• Journal of The Korean Society of Grassland and Forage Science
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• v.35 no.2
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• pp.125-130
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• 2015

The objective of this study was to investigate the feasibility of using near infrared spectroscopy (NIRS) to discriminate between grass spices. A combination of NIRS and chemometrics was used to discriminate between Italian ryegrass, perennial ryegrass, and tall fescue seeds. A total of 240 samples were used to develop the best discriminant equation, whereby three spectra range (visible, NIR, and full range) were applied within a 680 nm to 2500 nm wavelength. The calibration equation for the discriminant analysis was developed using partial least square (PLS) regression and discrimination equation (DE) analysis. A PLS discriminant analysis model for the three spectra range that was developed with the mathematic pretreatment "1,8,8,1" successfully discriminated between Italian ryegrass, perennial ryegrass, and tall fescue. An external validation indicated that all of the samples were discriminated correctly. The discriminant accuracy was shown as 68%, 78%, and 73% for Italian ryegrass, perennial ryegrass, and tall fescue, respectively, with the NIR full-range spectra. The results demonstrate the usefulness of the NIRS-chemometrics combination as a rapid method for the discrimination of grass species by seed.

• Analytical Science and Technology
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• v.32 no.1
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• pp.7-16
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• 2019

The objective of this study was the development of a discrimination model for the cultivational origin of paper mulberry bast fiber and Hanji using near infrared (NIR) and mid infrared (MIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA). Paper mulberry bast fiber was purchased in 10 different regions of Korea, and used to make Hanji. PLS-DA was performed using pre-treated FT-NIR and FT-MIR spectral data for paper mulberry bast fiber and Hanji. PLS-DA of paper mulberry bast fiber and Hanji samples, using FT-NIR spectral data, showed 100 % performance in cross validation and the confusion matrix (accuracy, sensitivity, and specificity). The discrimination models showed four regional groups which demonstrated clearer separation and much superior score plots in the NIR spectral data-based model than in the MIR spectral data-based model. Furthermore, the discrimination model based on the NIR spectral data of paper mulberry bast fiber had highly similar score morphology to that of the discrimination model based on the NIR spectral data of Hanji.

• Korean Journal of Oriental Medicine
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• v.13 no.2 s.20
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• pp.59-63
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• 2007

The purpose of this study was to diagnosis that what patterns identification using the statistical method. Discriminant analysis using the medical specialist and resident pattern identification agree case in stroke patients within 1 month of onset. The agreement rate of dificiency of Gi(75%), heat-transformation(74%), dampphlegm syndrome(69%), deficiency of Eum(51%) and syndrome of blood stagnation(43%) are respectively 0.75, 0.74, 0.69, 0.51 and 0.43 in medical specialist and using linear discriminant function pattern identification are same. The study of inspection, pulse feeling and palpitation will be continued to evaluate concordance rate. Discrimination model will be make to get higher Accuracy and prediction, it means becomes the help in pattern identification diagnosis objectivity and scientific.

• Journal of Ginseng Research
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• v.38 no.1
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• pp.52-58
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• 2014

To determine whether Fourier transform (FT)-IR spectral analysis combined with multivariate analysis of whole-cell extracts from ginseng leaves can be applied as a high-throughput discrimination system of cultivation ages and cultivars, a total of total 480 leaf samples belonging to 12 categories corresponding to four different cultivars (Yunpung, Kumpung, Chunpung, and an open-pollinated variety) and three different cultivation ages (1 yr, 2 yr, and 3 yr) were subjected to FT-IR. The spectral data were analyzed by principal component analysis and partial least squares-discriminant analysis. A dendrogram based on hierarchical clustering analysis of the FT-IR spectral data on ginseng leaves showed that leaf samples were initially segregated into three groups in a cultivation age-dependent manner. Then, within the same cultivation age group, leaf samples were clustered into four subgroups in a cultivar-dependent manner. The overall prediction accuracy for discrimination of cultivars and cultivation ages was 94.8% in a cross-validation test. These results clearly show that the FT-IR spectra combined with multivariate analysis from ginseng leaves can be applied as an alternative tool for discriminating of ginseng cultivars and cultivation ages. Therefore, we suggest that this result could be used as a rapid and reliable F1 hybrid seed-screening tool for accelerating the conventional breeding of ginseng.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2826-2840
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• 2018

Traditional methods based active contours or region merging are powerless in processing images with blurring border or hair occlusion. In this paper, a structure based convolutional neural networks is proposed to solve segmentation of skin lesion image. The structure mainly consists of two networks which are segmentation net and discrimination net. The segmentation net is designed based U-net that used to generate the mask of lesion, while the discrimination net is designed with only convolutional layers that used to determine whether input image is from ground truth labels or generated images. Images were obtained from "Skin Lesion Analysis Toward Melanoma Detection" challenge which was hosted by ISBI 2016 conference. We achieved segmentation average accuracy of 0.97, dice coefficient of 0.94 and Jaccard index of 0.89 which outperform the other existed state-of-the-art segmentation networks, including winner of ISBI 2016 challenge for skin melanoma segmentation.

• Korean Journal of Food Science and Technology
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• v.38 no.6
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• pp.835-837
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• 2006

The geographical origin of Tricholoma matsutake mushrooms was studied using near-infrared spectroscopy. Modified partial least-square regression analyses were used to discriminate geographical origin. Two-hundred fifty-six of 259 actual domestic Tricholoma matsutake were classified as domestic produce, Sixty of 81 actual imported mushrooms were correctly classified as imported, while the other 21 imported from North Korea were not clearly classified. The accuracy of geographical origin discrimination was 92.94% The correlation coefficient, standard error of calibration, and standard error of prediction from modified partial least-square regression analysis were 0.84, 15.10% and 18.30% respectively.

• Journal of Biosystems Engineering
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• v.32 no.4
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• pp.256-262
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• 2007

Image processing systems have been used to measure the plant parameters such as size, shape and structure of plants. There are yet some limited applications for evaluating plant colors due to illumination conditions. This study was focused to present adaptive methods to analyze plant leaf color regardless of illumination conditions. Color patches attached on the calibration bars were selected to represent leaf colors of lettuces and to test a possibility of health monitoring of lettuces. Repeatability of assigning leaf colors to color patches was investigated by two-tailed t-test for paired comparison. It resulted that there were no differences of assignment histogram between two images of one lettuce that were acquired at different light conditions. It supported that use of the calibration bars proposed for leaf color analysis provided color constancy, which was one of the most important issues in a video color analysis. A health discrimination equation was developed to classify lettuces into one of two classes, SOUND group and POOR group, using the machine vision. The classification accuracy of the developed health discrimination equation was 80.8%, compared to farmers' decision. This study could provide a feasible method to develop a standard color chart for evaluating leaf colors of plants and plant health monitoring system using the machine vision.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.7
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• pp.72-79
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• 2021

This study reports on the experiment of using deep learning algorithms to determine the machining process of aluminium and steel. A face cutting milling tool was used for machining and the cutting speed was set between 3 and 4 mm/s. Both materials were machined with a depth to 0.5mm and 1.0mm. To demonstrate the developed deep learning algorithm, simulation experiments were performed using the VGGish algorithm in MATLAB toobox. Downcutting was used to cut aluminum and steel as a machining process for high quality and precise learning. As a result of learning algorithms using audio data, 61%-99% accuracy was obtained in four categories: Al 0.5mm, Al 1.0mm, Steel 0.5mm and Steel 1.0mm. Audio discrimination using deep learning is derived as a probabilistic result.

• Tribology and Lubricants
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• v.38 no.4
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• pp.128-135
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• 2022

In the product development process, the reliability of the product can be secured through durability tests. However, since the durability test method is expensive and time consuming, a method to save time and money by utilizing virtual product development (VPD) is required. However, research on the accuracy of the results of virtual product development is required. In this paper, an accelerated durability test was designed and conducted using a planetary gear decelerator. And an analysis model under the same conditions was created and simulated. To correlate the results of the experiment with the results of the analytical model, created a model that can discriminate the wear region using one of the data mining methods, the k-means algorithm method and HSV (Hue, Saturation, Value). The wear area is compared by counting the number of pixels defined as wear through a discrimination model. A similar ratio was calculated by comparing the pixel ratio of the area determined as wear in the entire area. It showed a similar ratio of about 70%, and it is necessary to improve the discrimination method.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.823-833
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• 1996

An ultimate purpose of this study is to develop an automatic brown rice quality inspection system using image processing technique. In this study emphasis was put on developing an algorithm for discriminating the brown rice kernels depending on their external quality with a color image processing system equipped with an adaptor for magnifying the input image and optical fiber for oblique illumination. Primarily , geometrical and optical features of sample images were analyzed with unhulled paddy and various brown rice kernel samples such as sound, cracked, green-transparent , green-opaque, colored, white-opaque and brokens. Secondary, an algorithm for discrimination of the rice kernels in static state was developed on the basis of the geometrical and optical parameters screened by a statistical analysis(STEPWISE and DISCRIM Procedure, SAS ver.6). Brown rice samples could be discriminated by the algorithm developed in this study with an accuracy of 90% to 96% for the sound , cracked, colored, broken and unhulled , about 81% for the green-transparent and the white-opaque and about 75% for the green-opaque, respectively. A total computing time required for classification was about 100 seconds/1000 kernels with the PC 80486-DX2, 66MHz.