• Ocean and Polar Research
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• v.36 no.1
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• pp.13-24
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• 2014

We established the first complete ice core processing method and analytical procedures for fundamental proxies, using a 40.2 m long ice core drilled on the Mt. Tsambagarav glacier in the Mongolian Altai mountains in July 2008. The whole core was first divided into two sub ice core sections and the measurements of the visual stratigraphy and electrical conductivity were performed on the surface of these sub core sections. A continuous sequence of samples was then prepared for chemical analyses (stable isotope ratios of oxygen ($^{18}O/^{16}O$) and hydrogen ($^2H/^1H$), soluble ions and trace elements). A total of 29 insoluble dust layers were identified from the measurement of visual stratigraphy. The electrical conductivity measurement (ECM) shows 11 peaks with the current more than 0.8 ${\mu}A$ Comparing the profiles of $SO_4{^{2-}}$ and $Cl^-$ concentrations to correlate with known volcanic eruptions, the first two ECM peaks appear to be linked to the eruptions (January and June 2007) of Kliuchevskoi volcano on the Kamchatka Peninsula of Russia, which supports the reliability of our ECM data. Finally, the composition of stable isotopes (${\delta}^{18}O$ and ${\delta}D$) shows a well-defined seasonal variation, suggesting that various chemical proxies may have been well preserved in the successive ice layers of Tsambagarav ice core. Our ice core processing method and analytical procedures for fundamental proxies are expected to be used for paleoclimate and paleoenvironmental studies from polar and alpine ice cores.

• Weed & Turfgrass Science
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• v.6 no.2
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• pp.86-108
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• 2017

The nationwide weed survey was conducted in arable land, paddy field, upland, orchard and pasture, over whole country of Korea during 3 years from 2013 to 2015. Also, these survey were conducted in order to determine a change of weed community and to identify a major dominant weed species, and problem weeds. Weeds of arable land were composed of 619 species belonging to 81 families. Weeds occurred in paddy field were 90 species belonging to 28 families, upland weeds were 375 species of 50 families, orchard weeds were 492 species of 63 families and pasture weeds 275 species of 52 families. Among total 81 families, the Asteraceae (15.5%) was the biggest family, followed by Poaceae (13.1%), Fabaceae (5.5%), Cyperaceae (5.2%), Polygonaceae (6.3%), respectively. And these weed species were composed of annual weeds of 209 species (33.8%), winter annual of 102 species (16.4%) and perennial weeds of 308 species (49.8%). Exotic weeds naturalized in Korea were investigated as 166 species. Weed flora in Korean cultivation area have been increased as much as 13 families and 186 species for 10 years compared to 2000~2004 weed survey result of 68 families and 433 species. The increment could be resulted from the change of weed population such as increase of the herbicide-resistant and the difference of investigation method.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.10 no.2
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• pp.71-96
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• 1977

Early development Linuparus trigonus(von Siebold) has been studied based on the samples collected monthly in Je-ju Island, Korea from February, 1975 to January, 1977. Gametogenesis, reproductive cycle, embryonic development were investigated by histological mettled, and morphological description was made on the first phyllosoma larva which reared in the laboratory. Testis is composed of two tubular duct which are symmetrical with H-shaped appearance. Outer layer of testis is of fibrous connective tissue capsule. In the lumen there is a convoluted seminiferous tubule with interstitial tissue. Ovary is a pair of symmetrical blind tubular lobes, and the midportions are connected each other. The ovary consists of a couple of ovarian sacs partitioned by two-layered connective tissue fibers. Proliferation of spermatogonia are observed all the year around on the germinal epithelium of seminiferous tubule. Partial spermatogenesis is always in progress, and the spermatozoa appear all the year around in the tubules. Nutrition of early oogonia is supplied by fibrous mesenchyme which is abundantly distributed in ovarian sacs. Oocytes grow and couplete maturation divisions in the follicle layers. They finally develop into mature ova before spawning. Reproductive cycle is classified into four successive stages; multiplication stage from September to December, growing stage from January to March, maturation division stage from April to May and mature stage from June to August. Spawning takes place from May to August with peak spawning from Into July to early August. Cleavage type is superficial. Blastopore is formed in blasto-disc region which is proliferation of blastoderm cells. Germinal layers are also derived from tile region. Mesoderm formation is originated from endodermal cells which are formed front the blasto-disc region. The endodermal cells are separated by the process of delamination from yolk sac and take part in the formation of the mid-gut. Morphological characteristics of first phyllosoma larva are different from the larvae of other Palinurid and Scyllarid species.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.123-139
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• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• Journal of Bio-Environment Control
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• v.5 no.2
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• pp.215-235
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• 1996

The thermal analysis by mathematical model simulation makes it possible to reasonably predict heating and/or cooling requirements of certain greenhouses located under various geographical and climatic environment. It is another advantages of model simulation technique to be able to make it possible to select appropriate heating system, to set up energy utilization strategy, to schedule seasonal crop pattern, as well as to determine new greenhouse ranges. In this study, the control pattern for greenhouse microclimate is categorized as cooling and heating. Dynamic model was adopted to simulate heating requirements and/or energy conservation effectiveness such as energy saving by night-time thermal curtain, estimation of Heating Degree-Hours(HDH), long time prediction of greenhouse thermal behavior, etc. On the other hand, the cooling effects of ventilation, shading, and pad ||||&|||| fan system were partly analyzed by static model. By the experimental work with small size model greenhouse of 1.2m$\times$2.4m, it was found that cooling the greenhouse by spraying cold water directly on greenhouse cover surface or by recirculating cold water through heat exchangers would be effective in greenhouse summer cooling. The mathematical model developed for greenhouse model simulation is highly applicable because it can reflects various climatic factors like temperature, humidity, beam and diffuse solar radiation, wind velocity, etc. This model was closely verified by various weather data obtained through long period greenhouse experiment. Most of the materials relating with greenhouse heating or cooling components were obtained from model greenhouse simulated mathematically by using typical year(1987) data of Jinju Gyeongnam. But some of the materials relating with greenhouse cooling was obtained by performing model experiments which include analyzing cooling effect of water sprayed directly on greenhouse roof surface. The results are summarized as follows : 1. The heating requirements of model greenhouse were highly related with the minimum temperature set for given greenhouse. The setting temperature at night-time is much more influential on heating energy requirement than that at day-time. Therefore It is highly recommended that night- time setting temperature should be carefully determined and controlled. 2. The HDH data obtained by conventional method were estimated on the basis of considerably long term average weather temperature together with the standard base temperature(usually 18.3$^{\circ}C$). This kind of data can merely be used as a relative comparison criteria about heating load, but is not applicable in the calculation of greenhouse heating requirements because of the limited consideration of climatic factors and inappropriate base temperature. By comparing the HDM data with the results of simulation, it is found that the heating system design by HDH data will probably overshoot the actual heating requirement. 3. The energy saving effect of night-time thermal curtain as well as estimated heating requirement is found to be sensitively related with weather condition: Thermal curtain adopted for simulation showed high effectiveness in energy saving which amounts to more than 50% of annual heating requirement. 4. The ventilation performances doting warm seasons are mainly influenced by air exchange rate even though there are some variations depending on greenhouse structural difference, weather and cropping conditions. For air exchanges above 1 volume per minute, the reduction rate of temperature rise on both types of considered greenhouse becomes modest with the additional increase of ventilation capacity. Therefore the desirable ventilation capacity is assumed to be 1 air change per minute, which is the recommended ventilation rate in common greenhouse. 5. In glass covered greenhouse with full production, under clear weather of 50% RH, and continuous 1 air change per minute, the temperature drop in 50% shaded greenhouse and pad ＆ fan systemed greenhouse is 2.6$^{\circ}C$ and.6.1$^{\circ}C$ respectively. The temperature in control greenhouse under continuous air change at this time was 36.6$^{\circ}C$ which was 5.3$^{\circ}C$ above ambient temperature. As a result the greenhouse temperature can be maintained 3$^{\circ}C$ below ambient temperature. But when RH is 80%, it was impossible to drop greenhouse temperature below ambient temperature because possible temperature reduction by pad ||||&|||| fan system at this time is not more than 2.4$^{\circ}C$. 6. During 3 months of hot summer season if the greenhouse is assumed to be cooled only when greenhouse temperature rise above 27$^{\circ}C$, the relationship between RH of ambient air and greenhouse temperature drop($\Delta$T) was formulated as follows : $\Delta$T= -0.077RH＋7.7 7. Time dependent cooling effects performed by operation of each or combination of ventilation, 50% shading, pad ＆ fan of 80% efficiency, were continuously predicted for one typical summer day long. When the greenhouse was cooled only by 1 air change per minute, greenhouse air temperature was 5$^{\circ}C$ above outdoor temperature. Either method alone can not drop greenhouse air temperature below outdoor temperature even under the fully cropped situations. But when both systems were operated together, greenhouse air temperature can be controlled to about 2.0-2.3$^{\circ}C$ below ambient temperature. 8. When the cool water of 6.5-8.5$^{\circ}C$ was sprayed on greenhouse roof surface with the water flow rate of 1.3 liter/min per unit greenhouse floor area, greenhouse air temperature could be dropped down to 16.5-18.$0^{\circ}C$, whlch is about 1$0^{\circ}C$ below the ambient temperature of 26.5-28.$0^{\circ}C$ at that time. The most important thing in cooling greenhouse air effectively with water spray may be obtaining plenty of cool water source like ground water itself or cold water produced by heat-pump. Future work is focused on not only analyzing the feasibility of heat pump operation but also finding the relationships between greenhouse air temperature(T$_{g}$ ), spraying water temperature(T$_{w}$ ), water flow rate(Q), and ambient temperature(T$_{o}$).