• Journal of the Korean Applied Science and Technology
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• v.18 no.1
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• pp.67-77
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• 2001

In search for several fatty acid with unusual structure in vegetable oils, we have found that unknown peaks were shown on GLC in the analysis of fatty acids of the lipids from the pulp of ripened jujube (Zizypus jujuba var. inermis) fruits. These fatty acids were identified as a series of cis-monoenoic acids with ${\omega}-5$ double bond system such as $C_{14:1{\omega}5}$, $C_{16:1{\omega}5}$ and $C_{18:1{\omega}5}$, including ${\omega}-7$ fatty acid as $C_{16:1{\omega}7}$ and $C_{18:1{\omega}7}$, by GLC, solid-phase extraction silver ion-column chromatographic, GLC-mass spectrometric and IR techniques. First of all, total fatty acid methyl esters were resolved into saturated and branched fatty acid, monoenoic acid, dienoic acid, and trienoic acid fraction, respectively, with 100% dichloromethane (DCM), DCM/acetone (9:1, v/v) 100% acetone, and acetone/ acetonitrile (97:3, v/v) solvent system. Unknown fatty acids were included in the monoenoic fraction and were confirmed to have cis-configuration by IR. Picolinyl esters of monoenoic fatty acids gave distinct molecular ion peak and dominant diagnostic peaks, for example, m/z 317, 220 and 260 fragment for $cis-C_{14:1{\omega}5}$, m/z 345, m/z 248 and 288 fragment for $cis-C_{16:1{\omega}5}$ and m/z 373, m/z 276 and 316 fragment for $cis-C_{18:1{\omega}5}$. In this way the occurrence of $cis-C_{16:1{\omega}7}$ and $cis-C_{18:1{\omega}7}$ could be deduced from the appearance of prominent fragments as m/z 345, 220 and 260, and m/z 373, 248 and 280. Level of total ${\omega}-5$ fatty acids amounted to about 30% in the fatty acid composition with the predominance of $C_{16:1{\omega}5}$ $ (18.7{\sim}25.0%)$, in the semi-ripened and/or ripened samples collected in September 14 ($C_{16:1{\omega}5}$ ; 18.7%, $C_{14:1{\omega}5}$ ; 3.6% and $C_{18:1{\omega}5}$ ; 3.0%), September 22 ($C_{16:1{\omega}5}$ ; 25.0%, $C_{14:1{\omega}5}$ ; 1.4% and $C_{18:1{\omega}5}$ ; 2.6%), and October $7 (C_{16:1{\omega}5}$ ; 24.7%, $C_{14:1{\omega}5}$ ; 7.7% and $C_{18:1{\omega}5}$ ; 2.5%). However, the lipids extracted from unripened jujube in July and August contain these unusual fatty acids as low as negligible. It could be observed that the level of ${\omega}-5$ fatty acids in the pulps increased sharply with an elapse of ripening time of jujube fruits. Other monoenoic fatty acids with ${\omega}-7$ series, $C_{16:1{\omega}7}$ (palmitoleic acid) and $C_{18:1{\omega}7}$ (cis-vaccenic acid) could be detected. And in the lipids of the kernel and leaf of jujube, none of ${\omega}-5$ fatty acids could be detected.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.6
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• pp.904-911
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• 2015

The objective of this study was to investigate the quality characteristics of traditional wine using an radio-frequency identification (RFID) system annexed to a fermenter. In this study, we proposed an RFID-based data transmission scheme for monitoring fermentation of traditional alcoholic beverages. The pH, total acidity, total sugar, soluble sugar, free sugar, alcohol content, and organic acids of were investigated and subjected to fermentation of traditional alcoholic beverages three times. The pH ranged from 7.98, 7.95, and 7.68 at day 0, decreased drastically to 3.31~2.96 at day 2, and then slowly increased to the end point, finally reaching 3.34 at day 20. Acidity tended to increase quickly with time, especially for all samples after day 2. The fermentation environment induced a sudden increase acidity in reactants and indicated a low pH. The total sugars during fermentation quickly decreased to the range of 20.3, 22.43, and 19.2% at day 2, and the slope of reduction steadily decreased to 5.1, 6.1, and 4.8% at day 10. On the other hand, the alcohol content showed the reverse trend as total sugars. The alcohol content also showed the same pattern as total acids, showing the highest alcohol content of 17.3% (v/v) on day 20. In this study on traditional wine fermentation using an RFID system, we showed that pH, soluble sugar, and alcohol content can be adopted as key indicators for quality control and standardization of traditional wine manufacturing.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.10-20
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• 2019

Irregular rainfall caused by climate change, in combination with non-point pollution, can cause water systems worldwide to suffer from frequent eutrophication and algal blooms. This type of water pollution is more common in agricultural prone to water system inflow of non-point pollution. Therefore, in this study, the correlation between Unmanned Aerial Vehicle(UAV) multi-spectral images and total phosphorus, total nitrogen, and chlorophyll-a with indirect association of algal blooms, was analyzed to identify the usability of UAV image to identify water quality characteristics in agricultural streams. The analysis the vegetation index Normalized Differences Index (NDVI), the Normalized Differences Red Edge(NDRE), and the Chlorophyll Index Red Edge(CIRE) for the detection of multi-spectral images and algal blooms collected from the target regions Yang cheon and Hamyang Wicheon. The analysis of the correlation between image values and water quality analysis values for the water sampling points, total phosphorus at a significance level of 0.05 was correlated with the CIRE(0.66), and chlorophyll-a showed correlation with Blue(-0.67), Green(-0.66), NDVI(0.75), NDRE (0.67), CIRE(0.74). Total nitrogen was correlated with the Red(-0.64), Red edge (-0.64) and Near-Infrared Ray(NIR)(-0.72) wavelength at the significance level of 0.05. The results of this study confirmed a significant correlations between multi-spectral images collected through UAV and the factors responsible for water pollution, In the case of the vegetation index used for the detection of algal bloom, the possibility of identification of not only chlorophyll-a but also total phosphorus was confirmed. This data will be used as a meaningful data for counterplan such as selecting non-point pollution apprehensive area in agricultural area.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.17-32
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• 2021

Speaker recognition is generally divided into speaker identification and speaker verification. Speaker recognition plays an important function in the automatic voice system, and the importance of speaker recognition technology is becoming more prominent as the recent development of portable devices, voice technology, and audio content fields continue to expand. Previous speaker recognition studies have been conducted with the goal of automatically determining who the speaker is based on voice files and improving accuracy. Speech is an important sociolinguistic subject, and it contains very useful information that reveals the speaker's attitude, conversation intention, and personality, and this can be an important clue to speaker recognition. The final ending used in the speaker's speech determines the type of sentence or has functions and information such as the speaker's intention, psychological attitude, or relationship to the listener. The use of the terminating ending has various probabilities depending on the characteristics of the speaker, so the type and distribution of the terminating ending of a specific unidentified speaker will be helpful in recognizing the speaker. However, there have been few studies that considered speech in the existing text-based speaker recognition, and if speech information is added to the speech signal-based speaker recognition technique, the accuracy of speaker recognition can be further improved. Hence, the purpose of this paper is to propose a novel method using speech style expressed as a sentence-final ending to improve the accuracy of Korean speaker recognition. To this end, a method called sentence sequencing that generates vector values by using the type and frequency of the sentence-final ending appearing in the utterance of a specific person is proposed. To evaluate the performance of the proposed method, learning and performance evaluation were conducted with a actual drama script. The method proposed in this study can be used as a means to improve the performance of Korean speech recognition service.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.2
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• pp.110-118
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• 2022

Candidemia is a major cause of nosocomial infections resulting in increased morbidity and mortality. It remains a serious risk in inpatients and increases medical treatment costs. From 2009 to 2018, Candida strains (3,533) isolated from blood culture tests at the S Hospital were analyzed according to the period, year, sex, age, ward, etc. During the entire period, 54,739 of 717,996 blood culture tests showed a positive rate (7.6%) and the Candida isolation rate was 3,533 (6.4%) out of 1,036 patients. Among the Candida isolates, C. albicans was most common (33.8%), followed by C. tropicalis (28.6%), C. glabrata (19.8%), C. parapsilosis (7.8%), and C. krusei (4.0%). In early (2009~2013)/late (2014~2018) isolation, C. tropicalis decreased by 3.8% and C. glabrata increased by 3.4%. After 50 years of age, the higher the separation frequency. C. parapsilosis (31.3%) in 1~10s, C. tropicalis (30.3%) and C. glabrata (27.6%) in 41~50s, and C. tropicalis (28.6%) in 80s are relatively frequent. has been separated C. krusei was isolated in a relatively high proportion from females (60.9%). Therefore, a systematic and continuous nosocomial infection control system should be established for appropriate treatment as per antifungal treatment guidelines. The system should continuously monitor the distribution of Candida species and provide rapid identification results.

• Development and Reproduction
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• v.12 no.3
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• pp.289-296
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• 2008

BPES (Blepharophimosis/Ptosis/Epicanthus inversus Syndrome) is an autosomal dominant disorder caused by mutations in FOXL2. Affected individuals have premature ovarian failure (POF) in addition to small palpebral fissures, drooping eyelids, and broad nasal bridge. FOXL2 is a member of the forkhead family transcription factors. In FOXL2-deficient ovaries, granulosa cell differentiation dose not progress, leading to arrest of folliculogenesis and oocytes atresia. Using yeast two-hybrid screening of rat ovarian cDNA library with FOXL2 as bait, we found that small ubiquitin-related modifier (SUMO)-conjugating E2 enzyme UBE2I protein interacted with FOXL2 protein. UBE2I also known as UBC9 is an essential protein for processing SUMO modification. Sumoylation is a form of post-translational modification involved in diverse signaling pathways including the regulation of transcriptional activities of many transcriptional factors. In the present study, we confirmed the protein-protein interaction between FOXL2 and UBE2I in human cells, 293T, by in vivo immunoprecipitation. In addition, we generated truncated FOXL2 mutants and identified the region of FOXL2 required for its association with UBE2I using yeast-two hybrid system. Therefore, the identification of UBE2I as an interacting protein of FOXL2 further suggests a presence of novel regulatory mechanism of FOXL2 by sumoylation.

• Korea Science and Art Forum
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• v.21
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• pp.31-43
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• 2015

Information and Communications Technology (ICT) is one of the technologies which represent the core value of the creative economy. It has served as a vehicle connecting the existing industry and corporate infrastructure, developing existing products and services and creating new products and services. In addition to the ICT, new devices including big data, mobile gadgets and wearable products are gaining a great attention sending an expectation for a new market-pioneering. Further, Internet of Things (IoT) is helping solidify the ICT-based social development connecting human-to-human, human-to-things and things-to-things. This means that the manufacturing-based hardware development needs to be achieved simultaneously with software development through convergence. The essential element the convergence between hardware and software is OS, for which world's leading companies such as Google and Apple have launched an intense development recognizing the importance of software. Against this backdrop, the status-quo of the software market has been examined for the study of the present report (Korea Evaluation Institute of Industrial Technology: Professional Design Technology Development Project). As a result, the software platform-based Google's android and Apple's iOS are dominant in the global market and late comers are trying to enter the market through various pathways by releasing web-based OS and similar OS to provide a new paradigm to the market. The present study is aimed at finding the way to utilize a smart content by which anyone can be a developer based on OS responding to such as social change, newly defining a smart content to be universally utilized and analyzing the market to deal with a rapid market change. The study method, scope and details are as follows: Literature investigation, Analysis on the app market according to a smart classification system, Trend analysis on the current content market, Identification of five common trends through comparison among the universal definition of smart content, the status-quo of application represented in the app market and content market situation. In conclusion, the smart content market is independent but is expected to develop in the form of a single organic body being connected each other. Therefore, the further classification system and development focus should be made in a way to see the area from multiple perspectives including a social point of view in terms of the existing technology, culture, business and consumers.

• Journal of Soil and Groundwater Environment
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• v.13 no.1
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• pp.92-100
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• 2008

Experimental studies were conducted to identify the active agents for reductive dechlorination of TCE in cement/Fe(II) systems focusing on cement components such as CaO, $Fe_2O_3$, and $Al_2O_3$. A hematite that was used to simulate an $Fe_2O_3$ component in cement was found to have degradation efficiencies (k = 0.641 $day^{-1}$) equivalent to that of cement/Fe(II) systems in the presence of CaO/Fe(II), only when it contained an aluminum impurity$(Al_2O_3)$. When the effect of $Al_2O_3$ content of hematite/CaO/$Al_2O_3$/Fe(II) system was tested, the mole ratio of $Al_2O_3$ to CaO affected the rate of TCE degradation with an optimum ratio around 1 : 10 that resulted in a rate constant of 0.895 $day^{-1}$. In the SEM images of hematite/CaO/$Al_2O_3$/Fe(II) systems, acicular crystals were also found that were also observed in cement/Fe(II) systems. Thus it was suspected that these crystals were reactive reductants and that they might be goethite or ettringite that are known to have acicular structures. An EDS element map analysis revealed that these crystals were not goethite crystals. A subsequent experiment that tested reactivities of compounds formed during the ettringite synthesis showed that ettringite and minerals associated with ettringite formation are not reactive reductants. These observations conclude that a mineral containing CaO and $Al_2O_3$ with a acicular structure could be a major reactive reductant of cement/Fe(II) systems.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.80-87
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• 2010

This study was conducted to develop a reservoir modelling workflow to reproduce the heterogeneous distribution of effective permeability that impacts on the performance of SAGD (Steam Assisted Gravity Drainage), the in-situ bitumen recovery technique in the Athabasca Oil Sands. Lithologic facies distribution is the main cause of the heterogeneity in bitumen reservoirs in the study area. The target formation consists of sand with mudstone facies in a fluvial-to-estuary channel system, where the mudstone interrupts fluid flow and reduces effective permeability. In this study, the lithologic facies is classified into three classes having different characteristics of effective permeability, depending on the shapes of mudstones. The reservoir modelling workflow of this study consists of two main modules; facies modelling and permeability modelling. The facies modelling provides an identification of the three lithologic facies, using a stochastic approach, which mainly control the effective permeability. The permeability modelling populates mudstone volume fraction first, then transforms it into effective permeability. A series of flow simulations applied to mini-models of the lithologic facies obtains the transformation functions of the mudstone volume fraction into the effective permeability. Seismic data contribute to the facies modelling via providing prior probability of facies, which is incorporated in the facies models by geostatistical techniques. In particular, this study employs a probabilistic neural network utilising multiple seismic attributes in facies prediction that improves the prior probability of facies. The result of using the improved prior probability in facies modelling is compared to the conventional method using a single seismic attribute to demonstrate the improvement in the facies discrimination. Using P-wave velocity in combination with density in the multiple seismic attributes is the essence of the improved facies discrimination. This paper also discusses sand matrix porosity that makes P-wave velocity differ between the different facies in the study area, where the sand matrix porosity is uniquely evaluated using log-derived porosity, P-wave velocity and photographically-predicted mudstone volume.