• Journal of Power Electronics
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• v.16 no.1
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• pp.1-8
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• 2016

Dead time is typically incorporated in voltage source inverter systems to prevent short circuit cases. However, dead time causes an error between the output voltage and reference voltage. Hence, voltage equation-based algorithms, such as motor parameter estimation and back electromotive force (EMF)-based sensorless algorithms, are prone to estimation errors. Several dead-time compensation methods have been developed to reduce output voltage errors. However, voltage errors are still common in zero current crossing areas, and an effect of the error is much worse in a low speed region. Therefore, employing voltage equation-based algorithms in low speed regions is difficult. This study analyzes the conventional dead-time compensation method and output voltage errors in low speed operation areas. A current shaping method that can reduce output voltage errors is also proposed. Experimental results prove that the proposed method reduces voltage errors and improves the accuracy of the parameter estimation method and the performance of the back EMF-based sensorless algorithm.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.6 s.99
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• pp.738-748
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• 2005

The ship radiated noise level fluctuates by the difference of interference and reverberation according to measurement methods and environmental conditions. These phenomena cause error of the source level estimation even in the same environment conditions. This paper describes a quantitative analysis and a reduction method for an error value to the source level estimation in spatial and temporal interference environment. The design criteria of the radiated noise measurement array composed of omni-directional hydrophones and the source level accuracy in the deep water range are given. The source level accuracy in the shallow water range is also derived based on the statistical model of the multiple reflection paths. The results are verified using the water tank experiment and the sea trial.

• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.16-23
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• 2015

This paper presents a method of improving the pose recognition accuracy of objects by using Kinect sensor. First, by using the SURF algorithm, which is one of the most widely used local features point algorithms, we modify inner parameters of the algorithm for efficient object recognition. The proposed method is adjusting the distance between the box filter, modifying Hessian matrix, and eliminating improper key points. In the second, the object orientation is estimated based on the homography. Finally the novel approach of Auto-scaling method is proposed to improve accuracy of object pose estimation. The proposed algorithm is experimentally tested with objects in the plane and its effectiveness is validated.

• Journal of Aerospace System Engineering
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• v.4 no.1
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• pp.19-22
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• 2010

The accuracy and integrity of global navigation satellite systems (GNSS) can be improved by using GNSS augmentation systems. Large ionospheric spatial gradient, during ionosphere storm, is a major threat for using GNSS augmentation systems by increasing the spatial decorrelation between a reference system and users. Ionosphere decorrelation behavior can be analyzed by using dual frequency GPS data. GNSS receivers have their own biases, called inter-frequency bias (IFB) between dual(P1 and P2) frequencies and they must be accurately estimated before computing ionosphere delays. GPS network data in Korea is used to compute each receiver's IFB, and their estimation accuracy and variability are analyzed. IFB estimation methodology to apply for ionosphere gradient analysis is discussed.

• International Journal of Reliability and Applications
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• v.3 no.1
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• pp.37-50
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• 2002

Simultaneous estimation of accuracy and probability corresponding to a prediction interval is considered in this study. Traditional application of confidence interval forecasting consists in evaluation of interval limits for a given significance level. The wider is this interval, the higher is probability and the lower is the forecast precision. In this paper a measure of stochastic forecast accuracy is introduced, and a procedure for balanced estimation of both the predicting accuracy and confidence probability is elaborated. Solution can be obtained in an optimizing approach. Suggested method is applied to constructing confidence intervals for parameters estimated by normal and t distributions

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.589-599
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• 2008

Accurate software effort estimation has always been a challenge for the software industrial and academic software engineering communities. Many studies have focused on effort estimation methods to improve the estimation accuracy of software effort. Although data quality is one of important factors for accurate effort estimation, most of the work has not considered it. In this paper, we investigate the influence of outlier elimination on the accuracy of software effort estimation through empirical studies applying two outlier elimination methods(Least trimmed square regression and K-means clustering) and three effort estimation methods(Least squares regression, Neural network and Bayesian network) associatively. The empirical studies are performed using two industry data sets(the ISBSG Release 9 and the Bank data set which consists of the project data collected from a bank in Korea) with or without outlier elimination.

• Journal of the Korean Society for Railway
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• v.20 no.5
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• pp.692-702
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• 2017

Recently, there has been a growing interest in using smart card data. However, there are restrictions on the utilization of data in many areas outside the Seoul metropolitan area because the data does not contain alighting information. This paper presents a methodology for estimating alighting stops of smart card data. Estimation results were verified by smart card data from Seoul and Gwangju. The estimation rates were 78.2% and 81.6% in Seoul and Gwangju, respectively. The matching accuracy was 54.2% and 33.4%, respectively. However, if up to two stops of error are allowed, the accuracy values were 93.6% and 94.0%, respectively. We also discussed changes in estimation results due to adjusting the allowable walking distance, which is a key parameter of trip chaining methods. As the allowable walking distance increases, the estimation rate increases, while the accuracy decreases, and it is found that the estimation results change by around 500m.

• Structural Monitoring and Maintenance
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• v.11 no.2
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• pp.127-148
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• 2024

Estimating cable tension is important in the maintenance of cable structures, such as cable-stayed bridges. In practice, the higher-order vibration method based on natural frequencies is used. In recent years, dampers have been installed onto cables to suppress aerodynamic vibration. Because the higher-order vibration method is suitable to cables without a damper, the damper must be removed before using this method. Because damper removal is time-consuming and labor-intensive, a previous study proposed a tension estimation method for a cable with a damper based on the natural frequencies, which does not require the damper's removal. However, the previous method relies on the modeling accuracy of the damper's complex stiffness. The damper design formula, while intended for design purposes, does not consistently reflect the damper's actual complex stiffness. Therefore, the estimation accuracy deteriorates when the damper's actual complex stiffness deviates from the damper design formula. With this background, this paper introduces a novel tension estimation method based on mode shapes, which circumvents damper modeling errors since mode shapes are independent of the damper's complex stiffness. In the numerical verification using 90 models, the proposed method estimated tension accurately with an estimation error within 0.59%. In the experimental verification, the proposed method estimated tension accurately with an estimation error within 4.17% except for one case, while the previous method had an estimation error of 44% when the damper design formula was used. The proposed method was found to be superior to the previous method in terms of accuracy and practicality by numerical simulation and experiment.

• Journal of Korea Multimedia Society
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• v.16 no.2
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• pp.131-137
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• 2013

Zoom motion estimation of video sequence is very complicated for implementation. In this paper, we propose a method to implement the zoom motion estimation using together the depth camera and color camera. Depth camera obtains the distance information between current block and reference block, then zoom ratio between both blocks is calculated from this distance information. As the reference block is appropriately zoomed by the zoom ratio, the motion estimated difference signal can be reduced. Therefore, the proposed method is possible to increase the accuracy of motion estimation with keeping zoom motion estimation complexity not greater. Simulation was to measure the motion estimation accuracy of the proposed method, we can see the motion estimation error was decreased significantly compared to conventional block matching method.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.6
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• pp.982-989
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• 2010

This paper shows accuracy comparison results of reliability estimation methods for one-shot systems such as ammunitions. Quantal-response data, following a binomial distribution at each sampling time, characterizes lifetimes of one-shot systems. Various quantal-response data of different sample sizes are simulated using lifetime data randomly sampled from assumed weibull distributions with different shape parameters but the identical scale parameter in this paper. Then, reliability estimation methods in open literature are applied to the simulated quantal-response data to estimate true reliability over time. Rankings in estimation accuracy for different sample sizes are determined using t-test of SSE. Furthermore, MSE at each time, including both bias and variance of estimated reliability metrics for each method are analyzed to investigate how much both bias and variance contribute the SSE. From the MSE analysis, MSE provides reliability estimation trend for each method. Parametric estimation method provides more accurate reliability estimation results than the other methods for most of sample sizes.