• Korean Journal of Soil Science and Fertilizer
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• v.42 no.5
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• pp.371-378
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• 2009

This study was carried out to investigate the improvement effect of soil fertility by incorporation of GMC(green manure crops) at rice cropping after cultivation GMC such as the barley for alternative rye in paddy field over the past two years(2006~2007). Plots, which consisted of incorporation time of GMC as rye; heading stage, barley; heading stage, heading stage of rye and 10days after heading stage were divided by amount of applied rates; standard fertilizer fertilization, diagnosis fertilization and non-fertilization. we investigated change of soil physico-chemical properties, degree of decomposition on GMC in soil, growth and yield potential. The obtained results were summarized as follows. The fresh weight of GMC at incorporation time on heading stage of rye, heading stage and 10days after heading stage of barley were $2,715,\;2,352,\;2,867kg\;10a^{-1}$ respectively. Content of total nitrogen at three incorporation times was 1.31, 1.46, 1.38% and the C/N ratio were 33.4, 28.7, and 34.6, respectively. Some soil physical properties, such as soil hardness and bulk density tended to decrease with incorporation of GMC, while surface soil depth and porosity were increased. Some soil chemical properties, such as content of exchangeable cations and cation exchangeable capasity(CEC) were increased with incorporation of GMC compared with before experiment. Rice yields was increased 3~9% in diagnosis application plots on application of barley compared with control($559kg\;10a^{-1}$) and incorporation of barley caused to improve perfect kernel ratio 73.6~78.7% in appearance characteristics of brown rice compared with cotrol(73.0%). It was found that incorporation with 10days after heading stage of barley was more effective to reduce chemical nitrogen fertilizer and to improve soil fertility.

• Journal of Korea Port Economic Association
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• v.37 no.2
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• pp.73-89
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• 2021

The handysize bulk carriers are capable of transporting a variety of cargo that cannot be transported by mid-large size ship, and the spot chartering market is active, and it is a market that is independent of mid-large size market, and is more risky due to market conditions and charterage variability. In this study, Granger causality test, the Impulse Response Function(IRF) and Forecast Error Variance Decomposition(FEVD) were performed using monthly time series data. As a result of Granger causality test, coal price for coke making, Japan steel plate commodity price, hot rolled steel sheet price, fleet volume and bunker price have causality to Baltic Handysize Index(BHSI) and charterage. After confirming the appropriate lag and stability of the Vector Autoregressive model(VAR), IRF and FEVD were analyzed. As a result of IRF, the three variables of coal price for coke making, hot rolled steel sheet price and bunker price were found to have significant at both upper and lower limit of the confidence interval. Among them, the impulse of hot rolled steel sheet price was found to have the most significant effect. As a result of FEVD, the explanatory power that affects BHSI and charterage is the same in the order of hot rolled steel sheet price, coal price for coke making, bunker price, Japan steel plate price, and fleet volume. It was found that it gradually increased, affecting BHSI by 30% and charterage by 26%. In order to differentiate from previous studies and to find out the effect of short term lag, analysis was performed using monthly price data of major cargoes for Handysize bulk carriers, and meaningful results were derived that can predict monthly market conditions. This study can be helpful in predicting the short term market conditions for shipping companies that operate Handysize bulk carriers and concerned parties in the handysize chartering market.

• Korean Journal of Heritage: History & Science
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• v.47 no.4
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• pp.74-91
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• 2014

The stone statues in the site of Mireuksaji Temple(Iksan, South Korea) were created after the stone pagoda was built in 639. They, transitional statues between animal sculptures and human shaped statues made from the late Goryeo dynasty until the early Joseon dynasty, were set up at the four corners of the stone pagoda by way of guardians. In the case of three statues, their surfaces were denudated and their iconographies have been indiscernible. However, the one in the southwest clearly shows its iconography. It is inferior in properties to the other three statues in the northwest, the northeast and the southeast respectively, but on the other hand its iconography has been well maintained. The reason is related to exposure to harmful environments; specifically, the retaining wall, built around the stone pagoda in the 17th century, had the southwest statue inside and could naturally worked as a buffer against harmful environments. As a result, for about 400 years there has been difference in weathering conditions between the three stone statues and the southwest statue, which brought denudation, the consequent indiscernibleness of iconography and biological invasion to the three statues, notwithstanding superior properties(northwest statue:$176kgf/cm^2$, northeast statue:$109kgf/cm^2$, southeast statue:$273kgf/cm^2$). In contrast, the southwest statue significantly shows its iconography with black contaminants and granule decomposition, despite inferior properties($133kgf/cm^2$). Defenseless exposure to external environment is not recommended for the stone statues, because it is hard to preserve the extant iconography. Herein lies the application of the data on microclimate around Mireuksaji Temple. As regards the weathering zone in which the stone statues are located, Conservation increases in acidity and frequency as years go by, Hereat, in the approach to the Conservation of stone statues, the first consideration needs to be morphological historicity rather than geographical location.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.2
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• pp.152-158
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• 2019

This study was conducted to develop and prove the effects of an agronomical pest control measure on ginseng cultivated by direct seeding in paddy field, and the results obtained are as follows. Decomposition of ginseng in field during overwintering was due to gray mold rot caused by Botrytis cinerea, which occurred in October or November of 2016 and intensified in February and March the following year. The occurrence rate of gray mold rot based on shading materials was 6.5%, 16.8%, and 29.5% with light-proof paper, PE shade net, and rice straw shade, respectively. The initial infection occurred in the leaves prior to wintering and secondary infection occurred in the stems after wintering. The rate of screrotium formation by gray mold in the above-ground parts of ginseng tended to increase: 26.6% on October 20, 33.7% in November 20, and 41.8% on December 20. The force needed to remove the leaves and stems from withered ginseng was 0.2, 0.94, 2.5, and 5 kg for 1-, 2-, 3-, and 4- and 5-year holds; the force required was 1 kg after wintering, making it relatively easy to remove. The disease incidence rate after the removal of leaves and stems was 2.5%, 1.2%, and 2.2% in 4-, 5-, and 6-year-old plants, respectively, and a disease high incidence rate of 8.8%, 13.0%, and 18.2%, respectively, was seen when the leaves and stems were not removed. In both transplanting and direct seeding, the miss-planted rate decreased and the germination rate increased when shading material was removed and the surface of ridge was covered with soil or vinyl.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.19-41
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• 2019

According to the rapidly increasing demand for text data analysis, research and investment in text mining are being actively conducted not only in academia but also in various industries. Text mining is generally conducted in two steps. In the first step, the text of the collected document is tokenized and structured to convert the original document into a computer-readable form. In the second step, tasks such as document classification, clustering, and topic modeling are conducted according to the purpose of analysis. Until recently, text mining-related studies have been focused on the application of the second steps, such as document classification, clustering, and topic modeling. However, with the discovery that the text structuring process substantially influences the quality of the analysis results, various embedding methods have actively been studied to improve the quality of analysis results by preserving the meaning of words and documents in the process of representing text data as vectors. Unlike structured data, which can be directly applied to a variety of operations and traditional analysis techniques, Unstructured text should be preceded by a structuring task that transforms the original document into a form that the computer can understand before analysis. It is called "Embedding" that arbitrary objects are mapped to a specific dimension space while maintaining algebraic properties for structuring the text data. Recently, attempts have been made to embed not only words but also sentences, paragraphs, and entire documents in various aspects. Particularly, with the demand for analysis of document embedding increases rapidly, many algorithms have been developed to support it. Among them, doc2Vec which extends word2Vec and embeds each document into one vector is most widely used. However, the traditional document embedding method represented by doc2Vec generates a vector for each document using the whole corpus included in the document. This causes a limit that the document vector is affected by not only core words but also miscellaneous words. Additionally, the traditional document embedding schemes usually map each document into a single corresponding vector. Therefore, it is difficult to represent a complex document with multiple subjects into a single vector accurately using the traditional approach. In this paper, we propose a new multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. This study targets documents that explicitly separate body content and keywords. In the case of a document without keywords, this method can be applied after extract keywords through various analysis methods. However, since this is not the core subject of the proposed method, we introduce the process of applying the proposed method to documents that predefine keywords in the text. The proposed method consists of (1) Parsing, (2) Word Embedding, (3) Keyword Vector Extraction, (4) Keyword Clustering, and (5) Multiple-Vector Generation. The specific process is as follows. all text in a document is tokenized and each token is represented as a vector having N-dimensional real value through word embedding. After that, to overcome the limitations of the traditional document embedding method that is affected by not only the core word but also the miscellaneous words, vectors corresponding to the keywords of each document are extracted and make up sets of keyword vector for each document. Next, clustering is conducted on a set of keywords for each document to identify multiple subjects included in the document. Finally, a Multi-vector is generated from vectors of keywords constituting each cluster. The experiments for 3.147 academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the proposed multi-vector based method, we ascertained that complex documents can be vectorized more accurately by eliminating the interference among subjects.

• Korean Journal of Heritage: History & Science
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• v.37
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• pp.327-348
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• 2004

In this study, after analyzing several local climate characteristics of South Korea, I validated distribution, invasion, foraging, underground activities, attack season as ecological characteristics and also temperature, relative humidity, and tree species as preference characteristics of Korean termites (Reticulitermes speratus kyushuensis Morimoto). Especially, southern part of the Korean peninsula is a suitable area for inhabitation and motion of termites holding same ecological characteristic like R. speratus kyushuensis. Busan is a neighboring district at field distribution north limiting temperature of Coptotermes formosanus Shiraki and Chuncheon is a passing area through the Korean Peninsula of field distribution north limiting temperature of Reticulitermes speratus Kolbe. The termite attack of wood devices was about 34.5% for 3 years in the forest of Jongmyo. Although the attack rate of termite increased each year, the detection rate decreased and the missing rate was high by degrees. I confirmed a foraging habits which is a part of termite colony was a role of continuous decomposition and another was a role of new food hunt as experimental results. The foraging termites were found under ground at Jongmyo in Seoul from April to November in the 2001 and the most active period was on July and August. The termite invasion rate of bait station increased in every monitoring. Through the increasing attack rate of bait station during 2nd monitoring (November, 2000) and 3rd monitoring(March, 2001), I confirmed that termites moved into the deep underground in winter, and were working continuously to forage. R. speratus kyushuensis inhabiting at the Korean Peninsula is a species which has food consumption rate with higher temperature. The termite revealed the greatest amount of food(filter paper) at $30^{\circ}C$(90% RH), but showed increasing death rate at over $32^{\circ}C$. Also, survival rate of this termite was 97% at 84% RH($30^{\circ}C$), but killed 100% at 52% RH($30^{\circ}C$) and 70% RH($30^{\circ}C$). For wood feeding, this was observed the preference in a pine tree(Pinus densiflora) above all others. Survival of termites was high(87%) at a pine tree, but low(13.5%) at a paulownia tree(Paulownia coreana). In this study, I presented the biological characteristic of termite(R. speratus kyushuensis Morimoto) and confirmed the deterioration degree of termite on wooden cultural heritage in Korea. Depending on climate and soil temperature, each area in the southern part of the Korea Peninsula, has some different active period and different distribution of R. speratus kyushensis. With these results, I expect that this report helps to prepare the integrated pest management(IPM) of the termite on wooden cultural heritage in Korea, and it may help to reduce the economical loss from termite damage in Korea.

• Korean Journal of Environment and Ecology
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• v.33 no.4
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• pp.453-461
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• 2019

This study was conducted to monitor the soil characteristics and growth of Pinus densiflora and to determine the effect of soil characteristics on growth rate five years after an ecological restoration project in Baekdudaegan ridge including Ihwaryeong, Yuksimnyeong, and Beoljae sites. The ecological restoration project was executed with the forest of P. densiflora in 2012-2013. In April 2018, we collected soil samples from each site and measured the height and the diameter at breach height (DBH) of P. densiflora. Although there was no significant change of soil pH compared to the early stage of restoration (one year after the project), it was high in Ihwaryeong, and Beoljae with values of 7.7 and 6.4, respectively. Also, the organic matter decreased by 70-80%, and the available phosphorus (P) was unchanged in three restoration sites. The decreased organic matter can be attributed to restriction of inflow and thus decomposition of litter in the early stage after the restoration. The tree height growth rate ($m\;yr^{-1}$) of P. densiflora in Yuksimnyeong was the highest at 1.02, followed by Beolja at 0.75 and Ihwaryeong at 0.17. The height growth rate showed negative relationships with soil pH and cations, including Na and Ca concentrations and a positive relationship with available phosphate. The low growth rate in the Ihwaryeong site, in particular, might result from the poor nutrient availability due to high soil pH and the decrease in water absorption into the root due to high Na and Ca concentrations. The substantial reduction of organic matter after five years indicates that the need for soil improvement using chemical fertilizer and biochar.

• Journal of Korean Society of Forest Science
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• v.108 no.1
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• pp.40-49
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• 2019

Coarse woody debris (CWD), which is a component of the forest ecosystem, plays a major role in forest energy flow and nutrient cycling. In particular, CWD isolates carbon for a long time and is important in terms of slowing the rate of carbon released from the forest to the atmosphere. Therefore, this study measured the physiochemical characteristics and respiration rate ($R_{CWD}$) of CWD for Larix kaempferi and Pinus rigida in temperate forests in central Korea. In summer 2018, CWD samples from decay class (DC) I to IV were collected in the 14 forest stands. $R_{CWD}$ and physiochemical characteristics were measured using a closed chamber with a portable carbon dioxide sensor in the laboratory. In both species, as CWD decomposition progressed, the density ($D_{CWD}$) of the CWD decreased while the water content ($WC_{CWD}$) increased. Furthermore, the carbon concentrations did not significantly differ by DC, whereas the nitrogen concentration significantly increased and the C/N ratio decreased. The respiration rate of L. kaempferi CWD increased significantly up to DC IV, but for P. rigida it increased to DC II and then unchanged for DC II-IV. Accordingly, except for carbon concentration, all the measured characteristics showed a significant correlation with $R_{CWD}$. Multiple linear regression showed that $WC_{CWD}$ was the most influential factor on $R_{CWD}$. $WC_{CWD}$ affects $R_{CWD}$ by increasing microbial activity and is closely related to complex environmental factors such as temperature and light conditions. Therefore, it is necessary to study their correlation and estimate the time-series pattern of CWD moisture.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of Life Science
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• v.31 no.1
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• pp.90-99
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• 2021

In this study, to reduce the production cost of poly(3-hydroxyalkanoates) (PHA), optimal cell growth and PHA biosynthesis conditions of the isolated strain Pseudomonas sp. EML8 were established using waste frying oil (WFO) as the cheap carbon source. Gas chromatography (GC) and GC mass spectrometry analysis of the medium-chain-length PHA (mcl-PHAWFO) obtained by Pseudomonas sp. EML8 of WFO indicated that it was composed of 7.28 mol% 3-hydrxoyhexanoate, 39.04 mol% 3-hydroxyoctanoate, 37.11 mol% 3-hydroxydecanoate, and 16.58 mol% 3-hydroxvdodecanoate monomers. When Pseudomonas sp. EML8 were culture in flask, the maximum dry cell weight (DCW) and the mcl-PHAWFO yield (g/l) were showed under WFO (20 g/l), (NH4)2SO4 (0.5 g/l), pH 7, and 25℃ culture conditions. Based on this, the highest DCW, mcl-PHAWFO content, and mcl-PHAWFO yield from 3-l-jar fermentation was obtained after 48 hr. Similar results were obtained using 20 g/l of fresh frying oil (FFO) as a control carbon source. In this case, the DCW, the mcl-PHAFFO content, and the mcl-PHAFFO yields were 2.7 g/l, 62 wt%, and 1.6 g/l, respectively. Gel permeation chromatography analysis confirmed the average molecular weight of the mcl-PHAWFO and mcl-PHAFFO to be between 165-175 kDa. Thermogravimetric analysis showed decomposition temperature values of 260℃ and 274.7℃ for mcl-PHAWFO and mcl-PHAFFO, respectively. In conclusion, Pseudomonas sp. EML8 and WFO could be suggested as a new candidate and substrate for the industrial production of PHA.