• Korean journal of applied entomology
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• v.53 no.3
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• pp.247-260
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• 2014

To identify the species of Trichogramma occurring in the corn fields of Korea as egg parasitoids of Ostrinia furnacalis, we sequenced the full-length of ITS2 nuclear rDNA from 112 parasitoids collected during this study. As a reference to distinguish species, we also retrieved full-length ITS2 sequences of 60 Trichogramma species from the NCBI GenBank database. On the basis of the size and 3'terminal sequence pattern of the ITS2 sequences, the Trichogramma samples collected in this study were divided into three groups (K-1, -2, and -3). Evolutionary distances (d) within and between groups based on ITS2 sequences were estimated to be ${\leq}0.005$ and ${\geq}0.080$, respectively. In the net average distance between groups or species, the d value between K-1 and T. ostriniae, K-2 and T. dendrolimi, and K-3 and T. confusum was the lowest, with values of 0.016, 0.001, and 0.002, respectively. In the phylogenetic tree, K-1 and K-2 were clustered with T. ostriniae and T. dendrolimi, respectively. However, K-3 was clustered with three different species, namely, T. confusum, T. chilonis, and T. bilingensis. NCBI BLAST results revealed that parasitoids belonging to K-1 and K-2 showed 99% identity with T. ostriniae and T. dendrolimi, respectively. Parasitoids in K-3 collected from Hongcheon showed 99-100% identity with T. confusum and T. chilonis, and one parasitoid in K-3 collected from Gochang had 98% identity with T. bilingensis, T. confusum, and T. chilonis. On the basis of these results, we infer that the species of Trichogramma collected in this study are closely related to T. ostriniae (K-1) and T. dendrolimi (K-2). However, it was not possible to distinguish species of K-3 using the ITS2 sequence alone.

• Journal of The Korean Society of Grassland and Forage Science
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• v.12 no.2
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• pp.111-122
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• 1992

This experiment was conducted to determine the effects of spring seeding dates on the growth, yield and quality of early and late maturing spring oat (Auena sativa L.) cultivars on the forage experimental field, College of Agriculture, Seoul National University, Suwon from March to June, 1991. The experiment was arranged as a split plot with three replications. Oat cultivars, Cayuse and Speed oat, were the main plots, and seeding dates consisted of March 15, 22, 29, April 5 and 12 were the subplots. 1. A 7-day delay in seeding represents approximately 3~8 days being early in heading. The heading date of the early maturing cultivar, Speed oat, was 14 days earlier than that of the late maturing cultivar, Cayuse. 2. The concentrations of Crude protein (CP), Acid detergent fiber (ADF), Neutral detergent fiber (NDF) and zn uitro dry matter digestibility (IVDMD) of the late maturing cultivar, Cayuse, harvested May 29 were 19.6, 30.0, 44.9, and 82.7 %, respectively, but those of the early maturing cultivar, Speed oat, were 14.8. 33.3. 52.3, and 71.2 %, respectively. Chemical analyses of oat forage indicated that the contents of crude protein and lVDMD were increased from March 15 to April 12 seeding, while crude fiber was decreased. 3. Theaverage dry matter, IVDDM and CP yields of oats harvested May 29 were 2,960, 2,435 and 572 kg per ha, respectively with the late maturing cultivar. Cayuse. while the early maturing cultivar, Speed oat, recorded 3,255, 2.298 and 475 kg per ha, respectively. No significant dry matter yield differences were found among the different seeding dates of March 15, 22 and 29 for the two oat cultivars. but a significant yield decrease was found from April 5 seeding. No interactions in dry matter yield were observed between oat cultivars and seeding dates. 4. Maximum Leaf area index (LAI) and Leaf area index duration (LAID) were observed with earlier seeding and the LA1 of Cayuse cultivar was twice or three times as much as that of Speed oat cult~var as the growth progresses. 5. As the seeding date was earlier. the Crop growth rate (CGR) of the late maturing cultivar, Cayuse. was increased continuously. but that of the early maturing cultivar, Speed oat, was declined after May 29. This trend was also found on the Net assimilation rate (NAR) of Speed oat cultivar. The present experiment indicates that spring oats can be successfully produced as forages by seeding in March with early maturing cultivars.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.2
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• pp.152-158
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• 2019

This study was conducted to develop and prove the effects of an agronomical pest control measure on ginseng cultivated by direct seeding in paddy field, and the results obtained are as follows. Decomposition of ginseng in field during overwintering was due to gray mold rot caused by Botrytis cinerea, which occurred in October or November of 2016 and intensified in February and March the following year. The occurrence rate of gray mold rot based on shading materials was 6.5%, 16.8%, and 29.5% with light-proof paper, PE shade net, and rice straw shade, respectively. The initial infection occurred in the leaves prior to wintering and secondary infection occurred in the stems after wintering. The rate of screrotium formation by gray mold in the above-ground parts of ginseng tended to increase: 26.6% on October 20, 33.7% in November 20, and 41.8% on December 20. The force needed to remove the leaves and stems from withered ginseng was 0.2, 0.94, 2.5, and 5 kg for 1-, 2-, 3-, and 4- and 5-year holds; the force required was 1 kg after wintering, making it relatively easy to remove. The disease incidence rate after the removal of leaves and stems was 2.5%, 1.2%, and 2.2% in 4-, 5-, and 6-year-old plants, respectively, and a disease high incidence rate of 8.8%, 13.0%, and 18.2%, respectively, was seen when the leaves and stems were not removed. In both transplanting and direct seeding, the miss-planted rate decreased and the germination rate increased when shading material was removed and the surface of ridge was covered with soil or vinyl.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.3
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• pp.101-114
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• 2019

In this study, we carry out the numerical simulation to trace the yearly shoreline change of Mang-Bang beach, which is suffering from erosion problem. We obtain the basic equation (One Line Model for shoreline) for the numerical simulation by assuming that the amount of shoreline retreat or advance is balanced by the net influx of longshore and cross-shore sediment into the unit discretized shoreline segment. In doing so, the energy flux model for the longshore sediment transport rate is also evoked. For the case of cross sediment transport, the modified Bailard's model (1981) by Cho and Kim (2019) is utilized. At each time step of the numerical simulation, we adjust a closure depth according to pertinent wave conditions based on the Hallermeier's analytical model (1978) having its roots on the Shield's parameter. Numerical results show that from 2017.4.26 to 2017.10.15 during which swells are prevailing, a shoreline advances due to the sustained supply of cross-shore sediment. It is also shown that a shoreline temporarily retreats due to the erosion by the yearly highest waves sequentially occurring from mid-October to the end of October, and is followed by gradual recovery of shoreline as high waves subdue and swells prevail. It is worth mentioning that great yearly circulation of shoreline completes when a shoreline retreats due to the erosion by the higher waves occurring from mid-March to the end of March. The great yearly circulation of shoreline mentioned above can also be found in the measured locations of shoreline on 2017.4.5, 2017.9.7, 2017.11.7, 2018.3.14. However, numerically simulated amount of shoreline retreat or advance is more significant than the physically measured one, and it should be noted that these discrepancies become more substantial for the case of RUN II where a closure depth is sustained to be as in the most morphology models like the Genesis (Hanson and Kraus, 1989).

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1526-1531
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• 2024

¹³¹I is a fission product produced in a nuclear reactor by irradiating tellurium dioxide, with a half-life of 8.02 day. The most important and widely used method for making ¹³¹I is irradiation using a nuclear reactor and post-irradiation followed by dry distillation. The advantage of the dry distillation process is that the process and the equipment are relatively simple, namely TeO₂ (m.p. 750 ℃), which can withstand heating during reactor irradiation. Based on TeO₂ irradiation by neutron following the technique of dry distillation was explained for production of ¹³¹I on a large scale. A dry distillation followed the radioisotope production operation using the 30 MW GA Siwabessy nuclear reactor to meet national demand. TeO₂ targets are 25 and 50 g irradiated for 87-100 h. The resulting ¹³¹I activity is 20.29339-368.50335GBq. According to the requirements imposed on the radionuclide purity of the preparation, the contribution of ¹³¹I training in the resulting preparation was not less than 99.9 %

• Journal of the Korean Geosynthetics Society
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• v.12 no.4
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• pp.67-76
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• 2013

Landfill require special care due to the dangers of nearby surface water and underground water pollution caused by leakage of leachate. The leachate does not leak due to the installation of the geomembrane but sharp wastes or landfill equipment can damage the geomembrane and therefore a means of protecting the geomembrane is required. In Korea, in accordance with the waste control act being modified in 1999, protecting the geosynthetics liner on top of the slope of landfill and installing a drainage layer to fluently drain leachate became mandatory, and technologies are being researched to both protect the geomembrane and quickly drain leachate simultaneously. Therefore, this research has its purpose in studying the drainage functions of leachate and protection functions of the geomembrane in order to examine the application possibilities of Geo-Multicell-Composite (GMC) as a Leachate Collection Removal and Protection System (LCRPs) at the slope on top of the geomembrane of landfill by observing methods of inserting filler with high-quality water permeability at the drainage net. GMC's horizontal permeability coefficient is $8.0{\times}10^{-4}m^2/s$ to legal standards satisfeid. Also crash gravel used as filler respected by vertical permeability is 5.0 cm/s, embroidering puncture strength 140.2 kgf. A result of storm drain using artificial rain in GMC model facility, maxinum flow rate of 1,120 L/hr even spray without surface runoff was about 92~97% penetration. Further study, instead of crash gravel used as a filler, such as using recycled aggregate utilization increases and the resulting construction cost is expected to savings.

• 한국산학경영학회:학술대회논문집
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• 2004.11a
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• pp.101-123
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• 2004

This study examines empirically the significant changes in tax payments when the consolidated tax return is introduced in the future. We estimate the consolidated tax payments under the eight cases which are classified as such : whether only 100% ownership subsidiaries should be included or 80% and over, whether all subsidiaries should be included or only subsidiaries with loss, and whether unrealized profits from intercompany transactions should be excluded or not. After estimating the consolidated tax payments, we test the difference between the consolidated tax payments and the sum of the individual tax payments of the subsidiaries. The results of the test show that the consolidated tax payments are significantly less than the sum of the individual tax payments of the subsidiaries. We interpret that the inclusion of the losses of the subsidiaries in the consolidated tax base makes the tax payment decrease. Based on our analysis about 3.8 billion Won per each parent company would decrease due to the introduction of the consolidated tax return. And we find that under the mandatory consolidated tax return system the significant difference between the consolidated and individual tax payment exists except that the only 100% ownership subsidiaries are included and unrealized profits from intercompany transactions are not excluded. However, when the parent companies have the discretion to select the consolidated subsidiaries, the consolidated tax payments are significantly less than the sum of the individual tax payments of the subsidiaries regardless of the ownership percentage, inclusion of the loss of the subsidiaries and exclusion of the unrealized profits.