• Journal of Astronomy and Space Sciences
• /
• v.22 no.2
• /
• pp.147-174
• /
• 2005

To understand the physical processes that control the high-latitude lower thermospheric dynamics, we quantify the forces that are mainly responsible for maintaining the high-latitude lower thermospheric wind system with the aid of the National Center for Atmospheric Research Thermosphere-Ionosphere Electrodynamics General Circulation Model (NCAR-TIEGCM). Momentum forcing is statistically analyzed in magnetic coordinates, and its behavior with respect to the magnitude and orientation of the interplanetary magnetic field (IMF) is further examined. By subtracting the values with zero IMF from those with non-zero IMF, we obtained the difference winds and forces in the high-latitude 1ower thermosphere(<180 km). They show a simple structure over the polar cap and auroral regions for positive($B_y$ > 0.8|$\overline{B}_z$ |) or negative($B_y$ < -0.8|$\overline{B}_z$|) IMF-$\overline{B}_y$ conditions, with maximum values appearing around -80$^{\circ}$ magnetic latitude. Difference winds and difference forces for negative and positive $\overline{B}_y$ have an opposite sign and similar strength each other. For positive($B_z$ > 0.3125|$\overline{B}_y$|) or negative($B_z$ < -0.3125|$\overline{B}_y$|) IMF-$\overline{B}_z$ conditions the difference winds and difference forces are noted to subauroral latitudes. Difference winds and difference forces for negative $\overline{B}_z$ have an opposite sign to positive $\overline{B}_z$ condition. Those for negative $\overline{B}_z$ are stronger than those for positive indicating that negative $\overline{B}_z$ has a stronger effect on the winds and momentum forces than does positive $\overline{B}_z$ At higher altitudes(>125 km) the primary forces that determine the variations of tile neutral winds are the pressure gradient, Coriolis and rotational Pedersen ion drag forces; however, at various locations and times significant contributions can be made by the horizontal advection force. On the other hand, at lower altitudes(108-125 km) the pressure gradient, Coriolis and non-rotational Hall ion drag forces determine the variations of the neutral winds. At lower altitudes(<108 km) it tends to generate a geostrophic motion with the balance between the pressure gradient and Coriolis forces. The northward component of IMF By-dependent average momentum forces act more significantly on the neutral motion except for the ion drag. At lower altitudes(108-425 km) for negative IMF-$\overline{B}_y$ condition the ion drag force tends to generate a warm clockwise circulation with downward vertical motion associated with the adiabatic compress heating in the polar cap region. For positive IMF-$\overline{B}_y$ condition it tends to generate a cold anticlockwise circulation with upward vertical motion associated with the adiabatic expansion cooling in the polar cap region. For negative IMF-$\overline{B}_z$ the ion drag force tends to generate a cold anticlockwise circulation with upward vertical motion in the dawn sector. For positive IMF-$\overline{B}_z$ it tends to generate a warm clockwise circulation with downward vertical motion in the dawn sector.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.59-83
• /
• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Sijohaknonchong
• /
• v.30
• /
• pp.263-300
• /
• 2009

Adoration for nature constitutes one of the primary subjects that literature has tackled since the origin of human history. Nature expressed through a poet's subjective imagination is the internalized and reorganized nature. This study examines the view on nature enacted in Ch'o-Jung's three-verse poems (sijo) in light of the traditional views on nature implicated in the ancient three-verse poems (koshijo), which is in line with the long-established Oriental view on nature. To dignitaris(sadaebu) in the Chosun Dynasty, nature appeared as the idealistic subject for moral culture ($shims{\breve{o}}ngsuyang$), which also becomes the literary space where the purity and justice of the world view of Neo-Confucianism(Sungrihak) is contained in the form of the three-verse poem, the lyrical poetic space where the "I" is united with nature by way of "enjoying of wind and moon"($umpungnongw{\breve{o}}i$) and "living in quiet retiremen"($yuyuchaj{\breve{o}}k$), and the object for the poetical perception of the surrounding world. Ch'o-Jung' s three-verse odes are found in Reed pipe ($Ch'oj{\breve{o}}k$), Sixty Five Pieces of Three-Verse Odes (Samhaengshi-$yukshipopy{\breve{o}}n$), Autumn Fragrance ($Hyangginam{\check{u}}n-ga{\check{u}}l$), and The Words of Zelko va Tree ($N{\check{u}}tinamu{\check{u}}i-mal$). This study analyzes 212 pieces of Ch'o-Jung' s three-verse poems chosen from theses books. In Ch'o-Jung's poems, the traditional view on nature expressed in the ancient three-verse poems is rendered in such a way that metaphysical understanding of nature is indirectly transmitted through the objective correlatives found nature. Nature is no longer the object of straightforward utterance, but transformed, displaced, and removed: that way, nature gets objectified to form a complicated and multi-layered structure. In conclusion, the view on nature manifested in Ch'o-Jung's three-verse poems is based on traditional metaphysics. Second, nature is the object of lyrical nostalgia and adoration. Third, nature is imbued with the fundamental affection for parents. Fourth, nature is associated with organic life. Fifth, the nature in Ch'o-Jung's poems reveals the beauty of stillness endorsed in Lao-tse's and Chung-tze's philosophy. And last, nature is the agent for self-realization and meditation.

• Korean Journal of Environment and Ecology
• /
• v.37 no.5
• /
• pp.347-366
• /
• 2023

In the present study, the vegetation was classified using the phytosociological method and canonical-correlation analysis (CCA) was implemented to analyze correlation between community structure and environmental factors in the natural habitats of forest byproducts, especially medicinal plants, such as Cudrania tricuspidata, Sorbus commixta, and Hovenia dulcis, in 2021-2022 to provide primary ecological data to establish environmental conditions for wild vegetable cultivation. A total of 11 plots in five regions, 8 plots in three regions, and 17 plots in 5 regions were selected for the natural habitats of C. tricuspidata in southern Korea, S. commixta in high mountains, and H. dulcis in valleys of central Korea, respectively. The importance value in each community was respectively analyzed as follows, in C. tricuspidata community, the importance value of C. tricuspidata (61.10) was the highest, followed by Celtis sinensis, Pinus thunbergii, Neolitsea aciculata, Styrax japonica, Carpinus coreana, Quercus serrata, and Q. acutissima. In Sorbus commixta community, Q. mongolica (57.21) was the highest, followed by, S. commixta (42.58), Betula ermani, Tilia amurensis, A. pseudosieboldianum, A. tschonoskii var. rubripes, Cornus controversa, Magnolia sieboldii, and Taxus cuspidata. In H. dulcis community, H. dulcis (64.58) was the highest, followed by Zelkova serrata, Cornus controversa, A. mono, Q. serrata, C. cordata, and Juglans mandshurica. As the result of the analysis on DBH of the major species having the high importance value, in C. tricuspidata community, C. tricuspidata, C. sinensis, Neolitsea aciculata, and C. coreana show the density of normal distribution, so the dominant status of these species is likely to continue. In S. commixta community, S. commixta show the density of reverse J-shaped curve, so the dominant status of these species is likely to be stable, and Q. mongolica, B. ermani and T. amurensis, show the density of normal distribution, so the dominant status of these species is likely to continue. In H. dulcis community, C. cordata, and J. mandshurica show the density of reverse J-shaped curve, so the dominant status of these species is likely to be stable, and H. dulcis, Z. serrata, C. controversa and A. mono had a formality distribution, suggesting a continuous domination of these species over the other species for the time being. The results of CCA ordination analysis using 11 environmental factors and 30 communities of three taxa classified by TWINSPAN analysis revealed that the altitude showed the strongest correlation with the vegetation. C. tricuspidata community was distributed on the moderate and gentle northeastern slope at low altitude with the highest pH, C.E.C, Ca²⁺, and Mg² and various P₂O₅, whereas S. commixta community was distributed on the steep slope at high altitude with the highest O.M and T-N and lower P₂O₅, Ca²⁺, Mg²⁺, C.E.C and pH, which is the opposite tendency of the environment of C. tricuspidata community. H. dulcis community was distributed on the gentle northern slope at lower altitude with an average pH, O.M, T-N, Ca²⁺, Mg²⁺, and C.E.C, except higher P₂O₅.