• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.38 no.1
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• pp.115-131
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• 2020

Through literature research and field investigation, this paper attempts to study the distribution, morphology and the typification of the visual and perceptual stone inscription in Wuyi-Gugok of China. The results are as follows: First, there are 350 stone inscriptions in total from the 1^st Gok to 9^th Gok in Wuyi-Gugok. Second, according to the analysis of the stone inscription distribution, 74(21.2%) stone inscriptions in the 5^th Gok, 67(19.2%) in the 6^th Gok, 65(18.6%) in the 1^st Gok, 60(17.2%) in the 2^nd Gok and 53(15.2%) in the 4^th Gok are confirmed. The above five Goks contain 319(91.1%) stone inscriptions, so they have rich cultural landscape. Third, according to the survey, the number of the stone inscriptions existed in the Sugwangseok of the 1^st Gok are 41(22.6%), in the Homagan of Cheonyubong of the 6^th Gok are 29(8.3%), in the Jesiam of the 4^th Gok are 23(6.6%), in the Nyeongam of the 2^nd Gok are 22(6.3%), in the Hyangseongam of the 6^th Gok are 21(6%), in the Unwa of the 5^th Gok are 19(5.4%), in the Bokhoam of the 5^th Gok are 18(5.1%), in the Eunbyeongbong of the 5^th Gok are 17(4.9%), in the Daejangbong of the 4^th Gok are 14(4%), in the Daewangbong of the 1^st Gok and the Geumgokam of the 4^th Gok are 12(3.4%). Thus, a total of 228 (65.1%) stone inscriptions are concentrated in these 11 sites, which represent the popularity and cultural value of these rocks. Fourth, the stone inscription of Wuyi-Gugok, praising the landform and topographical geological landscape of Mount Wuyi, mainly describe the scenic name of each Gok related to Zhu Xi's Gugok culture, appreciate Zhu Xi's tracks and the stone inscription in the sacred land of Neo-Confucianism culture, and also record the Confucian edification of mencius thoughts, Muigun(武夷君) and the myths and legends related to the site names of Wuyi mountain, which can remind people of the worldview of the celestial paradise where the gods live and the fairyland of the land of peach blossoms. In addition, it indicates that the historical and cultural landscape, which is full of colorful history and myths and legends, including allusions related to Confucian, buddhist and Taoist celebrities and the ancestor ancient things related to traditional culture of China is very diverse. Fifth, the results of the classification, based on the content of the stone inscription in Wuyi-Gugok, are classified as the scenery name inscription, the praise scene inscription, the recording travel inscription, the recording event inscription, the philosophy inscription, the expressing emotion inscription, the religion inscription, the inscription for auspiciousness, the slogan and expressing ambition inscription and the official document notice inscription, among which there are 102(29.1%) praise scene inscriptions, 93(26.6%) scenery name inscriptions and 61(17.4%) recording travel inscriptions. The stone inscriptions of Wuyi-Gugok have the characteristics of the special emphasis on scenery names, landscape praise and commemorative tours. Sixth, the analysis of the intertext between the 「Figure of Wuyi-Gugok」 and Wuyi-Gugok rock letters, in the study found that the method of propagation between media was mostly the method of propagation of quotations and maintained intermedia through extension, repetition, extension, and compression.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Korean journal of applied entomology
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• v.13 no.3 s.20
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• pp.105-133
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• 1974

The present study is an attempt to solve the basic problems involved in the control of the Sclerotium disease. The biologic stranis of Sclerotium rolfsii Sacc., pathogen of Sclerotium disease of Magnolia kobus, were differentiated, and the effects of vitamins, various nitrogen and carbon sources on its mycelial growth and sclerotial production have been investigated. In addition the relationship between the cultural filtrate of Penicillium sp. and the growth of Sclerotium rolfsii, the tolerance of its mycelia or sclerotia to moist heat or drought and to Benlate (methyl-(butylcarbamoy 1)-2-benzimidazole carbamate), Tachigaren (3-hydroxy-5-methylisoxazole) and other chemicals were also clarified. The results are summarizee as follows: 1. There were two biologic strains, Type-l and Type-2 among isolates. They differed from each other in the mode of growth and colonial appearance on the media, aversion phenomenon and in their pathogenicity. These two types had similar pathogenicity to the Magnolia kobus and Robinia pseudoacasia, but behaved somewhat differently to the soybaen and cucumber, the Type-l being more virulent. 2. Except potassium nitrite, sodium nitrite and glycine, all of the 12 nitrogen sources tested were utilized for the mycelial growth and sclerotial production of this fungus when 10r/l of thiamine hydrochloride was added in the culture solution. Considering the forms of nitrogen, ammonium nitrogen was more available than nitrate nitrogen for the growth of mycelia, but nitrate nitrogen was better for sclerotia formation. Organic nitrogen showed different availabilities according to compounds used. While nitrite nitrogen was unavailable for both mycelial growth and sclerotial formation whether thiamine hydrochlioride was added or not. 3. Seven kinds of carbon sources examined were not effective in general, as long as thiamine hydrochloride was not added. When thiamine hydrochloride was added, glucose and saccharose exhibited mycelial growth, while rnaltose and soluble starch gave lesser, and xylose, lactose, and glycine showed no effect at all,. In the sclerotial production, all the tested carbon sources, except lactose, were effective, and glucose, maltose, saccharose, and soluble starch gave better results. 4. At the same level of nitrogen, the amount of mycelial growth increased as more carbon Sources were applied but decreased with the increase of nitrogen above 0.5g/1. The amount of sclerotial production decreased wi th the increase of carbon sources. 5. Sclerotium rolfsii was thiamine-defficient and required thiamine 20r/l for maximun growth of mycelia. At a higher concentration of more than 20r/l, however, mycelial growth decreased as the concentration increased, and was inhibited at l50r/l to such a degree of thiamine-free. 6. The effect of the nitrogen sources on the mycelial growth under the presence of thiamine were recognized in the decreasing order of $NH_4NO_3,\;(NH_4)_2SO_4,\;asparagine,\;KNO_3$, and their effects on the sclerotial production in the order of $KNO_3,\;NH_4NO_3,\;asparagine,\;(NH_4)_2SO_4$. The optimum concentration of thiamine was about 12r/l in $KNO_3$ and about 16r/l in asparagine for the growth of mycelia; about 8r/l in $KNO_3$ and $NH_4NO_3$, and 16r/l in asparagine for the production of sclerotia. 7. After the fungus started to grow, the pH value of cultural filtrate rapidly dropped to about 3.5. Hereafter, its rate slowed down as the growth amount increased and did not depreciated below pH2.2. 8. The role of thiamine in the growth of the organism was vital. If thiamine was not added, the combination of biotin, pyridoxine, and inositol did not show any effects on the growth of the organism at all. Equivalent or better mycelial growth was recognized in the combination of thiamine+pyridoxine, thiamine+inositol, thiamine+biotin+pyridoxine, and thiamine+biotin+pyridoxine+inositol, as compared with thiamine alone. In the combinations of thiamine+biotin and thiamine+biotin+inositol, mycelial growth was inhibited. Sclerotial production in dry weight increased more in these combinations than in the medium of thiamine alone. 9. The stimulating effects of the Penicillium cultural filtrate on the mycelial growth was noticed. It increased linearly with the increase of filtrate concentration up to 6-15 ml/50ml basal medium solution. 10. $NH_4NO_3$. as a nitrogen source for mycelial growth was more effective than asparasine regardless of the concentration of cultural filtrate. 11. In the series of fractionations of the cultural filtrate, mycelial growth occured in unvolatile, ether insoluble cation-adsorbed or anion-unadsorbed substance fractions among the fractions of volatile, unvolatile acids, ether soluble organic acids, ether insoluble, cation-adsorbed, cation-unadsorbed, anion-adsorbed and anion-unadsorbed. and anion-un-adsorbed substance tested. Sclerotia were produced only in cation-adsorbed fraction. 12. According to the above results, it was assumed that substances for the mycelial growth and sclerotial formation and inhibitor of sclerotial formation were include::! in cultural filtrate and they were quite different from each other. I was further assumed that the former two substances are un volatile, ether insotuble, and adsorbed to cation-exchange resin, but not adsorbed to anion, whereas the latter is unvolatile, ether insoluble, and not adsorbed to cation or anion-exchange resin. 13. Seven amino acids-aspartic acid, cystine, glysine, histidine, Iycine, tyrosine and dinitroaniline-were detected in the fractions adsorbed to cation-exchange resin by applying the paper chromatography improved with DNP-amino acids. 14. Mycelial growth or sclerotial production was not stimulated significantly by separate or combined application of glutamic acid, aspartic acid, cystine, histidine, and glysine. Tyrosine gave the stimulating effect when applied .alone and when combined with other amino acids in some cases. 15. The tolerance of sclerotia to moist heat varied according to their water content, that was, the dried sclerotia are more tolerant than wet ones. The sclerotia harvested directly from the media, both Type-1 and Type-2, lost viability within 5 minutes at $52^{\circ}C$. Sclerotia dried for 155 days at$26^{\circ}C$ had more tolerance: sclerotia of Type-l were killed in 15 mins. at $52^{\circ}C$ and in 5 mins. at $57^{\circ}C$, and sclerotia of Type-2 were killed in 10 mins. both at $52^{\circ}C$ or $57^{\circ}C$. 16. Cultural sclerotia of both strains maintained good germinability for 132 days at$26^{\circ}C$. Natural sclerotia of them stored for 283 days under air dry condition still had good germinability, even for 443 days: type-l and type-2 maintained $20\%$ and $26.9\%$ germinability, respectively. 17. The tolerance to low temperature increased in the order of mycelia, felts and sclerotia. Mycelia completely lost the ability to grow within 1 week at $7-8^{\circ}C$> below zero, while mycelial felts still maintained the viability after .3 weeks at $7-20^{\circ}C$ below zero, and sclerotia were even more tolerant. 18. Sclerotia of type-l and type-2 were killed when dipped into the $0.05\%$ solution of mercury chloride for 180 mins. and 240 mins. respectively: and in the $0.1\%$ solution, Type-l for 60 mins. and Type-2 for 30 mins. In the $0.125\%$ uspulun solution, Type-l sclerotia were killed in 180 mins., and those of Type-2 were killed for 90 mins. in the$0.125\%$solution. Dipping into the $5\%$ copper sulphate solution or $0.2\%$ solution of Ceresan lime or Mercron for 240 mins. failed to kill sclerotia of either Type-l or Type-2. 19. Inhibitory effect on mycelial growth of Benlate or Tachi-garen in the liquid culture increased as the concentration increased. 6 days after application, obvious inhibitory effects were found in all treatments except Benlate 0.5ppm; but after 12 days, distingushed diflerences were shown among the different concentrations. As compared with the control, mycelial growth was inhibited by $66\%$ at 0.5ppm and by $92\%$ at 2.0ppm of Benlate, and by$54\%$ at 1ppm and about $77\%$ at 1.5ppm or 2.0ppm of Tachigaren. The mycelial growth was inhibited completely at 500ppm of both fungicides, and the formation of sclerotia was checked at 1,000ppm of Benlate ant at 500ppm or 1,000ppm of Tachigaren. 20. Consumptions of glucose or ammonium nitrogen in the culture solution usually increased with the increment of mycelial growth, but when Benlate or Tachigaren were applied, consumptions of glucose or ammonium nitrogen were inhibited with the increment of concentration of the fungicides. At the low concentrations of Benlate (0.5ppm or 1ppm), however, ammonium nitrogen consumption was higher than that of the ontrol. 21. The amount of mycelia produced by consuming 1mg of glucose or ammonium nitrogen in the culture solution was lowered markedly by Benlate or Tachigaren. Such effects were the severest on the third day after their treatment in all concentrations, and then gradually recovered with the progress of time. 22. In the sand culture, mycelial growth was not inhibited. It was indirectly estimated by the amount of $CO_2$ evolved at any concentrations, except in the Tachigaren 100mg/g sand in which mycelial growth was inhibited significantly. Sclerotial production was completely depressed in the 10mg/g sand of Benlate or Tachigaren. 23. There was no visible inhibitory effect on the germination of sclerotia when the sclerotia were dipped in the solution 0.1, 1.0, 100, 1.000ppm of Benlate or Tachigaren for 10 minutes or even 20 minutes.