• Horticultural Science & Technology
• /
• v.29 no.5
• /
• pp.461-466
• /
• 2011

In genetic and molecular breeding studies of plants, researchers need to design various kinds of primers based on their research purposes. So far many kinds of web- or script-based non-commercial programs for primer design are available. Because most of them do not include user interface for multipurpose usage including gene structure prediction and direct target selection on sequences, it has been a laborious work to design primers targeting on the exon or intron regions of interesting genes. Here we report a primer designing graphic user interface program, Pickprimer, that includes gene structure prediction and primer design modules by combining source codes of the Spidey and Primer3 programs. This program provides simple graphic user interface to input sequences and design primers. Genomic sequence and mRNA or coding sequence of genes can be copy and pasted or input as fasta or text files. Based on alignment of the input sequences using the Spidey module, a putative gene structure is graphically visualized along with exon-intron sequences of color codes. Primer design can be easily performed by dragging mouse on the displayed sequences or input primer targeting position with desirable values of primers. The output of designed primers with detailed information is provided by the Primer3 module. PCR evaluation of 24 selected primer sets successfully amplified single amplicons from six Brassica rapa cultivars. The Pickprimer will be a convenient tool for genetic and molecular breeding studies of plants.

• Journal of Oriental Neuropsychiatry
• /
• v.23 no.2
• /
• pp.99-120
• /
• 2012

Objectives : The oriental medicine Jangwonhwan, a boiled extract of 12 medicinal herbs/mushrooms, has been prescribed to patients with cognitive dysfunction, as originally described in the Korean medical text, DonguiBogam(amnesia chapter). Recently, a modified formula of Jangwonhwan (LMK02-Jangwonhwan) consisting of seven medicinal plants/mushrooms, was shown to reduce the ${\beta}$-amyloid deposition in the brain of Tg-APPswe/PS1dE9 mouse model for Alzheimer's disease. The toxicity of LMK02-Jangwonhwan was investigated in SD rats, by a daily oral administration for 13 weeks and NOAEL(No observed adverse effect dose), a definite toxic dose and target organ, as well. Methods : Quality control of the tablet form of LMK02-Jangwonhwan was established by estimating the indicative components, Ginsenoside Rg3 of Red Ginseng and Decursin of Angelicagigas Nakai. The toxicity of LMK02-Jangwonhwan was investigated in 6 week old, specific pathogen free (SPF), Sprageu-Dawley rats by oral administration. Each test group consisted of 10 male and 10 female rats. The groups received doses of 500, 1,000 or 2,000 mg/kg/day of test substance for 13 weeks. The clinical signs, death rate, body weight, food consumption, ophthalmic examination, urinalysis, hematological and serum biochemistry, organ weight and pathological changes were examined and compared with those of the control group. Results : The 13-week repeated oral treatment doses didn't result in any specific symptoms or death. There were no significant changes in the rat's weight and food consumption. Further, ophthalmic examination, urinalysis, hematological, serum biochemistry test and organ weight revealed no significant differences. Conclusions : The no-observed-adverse-effect level(NOAEL) of LMK02 for male and female Sprague-Dawley rats was determined as 2,000mg/kg/day and the target organ wasn't confirmed. Because no significant adverse effects were observed, the target organ could not be determined.

• The KIPS Transactions:PartB
• /
• v.14B no.5
• /
• pp.383-390
• /
• 2007

Interactions between proteins and genes are often considered essential in the description of biomolecular phenomena and networks of interactions are considered as an entre for a Systems Biology approach. Recently, many works try to extract information by analyzing biomolecular text using natural language processing technology. Previous researches insist that linguistic information is useful to improve the performance in detecting gene interactions. However, previous systems do not show reasonable performance because of low recall. To improve recall without sacrificing precision, this paper proposes a new method for detection of gene interactions based on syntactic relations. Without biomolecular knowledge, our method shows reasonable performance using only small size of training data. Using the format of LLL05(ICML05 Workshop on Learning Language in Logic) data we detect the agent gene and its target gene that interact with each other. In the 1st phase, we detect encapsulation types for each agent and target candidate. In the 2nd phase, we construct verb lists that indicate the interaction information between two genes. In the last phase, to detect which of two genes is an agent or a target, we learn direction information. In the experimental results using LLL05 data, our proposed method showed F-measure of 88% for training data, and 70.4% for test data. This performance significantly outperformed previous methods. We also describe the contribution rate of each phase to the performance, and demonstrate that the first phase contributes to the improvement of recall and the second and last phases contribute to the improvement of precision.

• Cartoon and Animation Studies
• /
• s.30
• /
• pp.201-219
• /
• 2013

Development of digital media in education field at America, UK, Japan etc bring big progress on digital device if education. Japan bring huge progress on digital education by nationally. UK use huge a national budget at digital education development and Sweden which is advanced country of education and a welfare state. Especially UK and Sweden's digital education markets are full now aspect more high quality design. Korea which is advanced country of IT adopted digital text book 2007 with mathematics, through science and English digital text book through the state. Korea's digital text book is in a transition period. that needs case study of advanced country of education for setting design guide and educational effect to digital education media and device plan. All researches are based on LG europe design center at London. Analysis by using KJ method, survey of questionnaire, heuristic method at 4 schools in UK and Sweden. Through analytical researches want to more reality simulation at digital education, and high quality contents with digital socialization. co-work with analog, can get any where, anytime user want without any difficulty. Also interactive GUI design of digital education device to easy to access for user. When plan Digital text book content and design needs methodical design guide for target who students and environment an in-depth study of the appraisal and method. The results of the research are introduce the design plan as a basic research and giving useful design plan to make digital educational media in Korea industrial aspect.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.59-83
• /
• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.219-240
• /
• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.1-19
• /
• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Journal of Korean Home Economics Education Association
• /
• v.20 no.2
• /
• pp.75-94
• /
• 2008

The purposes of this paper are to describe the newly established reference values of nutrient intakes: to apply the changed dietary reference intakes to the new text book based on the revised curriculum: and to contrive substantial contents in the domain of dietary life(foods & nutrition) of new text book. Dietary Reference Intakes for Koreans(KDRIs) is newly established reference values of nutrient intakes that are considered necessary to maintain the health of Koreans at the optimal state and to prevent chronic diseases and overnutrition. Unlike previously used Recommended Dietary Allowances for Koreas(KRDA), which presented a single reference value for intake of each nutrient, multiple values are set at levels for nutrients to reduce risk of chronic diseases and toxicity as well as prevention of nutrient deficiency. The new KDRIs include the Estimated Average Requirement(EAR), Recommended Intake(RI), Adequate Intake(AI), and Tolerable Upper Intake Level(UL). The EAR is the daily nutrient intake estimated to meet the requirement of the half of the apparently healthy individuals in a target group and thus is set at the median of the distribution of requirements. The RI is set at two standard deviations above the EAR. The AI is established for nutrients for which existing body of knowledge are inadequate to establish the EAR and RI. The UL is the highest level of daily nutrient intake which is not likely to cause adverse effects for the human health. Age and gender subgroups are established in consideration of physiological characteristics and developmental stages: infancy, toddler, childhood, adolescence, adulthood and old age. Pregnancy and lactation periods were considered separately and gender is divided after early childhood. Reference heights and weights are from the Korean Agency for Technology and Standards, Ministry of Commerce, Industry and Energy. The practical application of DRIs to the new books based on the revision in the 7th curriculum is to assess the dietary and nutrient intake as well as to plan a meal. It can be utilized to set an appropriate nutrient goal for the diet as usually eaten and to develop a plan that the individual will consume using a nutrient based food guidance system in the new books based on the revision in the 7th curriculum.

• Journal of Internet Computing and Services
• /
• v.21 no.4
• /
• pp.97-107
• /
• 2020

This study aims to conduct a comprehensive meta-study from the perspective of content analysis to explore trends in Korean academic research on the sharing economy by using the big data analytics. Comprehensive meta-analysis methodology can examine the entire set of research results historically and wholly to illuminate the tendency or properties of the overall research trend. Academic research related to the sharing economy first appeared in the year in which Professor Lawrence Lessig introduced the concept of the sharing economy to the world in 2008, but research began in earnest in 2013. In particular, between 2006 and 2008, research improved dramatically. In order to grasp the overall flow of domestic academic research of trends, 8 years of papers from 2013 to the present have been selected as target analysis papers, focusing on titles, keywords, and abstracts using database of electronic journals. Big data analysis was performed in the order of cleaning, analysis, and visualization of the collected data to derive research trends and insights by year and type of literature. We used Python3.7 and Textom analysis tools for data preprocessing, text mining, and metrics frequency analysis for key word extraction, and N-gram chart, centrality and social network analysis and CONCOR clustering visualization based on UCINET6/NetDraw, Textom program, the keywords clustered into 8 groups were used to derive the typologies of each research trend. The outcomes of this study will provide useful theoretical insights and guideline to future studies.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.17 no.4
• /
• pp.290-305
• /
• 2015

To improve the practicality of 'Early warning service about agrometeorological weather hazards' and operation efficiency to deliver site-specific about a lot of land unit possibility of weather hazard occurrence with the suitable counterplan to farmer, site-specific early warning service system that was built at the National Academy of Agricultural Science in Korea passed some of the error supplementation and service's stabilization stage during operation period for trial services from October 2014 to March 2015. Field service system covered about 470 volunteered farmer and 950 lots in Seomjin river downstream areas (part of Gwangyang-si, Hadong-gun, Gurye-gun). This system (Two track system) consists of early warning system (a lot of land unit) to inform farmer by individual text message and dispersal prior alert system that can see the jurisdiction's situation of local government. Individual text message about Seomjin river downstream that is our first study area was launched since $2^{nd}$ March 2015, and online site (http://www.agmet.kr) started business since April 2015. Service offers currently information of farm weather, farm weather hazard, nationwide weather risk and special weather alert, also our system will consistently expand the service target area and contents and improve the service quality until 2017 when our study finished. To prevent crops damage that was caused by crisis situation like farm weather and weather damage offer prior alert about agrometeorological weather harzard to volunteered farmer, thereby our study expects to help the reduction of farm's damage caused by weather derivatives.