• 한국의류학회지
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• 제37권6호
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• pp.764-771
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• 2013

The metal component of Chinese and Korean traditional gold thread was analyzed nondestructively using P-XRF and classified morphologically. In the nondestructive analysis of 22 Chinese and Korean artifacts, there were 10 gold threads made up of Au in China and 7 in Korea; in addition, there were 4 silver threads made up of Ag in Korea and 1 copper thread made up of Cu in China. In the morphological classification, 7 gilt paper strips were confirmed in China and Korea and 4 wrapped threads were identified in China and Korea. Zn, Sn and Fe (minor components of the threads) were detected. These components were assumed to be transferred from the metal found in burial goods.

• 대한기계학회논문집A
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• 제31권8호
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• pp.870-879
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• 2007

In this study, various damage modes in bending unimorph piezoelectric composite actuators with a thin sandwiched PZT plate during bending fracture tests have been evaluated by monitoring acoustic emission (AE) signals in terms of waveform and peak frequency as well as AE parameters. Three kinds of actuator specimens consisting of woven fabric fiber skin layers and a PZT ceramic core layer are loaded with a roller and an AE activity from the specimen is monitored during the entire loading using an AE transducer mounted on the specimen. AE characteristics from a monolithic PZT ceramic with a thickness of $250{\mu}m$ are examined first in order to distinguish different AE signals from various possible damage modes in piezoelectric composite actuators. Post-failure observations and stress analyses in the respective layers of the specimens are conducted to identify particular features in the acoustic emission signal that correspond to specific types of damage modes. As a result, the signal classification based on waveform and peak frequency analyses successfully describes the failure process of the bending piezoelectric composite actuator exhibiting diverse failure mechanisms. Furthermore, it is elucidated that when the PZT ceramic embedded actuators are loaded mechanical bending loads, the failure process of actuator specimens with different lay-up configurations is almost same irrespective of their lay-up configurations.

• 비파괴검사학회지
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• 제22권5호
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• pp.532-538
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• 2002

3가지 종류의 알루미늄 합금강의 피로균열 진전 시 균열 열림 및 닫힘에 따른 음향방출 신호를 분류하기 위하여 주성분 분석 방법과 인공신경망 기법을 적용하였다. 재료의 균열 열림과 닫힘, 마찰 등과 같은 여러 가지 AE 신호를 얻기 위하여 피로시험을 수행하였다. 주성분 분석결과 AE 파라미터의 제 1 및 제 2 주성분만으로도 균열 열렴 및 닫힘에 대한 AE 신호의 변이를 94% 이상 설명할 수 있는 것으로 분석되어 주성분 분석 기법을 이용한 균열 열림 및 닫힘에 대한 신호해석이 가능한 것으로 나타났다. AE 신호의 주성분들을 입력변수로 사용한 인공신경망을 이용하여 균열 열림 및 닫힘을 분류할 수 있는 분류기를 개발하고 평가한 결과 분류기의 입력 변수로서 2개의 주성분을 이용 할 경우 전체 AE 파라미터를 입력변수로 사용한 경우 보다 분류 성능이 향상되었다.

• Applied Biological Chemistry
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• 제41권3호
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• pp.234-239
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• 1998

근적외 분광분석법으로 사과의 유리산 함량을 비파괴적으로 측정함에 있어서 사과 착즙액의 갈변에 의한 영향은 있었으나 실활처리 및 적정 알칼리액 농도는 측정 정확도에 영향을 주지 않았다. 수확기 사과의 유리산 함량은 수확 시기를 달리한 시료로 작성한 검량식을 사용하여 측정하였는데 중회귀분석 결과, 중상관계수(R)는 0.77이었고 측정오차(SEP)는 0.03%이었다. 저장 중인 사과의 유리산 함량은 저장하여 유리산 함량폭이 넓은 시료로 작성한 검량식을 사용하여 측정하였으며 그 결과 R은 0.90, SEP는 약 0.04%이었다. 근적외 분광분석법을 응용하여 사과 중의 유리산 함량을 정확히 정량하기는 다소 미흡하나 높은 산 함량치와 낮은 산 함량치를 가지는 사과로 분류하는 것은 가능한 것으로 판단되었다.

• Journal of Biosystems Engineering
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• 제33권1호
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• pp.30-37
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• 2008

Dynamic excitation and response analysis is an acceptable method to determine some of physical properties of agricultural product for quality evaluation. There is a difference in the internal viscoelasticity between sound and defective fruits due to the difference of geometric structures, thereby showing different vibration characteristics. This study was carried out to develop a portable piezoelectric film-based glove sensor system that can separate internally damaged watermelons from sound ones using an acoustic impulse response technique. Two piezoelectric sensors based on polyvinylidene fluoride (PVDF) films to measure an impact force and vibration response were separately mounted on each glove. Various signal parameters including number of peaks, energy ratio, standard deviation of peak to peak distance, zero-crossing rate, and integral value of peaks were examined to develop a regression-estimated model. When using SMLR (Stepwise Multiple Linear Regression) analysis in SAS, three parameters, i.e., zeros value, number of peaks, and standard deviation of peaks were selected as usable factors with a coefficient of determination ($r^2$) of 0.92 and a standard error of calibration (SEC) of 0.15. In the validation tests using twenty watermelon samples (sound 9, defective 11), the developed model provided good capability showing a classification accuracy of 95%.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1131-1131
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• 2001

To develop a nondestructive quality evaluation technique of fruits, a K-mean algorism is applied to near infrared (NIR) spectroscopy of apples. The K-mean algorism is one of neural network partition methods and the goal is to partition the set of objects O into K disjoint clusters, where K is assumed to be known a priori. The algorism introduced by Macqueen draws an initial partition of the objects at random. It then computes the cluster centroids, assigns objects to the closest of them and iterates until a local minimum is obtained. The advantage of using neural network is that the spectra at the wavelengths having absorptions against chemical bonds including C-H and O-H types can be selected directly as input data. In conventional multiple regression approaches, the first wavelength is selected manually around the absorbance wavelengths as showing a high correlation coefficient between the NIR $2^{nd}$ derivative spectrum and Brix value with a single regression. After that, the second and following wavelengths are selected statistically as the calibration equation shows a high correlation. Therefore, the second and following wavelengths are selected not in a NIR spectroscopic way but in a statistical way. In this research, the spectra at the six wavelengths including 900, 904, 914, 990, 1000 and 1016nm are selected as input data for K-mean analysis. 904nm is selected because the wavelength shows the highest correlation coefficients and is regarded as the absorbance wavelength. The others are selected because they show relatively high correlation coefficients and are revealed as the absorbance wavelengths against the chemical structures by B. G. Osborne. The experiment was performed with two phases. In first phase, a reflectance was acquired using fiber optics. The reflectance was calculated by comparing near infrared energy reflected from a Teflon sphere as a standard reference, and the $2^{nd}$ derivative spectra were used for K-mean analysis. Samples are intact 67 apples which are called Fuji and cultivated in Aomori prefecture in Japan. In second phase, the Brix values were measured with a commercially available refractometer in order to estimate the result of K-mean approach. The result shows a partition of the spectral data sets of 67 samples into eight clusters, and the apples are classified into samples having high Brix value and low Brix value. Consequently, the K-mean analysis realized the classification of apples on the basis of the Brix values.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제7권10호
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• pp.377-382
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• 2018

본 연구는 홍삼 등급 판정을 위한 내부 상태 및 조직의 치밀도 분석 방법에 관한 것이다. 홍삼 내부 측정을 위해 1990년대 이후부터는 자기공명영상법(MRI), X-ray 판별 등의 비파괴 검사 방법에 대한 연구가 다양하게 이루어졌지만, 등급 판정에 가장 중요한 내공(內空), 내백(內白)을 파악하는데 어려움이 있어 정확한 내부 판정이 불가능하였다. 그리하여 본 연구에서는 적외선 조명 환경의 폐쇄형 영상 취득 장치를 제작하고 내공, 내백의 유무와 직경을 파악할 수 있는 내부 측정 시스템을 개발하였다. 제작한 장치는 홍삼 내부 투과율이 높은 950nm 파장대역의 적외선 조명, 적외선 대역 촬영이 가능한 카메라, 카메라에 홍삼의 초점을 자동제어 할 수 있는 Y축 제어 액추에이터 그리고 홍삼을 $1^{\circ}$의 간격으로 $360^{\circ}$ 회전하며 영상을 취득할 수 있는 회전 액추에이터로 구성이 되어있다. 제안하는 알고리즘은 Y축 액추에이터에서 Auto-Focus 알고리즘을 수행하여 홍삼의 크기와 두께 변화에 따라 객체의 선명한 초점을 자동으로 맞춰준다. 그다음 홍삼을 $1^{\circ}$ 간격으로 $360^{\circ}$ 회전하며 총 360장의 홍삼 영상을 취득하면 라돈 변환(Radon transform)을 통해 사이노그램(Sinogram)으로 재구성하고, 역 라돈 변환(Inverse Radon transform)을 통해 단층영상복원(Back-projection) 알고리즘이 수행되어 홍삼 내부 영상을 획득하였다. 알고리즘 수행 결과 홍삼 두께나 모양에 관계없이 내부 단면영상 획득이 가능하였고 영상을 통해 내공, 내백의 유무와 직경을 파악할 수 있었다. 추후 10,000개 이상의 다양한 모양과 크기를 가지는 홍삼에 대하여 내부 영상을 취득하여 등급 판별 기준을 적용한다면 신뢰성 있는 홍삼 등급 자동화 측정 방법으로 사용가능 할 것이다.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1031-1031
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• 2001

The mammary gland is made up of remarkably sensitive tissue, which has the capability of producing a large volume of secretion, milk, under normal or healthy conditions. When bacteria enter the gland and establish an infection (mastitis), inflammation is initiated accompanied by an influx of white cells from the blood stream, by altered secretory function, and changes in the volume and composition of secretion. Cell numbers in milk are closely associated with inflammation and udder health. These somatic cell counts (SCC) are accepted as the international standard measurement of milk quality in dairy and for mastitis diagnosis. NIR Spectra of unhomogenized composite milk samples from 14 cows (healthy and mastitic), 7days after parturition and during the next 30 days of lactation were measured. Different multivariate analysis techniques were used to diagnose the disease at very early stage and determine how the spectral properties of milk vary with its composition and animal health. PLS model for prediction of somatic cell count (SCC) based on NIR milk spectra was made. The best accuracy of determination for the 1100-2500nm range was found using smoothed absorbance data and 10 PLS factors. The standard error of prediction for independent validation set of samples was 0.382, correlation coefficient 0.854 and the variation coefficient 7.63%. It has been found that SCC determination by NIR milk spectra was indirect and based on the related changes in milk composition. From the spectral changes, we learned that when mastitis occurred, the most significant factors that simultaneously influenced milk spectra were alteration of milk proteins and changes in ionic concentration of milk. It was consistent with the results we obtained further when applied 2DCOS. Two-dimensional correlation analysis of NIR milk spectra was done to assess the changes in milk composition, which occur when somatic cell count (SCC) levels vary. The synchronous correlation map revealed that when SCC increases, protein levels increase while water and lactose levels decrease. Results from the analysis of the asynchronous plot indicated that changes in water and fat absorptions occur before other milk components. In addition, the technique was used to assess the changes in milk during a period when SCC levels do not vary appreciably. Results indicated that milk components are in equilibrium and no appreciable change in a given component was seen with respect to another. This was found in both healthy and mastitic animals. However, milk components were found to vary with SCC content regardless of the range considered. This important finding demonstrates that 2-D correlation analysis may be used to track even subtle changes in milk composition in individual cows. To find out the right threshold for SCC when used for mastitis diagnosis at cow level, classification of milk samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two levels of SCC - 200 000 cells/$m\ell$ and 300 000 cells/$m\ell$, respectively, were set up and compared as thresholds to discriminate between healthy and mastitic cows. The best detection accuracy was found with 200 000 cells/$m\ell$ as threshold for mastitis and smoothed absorbance data: - 98% of the milk samples in the calibration set and 87% of the samples in the independent test set were correctly classified. When the spectral information was studied it was found that the successful mastitis diagnosis was based on reviling the spectral changes related to the corresponding changes in milk composition. NIRS combined with different ways of spectral data ruining can provide faster and nondestructive alternative to current methods for mastitis diagnosis and a new inside into disease understanding at molecular level.