• Korean Journal of Heritage: History & Science
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• v.55 no.1
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• pp.63-84
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• 2022

One of the ancient nomadic cultures, the Saka is generally regarded as an important intermediary in the ancient Eurasian cultural network. This study is the reinterpretation of the excavations conducted on the Katartobe tombs site of the Saka culture through a joint three-year-long project by the National Research Institute of Cultural Heritage in Korea in collaboration with the Cultural Heritage Research Institute under the National Museum of the Republic of Kazakhstan. The main discussion of the study deals with the burial rituals performed by the community who built the Katartobe tombs by the comparison and review of the various researches on the Saka tombs based on the archaeological artifacts discovered during excavation. The research has shown that the Saka tribes maintained the tradition of burying domesticated animals, such as horses, with its owner and performed burial rituals which often involved the use of fire. The archaeological remains of the Saka also show that the burial rituals like these formed the key aspect of their cultural heritage. The archaeological discoveries also show that the Saka mourners built wooden cists under a single mound when they needed to bury multiple corpses at once and sustained the practice of excarnation when burying the bodies of those who died in the different periods of time. Some burials included a tomb passage which was used not only for carrying the deceased but also for a separate burial ritual. The main discussion of this study also deals with the remnants of bones of animals buried with their deceased owners in the same kurgan, as well as the animal species and their locations in the kurgan, resulting in the discovery of diverse meanings connected with them. The pottery buried in the tombs were largely ceremonial offering vessels, just like others excavated at nearby Saka tombs and located around the buried corpse's head facing toward the west. The excavation of the tombs also shows that two vessels were arranged at the corners of the coffin where the feet are located, revealing the characteristic features of the burial practices maintained by the tribe who built the Katartobe tombs. It may be too early to come to a definite conclusion on the burial practices of the Saka due to the relative lack of research on the kurgans across Central Asia. Excavations so far show that the kurgans clustered in a single archaeological site tend to display differences as well as uniformities. In conclusion, the ancient Central Asian tombs need more detailed surveys and researches to be able to make strides in an effort to restore the cultural heritage of the ancient Central Asian tribes who played a crucial role in the Eurasian cultural landscape.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.1
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• pp.113-129
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• 2020

In this study we examined the topic analysis and correlation analysis by text mining to extract meaningful information or rules from the self introduction letter of freshman at Korea National College of Agriculture and Fisheries in 2020. The analysis items are described in items related to 'academic' and 'in-school activities' during high school. In the text mining results, the keywords of 'academic' items were 'study', 'thought', 'effort', 'problem', 'friend', and the key words of 'in-school activities' were 'activity', 'thought', 'friend', 'club', 'school' in order. As a result of the correlation analysis, the key words of 'thinking', 'studying', 'effort', and 'time' played a central role in the 'academic' item. And the key words of 'in-school activities' were 'thought', 'activity', 'school', 'time', and 'friend'. The results of frequency analysis and association analysis were visualized with word cloud and correlation graphs to make it easier to understand all the results. In the next study, TF-IDF(Term Frequency-Inverse Document Frequency) analysis using 'frequency of keywords' and 'reverse of document frequency' will be performed as a method of extracting key words from a large amount of documents.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1691-1707
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• 2022

Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

• Korean Journal of Environmental Biology
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• v.40 no.4
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• pp.413-422
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• 2022

Barnea manilensis is a bivalve which bores soft rocks, such as, limestone or mudstone in the low intertidal zone. They make burrows which have narrow entrances and wide interiors and live in these burrows for a lifetime. In this study, the morphology and the microstructure of the valve of rock-boring clam B. manilensis were observed using a stereoscopic microscope and FE-SEM, respectively. The chemical composition of specific part of the valve was assessed by energy dispersive X-ray spectroscopy (EDS) analysis. 3D modeling and structural dynamic analysis were used to simulate the boring behavior of B. manilensis. Microscopy results showed that the valve was asymmetric with plow-like spikes which were located on the anterior surface of the valve and were distributed in a specific direction. The anterior parts of the valve were thicker than the posterior parts. EDS results indicated that the valve mainly consisted of calcium carbonate, while metal elements, such as, Al, Si, Mn, Fe, and Mg were detected on the outer surface of the anterior spikes. It was assumed that the metal elements increased the strength of the valve, thus helping the B. manilensis to bore sediment. The simulation showed that spikes located on the anterior part of the valve received a load at all angles. It was suggested that the anterior part of the shell received the load while drilling rocks. The boring mechanism using the amorphous valve of B. manilensis is expected to be used as basic data to devise an efficient drilling mechanism.

• Journal of fish pathology
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• v.36 no.2
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• pp.263-275
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• 2023

Chum salmon (Oncorhynchus keta) is a species which returns to Korea for spawning and was produced as seed production at the Fisheries Resources Agency located in Uljin-gun, Gyeongsangbuk-do to preserve the species. However, farmed chum salmon showed symptoms of bacterial infection. Therefore, in this study, bacteria were isolated to identify the causative agent from chum salmon in October 2021. The isolated bacteria were identified based on the sequences of 16S rDNA, rpoD (RNA polymerase sigma factor σ⁷⁰), and vapA (A-layer) genes. Also, salinity-growth curve, biochemical characterization, antibiotic susceptibility test, and pathogenicity analysis were performed in four strains. As a result, four isolated strains were identified as Aeromonas salmonicida subsp. salmonicida. Additionally, the bacterial strains showed a decrease in growth as the salt concentration increased in the medium. All of the isolated strains exhibited γ-hemolysis, and the same biochemical properties. In the antimicrobial susceptibility test, all strains showed an inhibition zone of 40 to 44 mm for oxolinic acid, flumequine, and florfenicol. Pathogenic factors were assessed by RT-PCR at the mRNA level, and found that the four strains expresses the outer membrane ring of T3SS (ascV), inner membrane ring of T3SS (ascC), vapA, enterotoxin (act), and lipase (lip) genes which are well known to significantly contribute to the pathogenicity of A. salmonicida. The results of this study can be used as basic data to prevent A. salmonicida subsp. salmonicida occurring in sea-chum salmon in the future.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of Animal Science and Technology
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• v.46 no.4
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• pp.645-658
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• 2004

To investigate the effects of conjugated linoleic acid added diet feeding on CLA accumulation and quality characteristics of manufactured press ham using CLA accwnulated pork loin meat. The CLA used to add in diet was chemically synthesized by alkaline isomerization method with com oil. Pigs were divided into 5 treatment groups(4 pigs/group) and subjected to one of five treatment diets(0, 1.25% CLA for 2weeks, 2.5% CLA for 2weeks, 1.25% CLA for 4weeks and 2.5% CLA for 4weeks, CLA diets; total fed diets) before slaughter. Pork loin were collected from the animals(110kg body weight) slaughtering at the commercial slaughter house. Manufacture press ham using CLA accumulated pork loin meat were vacuum packaged and then stored during 1, 7, 14, 21 and 28 days at 4$^{\circ}C$. Samples were analyzed for general compositions, physico-chemical properties(pH, color, shear force value), TBARS. pH value of CLA treatment(T4) was increased significantly than that of oontrol(P<0.05). pH of control and CLA treatments were increased significantly as the storage period passed(P< 0.05). Crude fat content of CLA treatment groups was significantly higher than the control pork (P<0.05). Meat color(CIE $L^*$, $a^*$$b^*$

• Tuberculosis and Respiratory Diseases
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• v.59 no.5
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• pp.522-529
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• 2005

Background : Several national societies have published guidelines for empirical antimicrobial therapy in patients with severe community-acquired pneumonia (SCAP). This study investigated the etiologies of SCAP in the Asan Medical Center and assessed the relationship between the initial empirical antimicrobial regimen and 30 day mortality rate. Method : retrospective analysis was performed on patients with SCAP admitted to the ICU between March 2002 and February 2004 in the Asan Medical Center. The basic demographic data, bacteriologic study results and initial antimicrobial regimen were examined for all patients. The clinical outcomes including the ICU length of stay, the ICU mortality rate, and 30 days mortality rates were assessed by the initial antimicrobial regimen. Results : One hundred sixteen consecutive patients were admitted to the ICU (mean age 66.5 years, 81.9 % male, 30 days mortality 28.4 %). The microbiologic diagnosis was established in 58 patients (50 %). The most common pathogens were S. pneumoniae (n=12), P. aeruginosae (n=9), K. pneumonia (n=9) and S. aureus (n=8). The initial empirical antimicrobial regimens were classified as: ${\beta}$-lactam plus macrolide; ${\beta}$-lactam plus fluoroquinolone; anti-Pseudomonal ${\beta}$-lactam plus fluoroquinolone; Aminoglycoside combination regimen; ${\beta}$-lactam plus clindamycin; and ${\beta}$-lactam alone. There were no statistical significant differences in the 30-day mortality rate according to the initial antimicrobial regimen (p = 0.682). Multivariate analysis revealed that acute renal failure, acute respiratory distress syndrome and K. pneumonae were independent risk factors related to the 30 day mortality rate. Conclusion : S. pneumoniae, P. aeruginosae, K. pneumonia and S. aureus were the most common causative pathogens in patients with SCAP and K. pneumoniae was an independent risk factor for 30 day mortality. The initial antimicrobial regimen was not associated with the 30-day mortality.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.