• Korean Journal of Environment and Ecology
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• v.36 no.2
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• pp.118-138
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• 2022

This study was conducted to identify the vegetation structure of major vegetation by region and elevation in the subalpine zone of Seoraksan National Park and prepare an inventory of flora. We reviewed the results of the previous subalpine studies and, through a preliminary survey, determined that the first appearance point of subalpine vegetation was about 800 m based on the south. Then we conducted a site survey by installing a total of 77 plots, including 12 plots on the northern Baekdamsa-Madeungnyeong trail (BD), 13 plots on the west Hangyeryeong-Kkeutcheong trail (HG), 13 plots on the east side of Sinheungsa-Socheongbong trail (SA), and 39 plots in the southern Osaek-Kkeutcheong, Osaek-Daecheongbong trail (OS), in an interval of 50 m above sea level. The analysis classified 7 communities, including Qercus mongolica-Abies holophylla-Acer pseudosieboldianumcommunity, Q. mongolica-Tilia amurensiscommunity, Q. mongolica-Pinus koraiensiscommunity, Q. mongolica-A. pseudosieboldianumcommunity, Betula ermanii-A. nephrolepiscommunity, P. koraiensis-A. nephrolepiscommunity, and mixed deciduous broad-leaf tree community according to the species composition based on the appearance of the major subalpine plants such as Quercus mongolica, Betula ermanii, and Abies nephrolepis, region, and elevation. 10.68±2.98 species appeared per plot (100 m²), and 110.87±63.89 individuals were identified. The species diversity analysis showed that the subalpine vegetation community of Seoraksan National Park was a mixed forest in which various species appeared as important species. Although there was a difference in the initial elevation for the appearance of major subalpine plants by region, they were distributed intensively in the elevation range of 1,100 to 1,300 m. In the Seoraksan National Park, 322 taxa, 83 families, 193 genera, 196 species, 1 subspecies, 26 varieties, and 4 forms of vascular plants were identified. One taxon of Trientalis europaeavar.arcticawas identified as the protected species. The endemic plants were 19 taxa, and 58 taxa were identified as subalpine plants.

• Korean Journal of Environment and Ecology
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• v.36 no.2
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• pp.139-149
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• 2022

This study aimed to prepare basic data for protecting the habitat of Fairy Pitta Pitta nympha and coping with climate change by detecting songs with bio-acoustic recording technology and identifying phenological characteristics in protected areas in Korea. The study sites were 36 protected areas nationwide. Data were collected between January and December 2019, and the analysis period was from May 1 to August 31, 2019. The main results are described as follows. Firstly, songs were detected in 22 out of 36 study sites. Frequency analysis results of songs show that high frequency was observed in southern inland, including Jeju island, and the area with the highest latitude was Seoraksan National Park. Secondly, the first song was observed in Hallyeohaesang National Park Geumsan on May 14, 2019, and the last song was observed in Ungok wetland in Gochang on August 6, 2019. Thirdly, circadian rhythm analysis results of songs show that the frequency rapidly increased at five o'clock in the morning, peaked at six o'clock, and then decreased afterward. Fourthly, seasonal cycle analysis results of songs show that they were observed from May 14, 2019 to August 6, and the day with the highest accumulated frequency of songs was June 3, 2019 (Julian date: 154). The average temperature of the day the songs were detected was 17.4℃, the average precipitation was 0.02mm, and the average humidity was 82.6%. Fifthly, a correlation analysis result between Fairy Pitta's songs and meteorological factors shows that temperature indicated a negative correlation with Fairy Pitta's songs (p<0.001), but precipitation (p=0.053) and humidity (p=0.077) did not indicate a statistical significance (df=471). This study is significant in that it confirmed the distribution of Fairy Pitta's songs using bio-acoustic recording technology in protected areas nationwide and identified their ecological characteristics by precisely analyzing the relationship between the song period and meteorological factors.

• Journal of Food Hygiene and Safety
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• v.37 no.2
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• pp.69-79
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• 2022

The purpose of this study is to monitor the pesticide residues in frozen fruits and vegetables distributed and sold in online and offline markets in Korea. For the study, 107 samples of 34 types of frozen fruits and vegetables were examined, and a total of 341 pesticide residues were analyzed by using multiclass pesticide multi-residue methods of the Korean Food Code. As a result, pesticide residues were detected from 16 of 64 frozen fruits samples and 15 of 43 frozen vegetables samples. Conclusively, residues were detected from 31 samples in total, showing a detection rate of 29.0%. Specifically, pyridaben exceeded the Maximum Residue Limits (MRLs) based on the Positive list system (PLS) in one of the frozen radish leaves, and the violation rate was 0.9%. Detection on frozen fruits and vegetables was made 23 times for 11 types and 36 times for 21 types. In total, 28 types of pesticide residues were detected 59 times. Fungicides were detected the most in frozen fruits, while insecticides were detected the most in frozen vegetables. The most detected pesticides were the insecticide, acaricide chlorfenapyr (5) and the fungicide boscalid (5). Chlorfenapyr was detected only in frozen vegetables, and boscalid was detected in frozen fruits except one.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.49-71
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• 2021

Users who intend to utilize knowledge to actively solve given problems proceed their jobs with cross- and sequential exploration of associated knowledge related each other in terms of certain criteria, such as content relevance. A knowledge map is the diagram or taxonomy overviewing status of currently managed knowledge in a knowledge-base, and supports users' knowledge exploration based on certain relationships between knowledge. A knowledge map, therefore, must be expressed in a networked form by linking related knowledge based on certain types of relationships, and should be implemented by deploying proper technologies or tools specialized in defining and inferring them. To meet this end, this study suggests a methodology for developing the knowledge graph-based knowledge map using the Graph DB known to exhibit proper functionality in expressing and inferring relationships between entities and their relationships stored in a knowledge-base. Procedures of the proposed methodology are modeling graph data, creating nodes, properties, relationships, and composing knowledge networks by combining identified links between knowledge. Among various Graph DBs, the Neo4j is used in this study for its high credibility and applicability through wide and various application cases. To examine the validity of the proposed methodology, a knowledge graph-based knowledge map is implemented deploying the Graph DB, and a performance comparison test is performed, by applying previous research's data to check whether this study's knowledge map can yield the same level of performance as the previous one did. Previous research's case is concerned with building a process-based knowledge map using the ontology technology, which identifies links between related knowledge based on the sequences of tasks producing or being activated by knowledge. In other words, since a task not only is activated by knowledge as an input but also produces knowledge as an output, input and output knowledge are linked as a flow by the task. Also since a business process is composed of affiliated tasks to fulfill the purpose of the process, the knowledge networks within a business process can be concluded by the sequences of the tasks composing the process. Therefore, using the Neo4j, considered process, task, and knowledge as well as the relationships among them are defined as nodes and relationships so that knowledge links can be identified based on the sequences of tasks. The resultant knowledge network by aggregating identified knowledge links is the knowledge map equipping functionality as a knowledge graph, and therefore its performance needs to be tested whether it meets the level of previous research's validation results. The performance test examines two aspects, the correctness of knowledge links and the possibility of inferring new types of knowledge: the former is examined using 7 questions, and the latter is checked by extracting two new-typed knowledge. As a result, the knowledge map constructed through the proposed methodology has showed the same level of performance as the previous one, and processed knowledge definition as well as knowledge relationship inference in a more efficient manner. Furthermore, comparing to the previous research's ontology-based approach, this study's Graph DB-based approach has also showed more beneficial functionality in intensively managing only the knowledge of interest, dynamically defining knowledge and relationships by reflecting various meanings from situations to purposes, agilely inferring knowledge and relationships through Cypher-based query, and easily creating a new relationship by aggregating existing ones, etc. This study's artifacts can be applied to implement the user-friendly function of knowledge exploration reflecting user's cognitive process toward associated knowledge, and can further underpin the development of an intelligent knowledge-base expanding autonomously through the discovery of new knowledge and their relationships by inference. This study, moreover than these, has an instant effect on implementing the networked knowledge map essential to satisfying contemporary users eagerly excavating the way to find proper knowledge to use.

• Journal of Nutrition and Health
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• v.55 no.3
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• pp.321-329
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• 2022

Riboflavin and its derivatives, flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD), are key components of mitochondrial energy metabolism and oxidation-reduction reactions. Proposed dietary reference intakes for Koreans (KDRIs), that is, estimated average requirements (EARs), for riboflavin, based on current knowledge of riboflavin and riboflavin derivative levels, and glutathione reductase activity, are 1.3 mg/d for men aged 19-64 years and 1.0 mg/d for women aged 19-64 years. By applying a coefficient of variance of 10%, reference nutrient intakes (RNIs) were set at 1.5 mg/d for men aged 19-64 years and 1.2 mg/d for women aged 19-64 years. Likewise, EARs and RNIs of riboflavin intake were proposed for all age groups and women in specific life stages such as pregnancy. Mean adult riboflavin intake for adults aged ≥ 19 years was 1.69 mg/d in Korea National Health and Nutrition Examination Survey (KNHANES) 2020, which was 124.9% of EAR according to the 2020 KDRIs. In the 2015-2017 KNHANES study, the mean riboflavin intake from foods and supplements was 2.79 mg/d for all age groups, and 32.7% of individuals consumed less riboflavin than EAR according to the 2020 KDRIs. For those that used supplements, mean intakes were 1.50 mg/d for riboflavin from foods, 10.26 mg/d from supplements, and 11.76 mg/d from food and supplements, and 5.5% of individuals consumed less riboflavin than EAR. Although the upper limit of riboflavin has not been established, the merits of increasing supplement use warrant further consideration. Also, additional epidemiologic and intervention studies are required to explore the role of riboflavin in the etiology of chronic diseases.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• Korean Journal of Environment and Ecology
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• v.36 no.3
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• pp.327-337
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• 2022

Ecological restoration is considered a good means to prevent biodiversity loss in terms of the ecosystem's health and sustainability. However, there are difficulties in putting it into practice as there is no comprehensive and objective standard for the selection of plant species, such as environmental, ecological factors, and restoration goal setting. Therefore, this study developed an evaluation index necessary for selecting plant species for restoration using the Delphi method that synthesizes the opinions of the expert group. A survey with 38 questionnaires was conducted twice for experts in ecological restoration, etc., and the importance and priority of evaluation indicators were analyzed by dividing the restoration targets into inland and island regions. The result of the importance analysis showed that "native plants" had the highest average of 4.9 among the evaluation indices in both inland and island regions, followed by "seed security", "propagation", and "root growth rate". In the inland region, the index priority was analyzed in the order of "native plants", "appearance frequency", "root growth rate", "distribution range", and "seed security" in the island region, it was analyzed in the order of "native plants", "root growth rate", "appearance frequency", "distribution range", and "tolerance", showing slight differences between the two indicators. As a result of the importance and priority indicator analysis, we set the mean importance and priority of 4.1 and 2.9, respectively, in the inland region and 4.2 and 2.9, respectively, in the island region. As for the criteria of selecting plant species for ecological restoration, the "native plants" had the highest importance and priority. "Seed securing", 'viability", "topography", "proliferation", "tolerance", "soil conditions", "growth characteristics", "early succession", "distribution range", "appearance frequency", and "germination rate" were classified into subgroups of low importance and priority. The lowest indicators were "final stage of succession", "transition period", 'transition stage", "root", "reproduction", "soil", "appearance", "technology", "landscape", "climate", and "germination rate". We expected that the findings through objective verification in this study would be used as evaluation indicators for selecting native plant species for ecological restoration.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.41-50
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• 2022

In this paper, a method to directly calculate the major elements of skin color such as melanin and hemoglobin from the RGB signal of the camera is proposed. The main elements of skin color typically measure spectral reflectance using specific equipment, and reconfigure the values at some wavelengths of the measured light. The values calculated by this method include such things as melanin index and erythema index, and require special equipment such as a spectral reflectance measuring device or a multi-spectral camera. It is difficult to find a direct calculation method for such component elements from a general digital camera, and a method of indirectly calculating the concentration of melanin and hemoglobin using independent component analysis has been proposed. This method targets a region of a certain RGB image, extracts characteristic vectors of melanin and hemoglobin, and calculates the concentration in a manner similar to that of Principal Component Analysis. The disadvantage of this method is that it is difficult to directly calculate the pixel unit because a group of pixels in a certain area is used as an input, and since the extracted feature vector is implemented by an optimization method, it tends to be calculated with a different value each time it is executed. The final calculation is determined in the form of an image representing the components of melanin and hemoglobin by converting it back to the RGB coordinate system without using the feature vector itself. In order to improve the disadvantages of this method, the proposed method is to calculate the component values of melanin and hemoglobin in a feature space rather than an RGB coordinate system using a feature vector, and calculate the spectral reflectance corresponding to the skin color using a general digital camera. Methods and methods of calculating detailed components constituting skin pigments such as melanin, oxidized hemoglobin, deoxidized hemoglobin, and carotenoid using spectral reflectance. The proposed method does not require special equipment such as a spectral reflectance measuring device or a multi-spectral camera, and unlike the existing method, direct calculation of the pixel unit is possible, and the same characteristics can be obtained even in repeated execution. The standard diviation of density for melanin and hemoglobin of proposed method was 15% compared to conventional and therefore gives 6 times stable.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.1
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• pp.17-26
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• 2022

Rice is one of the three major grains globally, and has been used as a staple food in Asian countries for a long time. In recent years, with the increase in the use of processed rice, the development and distribution of rice flour varieties have become a research focus. However, rice flour varieties are susceptible to pre-harvest sprouting (PHS). In this study, the optimal transplanting date for rice flour varieties for maximum yield production with PHS avoidance was examined. Four rice flour varieties with different maturity types (early maturing type, Garumi2 and medium-late maturing type, Seolgaeng, Hangaru, and Singil) were selected. The field experiment was conducted in Chuncheon (Central Plain area) and Cheorwon (Northern Plain area), Gangwon Province, Republic of Korea, from 2017 to 2019. The transplanting dates used were May 10, May 20, May 30, June 10, and June 20 in Chuncheon and April 30, May 10, May 20, May 30, and June 10 in Cheorwon. In Chuncheon, late transplantation decreased PHS in Garumi2. In Cheorwon, PHS in Garumi2 decreased with transplantation dates after May 20. The PHS decreased in Seolgaeng, Hangaru, and Singil with late transplantation in Chuncheon and Cheorwon. The optimal transplanting date for maximum yield production while avoiding PHS for Garumi2 was estimated to be June 10 in Chuncheon and May 25 in Cheorwon; for Seolgaeng, the optimal transplanting dates were May 20 in Chuncheon and May 15 in Cheorwon; for Hangaru, it was estimated to be May 30 in Chuncheon and May 15 in Cheorwon; and for Singil, the optimal dates were May 25 in Chuncheon and May 15 in Cheorwon.