• Radiation Oncology Journal
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• v.35 no.2
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• pp.101-111
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• 2017

The number of imaging data sets has significantly increased during radiation treatment after introducing a diverse range of advanced techniques into the field of radiation oncology. As a consequence, there have been many studies proposing meaningful applications of imaging data set use. These applications commonly require a method to align the data sets at a reference. Deformable image registration (DIR) is a process which satisfies this requirement by locally registering image data sets into a reference image set. DIR identifies the spatial correspondence in order to minimize the differences between two or among multiple sets of images. This article describes clinical applications, validation, and algorithms of DIR techniques. Applications of DIR in radiation treatment include dose accumulation, mathematical modeling, automatic segmentation, and functional imaging. Validation methods discussed are based on anatomical landmarks, physical phantoms, digital phantoms, and per application purpose. DIR algorithms are also briefly reviewed with respect to two algorithmic components: similarity index and deformation models.

• YAKHAK HOEJI
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• v.48 no.1
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• pp.60-64
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• 2004

Near-infrared (NIR) spectroscopy was used to determine rapidly and nondestructively the content of ambroxol in intact ambroxol tablets containing 30 mg (12.5％ m/m nominal concentration) by collecting NIR spectra in range 1100-1750 nm. The laboratory-made samples had 10.3∼15.9％ m/m nominal ambroxol concentration. The measurements were made by reflection using a fiber-optic probe and calibration was carried out by partial least square regression (PLSR) with autoscaling. Model validation was performed by randomly splitting the data set into calibration and validation data set (7 samples as a calibration data set and 5 samples as a validation data set). The developed NIR method gave results comparable to the known values of tablets in a laboratorial manufacturing Process, standard error of calibration (SEC) and standard error of prediction (SEP) being 0.49％ and 0.49％ m/m respectively. The method showed good accuracy and repeatability NIR spectroscopic determination in intact tablets allowed the potential use of real time monitoring for a running production process.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.436-439
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• 2004

Output data streams of the MSC contain flags, Headers and image data according to the established protocols and data formats. Especially the Header added to each data lines contain information of a line sync, a line counter and, ancillary data which consist of ancillary identification bit and one ancillary data byte. This information is used by ground station to calculate the geographic coordinates of the image and get the on-board time and several EOS(Electro-Optical Subsystem) parameters used at the time of imaging. Therefore, the EGSE(Electrical Ground Supporting Equipment) that is used for testing MSC has to have functions of interpreting and displaying this Header information correctly following the protocols. This paper describes the design of the header data processing module which is in EOS­EGSE. This module provides users with various test functions such as header validation, ancillary block validation, line-counter and In-line counter validation checks which allow convenient and fast test on imagery data.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.746-749
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• 2002

This paper is introducing a radio control helicopter as a new platform of ground truth measurement. This helicopter is normally used for spraying an agricultural chemical. It can do pinpoint hovering and programing flight using DGPS etc., A spectrometer with dual port can measure ground surface and white reference plate at the same time. And it can also take digital images by digital camera. It is needed to collect ground reflectance information as satellite sensor footprint size for satellite data validation. Generally it is possible to get such ground reflectance by an airplane measurement. But it is high cost and not so easy to make a measurement by airplane. Developed validation system can provide such ground reflectance in low cost and easy.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.151-165
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• 2006

It is important to estimate the true misclassification rate of a given classifier when an independent set of test data is not available. Cross-validation and bootstrap are two possible approaches in this case. In related literature bootstrap estimators of the true misclassification rate were asserted to have better performance for small samples than cross-validation estimators. We compare the two estimators empirically when the classification rule is so adaptive to training data that its apparent misclassification rate is close to zero. We confirm that bootstrap estimators have better performance for small samples because of small variance, and we have found a new fact that their bias tends to be significant even for moderate to large samples, in which case cross-validation estimators have better performance with less computation.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.498-511
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• 2017

This work conducts a validation study for loads analysis of the UH-60A slowed rotor at high advance ratios. The nonlinear flexible multibody dynamics analysis code, DYMORE II, is used with a freewake model for the rotorcraft comprehensive analysis. Wind tunnel test data of airloads and structural loads of a full-scale UH-60A slowed rotor are used for this validation study. This analysis predicts well the thrust reversal phenomenon at the advance ratio of 1.0. The section airloads such as normal forces and pitching moments and the oscillatory blade structural moments in this analysis are compared well or moderately with the measured data, although the higher harmonics components of blade torsion moments are not captured well. This validation study assesses the prediction accuracy and investigates the unique aeromechanics characteristics of a slowed rotor at high advance ratio.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.743-758
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• 2005

This paper consider penalized likelihood regression with data from exponential family. The fast computation method applied to Gaussian data(Kim and Gu, 2004) is extended to non Gaussian data through asymptotically efficient low dimensional approximations and corresponding algorithm is proposed. Also smoothing parameter selection is explored for various exponential families, which extends the existing cross validation method of Xiang and Wahba evaluated only with Bernoulli data.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.577-587
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• 2023

The utilization of crowdsourced spatial data has been actively researched; however, issues stemming from the uncertainty of data quality have been raised. In particular, when low-quality data is mixed into drone imagery datasets, it can degrade the quality of spatial information output. In order to address these problems, the study presents a methodology for automatically validating the geometric quality of crowdsourced imagery. Key quality factors such as spatial resolution, resolution variation, matching point reprojection error, and bundle adjustment results are utilized. To classify imagery suitable for spatial information generation, training and validation datasets are constructed, and machine learning is conducted using a radial basis function (RBF)-based support vector machine (SVM) model. The trained SVM model achieved a classification accuracy of 99.1%. To evaluate the effectiveness of the quality validation model, imagery sets before and after applying the model to drone imagery not used in training and validation are compared by generating orthoimages. The results confirm that the application of the quality validation model reduces various distortions that can be included in orthoimages and enhances object identifiability. The proposed quality validation methodology is expected to increase the utility of crowdsourced data in spatial information generation by automatically selecting high-quality data from the multitude of crowdsourced data with varying qualities.

• The KIPS Transactions:PartC
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• v.8C no.5
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• pp.517-524
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• 2001

There are cases that revoke the certification because of disclosure of private key, deprivation of qualification and the expiration of a term of validity based on PKI. So, a user have to confirm the public key whether valid or invalid in the certification. There are many method such as CRL, Delta-CRL, OCSP for the cert-validation of certification. But these method many problems which are overload traffic on network and the CRL server because of processing for cert-validation of certification. In this paper we proposed the realtime cert-validation of certification method which solved problems that are data integrity by different time between transmission and receiving for CRL, and overload traffic on network and the CRL server based on DARC.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.298-301
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• 2004

The aim of this study is to cross-validate of spatial probability model, artificial neural network at Boun, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the Boun, Janghung and Youngin areas from interpretation of aerial photographs, field surveys, and maps of the topography, soil type, forest cover and land use were constructed to spatial data-sets. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography, were calculated from the topographic database. Topographic type, texture, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter, age and density of forest were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using the landslide­occurrence factors by artificial neural network model. For the validation and cross-validation, the result of the analysis was applied to each study areas. The validation and cross-validate results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.