• Journal of The Korean Astronomical Society
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• v.50 no.1
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• pp.1-5
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• 2017

Like Hipparcos, Gaia is designed to give absolute parallaxes, independent of any astrophysical reference system. And indeed, Gaia's internal zero-point error for parallaxes is likely to be smaller than any individual parallax error. Nevertheless, due in part to mechanical issues of unknown origin, there are many astrophysical questions for which the parallax zero-point error ${\sigma}({\pi}_0)$ will be the fundamentally limiting constraint. These include the distance to the Large Magellanic Cloud and the Galactic Center. We show that by using the photometric parallax estimates for RR Lyrae stars (RRL) within 8kpc, via the ultra-precise infrared period-luminosity relation, one can independently determine a hyper-precise value for ${\pi}_0$. Despite their paucity relative to bright quasars, we show that RRL are competitive due to their order-of-magnitude improved parallax precision for each individual object relative to bright quasars. We show that this method is mathematically robust and well-approximated by analytic formulae over a wide range of relevant distances.

• Wind and Structures
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• v.32 no.2
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• pp.161-167
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• 2021

In this study, the artificial neural network (ANN) method was used for estimating the monthly mean wind speed of Sivas, in the central part of Turkey. Eighteen years of wind speed data obtained from nine measurement stations during the period of 2000-2017 at 10 m height was used for ANN analysis. It was found that mean absolute percentage error (MAPE) ranged from 3.928 to 6.662, mean bias error (MBE) ranged from -0.089 to -0.003, while root mean square error (RMSE) ranged from 0.050 to 0.157 and R2 ranged from 0.86 to 0.966. ANN models provide a good approximation of the wind speed for all measurement stations, however, a tendency to underestimate is also obvious.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.829-832
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• 2010

Wireless Sensor Networks have been proposed for several location-dependent applications. For such systems, the cost and limitations of the hardware on sensing nodes prevent the use of range-based localization schemes that depend on absolute point to point distance estimates. Because coarse accuracy is sufficient for most sensor network applications, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. In this paper, we proposed a modified DV-Hop (range-free localization) algorithm which reduces node's location error and cumulated distance error by minimizing localization error. Simulation results have verified the high estimation accuracy with our approach which outperforms the classical DV-Hop.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.387-390
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• 2002

In this paper, we accomplish uncertainty evaluation and performance test of the volumetric interferometer using two spherical wavefronts emitted from the ends of two single mode fibers. We verify that the volumetric interferometer has the volume uncertainty of 690nm through the error analysis and it has the resolution of 0.1 0.1$\mu\textrm{m}$ for x axis which is the same order of repeatability for x axis. Also, we obtain the systematic error of $1\mu\textrm{m}$ for $60\times 60\times 20 mm^3$ working volume using self-calibration with an artifact plate.

• Journal of Wind Energy
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• v.10 no.3
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• pp.5-11
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• 2019

This paper suggests and compares two algorithms, a moving average filter method and a method developed by the National Renewable Energy Laboratory (NREL), to verify the yaw control algorithm characteristic to reduce yaw error for a wind turbine. A characteristic change for yaw movement in accordance with control parameter change that consists of each control method has been verified. Also, yaw simulations were performed using nacelle wind data measured from two areas with different turbulence intensities and the yaw movement data in each area was compared. These two algorithms and real data were compared by calculating mean absolute error (MSE) and the number of yawing (NY). As a result of the analysis, the MSE values were not significantly different between the two algorithms, but the algorithm proposed by the NREL was found to reduce yaw movement by up to 50 percent more than the moving average filter method.

• Journal of the Korea Convergence Society
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• v.6 no.4
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• pp.249-255
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• 2015

The study was to investigate the effects of stimulus velocity and passer's skill level on anticipation timing performance. Fourteen subjects(seven novices and seven experts) were required to make a total 12 passes in coincidence with an experimentally manipulated moving light signal in randomly presented three different conditions(4m/s, $3m/s{\rightarrow}5m/s$, $5m/s{\rightarrow}3m/s$). Results of analyses showed that absolute error(AE) and constant error(CE) were greater in constant acceleration of the moving stimulus. In addition, experts were more accuracy and consistency than novices on absolute, constant and variable error(VE). These findings indicated that stimulus velocity served as the major determination of anticipation timing performance of passing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.1
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• pp.142-152
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• 2016

GPS is not able to be used for indoor positioning and currently most of techniques emerging to overcome the limit of GPS utilize private wireless networks. However, these methods require high costs for installation and maintenance, and they are inappropriate to be used in the place where precise positioning is needed as in indoor parking lots. This paper proposes a vehicular indoor positioning method based on QR-code recognition. The method gets an absolute coordinate through QR-code scanning, and obtain the location (an relative coordinate) of a black-box camera using the tilt and roll angle correction through affine transformation, scale transformation, and trigonometric function. Using these information of an absolute coordinate and an relative one, the precise position of a car is estimated. As a result, average error of 13.79cm is achieved and it corresponds to just 27.6% error rate in contrast to 50cm error of the recent technique based on wireless networks.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.153-160
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• 2020

Energy Consumption Predictions for Industries has a prominent role to play in the energy management and control system as dynamic and seasonal changes are occurring in energy demand and supply. This paper introduces and explores the steel industry's predictive models of energy consumption. The data used includes lagging and leading reactive power lagging and leading current variable, emission of carbon dioxide (tCO2) and load type. Four statistical models are trained and tested in the test set: (a) Linear Regression (LR), (b) Radial Kernel Support Vector Machine (SVM RBF), (c) Gradient Boosting Machine (GBM), and (d) Random Forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for calculating regression model predictive performance. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• Journal of the Korean Society of Safety
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• v.19 no.2
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• pp.54-60
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• 2004

Flammable substances are frequently used chemical industry processes. An accurate knowledge of the ALTs(Autoignition Temperatures) is important in developing appropriate prevention and control measures in industrial fire protection. The AITs describe the minimum temperature to which a substance must be heated, without the application of a flame or spark, which will cause that substance to ignite. The AITs are dependent upon many factors, namely initial temperature, pressure, volume, fuel/air stoichiometry, catalyst material, concentration of vapor, ignition delay. This study measured relationship between the AITs and the ignition delay times by using ASTM E659-78 apparatus for methanol and ethanol. The A.A.P.E.(Average Absolute Percent Error) and the A.A.D.(Average Absolute Deviation) of the experimental and the calculated delay times by the AITs for methanol were 14.59 and 1.76 respectively. Also the A.A.P.E. and the A.A.D. of the experimental and the calculated delay times by the ATIs for ethanol were 8.33 and 0.88.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.10
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• pp.2303-2309
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• 1997

This paper presents a convergence analysis of the stocastic gradient adaptive algorithm based on the least mean absolute third (LMAT) error criteriohn. Under the assumption that the signals involved are zero-mean, wide-sense sateionaryand gaussian, a set of nonlinear difference equations that characterizes the mean and mean-squared behavior of the algorithm is derived. Computer simulation resutls show fairly good agreements between the theoetical and empirical behaviors of the algorithm.