• Journal of Korean Institute of Industrial Engineers
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• v.39 no.2
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• pp.109-118
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• 2013

We propose a method for estimating coefficients of AR (autoregressive) model which named MLPAR (Maximum Likelihood of Pearson system for Auto-Regressive model). In the present method for estimating coefficients of AR model, there is an assumption that residual or error term of the model follows the normal distribution. In common cases, we can observe that the error of AR model does not follow the normal distribution. So the normal assumption will cause decreasing prediction accuracy of AR model. In the paper, we propose the MLPAR which does not assume the normal distribution of error term. The MLPAR estimates coefficients of auto-regressive model and distribution moments of residual by using pearson distribution system and maximum likelihood estimation. Comparing proposed method to auto-regressive model, results are shown to verify improved performance of the MLPAR in terms of prediction accuracy.

• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.886-894
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• 2015

In this paper, we conduct sensitivity analysis of parameters used for inverse modeling in order to estimate the PM₁₀ emissions from the 16 areas in East Asia accurately. Parameters used in sensitivity analysis are R, the observational error covariance matrix, and B, a priori (background) error covariance matrix. In previous studies, it was used with the predetermined parameter empirically. Such a method, however, has difficulties in estimating an accurate emissions. Therefore, an automatically determining method for the most suitable value of R and B with an error measurement criteria and posteriori emissions accuracy is required. We determined the parameters through a sensitivity analysis, and improved the accuracy of posteriori emissions estimation. Inverse modeling methods used in the emissions estimation are pseudo inverse, NNLS (Nonnegative Least Square), and BA(Bayesian Approach). Pseudo inverse has a small error, but has negative values of emissions. In order to resolve the problem, NNLS is used. It has a unrealistic emissions, too. The problems are resolved with BA(Bayesian Approach). We showed the effectiveness and the accuracy of three methods through case studies.

• International Journal of Aeronautical and Space Sciences
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• v.5 no.1
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• pp.94-100
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• 2004

Ionospheric time delay is the biggest error source for single-frequency DGPSapplications, including time transfer and Wide Area Differential GPS (WADGPS).Currently, there are many attempts to develop real-time ionospheric time delayestimation techniques to reduce positioning error due to the ionospheric time delay.Klobuchar model is now widely used for ionosphehc time delay calculation forsingle-frequency users. It uses flat surface at night time and cosine surface atdaytime[1], However, the model was developed for worldwide ionosphere fit, it isnot adequate for local area single-frequency users who want to estimateionospheric time delay accurate1y[2]. Therefore, 3-D ionosphere model usingtomographic estimation has been developed. 3-D tomographic inversion modelshows better accuracy compared with prior a1gorithms[3]. But that existing 3-Dmodel still has problem that it requires many coefficients and measurements forgood accuracy. So, that algorithm has Umitation with many coefficients incontinuous estimation at the small region which is obliged to have fewermeasurements.In this paper, we developed an modified 3-D ionosphehc time delay modelusing tomography, which requires only fewer coefficients. Because the combinationsof our base coefficients correspond to the full coefficients of the existing model, ourmodel has equivalent accuracy to the existing. We confirmed our algorithm bysimulations. The results proved that our modified algohthm can perform continuousestimation with fewer coefficients.

• Structural Engineering and Mechanics
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• v.75 no.6
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• pp.771-784
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• 2020

Reliability analysis techniques combining with various surrogate models have attracted increasing attention because of their accuracy and great efficiency. However, they primarily focus on the structures with continuous response, while very rare researches on the reliability analysis for structures with discontinuous response are carried out. Furthermore, existing adaptive reliability analysis methods based on importance sampling (IS) still have some intractable defects when dealing with small failure probability, and there is no related research on reliability analysis for structures involving discontinuous response and small failure probability. Therefore, this paper proposes a novel reliability analysis method called AGPC-IS for such structures, which combines adaptive Gaussian process classification (GPC) and adaptive-kernel-density-estimation-based IS. In AGPC-IS, an efficient adaptive strategy for design of experiments (DoE), taking into consideration the classification uncertainty, the sampling uniformity and the regional classification accuracy improvement, is developed with the purpose of improving the accuracy of Gaussian process classifier. The adaptive kernel density estimation is introduced for constructing the quasi-optimal density function of IS. In addition, a novel and more precise stopping criterion is also developed from the perspective of the stability of failure probability estimation. The efficiency, superiority and practicability of AGPC-IS are verified by three examples.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.16 no.1
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• pp.10-17
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• 2004

In this paper, the affine transformation method that is more simpler compare with digital orthophoto method is used analyzed the long-term shoreline change, and accuracy estimation was carried out. As a result of this study, it was able to check that the shoreline change on Namhangjin coast had eroded significantly compare with the past. Moreover, as a result of accuracy estimation, it shows that the RMS error around shoreline was about 1-2 m. In consideration that maximum allowable error shown in aerial photogrammetry specification is within 2 m, therefore, analysis results of shoreline change using affine transformation method on aerial images is reliable.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.248-254
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• 2013

In location finding system using spaced multi-sensor, there is the phenomenon that the position estimation accuracy is degraded by the location of signal sources and the sensors. This phenomenon is called GDOP(Geometric Dilution Of Precision) effect. and to minimize these effects, research is needed on how. In this paper, I will describe how to minimize GDOP effect, estimating possibility of GDOP using AOA(angle of arrival) information of spaced multi sensors, and removing sensor error factor in position estimation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.23-32
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• 2004

The query optimizer's important task while a query is invoked is to estimate the fraction of records in the databases that satisfy the given query condition. The query result size estimation in spatial databases, like relational databases, proceeds to partition the whole input into a small number of subsets called “buckets” and then estimate the fraction of the input in the buckets. The accuracy of estimation is determined by the difference between the real data counts and approximations in the buckets, and is dependent on how to partition the buckets. Existing techniques for spatial databases are equi-area and equi-count techniques, which are respectively analogous in relation databases to equi-height histogram that divides the input value range into buckets of equal size and equi-depth histogram that is equal to the number of records within each bucket. In this paper we propose a new partitioning technique that determines buckets according to the maximal difference of area which is defined as the product of data ranges End frequencies of input. In this new technique we consider both data values and frequencies of input data simultaneously, and thus achieve substantial improvements in accuracy over existing approaches. We present a detailed experimental study of the accuracy of query result size estimation comparing the proposed technique and the existing techniques using synthetic as well as real-life datasets. Experiments confirm that our proposed techniques offer better accuracy in query result size estimation than the existing techniques for space query size, bucket number, data number and data size.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.1
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• pp.75-82
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• 1991

The objective of this paper is to investigate the optimal Robust estimation and scale estimator that could be used to treat the gross errors in a self calibrating bundle adjustment. In order to test the variability in performance of the different weighting schemes in accurately detecting gross error, five robust estimation methods and three types of scale estimators were used. And also, two difference control point patterns(high density control, sparse density control) and three types of gross errors(4$\sigma o$, 20$\sigma o$, 50$\sigma o$) were used for comparison analysis. As a result, Anscombe's robust estimation produced the best results in accuracy among the robust estimation methods considered. when considering the scale estimator about control point patterns, It can be seen that Type II scale estimator provided the best accuracy in high density control pattern. On the other hand, In the case of sparse density control pattern, Type III scale estimator showed the best results in accuracy. Therefore it is expected to apply to robustified bundle adjustment using the optimal scale estimator which can be used for eliminating the gross error in precise structure analysis.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.9
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• pp.783-790
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• 2016

One of the main factors related to the deterioration of estimation accuracy in inertial measurement unit (IMU)-based orientation determination is the object's acceleration. This is because accelerometer signals under accelerated motion conditions cannot be longer reference vectors along the vertical axis. In order to deal with this issue, some orientation estimation algorithms adopt acceleration-compensating mechanisms. Such mechanisms include the simple switching techniques, mechanisms with adaptive estimation of acceleration, and acceleration model-based mechanisms. This paper compares these three mechanisms in terms of estimation accuracy. From experimental results under accelerated dynamic conditions, the following can be concluded. (1) A compensating mechanism is essential for an estimation algorithm to maintain accuracy under accelerated conditions. (2) Although the simple switching mechanism is effective to some extent, the other two mechanisms showed much higher accuracies, particularly when test conditions were severe.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.583-588
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• 2024

With the emergence of Brain-Computer Interface (BCI) technology, analyzing mirror neurons has become more feasible. However, evaluating the accuracy of BCI systems that rely on human thoughts poses challenges due to their qualitative nature. To harness the potential of BCI, we propose a new approach to measure accuracy based on the characteristics of mirror neurons in the human brain that are influenced by speech speed, depending on the ultimate goal of movement. In Chapter 2 of this paper, we introduce mirror neurons and provide an explanation of human posture estimation for mirror neurons. In Chapter 3, we present a powerful pose estimation method suitable for real-time dynamic environments using the technique of human posture estimation. Furthermore, we propose a method to analyze the accuracy of BCI using this robotic environment.