• 한국공간정보시스템학회:학술대회논문집
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• 2004.12a
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• pp.169-181
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• 2004

SK Telecom has already constructed GIMS system as the base common framework of LBS/GIS service system based on OGC(OpenGIS Consortium)'s international standard for the first mobile vector map service in 2002, But as service content appears more complex, renovation has been needed to satisfy multi-purpose, multi-function and maximum efficiency as requirements have been increased. This research is for preparation ion of GML3-based platform to upgrade service from GML2 based GIMS system. And with this, it will be possible for variety of application services to provide location and geographic data easily and freely. In GML 3.0, it has been selected animation, event handling, resource for style mapping, topology specification for 3D and telematics services for mobile LBS multimedia service. And the schema and transfer protocol has been developed and organized to optimize data transfer to MS(Mobile Stat ion) Upgrade to GML 3.0-based GIMS system has provided innovative framework in the view of not only construction but also service which has been implemented and applied to previous research and system. Also GIMS channel interface has been implemented to simplify access to GIMS system, and service component of GIMS internals, WFS and WMS, has gotten enhanded and expanded function.

• Proceedings of the Botanical Society of Korea Conference
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• 1996.07a
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• pp.61-74
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• 1996

Human and monogastric animals can not synthesize 10 out of the 20 amino asids and therefor need to obtain these from their diet. The plant seed is a major source of dietary protein. It is particular important in their study to increase nutritional quality of the seed storage proteins. The low contents of lysine, asparagine and threonenein various cereal seeds and of cystein and methionine. In legume seeds is due to the low proportions of these amino acids in the major storage proteins, we have tried to apply the three strategies; (1) mutagenesis and selection of specific amino acid analogue resistance, (2) cloning and expression study of lysine biosynthesis related gene, (3) transfomation of lysine rich soybean glycinin gene. The 5-methyltryptophan (5MT) resistant cell lines, SAR1, SAR2 and SAR3 were selected from anther derived callus of rice (Oryza sativa L. "Sasanishiki"). Among these selected cell lines, two (SAR1 and SAR3) were able to grow stably at 200 mg/L of 5MT. Analysis of the freed amino acids in callus shows that 5MT resistant cells (SAR3) accumulated free tryptophan at least up to 50 times higher than those that of the higher than of SAS. These results indicated that the 5MT resistant cell lines are useful in studies of amino acid biosynthesis. Tr75, a rice (Oryza sativa L., var. Sasanishiki) mutant resistant to 5MT was segregated from the progenies of its initial mutant line, TR1. The 5MT resistant of TR75 was inherited in the M8 generations as a single dominant nuclear gene. The content of free amino acids in the TR75 homozygous seeds increased approximately 1.5 to 2.0 fold compared to wild-type seeds. Especially, the contents of tryptophan, phenylalanine and aspartic acid were 5.0, 5.3 and 2.7 times higher than those of wild-type seeds, respectively. The content of lysine is significantly low in rice. The lysine is synthesized by a complex pathway that is predominantly regulated by feedback inhibition of several enzymes including asparginase, aspatate kinase, dihydrodipicolinat synthase, etc. For understanding the regulation mechanism of lysine synthesis in rice, we try to clone the lysine biosynthetic metabolism related gene, DHPS and asparaginase, from rice. We have isolated a rice DHPS genomic clone which contains an ORF of 1044 nucleotides (347 amino acids, Mr. 38, 381 daltons), an intron of 587 nucleotides and 5'and 3'-flanking regions by screening of rice genomic DNA library. Deduced amino acid sequence of mature peptide domain of GDHPS clone is highly conserved in monocot and dicot plants whereas that of transit peptide domain is extremely different depending on plant specie. Southern blot analysis indicated that GDHPS is located two copy gene in rice genome. The transcripts of a rice GDHPS were expressed in leaves and roots but not detected in callus tissues. The transcription level of GDHPS is much higher in leaves indicating enormous chloroplast development than roots. Genomic DNA clones for asparaginase genes were screened from the rice genomic library by using plaque hybridization technique. Twelve different genomic clones were isolated from first and second screening, and 8 of 12 clones were analyzed by restriction patterns and identified by Southern Blotting, Restriction enzyme digestion patterns and Southern blot analysis of 8 clones show the different pattern for asparaginase gene. Genomic Southern blot analysis from rice were done. It is estimated that rice has at least 2-3 copy of asparaginase gene. One of 8 positive clones was subcloned into the pBluescript SK(+) vector, and was constructed the physical map. For transformation of lysine rich storage protein into tobacco, soybean glycinin genes are transformed into tobacco. To examine whether glycinin could be stably accumulated in endosperm tissue, the glycinin cDNA was transcriptionally fused to an endosperm-specific promotor of the rice storage protein glutelin gene and then introduced into tobacco genomic via Agrobacterium-mediated transformation. Consequently the glycinin gene was expressed in a seed-and developmentally-specific manner in transgenic tobacco seeds. Glycinin were targeted to vacuole-derived protein bodies in the endosperm tissue and highly accumulated in the matrix region of many transgenic plant (1-4% of total seed proteins). Synthesized glycinin was processed into mature form, and assembled into a hexamer in a similar manner as the glycinin in soybean seed. Modified glycinin, in which 4 contiguous methionine residues were inserted at the variable regions corresponding to the C - teminal regions of the acidic and basic polypeptides, were also found to be accumulated similarly as in the normal glycinin. There was no apparent difference in the expression level, processing and targeting to protein bodies, or accumulation level between normal and modified glycinin. glycinin.

• Journal of Life Science
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• v.20 no.3
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• pp.411-415
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• 2010

Anisakis simplex is one of the parasitic nematodes, and has a complex life cycle in crustaceans, fish, squid or whale. When people eat under-processed or raw fish, it causes anisakidosis and also plays a critical role in inducing serious allergic reactions in humans. However, no web-based database on A. simplex at the level of DNA or protein has been so far reported. In this context, we constructed a web-based database for Anisakis research. To build up the web-based database for Anisakis research, we proceeded with the following measures: First, sequences of order Ascaridida were downloaded and translated into the multifasta format which was stored as database for stand-alone BLAST. Second, all of the nucleotide and EST sequences were clustered and assembled. And EST sequences were translated into amino acid sequences for Nuclear Localization Signal prediction. In addition, we added the vector, E. coli, and repeat sequences into the database to confirm a potential contamination. The web-based database gave us several advantages. Only data that agrees with the nucleotide sequences directly related with the order Ascaridida can be found and retrieved when searching BLAST. It is also very convenient to confirm contamination when making the cDNA or genomic library from Anisakis. Furthermore, BLAST results on the Anisakis sequence information can be quickly accessed. Taken together, the Web-based database on A. simplex will be valuable in developing species specific PCR markers and in studying SNP in A. simplex-related researches in the future.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.