• Proceedings of the CALSEC Conference
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• 2005.03a
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• pp.55-61
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• 2005

We will introduce a new web-based knowledge management system in progress, in which XML-based web information extraction and our structuring knowledge technologies are combined using ontology-based natural language processing. Our aim is to provide efficient access to heterogeneous information on the web, enabling users to use a wide range of textual and non textual resources, such as newspapers and databases, effortlessly to accelerate knowledge acquisition from such knowledge sources. In order to achieve the efficient knowledge management, we propose at first an XML-based Web information extraction which contains a sophisticated control language to extract data from Web pages. With using standard XML Technologies in the system, our approach can make extracting information easy because of a) detaching rules from processing, b) restricting target for processing, c) Interactive operations for developing extracting rules. Then we propose a structuring knowledge system which includes, 1) automatic term recognition, 2) domain oriented automatic term clustering, 3) similarity-based document retrieval, 4) real-time document clustering, and 5) visualization. The system supports integrating different types of databases (textual and non textual) and retrieving different types of information simultaneously. Through further explanation to the specification and the implementation technique of the system, we will demonstrate how the system can accelerate knowledge acquisition on the Web even for novice users of the field.

• The Journal of the Acoustical Society of Korea
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• v.32 no.1
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• pp.79-85
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• 2013

Feature extraction of CASA system uses time continuity and channel similarity and makes correlogram of auditory elements for the use. In case of using feature extraction with cross correlation coefficient for channel similarity, it has much computational complexity in order to display correlation quantitatively. Therefore, this paper suggests feature extraction method using non-parametric correlation coefficient in order to reduce computational complexity when extracting the feature and tests to segregate target speech by CASA system. As a result of measuring SNR (Signal to Noise Ratio) for the performance evaluation of target speech segregation, the proposed method shows a slight improvement of 0.14 dB on average over the conventional method.

• Journal of information and communication convergence engineering
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• v.13 no.3
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• pp.145-151
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• 2015

We describe in this paper a hardware-based improvement scheme of a real-time automatic speech recognition (ASR) system with respect to speed by designing a parallel feature extraction algorithm on a Field-Programmable Gate Array (FPGA). A computationally intensive block in the algorithm is identified implemented in hardware logic on the FPGA. One such block is mel-frequency cepstrum coefficient (MFCC) algorithm used for feature extraction process. We demonstrate that the FPGA platform may perform efficient feature extraction computation in the speech recognition system as compared to the generalpurpose CPU including the ARM processor. The Xilinx Zynq-7000 System on Chip (SoC) platform is used for the MFCC implementation. From this implementation described in this paper, we confirmed that the FPGA platform is approximately 500× faster than a sequential CPU implementation and 60× faster than a sequential ARM implementation. We thus verified that a parallelized and optimized MFCC architecture on the FPGA platform may significantly improve the execution time of an ASR system, compared to the CPU and ARM platforms.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.12 s.177
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• pp.170-174
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• 2005

A numerical system to extract effective elastic properties of polycrystalline thin-films for MEMS devices is developed. In this system, the statistical model based on lattice system is used for modeling the microstructure evolution simulation and the key kinetics parameters of given micrograph, grain distributions and deposition process can be extracted by inverse method proposed in the system. In this work, effects of kinetics parameters on the extraction of effective elastic properties of polycrystalline thin-films are studied by using statistical method. Effects of the fraction of the potential site($f_p$) among the parameters for deposition process of microstructure on the extraction of effective elastic properties of polycrystalline thin-films are investigated. For this research, polysilicon is applied to this system as the polycrystalline thin-films.

• Journal of information and communication convergence engineering
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• v.16 no.3
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• pp.160-165
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• 2018

This paper designed and developed the image processing system of integrating feature extraction and matching by using convolutional neural network (CNN), rather than relying on the simple method of processing feature extraction and matching separately in the image processing of conventional image recognition system. To implement it, the proposed system enables CNN to operate and analyze the performance of conventional image processing system. This system extracts the features of an image using CNN and then learns them by the neural network. The proposed system showed 84% accuracy of recognition. The proposed system is a model of recognizing learned images by deep learning. Therefore, it can run in batch and work easily under any platform (including embedded platform) that can read all kinds of files anytime. Also, it does not require the implementing of feature extraction algorithm and matching algorithm therefore it can save time and it is efficient. As a result, it can be widely used as an image recognition program.

• Journal of the Korean Society of Food Science and Nutrition
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• v.28 no.5
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• pp.1087-1091
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• 1999

Microwave extraction system equipped with closed vessels, which is known to rapidly extract target compounds from natural products, was applied to monitor the changes in phenolic compounds, browning color intensity and electron donating ability by using response surface methodology(RSM). Maximum content of phenolic compound was 21.65mg/100ml in 67.88% of ethanol concentration, 145oC of extraction temperature, and 6.24min of extraction time. The phenolic compounds in extracts are dependent on the increase of the extraction temperature and the ethanol concentration. Browning color intensity, which was maximized in 67.21%, 147oC, and 6.02min, was proportional to the increase of the extraction temperature. Maximum value of electron donating ability was 24.50units in 54.33%, 147oC, and 6.11 min. The electron donating ability of extracts was dependent on the increase of extraction temperature and maximized in the range from 50 to 65% of ethanol concentration.

• Bulletin of the Korean Chemical Society
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• v.19 no.12
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• pp.1351-1355
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• 1998

A simple apparatus for adding a modifying solvent to supercritical CO2 extractant was described. Small, fixed volumes (typically 100 μL) of liquid modifying solvents were delivered during the extraction process by use of an in-line high pressure loop injector and an air pump. Without disconnecting the extraction cell from the supercritical fluid extraction system, the modifying solvent was repeatedly delivered. The solvent modification device was optimized during the extraction of carbaryl and bis(acetylacetonato) copper(Ⅱ). Extraction recoveries from spiked filter paper and soil samples ranged between 22% and 109%, depending on the analyte and matrix components. The addition of polar modifying solvents were necessary to improve the extractability of the nonpolar CO2.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.255-255
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• 2017

Sesame has been consumed for centuries as flavoring ingredient in eastern Asian countries, especially Korea. Sesame seeds have been used as health food for traditional medicine to prevent disease in Asian countries for several thousand years. Sesame seed has higher oil content (around 50%) than most of the known oilseeds. Sesame oil is rich in monounsaturated and polyunsaturated fatty acids. Extraction of sesame has developed significantly over the years. The mechanical method was an early means of separation which was physical pressure to squeeze the oil out. Nowadays, solvent extraction becomes the commonly used commercial technique to recover oil from oilseeds. In this study, we investigated extraction efficiency and quality of oil affected by cultivars and extraction methods of sesame seed. Different variables were investigated; roasting temperature ($170{\sim}220^{\circ}C$), extraction methods (solvent and physical pressure), forced ventilation system and cultivars. The Contents of B(a)P in sesame oil after roasting at $170{\sim}220^{\circ}C$ were 0.30~2.53 ppm. When we introduced forced ventilation system during roasting, B(a)P Contents were decreased up to 36%. The Oil extraction efficiency on sesame seed was statistically depending on the cultivars and extraction methods. The oil extraction yields of solvent and physical pressure extraction were 56.3% and 44.6%, respectively. Many of sesame cultivars and genetic resources are linolenic acid content of less than 0.5%. The results supported that we have developed a safe and high quality sesame oil processing methods for small and medium-sized companies.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.82-86
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• 2006

The design of a pattern recognition system generally involves the three aspects: preprocessing, feature extraction, and decision making. Among them, a feature extraction method determines an appropriate subspace of dimensionality in the original feature space of dimensionality so that it can reduce the complexity of the system and help to improve successful recognition rates. Linear transforms, such as principal component analysis, factor analysis, and linear discriminant analysis have been widely used in pattern recognition for feature extraction. This paper shows that singular value decomposition (SVD) can be applied usefully in feature extraction stage of pattern recognition. As an application, a remote sensing problem is applied to verify the usefulness of SVD. The experimental result indicates that the feature extraction using SVD can improve the recognition rate about 25% compared with that of PCA.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.71-79
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• 2017

Recently, Fire watching and dangerous substances monitoring system has been being developed to enhance various fire related security. It is generally assumed that fire flame extraction plays a very important role on this monitoring system. In this study, we propose the fire flame extraction method of Non-Residential Facilities based on core object extraction in image. A core object is defined as a comparatively large object at center of the image. First of all, an input image and its decreased resolution image are segmented. Segmented regions are classified as the outer or the inner region. The outer region is adjacent to boundaries of the image and the rest is not. Then core object regions and core background regions are selected from the inner region and the outer region, respectively. Core object regions are the representative regions for the object and are selected by using the information about the region size and location. Each inner region is classified into foreground or background region by comparing its values of a color histogram intersection of the inner region against the core object region and the core background region. Finally, the extracted core object region is determined as fire flame object in the image. Through experiments, we find that to provide a basic measures can respond effectively and quickly to fire in non-residential facilities.