• Journal of Ginseng Research
• /
• v.44 no.4
• /
• pp.552-562
• /
• 2020

Background: Ginsenosides are the unique and bioactive components in ginseng. Ginsenosides are affected by the growing environment and conditions. In New Zealand (NZ), Panax ginseng Meyer (P. ginseng) is grown as a secondary crop under a pine tree canopy with an open-field forest environment. There is no thorough analysis reported about NZ-grown ginseng. Methods: Ginsenosides from NZ-grown P. ginseng in different parts (main root, fine root, rhizome, stem, and leaf) with different ages (6, 12, 13, and 14 years) were extracted by ultrasonic extraction and characterized by Liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry. Twenty-one ginsenosides in these samples were accurately quantified and relatively quantified with 13 ginsenoside standards. Results: All compounds were separated in 40 min, and a total of 102 ginsenosides were identified by matching MS spectra data with 23 standard references or published known ginsenosides from P. ginseng. The quantitative results showed that the total content of ginsenosides in various parts of P. ginseng varied, which was not obviously dependent on age. In the underground parts, the 13-year-old ginseng root contained more abundant ginsenosides among tested ginseng samples, whereas in the aboveground parts, the greatest amount of ginsenosides was from the 14-year-old sample. In addition, the amount of ginsenosides is higher in the leaf and fine root and much lower in the stem than in the other parts of P. ginseng. Conclusion: This study provides the first-ever comprehensive report on NZ-grown wild simulated P. ginseng.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39C no.9
• /
• pp.811-819
• /
• 2014

Activity recognition is a key component in identifying the context of a user for providing services based on the application such as medical, entertainment and tactical scenarios. Instead of applying numerous sensor devices, as observed in many previous investigations, we are proposing the use of smartphone with its built-in multimodal sensors as an unobtrusive sensor device for recognition of six physical daily activities. As an improvement to previous works, accelerometer, gyroscope and magnetometer data are fused to recognize activities more reliably. The evaluation indicates that the IBK classifier using window size of 2s with 50% overlapping yields the highest accuracy (i.e., up to 99.33%). To achieve this peak accuracy, simple time-domain and frequency-domain features were extracted from raw sensor data of the smartphone.

• Journal of Environmental Health Sciences
• /
• v.40 no.3
• /
• pp.215-224
• /
• 2014

Objective: This study was carried out in order to determine the depositional characteristics of pine needles, pine bark, and soil used as a passive air sampler (PAS) for atmospheric polybrominated diphenyl ethers (PBDEs). Methods: All three media were sampled from the same site. The PBDE concentrations were analyzed by HRGC/HRMS, and the lipid contents were measured using the gravimetric method by n-hexane extraction. Results: The total PBDE concentration was the highest in soil (22,274.57 pg/g dry), followed by pine bark (20,266.39 pg/g dry), and then pine needles (7,380.22 pg/g dry). Pine needles contained the highest lipid contents (21.31 mg/g dry), whereas soil (10.01 mg/g dry), and pine bark (4.85 mg/g dry) contained less. There were poor correlations between lipid content and total PBDE concentrations in the media ($R^2$=0.8216, p=0.2814). Congeners BDE 47, 99, 183, 196, 197, 206, 207 and 209 showed peak concentrations. Among these, BDE 206, 207, and 209 are highly brominated PBDEs that exist as particulates in ambient air. They accounted for 81.2% [69.2 (pine needles) - 89.0% (tree bark)] of the concentration and therefore are noted as the main congener of the total PBDEs. Conclusions: It can therefore be concluded that for reducing error by improper sampling, the same species of media should be recommended for use as a PAS for atmospheric PBDEs due to the differences in depositional characteristics.

• Journal of the Korean Society of Food Culture
• /
• v.9 no.4
• /
• pp.355-367
• /
• 1994

This study aims at exploring the nature of the traditional Korean wines brewed throughout the Southern Region of Korea-Chulla-do, Kyungsang-do and Cheju-do describing their varieties and brewing methods and also comparing the similarities and differences of their features. When compared with the wines produced in the Central Region, the Southern varieties are very fastidious and complex in their brewing methods, which in turn show a wide range of diversity. First of all, all the 29 kinds of wines investigated, not a single one shows any resemblance to any one of the remaining, each exhibiting peculiar and particular characteristic features of its own. Especially, the distilling methods demonstrate very complex processes. Secondly, the majority of the Southern spirits are made from grains, added with fragrant flavor of pine tree, wormwood, chrysanthemum leaves and other medicine herbs such as Chinese matrimony vine and tankui. Thirdly, they are brewed with yeast made from wheat into kodupap(steamed rice) type of spirits, emerging as in the form of blended liquor. Fourthly, in brewing, different fermenting temperature and duration are required. Typewise, the temperature required for the basic spirit is $15{\sim}20^{\circ}C\;or\;25{\sim}30^{\circ}C$ : in the case of blended secondarily fermented liquor, from the minimum of $0{\sim}5^{\circ}C$ to the maximum of $75{\sim}80^{\circ}C$. The brewing duration is $3{\sim}5$ days for the basic spirits. In some cases, from the minimum of 3 days to the maximum of 100 days are consumed for fermenting. Fifthly, the wine extraction gadgets are yongsu (wine strainer), the sieve, filter paper, Korean traditional paper, the utilization of which implies that the brewers endeavor to observe and preserve the traditional and indigenous methods of wine making.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.15 no.2
• /
• pp.137-146
• /
• 1990

A series of images whose sized and resolutions differ by a constant factor are called an image pyramid. Because the images at high levels are small, large object can be detected on high levels of the pyramid at low cost, But in this way, the boundaries of objects are not accurately localized. Therefore the pyramid algorithms extracte the objects by segmentation the constructed image using bottom-up method and description it in an original resolution using inverse bottom-up method. In this paper, we can project an object down to the next lower level of the pyramid and apply to the boundary adjustment algorithm at that level to localize it more precisely. We repeat the process at successively lower levels. In this paper, we present a method of boundary adjustment using an image pyramid to obtain optimal boundary. The performance of the proposed algorithm is compared to those of the conventional method in term of subjective quality of object boundary.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.7
• /
• pp.956-962
• /
• 2018

As the study of the object feature detection from image, we explain methods to identify the species of the tree in forest using the picture taken from dron. Generally there are three kinds of methods, which are GLCM (Gray Level Co-occurrence Matrix) and Gabor filters, in order to extract the object features. We proposed the object extraction method using the statistical properties of trees in this research because of the similarity of the leaves. After we extract the sample images from the original images, we detect the objects using cross correlation techniques between the original image and sample images. Through this experiment, we realized the mean value and standard deviation of the sample images is very important factor to identify the object. The analysis of the color component of the RGB model and HSV model is also used to identify the object.

• Journal of KIISE:Databases
• /
• v.32 no.6
• /
• pp.603-615
• /
• 2005

As recent business environments are changed and become complex, a more efficient and effective business process management are needed. This paper proposes a new approach to the automatic execution of business processes using Event-Condition-Action (ECA) rules that can be automatically triggered by an active database. First of all, we propose the concept of blocks that can classify process flows into several patterns. A block is a minimal unit that can specify the behaviors represented in a process model. An algorithm is developed to detect blocks from a process definition network and transform it into a hierarchical tree model. The behaviors in each block type are modeled using ACTA formalism. This provides a theoretical basis from which ECA rules are identified. The proposed ECA rule-based approach shows that it is possible to execute the workflow using the active capability of database without users' intervention.

• Applied Biological Chemistry
• /
• v.39 no.5
• /
• pp.389-397
• /
• 1996

The volatile concentrate obtained from the edible Korean dureup plant (Aralia elata Seem.) by a distillation-extraction system was separated into hydrocarbon and oxygen-containing fractions, and the latter was further separated into nine subfractions by silica gel column chromatography. Gas chromatography (GC) and gas chromatography/mass spectrometry (GC/MS) were utilized to identify 167 volatile compounds in the fractions. The volatile compounds included 72 hydrocarbons, 31 alcohols, 23 aldehydes, 16 esters, 10 acids, 6 ketons, 3 furans, 2 phenols, 1 indole, 1 oxide, 1 sulfide, and 1 lactone. ${\beta}-Caryopyllene$, a sesquiterpene hydrocarbon, was the most abundant volatile compound identified in Korean dureup (19.53%). Dureup oil was found to possess a woody or herbaceous aroma following sensory evaluation of each fraction and individual volatile component using a GC-sniff apparatus.

• Journal of Plant Biotechnology
• /
• v.47 no.1
• /
• pp.15-25
• /
• 2020

Allium L. is one of the largest genera of the Amaryllidaceae family, with more than 920 species including many economically important species used as vegetables, spices, medicines, or ornamental plants. Currently, DNA barcoding tools are being successfully used for the molecular taxonomy of Allium. A total of 46 Allium species were collected from their native areas, and DNA was extracted using the IBRC DNA extraction kit. We used specific primers to PCR amplify matK. DNA sequences were edited and aligned for homology, and a phylogenetic tree was constructed using the neighbor-joining method. The results show thymine (38.5%) was the most frequent and guanine (13.9%) the least frequent nucleotide. The matK regions of the populations were quite highly conserved, and the amount of C and CT was calculated at 0.162 and 0.26, respectively. Analysis of the nucleotide substitution showed C-T (26.22%) and A-G (8.08%) to have the highest and lowest percent, respectively. The natural selection process dN/dS was 1.16, and the naturality test results were -1.5 for Tajima's D and -1.19 for Fu's Fs. The NJ dendrogram generated three distinct clades: the first contained Allium austroiranicum and A. ampeloprasum; the second contained A. iranshahrii, A. bisotunense, and A. cf assadi; and the third contained A. rubellum and other species. In this study, we tested the utility of the matK region as a DNA barcode for discriminating Allium. species.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.41 no.4
• /
• pp.189-195
• /
• 2018

With the advent of the digital age, production and distribution of web pages has been exploding. Internet users frequently need to extract specific information they want from these vast web pages. However, it takes lots of time and effort for users to find a specific information in many web pages. While search engines that are commonly used provide users with web pages containing the information they are looking for on the Internet, additional time and efforts are required to find the specific information among extensive search results. Therefore, it is necessary to develop algorithms that can automatically extract specific information in web pages. Every year, thousands of international conference are held all over the world. Each international conference has a website and provides general information for the conference such as the date of the event, the venue, greeting, the abstract submission deadline for a paper, the date of the registration, etc. It is not easy for researchers to catch the abstract submission deadline quickly because it is displayed in various formats from conference to conference and frequently updated. This study focuses on the issue of extracting abstract submission deadlines from International conference websites. In this study, we use three machine learning models such as SVM, decision trees, and artificial neural network to develop algorithms to extract an abstract submission deadline in an international conference website. Performances of the suggested algorithms are evaluated using 2,200 conference websites.