• Journal of Information Processing Systems
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• v.6 no.3
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• pp.347-358
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• 2010

This paper presents a measurement system for 3D hand-drawn gesture motion. Many pen-type input devices with Inertial Measurement Units (IMU) have been developed to estimate 3D hand-drawn gesture using the measured acceleration and/or the angular velocity of the device. The crucial procedure in developing these devices is to measure and to analyze their motion or trajectory. In order to verify the trajectory estimated by an IMU-based input device, it is necessary to compare the estimated trajectory to the real trajectory. For measuring the real trajectory of the pen-type device, a PHANToMTM haptic device is utilized because it allows us to measure the 3D motion of the object in real-time. Even though the PHANToMTM measures the position of the hand gesture well, poor initialization may produce a large amount of error. Therefore, this paper proposes a calibration method which can minimize measurement errors.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.1022-1032
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• 2000

We propose a sliding mode controller tracking the states of a time-varying reference model. The reference model generates the desired trajectories of the states, and the sliding mode controller regulates robustly the errors between the desired states and the measured states. We apply this controller to the overhead crane. Its reference model generates the trajectories of the damped-out swing angle and the swing angular velocity to suppress the swinging motion caused by the acceleration and the deceleration of crane transportation. Also, this model generates the desired trajectories of the position and velocity of the crane. The crane model is identified from the experimental data using an orthogonal function. Kalman filtering is applied to estimate the crane states. The designed controller is simulated on a computer and is tested through a 2-ton industrial overhead crane using the vector-controlled servo motor system. It is verified that, from the simulated and experimental results, the sliding mode controller tracking a time-varying reference model works well.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.119-124
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• 2009

In this paper, we propose a novel approach to estimating the real-time moving trajectory of an object is proposed in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the input-output relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.

• Journal of The Korean Astronomical Society
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• v.53 no.5
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• pp.99-102
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• 2020

I show that when the observables (π_E, t_E, θ_E, π_s, µ_s) are well measured up to a discrete degeneracy in the microlensing parallax vector π_E, the relative likelihood of the different solutions can be written in closed form P_i = KH_iB_i, where H_i is the number of stars (potential lenses) having the mass and kinematics of the inferred parameters of solution i and B_i is an additional factor that is formally derived from the Jacobian of the transformation from Galactic to microlensing parameters. Here t_E is the Einstein timescale, θ_E is the angular Einstein radius, and (π_s, µ_s) are the (parallax, proper motion) of the microlensed source. The Jacobian term B_i constitutes an explicit evaluation of the "Rich Argument", i.e., that there is an extra geometric factor disfavoring large-parallax solutions in addition to the reduced frequency of lenses given by H_i. I also discuss how this analytic expression degrades in the presence of finite errors in the measured observables.

• Journal of the Optical Society of Korea
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• v.18 no.4
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• pp.335-340
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• 2014

Based on Zernike polynomial fitting, we propose an algorithm believed to be new for interferometric measurements of rotationally asymmetric surface deviation of optics. This method tests and calculates each angular surface by choosing specified rotation angles with lowest error. The entire figure can be obtained by superimposing these sub-surfaces. Simulation and experiment studies for verifying the proposed algorithm are presented. The results show that the accuracy of the proposed method is higher than single-rotation algorithm and almost comparable to the rotation-averaging algorithm with fewer rotation measurements. The new algorithm can achieve a balance between the efficiency and accuracy.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.2
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• pp.49-57
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• 2003

In this paper, is proposed an anti-swing control algorithm for the automation of overhead crane. The algorithm consists of three parts, the FCL with compensatory FLC which generates acceleration, velocity and position reference to reduce swing angle and acceleration feedback controller which feedback control errors. Especially the algorithm dose not need angular sensor which detect swing angle of payload and requires high cost. By the simulation study and experiment with prototype crane, we showed the usefulness of the proposed algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.2
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• pp.101-106
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• 2010

We propose an approach to estimate the real-time moving trajectory of an object in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the inputoutput relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.1
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• pp.65-70
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• 2002

An analysis method for calculating motion accuarcy of double sides hydrostatic table is proposed in this paper. In this method, profiles of each rails are assumed as periodic function, therefore it is represented as the sum of spacial frequencies. Bearing clearance at any position rail is depended on the variation of linear, angular motion error of table and the form errors of both sides of a rail. Finite element method is applied to calculate pressure distributions in bearing clearance. In order to simplify the analyzing process, double sides table model is converted into equivalent single side table model. Results calculated by the proposed modeling method agree well with the results directly caculated by double sides modeling method, and also agree well with experimental results. From the theoretical and experimental analysis, it is verified that the proposed analysis method is very effective to analyze the motion accuracy of double sides hydrostatic table.

• East Asian Journal of Business Economics (EAJBE)
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• v.3 no.3
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• pp.14-26
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• 2015

A wind speed forecast is a crucial and sophisticated task in a wind farm for planning turbines and corresponds to an estimate of the expected production of one or more wind turbines in the near future. By production is often meant available power for wind farm considered (with units KW or MW depending on both the wind speed and direction. Such forecasts can also be expressed in terms of energy, by integrating power production over each time interval. In this study, we technically focused on mathematical modeling of wind speed and direction forecast based on locally data set gathered from Aghdasiyeh station in Tehran. The methodology is set on using most common techniques derived from literature review. Hence we applied the most sophisticated forecasting methods to embed seasonality, trend, and irregular pattern for wind speed as an angular variables. Through this research, we carried out the most common techniques such as the Box and Jenkins family, VARMA, the component method, the Weibull function and the Fourier series. Finally, the best fit for each forecasting method validated statistically based on white noise properties and the final comparisons using residual standard errors and mean absolute deviation from real data.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.124.1-124.1
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• 2012

We have made a comparison of the WSA-ENLIL CME propagation model with three cone types and an empirical model using 29 halo CMEs from 2001 to 2002. These halo CMEs have cone model parameters from Michalek et al. (2007) as well as their associated interplanetary (IP) shocks. For this study we consider three different cone models (an asymmetric cone model, an ice-cream cone model and an elliptical cone model) to determine CME cone parameters (radial velocity, angular width and source location), which are used for input parameters of the WSA-ENLIL CME propagation model. The mean absolute error (MAE) of the arrival times at the Earth for the elliptical cone model is 10 hours, which is about 2 hours smaller than those of the other models. However, this value is still larger than that (8.7 hours) of an empirical model by Kim et al. (2007). We are investigating several possibilities on relatively large errors of the WSA-ENLIL cone model, which may be caused by CME-CME interaction, background solar wind speed, and/or CME density enhancement.