• Title, Summary, Keyword: K-mean algorism

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Near infrared spectroscopy for classification of apples using K-mean neural network algorism

  • Muramatsu, Masahiro;Takefuji, Yoshiyasu;Kawano, Sumio
    • Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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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.

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Analysis of the Inner Degradation Pattern by Clustering Algorism at Distribution Line (군집화 알고리즘을 이용한 배전선로 내부 열화 패턴 분석)

  • Choi, Woon-Shik;Kim, Jin-Sa
    • Journal of the Korean Institute of Electrical and Electronic Material Engineers
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    • v.29 no.1
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    • pp.58-61
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    • 2016
  • Degradation in power cables used in distribution lines to the material of the wire, manufacturing method, but also the line of the environment, generates a variety of degradation depending upon the type of load. The local wire deterioration weighted wire breakage accident can occur frequently, causing significant proprietary damage can lead to accidents and precious. In this study, the signal detected by the eddy current aim to develop algorithms capable of determining the signals for the top part and at least part of the signal by using a signal processing technique called K-means algorithm.

A study on evaluation of the image with washed-out artifact after applying scatter limitation correction algorithm in PET/CT exam (PET/CT 검사에서 냉소 인공물 발생 시 산란 제한 보정 알고리즘 적용에 따른 영상 평가)

  • Ko, Hyun-Soo;Ryu, Jae-kwang
    • The Korean Journal of Nuclear Medicine Technology
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    • v.22 no.1
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    • pp.55-66
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    • 2018
  • Purpose In PET/CT exam, washed-out artifact could occur due to severe motion of the patient and high specific activity, it results in lowering not only qualitative reading but also quantitative analysis. Scatter limitation correction by GE is an algorism to correct washed-out artifact and recover the images in PET scan. The purpose of this study is to measure the threshold of specific activity which can recovers to original uptake values on the image shown with washed-out artifact from phantom experiment and to compare the quantitative analysis of the clinical patient's data before and after correction. Materials and Methods PET and CT images were acquired in having no misalignment(D0) and in 1, 2, 3, 4 cm distance of misalignment(D1, D2, D3, D4) respectively, with 20 steps of each specific activity from 20 to 20,000 kBq/ml on $^{68}Ge$ cylinder phantom. Also, we measured the distance of misalignment of foley catheter line between CT and PET images, the specific activity which makes washed-out artifact, $SUV_{mean}$ of muscle in artifact slice and $SUV_{max}$ of lesion in artifact slice and $SUV_{max}$ of the other lesion out of artifact slice before and after correction respectively from 34 patients who underwent $^{18}F-FDG$ Fusion Whole Body PET/CT exam. SPSS 21 was used to analyze the difference in the SUV between before and after scatter limitation correction by paired t-test. Results In phantom experiment, $SUV_{mean}$ of $^{68}Ge$ cylinder decreased as specific activity of $^{18}F$ increased. $SUV_{mean}$ more and more decreased as the distance of misalignment between CT and PET more increased. On the other hand, the effect of correction increased as the distance more increased. From phantom experiments, there was no washed-out artifact below 50 kBq/ml and $SUV_{mean}$ was same from origin. On D0 and D1, $SUV_{mean}$ recovered to origin(0.95) below 120 kBq/ml when applying scatter limitation correction. On D2 and D3, $SUV_{mean}$ recovered to origin below 100 kBq/ml. On D4, $SUV_{mean}$ recovered to origin below 80 kBq/ml. From 34 clinical patient's data, the average distance of misalignment was 2.02 cm and the average specific activity which makes washed-out artifact was 490.15 kBq/ml. The average $SUV_{mean}$ of muscles and the average $SUV_{max}$ of lesions in artifact slice before and after the correction show a significant difference according to a paired t-test respectively(t=-13.805, p=0.000)(t=-2.851, p=0.012), but the average $SUV_{max}$ of lesions out of artifact slice show a no significant difference (t=-1.173, p=0.250). Conclusion Scatter limitation correction algorism by GE PET/CT scanner helps to correct washed-out artifact from motion of a patient or high specific activity and to recover the PET images. When we read the image occurred with washed-out artifact by measuring the distance of misalignment between CT and PET image, specific activity after applying scatter limitation algorism, we can analyze the images more accurately without repeating scan.