we derive the optimal performance based on the CRLB(Cramer-Lao lower bound) to check the extraction performance of the proposed method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.11 s.114
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• pp.1120-1131
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• 2006

This paper describes the numerical analysis techniques using the Combined Integration/Matrix Method to calculate ground potential rise which can be occurred in the various grounding systems. Combined Integration/Matrix Method is used to reduce the error and computation time with the analytical integration equation and the proper segmentaion of earth embedded conductor. To do it, optimal segmentaion method for the buried conductors is presented through error analysis which is capable of applying the practical scaled various grounding systems. The optimum length of segmented element is much co-related with the buried depth of grounding electrode and the maximum length of buried electrode. As a result, less 3 precent errors was obtained by proposed model. The proposed model is applied to verify an effect of multi-grounding problems which was aroused much controversy with separated or common grounding between the high power grounding system and low power grounding system such as signal and telecommunication grounding.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.368-376
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• 2021

Artificial intelligence clouds help to efficiently develop the autonomous things integrating artificial intelligence technologies and control technologies by sharing the learned models and providing the execution environments. The existing autonomous things development technologies only take into account for the accuracy of artificial intelligence models at the cost of the increment of the complexity of the models including the raise up of the number of the hidden layers and the kernels, and they consequently require a large amount of computation. Since resource-constrained computing environments, could not provide sufficient computing resources for the complex models, they make the autonomous things violate time criticality. In this paper, we propose a digital twin software development framework that selects artificial intelligence models optimized for the computing environments. The proposed framework uses a load estimation DNN model to select the optimal model for the specific computing environments by predicting the load of the artificial intelligence models with digital twin data so that the proposed framework develops the control software. The proposed load estimation DNN model shows up to 20% of error rate compared to the formula-based load estimation scheme by means of the representative CNN models based experiments.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.3
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• pp.122-130
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• 2021

A wind-speed estimation at the arbitrary elevations is key component for the design of the offshore wind energy structures and the computation of the wind-wave generation. However, the wind-speed estimation of the target elevation has been carried out by using the typical functions and their typical parameters, e.g., power and logarithmic functions because the available wind speed data is limited to the specific elevation, such as 2~3m, 10 m, and so on. In this study, the parameters of the vertical profile functions are estimated with optimal and analyzed the parameter ranges using the HeMOSU-1 platform wind data monitored at the eight different locations. The results show that the mean value of the exponent of the power function is 0.1, which is significantly lower than the typically recommended value, 0.14. The values of the exponent, the friction velocity, and the roughness parameters are in the ranges 0.0~0.3, 0~10 (m/s), and 0.0~1.0 (m), respectively. The parameter ranges differ from the typical ranges because the atmospheric stability condition is assumed as the neutral condition. To improve the estimation accuracy, the atmospheric condition should be considered, and a more general (non-linear) vertical profile functions should be introduced to fit the diverse profile patterns and parameters.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.6
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• pp.385-391
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• 2022

This paper presents to develop a path planning algorithm combining gradient descent-based path planning (GBPP) and particle swarm optimization (PSO) for considering prohibited flight areas, terrain information, and characteristics of fixed-wing unmmaned aerial vehicle (UAV) in 3D space. Path can be generated fast using GBPP, but it is often happened that an unsafe path can be generated by converging to a local minimum depending on the initial path. Bio-inspired swarm intelligence algorithms, such as Genetic algorithm (GA) and PSO, can avoid the local minima problem by sampling several paths. However, if the number of optimal variable increases due to an increase in the number of UAVs and waypoints, it requires heavy computation time and efforts due to increasing the number of particles accordingly. To solve the disadvantages of the two algorithms, hierarchical path planning algorithm associated with hierarchical particle swarm optimization (HPSO) is developed by defining the initial path, which is the input of GBPP, as two variables including particles variables. Feasibility of the proposed algorithm is verified by software-in-the-loop simulation (SILS) of flight control computer (FCC) for UAVs.