• Horticultural Science & Technology
• /
• v.28 no.3
• /
• pp.442-448
• /
• 2010

The objectives of this study were to analyze mutant lines of Chinese cabbage ($Brassica$ $rapa$ ssp. $pekinensis$) using gene tagging system (plasmid rescue and inverse polymerase chain reaction) and to observe the phenotypic characteristics. Insertional mutants were derived by transferring DNA (T-DNA) of $Agrobacterium$ for functional genomics study in Chinese cabbage. The hypocotyls of Chinese cabbage 'Seoul' were used to obtain transgenic plants with $Agrobacterium$ $tumefaciens$ harboring pRCV2 vector. To tag T-DNA from the Chinese cabbage genomic DNA, plasmid rescue and inverse PCR were applied for multiple copies and single copy insertional mutants. These techniques were successfully conducted to Chinese cabbage plant with high efficiency, and as a result, T-DNA of pRCV2 vector showed distinct various integration patterns in the transgenic plant genome. The polyploidy level analysis showed the change in phenotypic characteristics of 13 mutant lines was not due to variation in somatic chromosome number. Compared with wild type, the $T_1$ progenies showed varied phenotypes, such as decreased stamen numbers, larger or smaller flowers, upright growth habit, hairless leaves, chlorosis symptoms, narrow leaves, and deeply serrated leaves. The polyploidy level analysis showed the change in phenotypic characteristics of 13 mutant lines was not due to variation in somatic chromosome number. To tag T-DNA from the Chinese cabbage genomic DNA, plasmid rescue and inverse PCR were applied for multiple copies and single copy insertional mutants. Mutants that showed distinct phenotypic difference compared to wild type with 1 copy of T-DNA by Southern blot analysis, and with 2n = 20 of chromosome number were selected. These selected mutant lines were sequenced flanking DNA, mapped genomic loci, and the genome information of the lines is being recorded in specially developed database.

• Journal of Multimedia Information System
• /
• v.6 no.4
• /
• pp.197-202
• /
• 2019

Society advances through technology, and technology has changed many lifestyles. The need for food is varying, but the availability of food is constantly changing as trends in production change. Combining the food industry and technology, a robot that delivers food and also cooks it has been developed. The time has come for a combination of food content and technology to advance the restaurant industry. This study discusses the application of a recommended food content media providing system using a curation engine that recommends contents according to individual tastes and preferences for the convenience of those who use food contents, using artificial intelligence speakers. We discuss the technologies required to develop video contents optimized for AI speakers with screens and shapes, combined with inset top boxes.

• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.253-266
• /
• 2018

This paper proposes a methodology applying sequence tagging methodology to improve the performance of NER(Named Entity Recognition) used in QA system. In order to retrieve the correct answers stored in the database, it is necessary to switch the user's query into a language of the database such as SQL(Structured Query Language). Then, the computer can recognize the language of the user. This is the process of identifying the class or data name contained in the database. The method of retrieving the words contained in the query in the existing database and recognizing the object does not identify the homophone and the word phrases because it does not consider the context of the user's query. If there are multiple search results, all of them are returned as a result, so there can be many interpretations on the query and the time complexity for the calculation becomes large. To overcome these, this study aims to solve this problem by reflecting the contextual meaning of the query using Bidirectional LSTM-CRF. Also we tried to solve the disadvantages of the neural network model which can't identify the untrained words by using ontology knowledge based feature. Experiments were conducted on the ontology knowledge base of music domain and the performance was evaluated. In order to accurately evaluate the performance of the L-Bidirectional LSTM-CRF proposed in this study, we experimented with converting the words included in the learned query into untrained words in order to test whether the words were included in the database but correctly identified the untrained words. As a result, it was possible to recognize objects considering the context and can recognize the untrained words without re-training the L-Bidirectional LSTM-CRF mode, and it is confirmed that the performance of the object recognition as a whole is improved.

• Journal of Plant Biotechnology
• /
• v.37 no.2
• /
• pp.125-132
• /
• 2010

Rice is the staple food of more than 50% of the worlds population. Cultivated rice has the AA genome (diploid, 2n=24) and small genome size of only 430 megabase (haploid genome). As the sequencing of rice genome was completed by the International Rice Genome Sequencing Project (IRGSP), many researchers in the world have been working to explore the gene function on rice genome. Insertional mutagenesis has been a powerful strategy for assessing gene function. In maize, well characterized transposable elements have traditionally been used to clone genes for which only phenotypic information is available. In rice endogenous mobile elements such as MITE and Tos (Hirochika. 1997) have been used to generate gene-tagged populations. To date T-DNA and maize transposable element systems has been utilized as main insertional mutagens in rice. A main drawback of a T-DNA scheme is that Agrobacteria-mediated transformation in rice requires extensive facilities, time, and labor. In contrast, the Ac/Ds system offers the advantage of generating new mutants by secondary transposition from a single tagged gene. Revertants can be utilized to correlate phenotype with genotype. To enhance the efficiency of gene detection, advanced gene-tagging systems (i.e. activation, gene or enhancer trap) have been employed for functional genomic studies in rice. Internationally, there have been many projects to develop large scales of insertionally mutagenized populations and databases of insertion sites has been established. Ultimate goals of these projects are to supply genetic materials and informations essential for functional analysis of rice genes and for breeding using agronomically important genes. In this report, we summarize the current status of Ac/Ds-mediated gene tagging systems that has been launched by collaborative works from 2001 in Korea.

• Annual Conference on Human and Language Technology
• /
• 2017.10a
• /
• pp.184-189
• /
• 2017

With the advent of robust deep learning method, Neural machine translation has recently become a dominant paradigm and achieved adequate results in translation between popular languages such as English, German, and Spanish. However, its results in under-resourced languages Korean and Vietnamese are still limited. This paper reports an attempt at constructing a bidirectional Korean-Vietnamese Neural machine translation system with the supporting of Korean analysis tool - UTagger, which includes morphological analyzing, POS tagging, and WSD. Experiment results demonstrate that UTagger can significantly improve translation quality of Korean-Vietnamese NMT system in both translation direction. Particularly, it improves approximately 15 BLEU scores for the translation from Korean to Vietnamese direction and 3.12 BLEU scores for the reverse direction.

• 한국어정보학회:학술대회논문집
• /
• 2017.10a
• /
• pp.184-189
• /
• 2017

With the advent of robust deep learning method, Neural machine translation has recently become a dominant paradigm and achieved adequate results in translation between popular languages such as English, German, and Spanish. However, its results in under-resourced languages Korean and Vietnamese are still limited. This paper reports an attempt at constructing a bidirectional Korean-Vietnamese Neural machine translation system with the supporting of Korean analysis tool - UTagger, which includes morphological analyzing, POS tagging, and WSD. Experiment results demonstrate that UTagger can significantly improve translation quality of Korean-Vietnamese NMT system in both translation direction. Particularly, it improves approximately 15 BLEU scores for the translation from Korean to Vietnamese direction and 3.12 BLEU scores for the reverse direction.

• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.267-270
• /
• 2006

COMS has two imaging payloads, MI (Meteorological Imager) and GOCI (Geostationary Ocean Colour Imager). In GOCI case, data are packaged per each slot - one part of 16 two-dimensional arrays for imaging sensors - so its generation algorithm is simple. But MI case, data are made up with sequences of 480 bit blocks and are transmitted to its ground station sequentially. Moreover there is no time information in each 480 bit MI block, so a system in its ground system should be attaching time information at received MI blocks. DM (Decomposition Module) is one module of IMPS that receives Raw Data from DATS and generates Level 0 Products that include time tagging. This paper explains DM design for MI of COMS payloads.

• Journal of Korea Multimedia Society
• /
• v.19 no.8
• /
• pp.1498-1504
• /
• 2016

The progress of mobile computing technology is bringing a large amount of multimedia contents such as image. Thus, we need an image retrieval system which searches semantically relevant image. In this paper, we propose a semantic image annotation and retrieval in mobile environments. Previous mobile-based annotation approaches cannot fully express the semantics of image due to the limitation of current form (i.e., keyword tagging). Our approach allows mobile devices to annotate the image automatically using the context-aware information such as temporal and spatial data. In addition, since we annotate the image using RDF(Resource Description Framework) model, we are able to query SPARQL for semantic image retrieval. Our system implemented in android environment shows that it can more fully represent the semantics of image and retrieve the images semantically comparing with other image annotation systems.

• Korean Journal of Plant Tissue Culture
• /
• v.27 no.4
• /
• pp.259-268
• /
• 2000

Senescence is a sequence of biochemical and physiological events that lead to death of a cell, organ, or whole organism. Senescence is now clearly regarded as a genetically determined and evolutionarilly acquired developmental process comprising the final stage of development. However, in spite of the biological and practical importance, genetic mechanism of senescence has been very limited. Through forward and reverse genetic approaches, we are trying to reveal the molecular and genetic mechanism of senescence in plants, employing leaf organs of Arabidopsis as a model system. Using forward genetic approach, we have initially isolated several delayed senescence mutants either from T-DNA insertional lines or chemical-mutagenized lines. In the case of ore 4 and ore 9 mutants, the mutated genes were identified. The recent progress on characterization of mutants and identification of the mutated genes will be reported. We are also screening mutations from other various sources of mutant pools, such as activation tagging lines and promoter trap lines. Two dominant senescence-delayed mutants were isolated from the activation tagging pool. Cloning of the genes responsible for this phenotype is in progress. For reverse genetic approach, the genes that induced during leaf senescence were first isolated by differential screening method. We are currently using PCR-based suppression subtractive hybridization, designed to enrich a cDNA library for rare differentially expressed transcripts. Using this method, we have identified over 35 new sequences that are upregulated at leaf senescence stage. We are investigating the function of these novel genes by systemically generating antisense lines.

• Journal of KIISE
• /
• v.41 no.12
• /
• pp.1117-1125
• /
• 2014

Folksonomy data, which is derived from social tagging systems, is a useful source for understanding a user's intention and interest. Using the folksonomy data, it is possible to create an accurate user profile which can be utilized to build a personalized search system. However there are limitations in some of the traditional methods such as Vector Space Model(VSM) for user profiling and similarity computation. This paper suggests a novel method with graph-based user and document profile which uses the proximity information of query terms to improve personalized search. We demonstrate the performance of the suggested method by comparing its performance with several state-of-the-art VSM based personalization models in two different folksonomy datasets. The results show that the proposed model constantly outperforms the other state-of-the-art personalization models. Furthermore, the parameter sensitivity results show that the proposed model is parameter-free in that it is not affected by the idiosyncratic nature of datasets.