• Title/Summary/Keyword: multi-stage extraction

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Extraction of Red Ginseng Extract by Impulse Vacuun System (Impulse-Vacuum System을 이용한 홍삼엑스의 추출)

  • 김천석;곽이성;신창식
    • Food Science and Preservation
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    • v.6 no.3
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    • pp.324-327
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    • 1999
  • This study was carried out to establish the extraction method of red ginseng extract without saponin decomposition. Red ginseng was extracted with impulse vacuum system and multi-stage extraction method. Crude saponin content of red ginseng extract (RGE) from impulse vacuum system was 5.4-5.9%, while that of RGE from multi-stage extraction method was 8.2-8.3%. However, HPLC Patterns indicated that saponins of RGE from impulse vacuum system were hardly decomposed, while those of RGE from multi-stage extraction method were decomposed, especially in ginsenoside -Rgl and -Re saponin. Also, the yields of red ginseng by impulse vacuum system were 15 to 20 times higher than that of multi-stage extraction method.

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Extraction and Concentration Method of Red Ginseng by Vacuum Impulse System (진공력적방식(Vacuum Impulse Stem)을 이용한 홍삼의 추출 방법)

  • Kim Cheon-Suk;Chang Gap-Moon
    • Journal of Ginseng Research
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    • v.23 no.2 s.54
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    • pp.88-92
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    • 1999
  • Hydrolysis properties of ginseng saponins in processing of extraction with vacuum impulse system extraction method were compared with multi-stage extraction methods. Crude saponin content of the extract produced by vacuum impulse system extraction method was $11.5\%,$ compared with multi-stage extraction method (about $8.13\%).$ Also the yield of the extract increased about $6.7\%.$ The flavor and aroma of ginseng extract with vacuum impulse system extraction method are stronger than multi-stage extraction methods and people have a tendency to like more. The color was similar to existing extraction items and the liquidity ratio was high. Vacuum impulse system extraction method could save human resources because of short extraction time and automatic operation of processing. With HPLC pattern, We could ascertain the truth that hydrolysis properties of ginseng saponin was restrained in the extraction processing, vacuum impulse system extraction method.

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A Study on Feature Extraction of Transformers Aging Signal using discrete Wavelet Transform Technique (이산 웨이블렛 변환 기법을 이용한 변압기 열화신호의 특징추출에 관한 연구)

  • Park, Jae-Jun;Kwon, Dong-Jin;Song, Yeong-Cheol;Ahn, Chang-Beom
    • The Transactions of the Korean Institute of Electrical Engineers C
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    • v.50 no.3
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    • pp.121-129
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    • 2001
  • In this paper, a new efficient feature extraction method based on Daubechies discrete wavelet transform is presented. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of aging(the early period, the middle period, the last period)

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Application of Technique Discrete Wavelet Transform for Acoustic Emission Signals (음향방출신호에 대한 이산웨이블릿 변환기법의 적용)

  • 박재준;김면수;김민수;김진승;백관현;송영철;김성홍;권동진
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • 2000.07a
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    • pp.585-591
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    • 2000
  • The wavelet transform is the most recent technique for processing signals with time-varying spectra. In this paper, the wavelet transform is utilized to improved the assessment and multi-resolution analysis of acoustic emission signals generating in partial discharge. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals in case of applied voltage 20[kv]. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We applied FIR(Finite Impulse Response)digital filter algorithm in discrete to suppression for random noise. The white noise be included high frequency component denoised as decomposition of discrete wavelet transform level-3. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of acting(the early period, the last period) .

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Fabrication of High Purity Ga-containing Solution using MOCVD dust (유기금속화학증착 분진(MOCVD dust)을 이용한 갈륨 함유 고순도 수용액 제조 연구)

  • Lee, Duk-Hee;Yoon, Jin-Ho;Park, Kyung-Soo;Hong, Myung-Hwan;Lee, Chan-Gi;Park, Jeung-Jin
    • Resources Recycling
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    • v.24 no.4
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    • pp.50-55
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    • 2015
  • In this study, we have investigated solvent extraction of Ga and recovery of high pure Ga solution from MOCVD dust for manufacturing of LED chip. Effect of extractan, concentration of extractant were examined for choosing the more effective extractant and high pure Ga solution was fabricated by multi-stage extraction/stripping process. For extraction/separation of Ga based on the analysis of raw-material in previous study, 3 different extractants PC 99A, DP-8R, Cyanex 272 has been investigated and the extraction efficiency of 1.5 M Cyanex 272 was 43.8%. It was conformed that extraction efficiency of Ga was 83% in multi-stage extraction and 5N high purity Ga stripping solution without impurities also obtained.

Feature Extraction and Classification of Multi-temporal SAR Data Using 3D Wavelet Transform (3차원 웨이블렛 변환을 이용한 다중시기 SAR 영상의 특징 추출 및 분류)

  • Yoo, Hee Young;Park, No-Wook;Hong, Sukyoung;Lee, Kyungdo;Kim, Yihyun
    • Korean Journal of Remote Sensing
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    • v.29 no.5
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    • pp.569-579
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    • 2013
  • In this study, land-cover classification was implemented using features extracted from multi-temporal SAR data through 3D wavelet transform and the applicability of the 3D wavelet transform as a feature extraction approach was evaluated. The feature extraction stage based on 3D wavelet transform was first carried out before the classification and the extracted features were used as input for land-cover classification. For a comparison purpose, original image data without the feature extraction stage and Principal Component Analysis (PCA) based features were also classified. Multi-temporal Radarsat-1 data acquired at Dangjin, Korea was used for this experiment and five land-cover classes including paddy fields, dry fields, forest, water, and built up areas were considered for classification. According to the discrimination capability analysis, the characteristics of dry field and forest were similar, so it was very difficult to distinguish these two classes. When using wavelet-based features, classification accuracy was generally improved except built-up class. Especially the improvement of accuracy for dry field and forest classes was achieved. This improvement may be attributed to the wavelet transform procedure decomposing multi-temporal data not only temporally but also spatially. This experiment result shows that 3D wavelet transform would be an effective tool for feature extraction from multi-temporal data although this procedure should be tested to other sensors or other areas through extensive experiments.

Road Extraction Based on Watershed Segmentation for High Resolution Satellite Images

  • Chang, Li-Yu;Chen, Chi-Farn
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.525-527
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    • 2003
  • Recently, the spatial resolution of earth observation satellites is significantly increased to a few meters. Such high spatial resolution images definitely will provide lots of information for detail-thirsty remote sensing users. However, it is more difficult to develop automated image algorithms for automated image feature extraction and pattern recognition. In this study, we propose a two-stage procedure to extract road information from high resolution satellite images. At first stage, a watershed segmentation technique is developed to classify the image into various regions. Then, a knowledge is built for road and used to extract the road regions. In this study, we use panchromatic and multi-spectral images of the IKONOS satellite as test dataset. The experiment result shows that the proposed technique can generate suitable and meaningful road objects from high spatial resolution satellite images. Apparently, misclassified regions such as parking lots are recognized as road needed further refinement in future research.

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Parallelizing Feature Point Extraction in the Multi-Core Environment for Reducing Panorama Image Generation Time (파노라마 이미지 생성시간을 단축하기 위한 멀티코어 환경에서 특징점 추출 병렬화)

  • Kim, Geon-Ho;Choi, Tai-Ho;Chung, Hee-Jin;Kwon, Bom-Jun
    • Journal of KIISE:Computing Practices and Letters
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    • v.14 no.3
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    • pp.331-335
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    • 2008
  • In this paper, we parallelized a feature point extraction algorithm to reduce panorama image generation time in multi-core environment. While we compose a panorama image with several images, the step to extract feature points of each picture is needed to find overlapped region of pictures. To perform rapidly feature extraction stage which requires much calculation, we developed a parallel algorithm to extract feature points and examined the performance using CBE(Cell Broadband Engine) which is asymmetric multi-core architecture. As a result of the exam, the algorithm we proposed has a property of linear scalability-the performance is increased in proportion the number of processors utilized. In this paper, we will suggest how Image processing operation can make high performance result in multi-core environment.

Two-stage Deep Learning Model with LSTM-based Autoencoder and CNN for Crop Classification Using Multi-temporal Remote Sensing Images

  • Kwak, Geun-Ho;Park, No-Wook
    • Korean Journal of Remote Sensing
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    • v.37 no.4
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    • pp.719-731
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    • 2021
  • This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

An Effective Retinal Vessel and Landmark Detection Algorithm in RGB images

  • Jung Eun-Hwa
    • International Journal of Contents
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    • v.2 no.3
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    • pp.27-32
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    • 2006
  • We present an effective algorithm for automatic tracing of retinal vessel structure and vascular landmark extraction of bifurcations and ending points. In this paper we deal with vascular patterns from RGB images for personal identification. Vessel tracing algorithms are of interest in a variety of biometric and medical application such as personal identification, biometrics, and ophthalmic disorders like vessel change detection. However eye surface vasculature tracing in RGB images has many problems which are subject to improper illumination, glare, fade-out, shadow and artifacts arising from reflection, refraction, and dispersion. The proposed algorithm on vascular tracing employs multi-stage processing of ten-layers as followings: Image Acquisition, Image Enhancement by gray scale retinal image enhancement, reducing background artifact and illuminations and removing interlacing minute characteristics of vessels, Vascular Structure Extraction by connecting broken vessels, extracting vascular structure using eight directional information, and extracting retinal vascular structure, and Vascular Landmark Extraction by extracting bifurcations and ending points. The results of automatic retinal vessel extraction using jive different thresholds applied 34 eye images are presented. The results of vasculature tracing algorithm shows that the suggested algorithm can obtain not only robust and accurate vessel tracing but also vascular landmarks according to thresholds.

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