• Korean Journal of Agricultural Science
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• v.39 no.1
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• pp.111-116
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• 2012

Nondestructive evaluation of seed viability is one of the highly demanding technologies for seed production industry. Conventional seed sorting technologies, such as tetrazolium and standard germination test are destructive, time consuming, and labor intensive methods. Near infrared spectroscopy technique has shown good potential for nondestructive quality measurements for food and agricultural products. In this study, FT-NIR spectroscopy was used to classify normal and artificially aged lettuce seeds. The spectra with the range of 1100~2500 nm were scanned for lettuce seeds and analyzed using the principal component analysis(PCA) method. To classify viable seeds from nonviable seeds, a calibration modeling set was developed with a partial least square(PLS) method. The calibration model developed from PLS resulted in 98% classification accuracy with the Savitzky-Golay $1^{st}$ derivative preprocessing method. The prediction accuracy for the test data set was 93% with the MSC(Multiplicative Scatter Correction) preprocessing method. The results show that FT-NIR has good potential for discriminating non-viable lettuce seeds from viable ones.

• Journal of the Korean Society for Nondestructive Testing
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• v.18 no.1
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• pp.17-26
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• 1998

The specimen with the wear between a roller and a vane and a normal specimen are classified by AE signal pattern recognition method with a neural network classifier in airconditioning operation test. Also the specimen with the scoring between a shaft and a bearing and a normal specimen are classified by the same method. As the internal pressure increases, the wear between the roller and the vane increases. The different pairs of oils and refrigerants five the effect on the wear.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.6
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• pp.658-662
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• 2001

The classification of surface flaw types was performed on the basis of angular dependence of backscattered ultrasound. The copper line adhered on the surface, cower line filled in groove, pure groove and the normal edge were adopted as various surface flaw patterns of glass specimen. A backward longitudinal profile was formed probably by the longitudinal wane scattering at and near 1st critical angle. The wave trains at the peak angles of the backward radiation profiles showed different shapes according to the superposition ratio of scattered and leaky waves. The asymmetry of the backward radiation profile arose due to the scattering effect of flaw. The additive resonance effect of copper line appeared in the left side of the profile. The peak angles of both the longitudinal and radiation profiles were shifted toward small angle by the scattering effect.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.11
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• pp.2457-2465
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• 2002

Numerical modeling of a nondestructive testing system plays an important role in many aspects of quantitative nondestructive evaluation (QNDE). The ultimate goal of a model is to predict test results for a specific flaw in a material. Thus, in ultrasonic testing, a system model should include the transducer, its radiation pattern, the beam reflection and propagation, and scattering from defects. In this paper attention is focused on the scattering model and the scattered fields by defects are observed by an elastodynamic boundary element method. Flaw types addressed are void-like and crack-like flaws. When transverse ultrasonic waves are obliquely incident on the flaw, the angular distribution of far-field scattered displacements are calculated and presented in the form of A-scan mode. The component signals obtained from each scattering problem are identified and their differences are addressed. The numerical results are also compared with those obtained by high frequency approximate solutions.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.2
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• pp.102-109
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• 2000

This paper describes two different theories used to model the scattering of ultrasound by a volumetric flaw and a crack-like flaw. The elastodynamic Kirchhoff approximation (EKA) and the geometrical theory of diffraction (GTD) are applied respectively to a cylindrical cavity and a semi-infinite crack. These methods are known as high frequency approximations. The 2-D elastodynamic scattering problems of a plane wave incident on these model defects are considered and the scattered fields are expressed in terms of the reflection and diffraction coefficients. The ratio of the scattered far field amplitude to the incident wave amplitude is computed as a function of the angular location and compared with the boundary element solutions.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.5
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• pp.518-525
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• 2012

In this study, the feasibility of hyperspectral reflectance imaging technique was investigated for the discrimination of viable and non-viable lettuce seeds. The spectral data of hyperspectral reflectance images with the spectral range between 750 nm and 1000 nm were used to develop PLS-DA model for the classification of viable and non-viable lettuce seeds. The discrimination accuracy of the calibration set was 81.6% and that of the test set was 81.2%. The image analysis method was developed to construct the discriminant images of non-viable seeds with the developed PLS-DA model. The discrimination accuracy obtained from the resultant image were 91%, which showed the feasibility of hyperspectral reflectance imaging technique for the mass discrimination of non-viable lettuce seeds from viable ones.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.5
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• pp.510-517
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• 2001

This paper describes an initial study for the application of eddy current pattern recognition approaches to more realistic flaw characterization in steam generator tubes. For this purpose, finite-element model-based theoretical eddy current testing (ECT) signals are simulated from 5 types of OD flaws with the variation in flaw size parameters and testing frequency. In addition, three kinds of software are developed for the convenience in the application of steps in pattern recognition approaches such as feature extraction feature selection and classification by probabilistic neural networks (PNNs). The cross point of the ECT signals simulated from flaws with non-symmetric cross-sections shows the deviation from the origin of the impedance plane. New features taking advantages of this phenomenon are added to complete the feature set with a total of 18 features. Then, classification with PNNs are performed based on this feature set. The PNN classifiers show high performance for the identification of symmetry in the cross-section of a flaw. However, they show very limited success in the interrogation of the sharpness of flaw tips.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.1
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• pp.31-37
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• 2014

The aim of this study was the non-destructive evaluation of bloody eggs using VIS/NIR spectroscopy. The bloody egg samples used to develop the sorting mode were produced by injecting chicken blood into the edges of egg yolks. Blood amounts of 0.1, 0.7, 0.04, and 0.01 mL were used for the bloody egg samples. The wavelength range for the VIS/NIR spectroscopy was 471 to 1154 nm, and the spectral resolution was 1.5nm. For the measurement system, the position of the light source was set to $30^{\circ}$, and the distance between the light source and samples was set to 100 mm. The minimum exposure time of the light source was set to 30 ms to ensure the fast sorting of bloody eggs and prevent heating damage of the egg samples. Partial least squares-discriminant analysis (PLS-DA) was used for the spectral data obtained from VIS/NIR spectroscopy. The classification accuracies of the sorting models developed with blood samples of 0.1, 0.07, 0.04, and 0.01 mL were 97.9%, 98.9%, 94.8%, and 86.45%, respectively. In this study, a novel nondestructive sorting technique was developed to detect bloody brown eggs using spectral data obtained from VIS/NIR spectroscopy.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.56-62
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• 2008

This paper proposes the use of decision tree classification for the measuring pattern recognition from industrial Computed Radiography(CR) images used in nondestructive evaluation(NDE) of steel-tubes. It appears that NDE problems are naturally desired to have machine learning techniques identify patterns and their classification. The attributes of decision tree are taken from NDE test procedure. Geometric features, such as radiative angle, gradient and distance, are estimated from the analysis of input image data. These factors are used to make it easy and accurate to classify an input object to one of the pre-specified classes on decision tree. This algerian is to simplify the characterization of NDE results and to facilitate the determination of features. The experimental results verify the usefulness of proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1335-1343
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• 1999

This paper presents concrete pattern recognition method to identify the strength of concrete by evidence accumulation with multiple parameters based on artificial intelligence techniques. At first, zero-crossing(ZCR), mean frequency(MEANF), median frequency(MEDF) and autoregressive model coefficient(ARC) are extracted as feature parameters from ultrasonic signal of concrete. Pattern recognition is carried out through the evidence accumulation procedure using distance measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for concrete pattern recognition.