• Journal of People, Plants, and Environment
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• v.22 no.6
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• pp.593-600
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• 2019

We identified how plants affected by the human behavior of crushing plants respond and what kind of plants responded sensitively. We investigated Lactuca sativa "Gaesebadak", Syneilesis palmata and Peucedanum japonicum as plants that humans use for edible purposes, and Achyranthes japonica and Bidens bipinnata as wild plants that stick to people's clothes and disperse seed. Plants exposed to human breathing air were compared with those exposed to human breathing air after being crushed. Methyl jasmonate (MeJA), a chemical word, was measured using Syft/MS, which detects real-time VOC, and related genes were analyzed. The amount of MeJA of Syneilesis palmata and Peucedanum japonicum as edible plants was greater than that of non-edible plants that disperse seeds using humans. The amount of MeJA ranged from 0.20 ppb to 0.35 ppb when the control group were not exposed to human breathing air. On the other hand, MeJA decreased after increasing for the first hour in human breathing air. Also, MeJA affected by human breathing after crushing plants was higher than that affected by just human breathing air. Peucedanum japonicum showed the most distinctive difference between the treatment with human breathing after crushing plants and the treatment with just human breathing. In addition, the gene activity of JAR1 and JMT increased 3 hours after the treatment with human breathing after crushing plants. Therefore, in the treatment with human breathing after crushing plants, the concentration of MeJA and the activity of related genes showed the same tendency to increase. As a result, the plant that responded sensitively to human behavior was Peucedanum japonicum. Plants released MeJA as a chemical word in the treatment with human breathing air after crushing plants.

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

• The KIPS Transactions:PartB
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• v.14B no.6
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• pp.453-460
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• 2007

In this paper, we present a method to extract the text lines in poorly structured documents. The text lines may have different orientations, considerably curved shapes, and there are possibly a few wide inter-word gaps in a text line. Those text lines can be found in posters, blocks of addresses, artistic documents. Our method based on the traditional perceptual grouping but we develop novel solutions to overcome the problems of insufficient seed points and vaned orientations un a single line. In this paper, we assume that text lines contained tone connected components, in which each connected components is a set of black pixels within a letter, or some touched letters. In our scheme, the connected components closer than an iteratively incremented threshold will make together a chain. Elongate chains are identified as the seed chains of lines. Then the seed chains are extended to the left and the right regarding the local orientations. The local orientations will be reevaluated at each side of the chains when it is extended. By this process, all text lines are finally constructed. The proposed method is good for extraction of the considerably curved text lines from logos and slogans in our experiment; 98% and 94% for the straight-line extraction and the curved-line extraction, respectively.

• Journal of the Korean Society of Costume
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• v.61 no.9
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• pp.50-59
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• 2011

Recently the word "digital" is widely used in almost every field and is dominating this generation. Digital has become the most significant characteristic representing the 21st century, and is leading change across wide range of our life-styles in our culture and thoughts. New art is in harmony with digital in the 21st century. Digital photography is simpler, faster and newer than the analog system of the past. From ancient times, the nature has been the subject of art, and many designers have studied the ways to create beauty from nature. In this study, I chose the lotus as the subject material of textile design development. The lotus invokes a sense of stillness, and nestles many fluid elements, including the curved fluid lotus, rhythmic lotus petal, sinuous lotus leaf, radial vein, lotus pip and oval seed. Therefore, I tried to use these elements of lotus for development of textile design. For this purpose, I photographed the lotus with a digital camera as equipment of design development. Then, on computer, I have developed six textile designs through the process of modification and editing by using the adobe illustrator program.

• Korean Journal of Agricultural Science
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• v.26 no.2
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• pp.116-129
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• 1999

The objectives of the study mainly concentrated on identifying the political implication on genetically modified crops considering production potentials and problems. It is found that the comprehensive evaluation of the genetically modified crops could not be concluded and compromised in a word on account of the polarization and parallelism of the positive and negative benefits of genetically modified crops just like as the rail way. The genetically modified crops will be contributed to solving the food shortage problems in the world, when the issues such as food safety, ecological disturbance and loss and degradation of biodiversity can be guaranteed in transparency. And when the trade morality of the multi-national enterprises be accepted by the genetically modified crops consumers, the potentiality of genetically modified crops will be realized greatly. By the way, the first problems will be expected to be solved by scientific development. If the food safety of the genetically modified technology be verified in transparency, it will be greatly contributed to solve food problems of human beings in the world. But the second problem could not be expected to be easily solved from the view point of capital property. In conclusion, the genetically modified technology will be made a severe sense of incongruity and a seed of fire on it will be remained persistently. According to the results based on the analysing the genetically modified crops potentiality and problems, it was identified that the ex-ante preparation of counter-measures and actions on it should be necessary. Accordingly this study has recommended that how and what the R&D policy on genetically modified crops be established and suggested how to carried out the industrial and economic policy together with international negotiation, and organizational and institutional rearrangement and etc.

• The Korea Journal of Herbology
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• v.29 no.5
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• pp.39-43
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• 2014

Objectives : The purpose of this sutdy was finding out the origin and daily dose of Allii Fistulosi Bulbus in Treatise on Cold Damage Diseases. Methods : In order to estimate the origin and daily dose of Allii Fistulosi Bulbus, I researched the Treatise on Cold Damage Diseases, Synopsis of Prescription of the Golden Chamber, Classified Emergency Materia Medica, Compendium of Materia Medica, and Huangdi's Internal Classic. Results : According to some important herbal textbooks, Allii Fistulosi Bulbus had no seed, was reproduced by rhizome, and its leaves were soft. Chongbaek in Treatise on Cold Damage Diseases did not include word 'raw'. The unit of the dose of it was the number of article. So Allii Fistulosi Bulbus in Treatise on Cold Damage Diseases was not Daepa but Jokpa, and was dried, and was round shaped bulb bigger than bean. The daily dose was 4, 9, 14 articles in Treatise on Cold Damage Diseases and Synopsis of Prescription of the Golden Chamber. These were estimated equal to 1, 2, 3 Ryang. 1 Ryang equals to 6.5 g in Treatise on Cold Damage Diseases, so the daily dose of Allii Fistulosi Bulbus was estimated 6.5 g, 13.0 g, and 19.5 g. I weighed the middle-sized Allii Fistulosi Bulbuses, the result was remarkably consistent with the estimated numerical value. Conclusions : In Treatise on Cold Damage Diseases, Allii Fistulosi Bulbus was dried bulb of Jokpa of Allium fistulosum Linn$\acute{e}$ and the daily dose was 4 or 9 articles, respectively equaled to 1 Ryang, 2 Ryang and 6.5 g, 13 g.

• Journal of the Korean Society of Radiology
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• v.81 no.3
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• pp.676-687
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• 2020

Purpose To analyze the altered brain regions and intrinsic brain activity patterns in trauma-exposed firefighters without posttraumatic stress disorder (PTSD). Materials and Methods Resting-state functional MRI (rsfMRI) was performed for all subjects. Thirty-one firefighters over 40 years of age without PTSD (31 men; mean age, 49.8 ± 4.7 years) were included. Twenty-six non-traumatized healthy controls (HCs) (26 men; mean age, 65.3 ± 7.84 years) were also included. Voxel-based morphometry was performed to investigate focal differences in the brain anatomy. Seed-based functional connectivity analysis was performed to investigate differences in spontaneous brain characteristics. Results The mean z-scores of the Seoul Verbal Learning Test for immediate and delayed recall, Controlled Oral Word Association Test (COWAT) score for animals, and COWAT phonemic fluency were significantly lower in the firefighter group than in the HCs, indicating decreased neurocognitive function. Compared to HCs, firefighters showed reduced gray matter volume in the left superior parietal gyrus and left inferior temporal gyrus. Further, in contrast to HCs, firefighters showed alterations in rsfMRI values in multiple regions, including the fusiform gyrus and cerebellum. Conclusion Structural and resting-state functional abnormalities in the brain may be useful imaging biomarkers for identifying alterations in trauma-exposed firefighters without PTSD.

• Journal of Sasang Constitutional Medicine
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• v.10 no.1
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• pp.253-267
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• 1998

'Maek(麥)' which means a barley at present is used as a general name of the kinds of 'Maek(麥)' including barley, what, rye, and so on. However 'Maek(麥)' had meant a wheat when the letter of 'Maek(麥)' had been created at the beginning. And gradually 'Maek(麥)' has been used as a word for the both meaning of a wheat and barley, and now a days become a word only for the meaning of a barley. Therefore 'Mo-Maek' should be translated into 'a wheat and barley', although it is translated into only a barley. In Korea, according to 'Nong-ga-wol-ryung(農家月令)' of Ko, Sang-un(高尙顔) in 1619, the vernalization had been utilized practically 300 years earlier than the Western countries. It is based on a concept of the oriental medicine that a 'Maek(麥)' of the spring seedtime, which keeps the only warmth('Won-yeol-ji-ki(溫熱之氣) (陽氣)' during the growth period in spring-summer time, has less vigorous than a 'Maek(麥)' of the autumn seedtime, which keeps the cold, hot, warmth equally 'Yeol-won-yang-han-ji-ki(溫熱凉寒之氣) (陽氣+陰氣)' during the growth period in the four seasons. If a autumn seedtime barley but which is actually not sowed on time in autumn season is sowed in the spring after the treatment of cold condition (陰氣 autumn-winter condition) artificially, it would be avoided from the abnormal phenomenon that a barley could not come to fruition in case of non-treated seed. Thus as a result of this the agricultural productivity has been able to be improved. An barley is recommended as a food for Um-chung-ji-ki(陰淸之氣) to be served as Bo-myung-ji-ju(保命之主) to be Soyangin. And a wheat which is used as an ingredient of bread because of its expanding feature is a suitable food for Ho-san-ji-ki(呼散之氣) to be served as Bo-myung-ji-ju(保命之主)to the Taeumin.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.374-390
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• 2021

Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.119-138
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• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.