• Journal of the Korean Society for Railway
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• v.16 no.4
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• pp.272-277
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• 2013

Power quality is a term that is now extensively used in power systems applications, and in this context the voltage, current, and phase angle are discussed widely. In particular, different algorithms that are capable of detecting the voltage sag and swell information in a real time environment have been proposed and developed. Voltage sag and swell play an important role in determining the stability, quality, and operation of a power system. This paper presents ANFIS (Adaptive Network based Fuzzy Inference System) models with different membership functions to build the voltage shape with the knowledge of known system parameters, and detect voltage sag and swell accurately. The performance of each method has been compared with each other/other methods to determine the effectiveness of the different models, and the results are presented.

• Journal of electromagnetic engineering and science
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• v.17 no.2
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• pp.51-56
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• 2017

This study proposes a modeling process of equivalent terrains to reduce the computational load and time of a full-wave electromagnetic (EM) simulation. To verify the suitability of the proposed process, an original terrain model with a size of $3m{\times}3m$ is equivalently quantized based on the minimum range resolution of a radar, and the radar image of the quantized model is compared with that of the original model. The results confirm that the simulation time can be reduced from 407 hours to 162 hours without a significant distortion of the radar images, and an average estimation error of the quantized model (20.4 mm) is similar to that of the original model (20.3 mm).

• Proceedings of the KAIS Fall Conference
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• 2004.11a
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• pp.234-239
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• 2004

This paper describes preliminary results we have obtained in developing a computer vision system based on active IR illumination for real time gaze tracking for interactive graphic display. Unlike most of the existing gaze tracking techniques, which often require assuming a static head to work well and require a cumbersome calibration process fur each person, our gaze tracker can perform robust and accurate gaze estimation without calibration and under rather significant head movement. This is made possible by a new gaze calibration procedure that identifies the mapping from pupil parameters to screen coordinates using the Generalized Regression Neural Networks (GRNN). With GRNN, the mapping does not have to be an analytical function and head movement is explicitly accounted for by the gaze mapping function. Furthermore, the mapping function can generalize to other individuals not used in the training. The effectiveness of our gaze tracker is demonstrated by preliminary experiments that involve gaze-contingent interactive graphic display.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.873-881
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• 2014

Speed sensorless modes of operation are becoming standard solution in the area of electric drives. This paper presents flux estimator and speed estimator for the speed sensorless vector control of induction motors. The proposed sensorless methods are based on the model reference adaptive system (MRAS) observer and adaptive speed observer (ASO). The proposed speed estimation algorithm can be employed in the power control of grid connected induction generator for wind power applications. Two proposed schemes are verified through computer simulation PSIM and compared their simulation results.

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.333-348
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• 2015

In this paper, we study a generalization of the modified Weibull distribution. The generalization follows the recent work of Cordeiro et al. (2013) and is based on a class of exponentiated generalized distributions that can be interpreted as a double construction of Lehmann. We introduce a class of exponentiated generalized modified Weibull (EGMW) distribution and provide a list of some well-known distributions embedded within the proposed distribution. We derive some mathematical properties of this class that include ordinary moments, generating function and order statistics. We propose a maximum likelihood method to estimate model parameters and provide simulation results to assess the model performance. Real data is used to illustrate the usefulness of the proposed distribution for modeling reliability data.

• Journal of the military operations research society of Korea
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• v.26 no.1
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• pp.15-33
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• 2000

This paper deals with an estimation method of the airfield capacity for the airlift operation. In the US Air Force, airfield capacities has been estimated using MOG(Maximum -On-the-Ground) concept, which is known to having several weaknesses. Recently, RAND suggests a personal-computer- based model called the Airfield Capacity Estimator(ACE), which is a more advanced and realistic technique compared to the MOG. This paper attempts to modify the ACE appropriate to the Korean airlift operation. While ACE is developed on the basis of strategic mobilization, Korean airlift operation is done on the tactical basis. A designed mdel is tested with simulation technique.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.47.5-47
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• 2001

In this paper. a road-adaptive LQG control for the semiactive Macpherson strut suspension system of hydraulic type is investigated. A new control oriented model, which incorporates the rotational motion of the unsprung mass, is introduced. A semi-active suspension controller adapting to road variations is proposed. First, based on the extended least squares estimation algorithm, a LQG controller adapting to the estimated road characteristics is designed. Through the computer simulations, the performance of the proposed semi-active suspension is compared with that of a non-adaptive one. The results show better control performance of the proposed system over the compared one.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1398-1401
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• 2020

We present the concept of a Medical Digital Twin (MDT) that can predict and analyze medical diseases using computer simulations and introduce a dynamic virtual body capture system to create it. The MDT is a technology that creates a 3D digital virtual human body by reflecting individual medical and biometric information. The virtual human body is composed of a static virtual human body that reflects an individual's internal and external information and a dynamic virtual human body that reflects his motion. Especially we describe an early version of the dynamic virtual body capture system that enables continuous simulation of musculoskeletal diseases.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.1
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• pp.30-37
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• 2021

Accurate 3D object tracking with camera images is a key enabling technology for augmented reality applications. Motivated by the impressive success of convolutional neural networks (CNNs) in computer vision tasks such as image classification, object detection, image segmentation, recent studies for 3D object tracking have focused on leveraging deep learning. In this paper, we review deep learning approaches for 3D object tracking. We describe key methods in this field and discuss potential future research directions.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.268-281
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• 2022

The development of information technology is bringing many changes to everyday life, and machine learning can be used as a technique to solve a wide range of real-world problems. Analysis and utilization of data are essential processes in applying machine learning to real-world problems. As a method of processing data in machine learning, we propose an approach based on applying multiple linear regression models by interlacing data to the task of classifying similar software. Linear regression is widely used in estimation problems to model the relationship between input and output data. In our approach, multiple linear regression models are generated by training on interlaced feature data. A combination of these multiple models is then used as the prediction model for classifying similar software. Experiments are performed to evaluate the proposed approach as compared to conventional linear regression, and the experimental results show that the proposed method classifies similar software more accurately than the conventional model. We anticipate the proposed approach to be applied to various kinds of classification problems to improve the accuracy of conventional linear regression.