• Journal of Biosystems Engineering
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• 제41권1호
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• pp.51-59
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• 2016

Purpose: This study examined the performance of two spectroscopy methods and multivariate classification methods to discriminate viable pepper seeds from their non-viable counterparts. Methods: A classification model for viable seeds was developed using partial least square discrimination analysis (PLS-DA) with Fourier transform near-infrared (FT-NIR) and Raman spectroscopic data in the range of $9080-4150cm^{-1}$ (1400-2400 nm) and $1800-970cm^{-1}$, respectively. The datasets were divided into 70% to calibration and 30% to validation. To reduce noise from the spectra and compare the classification results, preprocessing methods, such as mean, maximum, and range normalization, multivariate scattering correction, standard normal variate, and $1^{st}$ and $2^{nd}$ derivatives with the Savitzky-Golay algorithm were used. Results: The classification accuracies for calibration using FT-NIR and Raman spectroscopy were both 99% with first derivative, whereas the validation accuracies were 90.5% with both multivariate scattering correction and standard normal variate, and 96.4% with the raw data (non-preprocessed data). Conclusions: These results indicate that FT-NIR and Raman spectroscopy are valuable tools for a feasible classification and evaluation of viable pepper seeds by providing useful information based on PLS-DA and the threshold value.

• 한국콘텐츠학회논문지
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• 제6권12호
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• pp.105-112
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• 2006

본 연구에서는 일반적으로 잘 알려진 기저 세포암 검출을 위한 간단한 전처리 방법을 제안하였다. 전처리 과정은 half Hanning 윈도우와 함께 데이터를 클리핑하고 PCA(principal components analysis)를 이용하여 차원을 감소하였다. Half Hanning 윈도우는 $1650cm^{-1}$ 피크 부근의 크기를 낮춤으로써 음성 오류율을 줄여 분류 성능을 향상시켰다. 이 실험에서 사용한 MAP(maximum a posteriori), KNN (k-nearest neighbor), PNN(probabilistic neural network), MLP(multilayer perceptron), SVM(support vector machine)와 MSE(minimum squared error)의 분류결과는 제안한 방법이 효과적임을 입증하고 있다. KNN 분류방법은 216개 라만 스펙트럼에 대한 분류실험에서 민감도가 약 97.3%로 제안한 윈도우를 적용한 이 실험에서 기저 세포암 검출 성능이 가장 많이 개선되었다.

• 한국지역지리학회지
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• 제12권4호
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• pp.496-507
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• 2006

본 연구는 고해상도 자료와 하이퍼스펙트럴 자료를 혼용하여 하와이 화산 국립공원 내 해안 지역의 식생을 분류하고자 하였다. 연구지역에 주로 나타나는 식생은 3종의 초본(broomsedge, natal redtop, and pili)과 작은 관목 등으로 대표되는 비초본으로 구분된다. 분류 기법으로는 unsupervised classification과 supervised classification을 결합한 하이브리드법을 이용하여 전체적으로 3단계 분류과정을 적용하였다. 첫째로는, IKONOS 고해상 위성자료를 이용하여, 식생 및 비식생지역을 unsupervised classification법을 통해 분류하였다. 두 번째로는, minimum noise fraction(MNF) transformation을 이용하여 AVIRIS하이퍼스펙트럴 자료로부터 주성분을 추출하여 자료를 압축하는 과정을 거쳤다. 20미터 해상도를 가진 AVIRIS 픽셀들은 대부분 용암면과 식생면으로부터 반사된 복사신호가 혼합되어 있기때문에, 용암과 식생의 지표피복 비율에 따른 선형모형을 적용하여 용암면이 갖는 반사 신호를 각 픽셀로부터 제거하였다. 최종적으로, 각 픽셀에 대하여, 식생피복 비율에 비례하는 AVIRIS 하이퍼스펙트럴 자료의 식생성분을 토대로 maximum likelihood algorithm에 따라 supervised classification법을 적용하여 초지 및 관목으로 대표되는 지표식생을 분류하였다.

• 생약학회지
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• 제41권4호
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• pp.303-307
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• 2010

To overcome the limit of morphological method for classification of herbal drug, a novel method to discriminate its origin using pyrolysis mass spectrometry-multivariate analysis was developed. This method was applied successfully to Scutellaria baicalensis Georgi, one of the most popular herbal drug in oriental countries. The ethylacetate soluble fractions were prepared by sonication from pulverized roots of S. baicalensis which were collected from various regions including Korea and China, and subjected to direct insertion probe (DIP) mass spectrometry to achieve mass spectra of pyrolizates of extracts. The probe temperature was elevated from $30^{\circ}C$ to $320^{\circ}C$ at increasing rate $64^{\circ}C/min$, and the average mass spectrum calculated from total ion chromatography (TIC) was obtained. The relative peak intensities versus m/z were subjected to SAS program, and the training set (9 from Korea origin and 22 from China origin) was clustered two groups as its origin. In the test set, 11 samples among total 13 test sample were successfully classified according to their origin by developed method with accuracy of 85%.

• International Journal of Ocean Engineering and Technology Speciallssue:Selected Papers
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• 제4권1호
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• pp.51-55
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• 2001

The characteristics of elastic waves emanating from crack initiation in 2024 and 5052 aluminum alloys subject to static and fatigue loading are investigated through laboratory experiments. The objective of the study is to determine difference in the properties of the signals generated from static and fatigue tests and also to examine if the sources of the waves could be identified from the temporal and spectral characteristics of the acoustic emission (AE) waveforms. The signals are recoded using non-resonant, flat, broadband transducers attached to the surface of the alloy specimens. The time dependence and power spectra of the signals recorded during the tests were examined and classified according to their special features. Three distinct types of signals were observed. The waveforms and their power spectra were found to be dependent on the material and the type of fracture associated with the signals. Analysis of the waveforms indicated that some signals could be attributed to plastic deformation associated with static tests. The potential application of the approach in health monitoring of aging aircraft structures using a network of surface mounted broadband sensors is discussed.

• 대한원격탐사학회지
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• 제21권5호
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• pp.405-415
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• 2005

It is difficult to classify tidal flat surface that is composed of a mixture of mud, sand, water and microphytobenthos. We used a Linear Spectral Unmixing (LSU) method for effectively classifying the tidal flat surface characteristics within a pixel. This study aims at 1) detecting algal mat using LSU in the Saemangeum tidal flats, 2) determining a suitable end-member selection method in tidal flats, and 3) find out a habitual characteristics of algal mat. Two types of end-member were built; one is a reference end-member derived from field spectrometer measurements and the other image end-member. A field spectrometer was used to measure spectral reflectance, and a spectral library was accomplished by shape difference of spectra, r.m.s. difference of spectra, continuum removal and Mann-Whitney U-test. Reference end-members were extracted from the spectral library. Image end-members were obtained by applying Principle Component Analysis (PCA) to an image. The LSU method was effective to detect microphytobenthos, and successfully classified the intertidal zone into algal mat, sediment, and water body components. The reference end-member was slightly more effective than the image end-member for the classification. Fine grained upper tidal flat is generally considered as a rich habitat for algal mat. We also identified unusual microphytobenthos that inhabited coarse grained lower tidal flats.

• Nuclear Engineering and Technology
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• 제55권5호
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• pp.1708-1717
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• 2023

The grade analysis of lead-zinc ore is the basis for the optimal development and utilization of deposits. In this study, a method combining Prompt Gamma Neutron Activation Analysis (PGNAA) technology and machine learning is proposed for lead-zinc mine borehole logging, which can identify lead-zinc ores of different grades and gangue in the formation, providing real-time grade information qualitatively and semi-quantitatively. Firstly, Monte Carlo simulation is used to obtain a gamma-ray spectrum data set for training and testing machine learning classification algorithms. These spectra are broadened, normalized and separated into inelastic scattering and capture spectra, and then used to fit different classifier models. When the comprehensive grade boundary of high- and low-grade ores is set to 5%, the evaluation metrics calculated by the 5-fold cross-validation show that the SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naive Bayes) and RF (Random Forest) models can effectively distinguish lead-zinc ore from gangue. At the same time, the GNB model has achieved the optimal accuracy of 91.45% when identifying high- and low-grade ores, and the F₁ score for both types of ores is greater than 0.9.

• 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.

• Journal of the Korean Wood Science and Technology
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• 제45권2호
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• pp.202-212
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• 2017

This study was to establish the interrelation between chemical compositions and near infrared (NIR) spectra for the classification on distinguishability of domestic gymnosperms. Traditional wet chemistry methods and infrared spectral analyses were performed. In chemical compositions of five softwood species including larch (Larix kaempferi), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), cypress (Chamaecyparis obtusa), and cedar (Cryptomeria japonica), their extractives and lignin contents provided the major information for distinction between the wood species. However, depending on the production region and purchasing time of woods, chemical compositions were different even though in same species. Especially, red pine harvested from Naju showed the highest extractive content about 16.3%, whereas that from Donghae showed about 5.0%. These results were expected due to different environmental conditions such as sunshine amount, nutrients and moisture contents, and these phenomena were also observed in other species. As a result of the principal component analysis (PCA) using NIR between five species (total 19 samples), the samples were divided into three groups in the score plot based on principal component (PC) 1 and principal component (PC) 2; group 1) red pine and Korean pine, group 2) larch, and group 3) cypress and cedar. Based on the chemical composition results, it was concluded that extractive content was highly relevant to wood classification by NIR analysis.

• 한국지진공학회논문집
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• 제10권2호
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• pp.51-62
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• 2006

동반논문 (I)에서는 국내 지반특성에 적합하도록 국내 내진설계기준이 개선되어야 한다는 결론을 얻었다. 본 논문에서는 우수한 지반분류 방법을 찾기 위하여 상부 토층 30m의 평균 전단파속도$(V_{S30})$, 지반의 고유주기$(T_G)$ 및 기반암 깊이를 이용한 지반분류 방법에 대하여 심도있게 검토하였다. 증폭계수$(F_a,\;F_v)$의 표준편차, 해석결과의 평균 스펙트럼 가속도와 재산정된 응답스펙트럼을 비교한 결과 각각의 방법에서 큰 차이가 발생하지 않아 특정한 방법이 우수하다고 판단하기 힘들었다. 그러나, $T_G$를 이용한 방법에서 RRS 값의 증폭구간이 좁은 구간에 집중되는 경향을 보여 지진시 유사한 거동특성을 나타내는 지반을 같은 지반그룹으로 분류할 수 있는 장점이 있었다. 또한, 증폭계수와 $T_G$의 상관관계를 나타내는 추세선의 경우, $V_{S30}$ 방법 보다 입력 가속도의 증가에 따른 지반의 비선형성 효과를 더욱 명확하게 나타낼 수 있었다. 마지막으로, $V_{S30}$을 이용하여 지반을 분류할 경우 기반암이 30m 보다 얕은 곳에 존재하는 경우에도 무조건 심도 30m까지 기반암의 전단파속도를 가정하여 계산해야 하나, $T_G$를 이용할 경우 이러한 불확실성을 제거할 수 있어 우수한 방법으로 판단된다. 본 논문에서는 지반의 고유주기를 이용한 방법을 기반암 깊이가 얕은 국내지반특성에 적합한 지반분류 방법으로 제안하였다.