• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.6
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• pp.710-717
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• 2015

Projection stereolithography is an additive manufacturing method that uses beam projection to cure the photo-reactive resin used. The light source of a cross-section layer-form illuminates photo-curable resin for building a three-dimensional (3D) model. This method has high accuracy and a fast molding speed because the processing unit is a face instead of a dot. This study describes a Scalable Projection Stereolithography 3D Printing System for improving the accuracy of the stereolithography. In a conventional projection 3D printer, when printing a small sized model, many pixels are not used in the projection or curing. The proposed system solves this problem through an optical adjustment, and keeps using the original image as possible as filling the whole projection area. The experimental verification shows that the proposed system can maintain the highest level of precision regardless of the output size.

• Nuclear Engineering and Technology
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• v.47 no.5
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• pp.596-607
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• 2015

In this paper, we describe a computer program, iBEST (inverse Burnup ESTimator), that we developed to accurately estimate the burnup histories of spent nuclear fuels based on sample measurement data. The burnup history parameters include initial uranium enrichment, burnup, cooling time after discharge from reactor, and reactor type. The program uses algebraic equations derived using the simplified burnup chains of major actinides for initial estimations of burnup and uranium enrichment, and it uses the ORIGEN-S code to correct its initial estimations for improved accuracy. In addition, we newly developed a stable bisection method coupled with ORIGEN-S to correct burnup and enrichment values and implemented it in iBEST in order to fully take advantage of the new capabilities of ORIGEN-S for improving accuracy. The iBEST program was tested using several problems for verification and well-known realistic problems with measurement data from spent fuel samples from the Mihama-3 reactor for validation. The test results show that iBEST accurately estimates the burnup history parameters for the test problems and gives an acceptable level of accuracy for the realistic Mihama-3 problems.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.204-213
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• 2008

In this paper, we present the practical application of an Unscented Kalman Filter (UKF) for an Indoor Mobile Localization System using ultrasonic sensors. It is true that many kinds of localization techniques have been researched for several years in order to contribute to the realization of a ubiquitous system; particularly, such a ubiquitous system needs a high degree of accuracy to be practical and efficient. Unfortunately, a number of localization systems for indoor space do not have sufficient accuracy to establish any special task such as precise position control of a moving target even though they require comparatively high developmental cost. Therefore, we developed an Indoor Mobile Localization System having high localization performance; specifically, the Unscented Kalman Filter is applied for improving the localization accuracy. In addition, we also present the additive filter named 'Pre-filtering' to compensate the performance of the estimation algorithm. Pre-filtering has been developed to overcome negative effects from unexpected external noise so that localization through the Unscented Kalman Filter has come to be stable. Moreover, we tried to demonstrate the performance comparison of the Unscented Kalman Filter and another estimation algorithm, such as the Unscented Particle Filter (UPF), through simulation for our system.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.178-183
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• 2013

In this study, we proposed a method for electroencephalogram (EEG) classification using invariant CSP at special channels for improving the accuracy of classification. Based on the naive EEG signals from left and right hand movement experiment, the noises of contaminated data set should be eliminate and the proposed method can deal with the de-noising of data set. The considering data set are collected from the special channels for right and left hand movements around the motor cortex area. The proposed method is based on the fit of the adjusted parameter to decline the affect of invariant parts in raw signals and can increase the classification accuracy. We have run the simulation for hundreds time for each parameter and get averaged value to get the last result for comparison. The experimental results show the accuracy is improved more than the original method, the highest result reach to 89.74%.

• 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 Electrical Engineering and Technology
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• v.9 no.3
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• pp.1060-1071
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• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.447-451
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• 2011

Recently, various in-vehicle networks have been developed respectively in order to accomplish their own purposes such as CAN and MOST. Especially, the MOST network is usually adapted to provide entertainment service. The car navigation system is also widely used for guiding driving paths to driver. The position for the navigation system is usually acquired by GPS technology. However, the GPS technique has two serious problems. The first is unavailability in urban canyons. The second is inherent positional error rate. The problems have been studied in many literatures. However, the second still leads to incorrect locational information in some area, especially parallel roads. This paper proposes a performance tuning method of image matching algorithm for the car navigation system. The method utilizes images obtained from in-vehicle MOST network and a real-time image matching algorithm which determines the direction of moving vehicle in parallel section of road. In order to accuracy improvement of image matching algorithm, three conditions are applied. The experimental tests show that the proposed system increases the accuracy.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.219-226
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• 2013

Feature selection is the one of important issues in the field of machine learning and pattern recognition. It is the technique to find a subset from the source data and can give the best classification performance. Ie, it is the technique to extract the subset closely related to the purpose of the classification. In this paper, we experimented to select the best feature subset for improving classification accuracy when classify success and failure factors in software reuse. And we compared with existing studies. As a result, we found that a feature subset was selected in this study showed the better classification accuracy.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1021-1023
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• 2003

Atmospheric water vapor is a key variable in numerical weather prediction (NWP) models, but it is a crucial factor to limit the accuracy of high-precision GPS positioning technique. For both issues, knowledge about the amount of water vapor is extremely important. In this study, we perform a simulation study to utilize GPS signals through a developed tomographic scheme to retrieve 3D structure of atmospheric wet refractivity, which may be assimilated into NWP models for advancing forecasting or position calculation for improving GPS positioning accuracy. For the purpose of knowing the absolute accuracy of the developed tomographic method, a well-defined temporal and spatial varying state of atmospheric profile is utilized. Under such circumstance, several factors that may influence the retrievals can be easily examined and their impacts may be clearly quantified. They include the values of the positional dilution of precision (PDOP) factors of the GPS signals, ... etc. Based upon the use of a variety spectrum of adjustable factors, many interesting findings are obtained. For example, the more is the number of the observed GPS signals the better becomes the retrievals as expected. Also, the smaller is the PDOP value the better becomes the retrievals.

• International Journal of Highway Engineering
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• v.18 no.1
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• pp.73-83
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• 2016

PURPOSES : The purpose of this study is to develop a method for improving the accuracy of analysis results obtained from a two-dimensional (2-D) numerical analysis model of continuously reinforced concrete pavement (CRCP). METHODS : The analysis results from the 2-D numerical model of CRCP are compared with those from more rigorous three-dimensional (3-D) models of CRCP, and the relationships between the results are recognized. In addition, the numerical analysis results are compared with the results obtained from field experiments. By performing these comparisons, the calibration factors used for the 2-D CRCP model are determined. RESULTS : The results from the comparisons between 2-D and 3-D CRCP analyses show that with the 2-D CRCP model, concrete stresses can be overestimated significantly, and crack widths can either be underestimated or overestimated by a slight margin depending on the assumption of plane stress or plane strain. The behaviors of crack width in field measurements are comparable to those obtained from the numerical model of CRCP. CONCLUSIONS : The accuracy of analysis results from the 2-D CRCP model can be improved significantly by applying calibration factors obtained from comparisons with 3-D analyses and field experiments.