• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.238-241
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• 2000

A fingerprint core-point detection algorithm is presented in this paper. Core-point is useful for fingerprint classification and also for the fingerprint verification since it giver a reference to a fingerprint. Traditional methods of finding the core-point is introduced. These methods are the method using poincare index and the method using sine component of ridge directions. The proposed method is modified algorithm of the latter using the poincare index. The experimental results show that the proposed algorithm achieves almost the same accuracy with faster speed.

• Proceedings of the Safety Management and Science Conference
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• 2011.11a
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• pp.551-556
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• 2011

This research presents an implementation strategy of Process Capability Index (PCI) according to the types of process characteristics. The types of process feature are classified as four perspectives of variation range, time period, error position, and process stage. The paper examines short-term or long-term PCI, within or between variation, position of precision or accuracy, and inclusion of measurement or calibration stage. Moreover, the study proposes normality test of unilateral PCI.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.493-502
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• 2021

Recently, because of changes in temperature and rising water temperatures due to increased pollution sources, many algae have been produced in the water system. Therefore, there has been a lot of research using satellite images for the generation and monitoring of green algae. However, in prior studies, it is difficult to consider the optical properties of the local water system by using only a single index, and by using medium and low-resolution satellite images to conduct large-scale algae detection, there is a problem of accuracy in narrow, broad rivers. Therefore, in this work, we utilize high-resolution images of Sentinel-2 satellites to perform green algae detection on a single index (NDVI, SEI, FGAI) and development index (NDVI & SEI, FGAI & SEI) that mixes single indices. In this study, POD, FAR, and PC values were utilized to evaluate the accuracy of green algae detection algorithms, and the FGAI & SEI index showed the highest accuracy with 98.29% overall accuracy PC.

• Journal of the Korea Safety Management & Science
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• v.11 no.2
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• pp.117-126
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• 2009

We develop methods for propagating and analyzing EPCI(Extended Process Capability Index) by using the error type that classifies into accuracy and precision. EPCI developed in this study can be applied to the three combined processes that consist of production, measurement and calibration. Little calibration work discusses while a great deal has been studied about SPC(Statistical Process Contol) and MSA(Measurement System Analysis). EPCI can be decomposed into three indexes such as PPCI(Production Process Capability Index), PPPI(Production Process Performance Index), MPCI(Measurement PCD, and CPCI(Calibration PCI). These indexs based on the type of error classification can be used with various statistical techniques and principles such as SPC control charts, ANOVA(Analysis of Variance), MSA Gage R&R, Additivity-of-Variance, and RSSM(Root Sum of Square Method). As the method proposed is simple, any engineer in charge of SPC. MSA and calibration can use efficientily in industries. Numerical examples are presentsed. We recommed that the indexes can be used in conjunction with evaluation criteria.

• Structural Monitoring and Maintenance
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• v.1 no.1
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• pp.89-110
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• 2014

Structural Health Monitoring (SHM) is an effective alternative to conventional inspections which are time-consuming and subjective. SHM can detect damage early and reduce maintenance cost and thereby help reduce the likelihood of catastrophic structural events to infrastructure such as bridges. After reviewing the Damage Index Method (DIM), an Iterative Damage Index Method (IDIM) is proposed to improve the accuracy of damage detection. These two damage detection techniques are compared based on damage on two structures, a simply supported beam and a pedestrian bridge. Compared to the traditional damage detection algorithm, the proposed IDIM is shown to be less arbitrary and more accurate.

• Korean Journal of Remote Sensing
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• v.31 no.1
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• pp.11-20
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• 2015

This study aimed to investigate the canopy growth conditions and the accuracy of phenological stages of paddy rice using ground-based remote sensing data. Plant growth variables including Leaf Area Index (LAI) and canopy reflectance of paddy rice were measured at the experimental fields of Chonnam National University, Gwangju, Republic of Korea during the crop seasons of 2011, 2012, and 2013. LAI values were also determined based on correlations with Vegetation Indices (VIs) obtained from the canopy reflectance. Three phenological stages (tillering, booting, and grain filling) of paddy rice could be identified using VIs and a spatial index (NIR versus red). We found that exponential relationships could be applied between LAI and the VIs of interest. This information, as well as the relationships between LAI and VIs obtained in the present study, could be used to estimate and monitor the relative growth and development of rice canopies during the growing season.

• Structural Engineering and Mechanics
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• v.59 no.1
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• pp.187-207
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• 2016

Practical ambient excitations of engineering structures usually do not comply with the stationary-white-noise assumption in traditional operational modal analysis methods due to heavy traffic, wind guests, and other disturbances. In order to eliminate spurious modes induced by non-white noise inputs, the improved stochastic subspace identification based on a delay index is proposed in this paper for a representative kind of stationary non-white noise ambient excitations, which have nonzero autocorrelation values near the vertical axis. It relaxes the stationary-white-noise assumption of inputs by avoiding corresponding unqualified elements in the Hankel matrix. Details of the improved stochastic subspace identification algorithms and determination of the delay index are discussed. Numerical simulations on a four-story frame and laboratory vibration experiments on a simply supported beam have demonstrated the accuracy and reliability of the proposed method in eliminating spurious modes under non-white noise ambient excitations.

• Journal of the Society of Disaster Information
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• v.14 no.4
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• pp.501-509
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• 2018

Purpose: UAV-based photo measurements are being researched using UAVs in the space information field as they are not only cost-effective compared to conventional aerial imaging but also easy to obtain high-resolution data on desired time and location. In this study, the UAV-based high-resolution images were used to perform the land cover classification. Method: RGB cameras were used to obtain high-resolution images, and in addition, multi-distribution cameras were used to photograph the same regions in order to accurately classify the feeding areas. Finally, Land cover classification was carried out for a total of seven classes using created ortho image by RGB and multispectral camera, DSM(Digital Surface Model), NDVI(Normalized Difference Vegetation Index), GLCM(Gray-Level Co-occurrence Matrix) using RF (Random Forest), a representative supervisory classification system. Results: To assess the accuracy of the classification, an accuracy assessment based on the error matrix was conducted, and the accuracy assessment results were verified that the proposed method could effectively classify classes in the region by comparing with the supervisory results using RGB images only. Conclusion: In case of adding orthoimage, multispectral image, NDVI and GLCM proposed in this study, accuracy was higher than that of conventional orthoimage. Future research will attempt to improve classification accuracy through the development of additional input data.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.145-154
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• 2009

High spatial resolution satellite image classification has a limitation when only using the spectral information due to the complex spatial arrangement of features and spectral heterogeneity within each class. Therefore, the extraction of the spatial information is one of the most important steps in high resolution satellite image classification. This study proposes a new spatial feature extraction method, named SSI(Shape-Size Index). SSI uses a simple region-growing based image segmentation and allocates spatial property value in each segment. The extracted feature is integrated with spectral bands to improve overall classification accuracy. The classification is achieved by applying a SVM(Support Vector Machines) classifier. In order to evaluate the proposed feature extraction method, KOMPSAT-2 and QuickBird-2 data are used for experiments. It is demonstrated that proposed SSI algorithm leads to a notable increase in classification accuracy.

• Journal of Astronomy and Space Sciences
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• v.26 no.3
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• pp.317-328
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• 2009

Relative dynamic models of satellites which describe the relative motion between two satellites is fundamental for research on the formation flying. The accuracy of various linearized or nonlinear models of relative motion is analyzed and compared. A 'Modeling Error Index (MEI)' is defined for evaluating the accuracy of models. The accuracy of the relative dynamic models in various orbit circumstance are obtained by calculating the modeling error with various eccentricities of the chief orbit and distances between the chief and the deputy. It is found that the modeling errors of the relative dynamic models have different values according to the eccentricity, J2 perturbation, and the distance between satellites. Since the evaluated accuracy of various models in this paper means the error of dynamic models of the formation flying, the results of this paper are very useful for choosing the appropriate relative model of the formation flying mission.