• Proceedings of the KAIS Fall Conference
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• 2004.11a
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• pp.299-301
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• 2004

A simple procedure for the extraction of the lipolytic enzyme from rice bran has been developed. High activity of lipolytic enzyme was obtained by first defatting the rice bran to remove lipid components with various extraction conditions. Then, after five cycles of aqueous extraction, rice bran lipolytic enzyme was purified using micro- and ultrafiltration apparatus. Lipolytic enzyme activity was estimated by its hydrolytic action of tributyrin. The result indicated that the standard activity curve of butyric acid showed that the potential rice bran enzyme is a hydrolytic lipase enzyme. In addition, it showed higher lipolytic activity and specific enzyme activity with further purification by micro- and ultrafiltration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1639-1658
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• 2019

Named entity recognition (NER) is an important technique for improving the performance of data mining and big data analytics. In previous studies, NER systems have been employed to identify named-entities using statistical methods based on prior information or linguistic features; however, such methods are limited in that they are unable to recognize unregistered or unlearned objects. In this paper, a method is proposed to extract objects, such as technologies, theories, or person names, by analyzing the collocation relationship between certain words that simultaneously appear around specific words in the abstracts of academic journals. The method is executed as follows. First, the data is preprocessed using data cleaning and sentence detection to separate the text into single sentences. Then, part-of-speech (POS) tagging is applied to the individual sentences. After this, the appearance and collocation information of the other POS tags is analyzed, excluding the entity candidates, such as nouns. Finally, an entity recognition model is created based on analyzing and classifying the information in the sentences.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.206-212
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• 2024

This study presents five selection rules for training algorithms to extract audio sources from noise. The five rules are Dynamics, Roots, Tonal Balance, Tonal-Noisy Balance, and Stereo Width, and the suitability of each rule for sound extraction was determined by spectrogram analysis using various types of sample sources, such as environmental sounds, musical instruments, human voice, as well as white, brown, and pink noise with sine waves. The training area of the algorithm includes both melody and beat, and with these rules, the algorithm is able to analyze which specific audio sources are contained in the given noise and extract them. The results of this study are expected to improve the accuracy of the algorithm in audio source extraction and enable automated sound clip selection, which will provide a new methodology for sound processing and audio source generation using noise.

• Preventive Nutrition and Food Science
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• v.4 no.3
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• pp.171-174
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• 1999

For monitoring lipid oxidation development in cooked ground pork during refrigerationm, malonaldehydethiobarbituric acid(MA-TBA) contents were measured using high performance liquid chromatography(HPLC). As the oxidation proceeded during refergeration, TBA-reaction substances(TBARS) absorbances increased and the corresponding HPLC peak areas also increased proportationately. The correlation coefficient between the HPLC peak areas and MA-TBA absorbance were 0.9979. The treatemtn of cetrimide, an ion pairing agent, gave a complete resolution of the MA-TBA complex and the butanol extraction of the complex increased its recovery by 37.8%. Both cetrimide treatment and butanol extraction are essential steps for analyzing MA-TBA complex in ground pork wiht HPLC. A reliable and specific measurement of NA-TBA in ground pork was successfully performed using HPLC.

• Applied Biological Chemistry
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• v.48 no.4
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• pp.331-334
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• 2005

In this study, the effects of five extraction methods for raw and processed corns were compared with respect to the integrity, yields and quality of DNA extracted from them and the results were assessed by PCR analysis. From the comparison of five extraction methods, DNA integrity showed a similar pattern. Amounts of genomic DNA obtained from the five extraction methods varies from $0.25{\mu}g\;to\;234{\mu}g$ per 1 g sample. The DNA yield extracted with CTAB method and DNeasy Plant Maxi kit is greater than that obtained from other extraction methods. These results would be applicable for the selection of an adequate extraction method for specific samples.

• Journal of the Korean Wood Science and Technology
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• v.52 no.5
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• pp.450-458
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• 2024

Litsea elliptica, a Southeast Asian tree with a rich history of medicinal applications, is attracting increasing research attention. This study investigated the effects of leaf maturity and solvent selection on the extraction of bioactive compounds from L. elliptica leaves, specifically with regard to their antioxidant activity. 2,2'-Azino-bis(3-ethylbenzothiazoline)-6-sulfonic acid (ABTS) method was employed to quantify the free radical scavenging capacity of L. elliptica leaf extracts prepared using three different solvents (n-hexane, ethyl acetate, and ethanol) at three different leaf stages (tender, immature, and mature). These results highlight the significant effects of leaf maturity and solvent selection on the extraction of phenolic compounds and flavonoids from L. elliptica leaves. Ethanol is the most effective solvent for the extraction of bioactive compounds, particularly from mature leaves. The ethanol extraction of tender leaves demonstrated potential for optimizing the antioxidant content. Further research is necessary to investigate the specific factors influencing the observed differences in antioxidant activity between leaves of varying ages and the potential impacts of other bioactive compounds present in the leaves.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.5
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• pp.443-453
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• 2020

The automatic land category extraction method was proposed, and the accuracy was evaluated by learning the aerial photo characteristics by land category in the border area with various restrictions on the acquisition of geospatial data. As experimental data, this study used four years' worth of published aerial photos as well as serial cadastral maps from the same time period. In evaluating the results of land category extraction by learning features from different temporal and spatial ranges of aerial photos, it was found that land category extraction accuracy improved as the temporal and spatial ranges increased. Moreover, the greater the diversity and quantity of provided learning images, the less the results were affected by the quality of images at a specific time to be extracted, thus generally demonstrating accurate and practical land category feature extraction.

• Analytical Science and Technology
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• v.37 no.5
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• pp.295-305
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• 2024

5-Hydroxymethyl-2-furaldehyde (5-HMF) is considered one of the main quality indexes of various food products. Its metabolism in humans can potentially lead to carcinogenic compounds. Metallic ions such as Zn, Mg, Mn, and Fe have been reported to enhance 5-HMF formation. Recently, studies on adsorbents that can extract specific organic and inorganic substances with one adsorbent have been conducted. However, simultaneous analysis of organic and inorganic materials typically requires distinct pre-treatment and analytical methods, which increase a lot of labor and cost. In this study, hybrid imprinted polymer (HIP) by mixing 5-HMF imprinted polymer (FIP) and zinc ion imprinted polymer (ZIIP) were generated to extract two analytes, Zn ion and 5-HMF, simultaneously. Physicochemical characterization of HIP was conducted by Fourier-transform infrared spectroscopy, scanning electron microscopy, and sorption tests. Extraction conditions including adsorbent mixing ratio, adsorbate mixing range, and equilibrium time were optimized. Freundlich adsorption model was as the best-fitting isotherm model to elucidate the adsorption mechanism. Affinity of Zn ion and 5-HMF on HIP was superior to non-HIP. In conclusion, HIP showed reasonable feasibility that could be used as an adsorbent to be used for simultaneous extraction of organic and inorganic compounds present in the sample.

• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.432-441
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• 2014

As the business model is receiving considerable attention these days, the ability to collect business model related information has become essential requirement for a company. The annual report is one of the most important external documents which contain crucial information about the company's business model. By investigating business descriptions and their future strategies within the annual report, we can easily analyze a company's business model. However, given the sheer volume of the data, which is usually over a hundred pages, it is not practical to depend only on manual extraction. The purpose of this study is to complement the manual extraction process by using text mining techniques. In this study, the text mining technique is applied in business model concept extraction and business model evolution analysis. By concept, we mean the overview of a company's business model within a specific year, and, by evolution, we mean temporal changes in the business model concept over time. The efficiency and effectiveness of our methodology is illustrated by a case example of three companies in the US video rental industry.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.194-199
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• 2003

Data are an expression of the language or numerical values that show some features. And the information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns or make a decision. Today, knowledge extraction and application of that are broadly accomplished for the easy comprehension and the performance improvement of systems in the several industrial fields. The knowledge extraction can be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge is drawn by rules with data mining techniques. Clustering (CL), input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for the knowledge expression based upon rules. In this paper, the various approaches of the knowledge extraction are surveyed and categorized by methodologies and applied industrial fields. Also, the trend and examples of each approaches are shown in the tables and graphes using the categories such as CL, ISP, NF, NN, EM, and so on.