• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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• pp.765-771
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• 1998

An artificial neural network(NN) technique is developed for hardware redundant sensor validation. Since the measurement space is a continuous space with many operating regions, it is difficult to train a NN to correctly detect failure in an accurate measurement system. A conventional backpropagation NN is modified to include an additional preprocessing layer that extracts classification features from scalar measurements. This feature extraction means transform the measurement space to parity space. The NN is independent of the state variable being measured, the instrument range, and the signal tolerance. This NN resembles the parity space approach to signal validation, except that analytical parity equations are unneeded and the NN pattern recognition capability is utilized for decision making.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.972-979
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• 1999

QFT(Quantitative Feedback Theory) is a very practical design technique that emphasizes the use of feedback for achieving the desired system performance tolerances in despite of plant uncertainty and disturbance. The fundamental concept of QFT is a loop shaping procedure that a suitable controller can be found by shaping a nominal loop transfer function. The loop shaping synthesis involves the identification of a structure and the specialization of parameter optimization of a desired system. This paper presents an improved loop shaping approach of QFT with model validation using GA(Genetic Algorithm). The method presented in this paper removes the problems of iterative operation, transformation error, and model validation in the conventional methods without consideration of frequency domain.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.291-296
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• 2003

This study estimated surface temperature by using split-window technique and NOAA/AVHRR data was used. For surface monitoring, cloud masking procedure was carried out using threshold algorithm. The daily maximum air temperature is estimated by multiple regression method using independent variables such as satellite-derived surface temperature, EDD, and latitude. When the EDD data added, the highest correlation shown. This indicates that EDD data is the necessary element for estimation of the daily maximum air temperature. We derived correlation and experience equation by three approaching method to estimate daily maximum air temperature. 1) non-considering landcover method as season, 2) considering landcover method as season, and 3) just method as landcover. The last approaching method shows the highest correlation. So cross-validation procedure was used in third method for validation of the estimated value. For all landcover type 5, the results using the cross-validation procedure show reasonable agreement with measured values(slope=0.97, intercept=-0.30, R$^2$=0.84, RMSE=4.24$^{\circ}C$). Also, for all landcover type 7, the results using the cross-validation procedure show reasonable agreement with measured values(slope=0.993, Intercept=0.062, R$^2$=0.84, RMSE=4.43$^{\circ}C$).

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1528-1539
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• 2023

Since the leakage of sodium in an SFR (sodium-cooled fast reactor) causes an explosion upon reaction with air and water, sodium leakages represent an important safety issue. In this study, a novel technique for improving the reliability of sodium leakage detection applying DDVR (dynamic data validation and reconciliation) is proposed and verified to resolve this technical issue. DDVR is an approach that aims to improve the accuracy of a target system in a dynamic state by minimizing random errors, such as from the uncertainty of instruments and the surrounding environment, and by eliminating gross errors, such as instrument failure, miscalibration, or aging, using the spatial redundancy of measurements in a physical model and the reliability information of the instruments. DDVR also makes it possible to estimate the state of unmeasured points. To validate this approach for supporting sodium leakage detection, this study applies experimental data from a sodium leakage detection experiment performed by the Korea Atomic Energy Research Institute. The validation results show that the reliability of sodium leakage detection is improved by cooperation between DDVR and hardware measurements. Based on these findings, technology integrating software and hardware approaches is suggested to improve the reliability of sodium leakage detection by presenting the expected true state of the system.

• Steel and Composite Structures
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• v.3 no.6
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• pp.439-450
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• 2003

The finite strip transition matrix technique, a semi analytical method, is employed to obtain the buckling loads and the natural frequencies of symmetric cross-ply laminated composite plates with edges elastically restrained against both translation and rotation. To illustrate the accuracy and the validation of the method several example of cross play laminated composite plates were analyzed. The buckling loads and the frequency parameters are presented and compared with available results in the literature. The convergence study and the excellent agreement with known results show the reliability of the purposed technique.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.110-112
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• 2003

This paper presents an alleviant technique for solving ill-conditioned linear systems using spectral adaptive mapping, which is based on spectral mapping theorem. The conventional approaches to solve the ill-conditioned linear systems are divided into reformulation and alleviant technique. So far, the alleviant technique is evaluated the most effective one. In this paper, an adaptive mapping of spectrum is adopted to alleviate the condition number of ill-conditioned linear systems. A shift constant, which is a dominant factor of the spectral adaptive mapping that are proposed, is assessed by the system spectrum. The proposed spectral adaptive mapping technique is tested to demonstrated the validation on several size Hilbert matrices and small scale power systems, which are provide the promising results.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.75-85
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• 2020

This study examines machine learning cross-validation to utilized create ROI for classification of land cover. The study area located in Sejong and one KOMPSAT-3A image was used in this analysis: procedure on October 28, 2019. We used four bands(Red, Green, Blue, Near infra-red) for learning cross validation process. In this study, we used K-fold method in cross validation and used SVM kernel type with cross validation result. In addition, we used 4 kernels of SVM(Linear, Polynomial, RBF, Sigmoid) for supervised classification land cover map using extracted ROI. During the cross validation process, 1,813 data extracted from 3,500 data, and the most of the building, road and grass class data were removed about 60% during cross validation process. Based on this, the supervised SVM linear technique showed the highest classification accuracy of 91.77% compared to other kernel methods. The grass' producer accuracy showed 79.43% and identified a large mis-classification in forests. Depending on the results of the study, extraction ROI using cross validation may be effective in forest, water and agriculture areas, but it is deemed necessary to improve the distinction of built-up, grass and bare-soil area.

• Journal of Korean Society of Transportation
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• v.25 no.4
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• pp.21-32
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• 2007

The households to be surveyed are usually huge number at the level of a city or metropolitan survey, not to mention a nationwide travel survey. Therefore, household travel surveys to figure out true origin-destination (O/D) trip patterns (population O/D) are conducted through a sampling method rather than by surveying all of the population in the system. Therefore, the population O/D pattern can only be estimated by expanding the sampled O/D patterns to the population. It is very difficult to avoid the errors involved in the process of sampling, surveying and expanding O/D data. In order to minimize such errors while estimating the true O/D patterns of the population, the validation and adjustment process should employed by doing a comparison between the expanded sample O/D data and observed link traffic volumes. This study suggests a method of validation and adjustment of the expanded sample O/D data by comparing observed link volumes at several screenlines. The study also suggests a practical technique to modify O/D pairs which are excluded in the screenline validation process by comparing observed traffic volume with the results of traffic assignment analysis. An empirical study was also conducted as an example applying the suggested methods of validation and adjustment with Korea's nationwide O/D data and highway network.

• Journal of Internet Computing and Services
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• v.11 no.4
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• pp.107-117
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• 2010

The feature models representing the common and variable concepts among the software products and the feature configurations generated by selecting the features to be included in the target product are the essential components in the software product lines methodology. Although the researches on the formal semantics and reasoning of the feature models and feature configurations are in progress, the researches on feature model ontologies and feature configuration validation using the semantic web technologies are yet insufficient. This paper defines the formal semantics of the feature models and proposes a feature configuration validation technique based on ontology and semantic web technologies. OWL(Web Ontology Language), a semantic web standard language, is used to represent the knowledge in the feature models and the feature configurations. SWRL(Semantic Web Rule Language), a semantic web rule languages, is used to define the rules to validate the feature configurations. The approach in this paper provides the formal semantic of the feature models, automates the validation of feature configurations, and enables the application of various semantic web technologies, such as SQWRL.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1591-1601
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• 2004

Clustering, a technique for the analysis of the genes, organizes the patterns into groups by the similarity of the dataset and has been used for identifying the functions of the genes in the cluster or analyzing the functions of unknown gones. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods which assign a sample to a group. In this paper, a Bayesian validation method is proposed to evaluate the fuzzy partitions effectively. Bayesian validation method is a probability-based approach, selecting a fuzzy partition with the largest posterior probability given the dataset. At first, the proposed Bayesian validation method is compared to the 4 representative conventional fuzzy cluster validity measures in 4 well-known datasets where foray c-means algorithm is used. Then, we have analyzed the results of Saccharomyces cell cycle expression data evaluated by the proposed method.