• Journal of the East Asian Society of Dietary Life
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• v.16 no.5
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• pp.573-577
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• 2006

The rutin content of Fagopyrum tataricum is 100-fold higher than that of Fagopyrum esculentum. For the development of a rutin-containing beverage, a suitable method to extract rutin from buckwheat (Fagopyrum tataricum) with high rutin yield was investigated. A roasting temperature range of $310/240^{\circ}C$ (Ed-confirm that this is indeed a range; otherwise perhaps, 'Roasting temperatures ranging from 310 to $240^{\circ}C$ were considered$\ldots$') was considered to be the best as the basic color reference. Rutin content varied according to the roasting time and heating temperature; i.e., it decreased with increasing roasting time and temperature. (Ed- this sentence is unnecessarily complicated and should be simplified to 'Rutin content decreased with increasing roasting time and heating temperature.') The optimal extraction temperature and processing time were obtained as $80^{\circ}C$ and 10 minutes to maximize the rutin concentration in the extract.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.345-350
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• 2018

This study is an algorithm for detecting vein pattern features important for finger vein recognition. The feature detection algorithm is important because it greatly affects recognition results in pattern recognition. The recognition rate is degraded because the reference is changed according to the finger position change. In addition, the image obtained by irradiating the finger with infrared light is difficult to separate the image background and the blood vessel pattern, and the detection time is increased because the image preprocessing process is performed. For this purpose, the presented algorithm can be performed without image preprocessing, and the detection time can be reduced. SWDA (Down Slope Trace Waveform) algorithm is applied to the finger vein images to detect the fingertip position and vein pattern. Because of the low infrared transmittance, relatively dark vein images can be detected with minimal detection error. In addition, the fingertip position can be used as a reference in the classification stage to compensate the decrease in the recognition rate. If we apply algorithms proposed to various recognition fields such as palm and wrist, it is expected that it will contribute to improvement of biometric feature detection accuracy and reduction of recognition performance time.

• Journal of Radiation Protection and Research
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• v.24 no.1
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• pp.1-7
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• 1999

Using the TBP slovent extraction method, a simple and precise method for determining uranium isotopes in the environment samples was developed. The soil sample was decomposed with $HNO_3$ and HF. Uranium isotopes were extracted with 15% TBP in $CCl_4$ from aqueous phase to organic phase, and Th fraction was removed with 8M HCl. Uranium fraction was purified in back extraction step with 1M HCl. Optimized electrode position conditions of uranium Isotopes were set using a new electrode position solution including a DTPA chelating agent. The new method of uranium isotopes determination was validated with a result of application to IAEA Reference Soils.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.115-125
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• 2011

Manufacturing companies usually manage the process to achieve high quality using various types of control chart in statistical process control. When an assignable cause occurs in a process, the data in the control chart changes with different patterns by the specific causes. It is important in process control to classify the CCP (Control Chart Pattern) recognition for fast decision making. In former research, gathered data from process used to apply as raw data, leads to degrade the performance of recognizer and to decrease the learning speed. Therefore, feature based recognizer, employing feature extraction method, has been studied to enhance the classification accuracy and to reduce the dimension of data. We propose the method to extract features that take the distances between CCP data and reference vector generated from BDK (Bi-Directional Kohonen Network). We utilize those features as the input vectors in ANN (Artificial Neural Network) and compare with raw data applied ANN to evaluate the performance.

• Journal of radiological science and technology
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• v.30 no.4
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• pp.419-426
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• 2007

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize taste(bitter, sweet, sour and salty) pattern vectors. The signal intensity of taste are used to compose the input pattern vectors. The SOM(Self Organizing Maps) algorithm for taste pattern recognition is used to learn initial reference vectors and the ot-star learning algorithm is used to determine the class of the output neurons of the sunclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ(Learning Vector Quantization) algorithm. The pattern vectors are classified into subclasses by neurons in the subclass layer, and the weights between subclass layer and output layer are learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors, the proposed algorithm is simulated with ones of the conventional LVQ, and it is confirmed that the proposed learning method is more successful classification than the conventional LVQ.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.95-103
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• 2006

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize EEG pattern vectors. The frequency and amplitude of alpha rhythms and beta rhythms are used to compose the input pattern vectors. And the algorithm for EEG pattern recognition is used SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of the subclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ algorithm, and pattern vectors is classified into subclasses by neurons which is being in the subclass layer, and the weights between subclass layer and output layer is learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors of EEG, the proposed algorithm is simulated with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional LVQ.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1C
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• pp.62-67
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• 2008

In this paper, wavelet based blind watermarking using self-reference method is proposed. First, we process wavelet transform of original image. Then, we set all domain except for the low-frequency domain to zero and make self-reference image after wavelet reverse transformation. By choosing specific domain according to the pixel value difference between original image and self-reference image, we make random sequence, use as watermark and embed. The experimental results of the watermark embedding and extraction on various images show that the proposed scheme not only has good image quality, but also has stability on JPEG lossy compression, filtering, sharpening, blurring and noise.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.127-136
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• 2021

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

• Applied Chemistry for Engineering
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• v.18 no.2
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• pp.168-172
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• 2007

The separation of indole from a model mixture comprising four kinds of nitrogen heterocyclic compounds [indole (In), quinoline (Q), iso-quinoline (iQ), quinaldine (Qu)], three kinds of bicyclic aromatic compounds [1-methylnaphthalene (1MN), 2-methylnaphthalene (2MN), dimethylnaphthalene (DMN)], biphenyl (Bp) and phenyl ether (Pe) was examined by batch cocurrent 4 stages equilibrium extraction. The model mixture used as a raw material in this work was prepared according to the components and compositions contained in coal tar fraction (the temperature ranges of fraction: $240{\sim}265^{\circ}C$). An aqueous solution of formamide was used as a solvent. Indole was recovered more than 99% through 4 stages of the equilibrium extraction. The range of selectivity of indole in reference to DMN obtained through the 5 stages equilibrium extraction was found to be 63~118. The process for separation and recovery of indole contained in coal tar was studied by using the experimental results obtained from this work and the previous work.

• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.95-102
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• 2022

Since the organic light-emitting diodes (OLEDs) have been widely investigated as next-generation displays, it has been successfully commercialized as a flexible and rollable display. However, there is still wide room and demand to improve the device characteristics such as power efficiency and lifetime. To solve this issue, there has been a wide research effort, and among them, the internal and the external light extraction techniques have been attracted in this research field by its fascinating characteristic of material independence. In this study, a micro-nano composite structured external light extraction layer was demonstrated. A reactive ion etching (RIE) process was performed on the surfaces of hexagonally packed hemisphere micro-lens array (MLA) and randomly distributed sphere diffusing films to form micro-nano composite structures. Random nanostructures of different sizes were fabricated by controlling the processing time of the O₂ / CHF₃ plasma. The fabricated device using a micro-nano composite external light extraction layer showed 1.38X improved external quantum efficiency compared to the reference device. The results prove that the external light extraction efficiency is improved by applying the micro-nano composite structure on conventional MLA fabricated through a simple process.