• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.1-7
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• 2011

Terrain, building location and area, and building shape information is in need of implementing 3D map. This paper proposes a method of extracting a building area by an improved semi-automatic snake algorithm. The method consists of 3-stage: pre-processing, initializing control points, and applying an improved snake algorithm. In the first stage, after transforming a satellite image to a gray image and detecting the approximate edge of the gray image, the method combines the gray image and the edge. In the second stage, the user looks for the center point of a building and the system sets the circular or rectangular initial control points by an procedural method. In the third stage, the enhanced snake algorithm extracts the building area. In particular, this paper sets the one tenn of the snake in a new way in order to use the proposed method for specializing building area extraction. Finally, this paper evaluated the performance of the proposed method using sky view satellite image and it showed that the matching percentage to the exact building area is 75%.

• The Mathematical Education
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• v.45 no.2 s.113
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• pp.145-154
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• 2006

The main purpose of this study is to investigate the effect of departures from normality and equal variance on the two-sample test when the variances are unknown. We have found that type I error brought about a little bit change which is ignorable in relation to kurtosis. But the change of type I error was mainly based on the skewness of the parent population. In introductory statistics classes where data analysis includes techniques for detecting skewness of two populations, we recommend the two-sample t-test when maximal skewness of two populations is smalter than the value 4 when the variances seem equal. Furthermore, our simulations reveal that the two-sample t-test appears somewhat more robust than that of z-test if the assumption of equal variance is satisfied. In the case of unequal variance, the two-sample t-test appears somewhat more robust provided the t-statistic using Satterthwaite's approximate degrees of freedom.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.2
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• pp.172-182
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• 2013

This paper focuses on the modeling and intelligent control of the new Eight-Rotor MAV, which is used to solve the problem of the low coefficient proportion between lift and gravity for the Quadrotor MAV. The Eight-Rotor MAV is a nonlinear plant, so that it is difficult to obtain stable control, due to uncertainties. The purpose of this paper is to propose a robust, stable attitude control strategy for the Eight-Rotor MAV, to accommodate system uncertainties, variations, and external disturbances. First, an interval type-II fuzzy neural network is employed to approximate the nonlinearity function and uncertainty functions in the dynamic model of the Eight-Rotor MAV. Then, the parameters of the interval type-II fuzzy neural network and gain of sliding mode control can be tuned on-line by adaptive laws based on the Lyapunov synthesis approach, and the Lyapunov stability theorem has been used to testify the asymptotic stability of the closed-loop system. The validity of the proposed control method has been verified in the Eight-Rotor MAV through real-time experiments. The experimental results show that the performance of the interval type-II fuzzy neural network based adaptive sliding mode controller could guarantee the Eight-Rotor MAV control system good performances under uncertainties, variations, and external disturbances. This controller is significantly improved, compared with the conventional adaptive sliding mode controller, and the type-I fuzzy neural network based sliding mode controller.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.5
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• pp.78-87
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• 2009

In the IEEE 802.16 Wireless MAN standard, the best effort service class is ranked on the lowest position in priority and is assisted by a MAC scheme based on reservation ALOHA. In such a MAC scheme, a collision among the requests is unavoidable so that the standard adopted a binary exponential back-off rule to arbitrate a collision. Aiming at improving throughput performance, we present two generic collision arbitration rules based on p-persistence rule, (identified as pristine and metamorphosed rules), as alternatives in a wireless MAN. For each of these rules, we then develop an analytical method to calculate an approximate value of saturated throughput. In comparison with simulation results, we confirm the high accuracy of the analytical method. Also, the pristine and metamorphosed rules are observed to exhibit higher saturated throughput compared with the binary exponential back-off rule.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.87-99
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• 2009

The top-down method for SOA delivery is recommended as a best way to take advantage of SOA. The core step of SOA delivery is the step of service modeling including service analysis and design based on ontology. Most enterprises know that the top-down approach is the best but they are hesitant to employ it because it requires them to invest a great deal of time and money without it showing any immediate results, particularly because they use well-defined component based systems. In this paper, we propose a service identification method to use a well-defined components maximally as a bottom-up approach. We assume that user's inputs generates events on a GUI and the approximate business process can be obtained from concatenating the event paths. We first find the core GUIs which have many outgoing event calls and form event paths by concatenating the event calls between the GUIs. Next, we adapt sequential pattern mining algorithms to find the maximal frequent event paths. As an experiment, we obtained business services with various granularity by applying a cohesion metric to extracted frequent event paths.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.3_4
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• pp.195-203
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• 2004

VolumePro is a real-time volume rendering hardware for consumer PCs. However it cannot be used for the applications requiring perspective projection such as virtual endoscopy since it provides only orthographic projection. Several methods have been presented to approximate perspective projection by decomposing a volume into slabs and applying successive parallel projection to thou. But it takes a lot of time since the entire region of every slab should be processed, which does not contribute to final image. In this paper, we propose an efficient perspective projection method that makes the use of several sub-volumes with cropping feature of VolumePro. It reduces the rendering time in comparison to slab-based method without image quality deterioration since it processes only the parts contained in the view frustum.

• Advances in nano research
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• v.16 no.3
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• pp.251-263
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• 2024

This article investigates the free vibration behavior of carbon nanotube reinforced composite (CNTRC) beams embedded using variational analytical methods and artificial neural networks (ANN). The material properties of layered functionally graded CNTRC (FG-CNTRC) beams are estimated using nonlocal parameters modified power-law with different types of CNT distributions through the thickness direction of the beam. Adopting Eringen's nonlocal elasticity theory to capture the small size effects, the nonlocal governing equations are derived and solved using the analytical method. And also, the problem was analyzed using the ANN method. The architecture of the proposed ANN model is 3-9-1. In the experiments, we used 112 different data to predict the natural frequency using ANN. Based on the nonlocal differential constitutive relations of Eringen, the equations of motion as well as the boundary conditions of the beam are derived using Hamilton's principle. The classical beam theory is used to formulate a governing equation for predicting the free vibration of laminated CNTRC beams. According to the experimental results, the prediction ability of the ANN model is very good and the natural frequency can be predicted in ANN without attempting any experiments.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.92-102
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• 2017

In this paper, we consider a typical health care system via the help of Wireless Sensor Network (WSN) for wireless patient tracking. The wireless patient tracking module of this system performs localization out of samples of Received Signal Strength (RSS) variations and tracking through a Particle Filter (PF) for WSN assisted by multiple transmit-power information. We propose a modified PF, Kullback-Leibler Distance (KLD)-resampling PF, to ameliorate the effect of RSS variations by generating a sample set near the high-likelihood region for improving the wireless patient tracking. The key idea of this method is to approximate a discrete distribution with an upper bound error on the KLD for reducing both location error and the number of particles used. To determine this bound error, an optimal algorithm is proposed based on the maximum gap error between the proposal and Sampling Important Resampling (SIR) algorithms. By setting up these values, a number of simulations using the health care system's data sets which contains the real RSSI measurements to evaluate the location error in term of various power levels and density nodes for all methods. Finally, we point out the effect of different power levels vs. different density nodes for the wireless patient tracking.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.305-315
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• 2010

In this paper, a frequentistic approach to impute the values of R, G, B-components in random missing pixels of color image is provided. Under assumption that the given image is a realization of Gaussian Markov random field, its model is designed such that each neighbor pixel values for a given pixel follows (independently) the normal distribution with covariance matrix scaled by an evaluates of the similarity between two pixel values, so that the imputation is not to be affected by the neighbors with different color. An approximate EM-based algorithm maximizing the underlying likelihood is implemented to estimate the parameters and to impute the missing pixel values. Some experiments are presented to show its effectiveness through performance comparison with a popular interpolation method.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.23-35
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• 2014

Recently, novel application areas in digital geometry processing, such as simulation, dynamics, and medical surgery simulations, have necessitated the representation of not only the surface data but also the interior volume data of a given 3D object. In this paper, we present an efficient framework for the shape approximations of spherical solid objects based on trivariate B-splines. To do this, we first constructed a smooth correspondence between a given object and a unit solid cube by computing their harmonic mapping. We set the unit solid cube as a rectilinear parametric domain for trivariate B-splines and utilized the mapping to approximate the given object with B-splines in a coarse-to-fine manner. Specifically, our framework provides user-controllability of shape approximations, based on the control of the boundary condition of the harmonic parameterization and the level of B-spline fitting. Experimental results showed that our method is efficient enough to compute trivariate B-splines for several models, each of whose topology is identical to a solid sphere.