• Journal of Climate Change Research
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• v.3 no.4
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• pp.245-257
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• 2012

Vulnerability assessment has been required for establish climate adaptation plan to prevent damage from climate change. In this study we assessed vulnerability with 1 km resolution and determined which sectors have the highest priority in each municipality of Gangwon province based on the result of vulnerability assessment. All sectors of vulnerability assessment are composed of three criteria; sensitivity, exposure and adaptation capacity. And suitable indicators of each sector were selected and spatial data set was prepared using GIS. Priority of vulnerability was classified with the degree of vulnerability in present and variation in vulnerability between present and future. The results of vulnerability assessment were different among municipalities due to the contribution of indicators. Present and future trends in vulnerability showed similar results but high vulnerable area was predicted to expand in the future. In addition increase in temperature led whole area to be more vulnerable generally. The result of prioritizing sectors of vulnerability indicated the most considerable sectors within a municipality. Also, the municipalities which have similar geographic, climatic and social conditions tended to be classified as the same priority class. The method of vulnerability assessment and determining priorities suggested in this study could be used to support decision makers to establish adaptation plan of local area.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.277-283
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• 2018

The crop growth model has been widely used for climate change impact assessment. Crop growth model require genetic coefficients for simulating growth and yield. In order to determine the genetic coefficients, regional growth monitoring data or yield trial data of crops has been used to calibrate crop growth model. The aim of this study is to verify that yield trial data of corn is appropriate to calibrate genetic coefficients of CERES-MAIZE. Field experiment sites were Suwon, Jinju, Daegu and Changwon. The distance from the weather station to the experimental field were from 1.3km to 27km. Genetic coefficients calibrated by yield trial data showed good performance in silking day. The genetic coefficients associated with silking are determined only by temperature. In CERES-MAIZE model, precipitation or irrigation does not have a significant effect on phenology related genetic coefficients. Although the effective distance of the temperature could vary depending on the terrain, reliable genetic coefficients were obtained in this study even when a weather observation site was within a maximum of 27 km. Therefore, it is possible to estimate the genetic coefficients by yield trial data in study area. However, the yield-related genetic coefficients did not show good results. These results were caused by simulating the water stress without accurate information on irrigation or rainfall. The yield trial reports have not had accurate information on irrigation timing and volume. In order to obtain significant precipitation data, the distance between experimental field and weather station should be closer to that of the temperature measurement. However, the experimental fields in this study was not close enough to the weather station. Therefore, When determining the genetic coefficients of regional corn yield trial data, it may be appropriate to calibrate only genetic coefficients related to phenology.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.63 no.4
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• pp.322-330
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• 2018

The correlation between the nutritional composition of soybeans and whole soymilk palatability was investigated using nine soybean cultivars (Teagwangkong, Daewonkong, Saedanback, Jinpung, Daechan, Miso, Cheongmiin, Cheongja 3, and Socheongja). Physicochemical analysis of soybeans, showed that the protein and lipid contents were 37.7-46.0 and 15.2-20.9%, respectively. Unsaturated fatty acids were 81.1-84.8% of total fatty acids, of which linoleic acids was 49.7-56.8%. Total tocopherol was $243.5-361.3{\mu}g/g$ of extract, of which ${\gamma}$-tocopherol was $67.14-86.49{\mu}g/g$. Total isoflavone contents varied within cultivars from $495.4-1,443.8{\mu}g/g$ of extract. Daidzin and genistin were 252.1-556.0 and $241.8-730.7{\mu}g/g$, respectively, which were major isoflavones in soybeans. For the sensory evaluation, whole soymilk was made from nine soybean cultivars and 20 panels investigate its palatability. The Daechan cultivar had the highest (9.1), and Cheongmiin the lowest (5.6), overall palatability score. Interestingly, sensory results were strongly correlated with linoleic acid (0.746) and stearic acid (-0.716) content. In summary, the fatty acid composition of soybeans is the key factor in determining the palatability of whole soymilk. This study could be applied to determine the suitability of cultivars for soybean products, including whole soymilk.

• Korean Journal of Environmental Biology
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• v.36 no.4
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• pp.479-487
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• 2018

The purpose of this study is to analyze photochemical activity of nursery seedlings under drought stress, using chlorophyll fluorescence reaction analysis. Young nursery seedlings of tomato (Lycopersicon esculentum Mill.) and cucumber (Cucumis sativa L.), were grown under drought stress for 8 days. Analysis of chlorophyll fluorescence reaction (OJIP) and parameters, were performed to evaluate photochemical fluctuation in nursery seedlings under drought stress. Chlorophyll fluorescence reaction analysis showed maximal recorded fluorescence (P) decreased from the 5 day after treatment in tomato seedlings, while an amount of chlorophyll fluorescence increased at the J-I step. Thus, physiological activity was reduced. In cucumber seedlings, maximal recorded fluorescence (P) and maximal variable fluorescence ($F_V$) lowered from the 4 day after treatment, and chlorophyll fluorescence intensity of J-I step increased. Chlorophyll fluorescence parameter analysis showed electron transfer efficiency of PSII and PSI were significantly inhibited with decreasing $ET2_O/RC$ and $RE1_O/RC$ from the 5 day after treatment, in tomato seedlings and from the 4 day after treatment, in cucumber seedlings. $ET2_O/RC$ and $PI_{ABS}$ significantly changed. In conclusion, 6 indices such as $F_V/F_M$, $DI_O/RC$, $ET2_O/RC$, $RE1_O/RC$, $PI_{ABS}$ and $PI_{TOTAL}ABS$ were selected for determining drought stress in nursery seedlings. Drought stress factor index (DFI) was used to evaluate whether the crop was healthy or not, under drought stress. Cucumber seedlings were less resistant to drought stress than tomato seedlings, in the process of drought stress.

• 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.

• Korean Journal of Ecology and Environment
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• v.52 no.2
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• pp.105-117
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• 2019

In order to investigate the characteristics of benthic macroinvertebrate communities in the Gihwa stream, a tributary of the Dong River, we surveyed the community and environmental factors in April and November 2013 at 6 sites. The benthic macroinvertebrate taxa represented total 63 species belonging to 29 families, 12 orders, 5 classes and 4 phyla. Total 48 (10~28 in each site) species were collected in April and 44 (13~24 in each site) in November. The number of individuals increased slightly from $560{\sim}2,290m^{-2}$ in April to $806{\sim}3,674m^{-2}$ in November. Chironomidae spp. was dominant species in April and Stenopsyche bergeri was dominant species in November. In the Functional Feeding Groups, Gathering-collector(53.9%) was dominant in April, while Filtering-collector (44.3%) increased in November. Intolerant order category (i.e. EPT species richness) in St.1, St.5 and St.6 increased in November compared to April due to the increase of Trichoptera. St.2, St.3 and St.4, which were located near the fish farm, were low EPT as a whole, but Benthic macroinvertebrate index (BMI) was good state in November than April due to decrease of Chironomidae spp.. The environmental factors in the survey site showed similar tendency except for St.1 between both seasons, and electrical conductivity, salinity, and water width showed seasonal differences. Cluster analysis and Nonmetric multidimensional scaling (NMDS) based on benthic macroinvertebrate community data were divided into two groups according to season. Electrical conductivity, salinity and substrate composition were the most influential factors determining the distribution patterns of macroinvertebrate communities.

• The Korean Journal of Air & Space Law and Policy
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• v.33 no.2
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• pp.291-314
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• 2018

In recent 3 years, Korea's low-cost airlines have expanded their areas of passenger transportation not only to domestic market but also to Japan, China, Southeast Asia and US territory as a total of 6 companies (8 airlines including small air operation business carriers). Currently, three more airlines have filed for air transportation business certification as future low-cost carriers, and this expansion is expected to continue. To cope with the aggressive airline operations of domestic and foreign low-cost carriers and to enhance their competitiveness, each low-cost airline is taking a number of strategies for promoting cabin service. Therefore, the workload of the cabin crew is increased in proportion to the expansion, and the fatigue directly connected with the safety task performance is increased. It is stipulated in the Enforcement Regulations of the Korea Aviation Safety Act that at minimum, one cabin crew is required per 50 passenger seating capacity, and all low cost carriers are boarding only the minimum cabin crew. Sometimes it is impossible for them to sit in a floor level emergency exit for evacuation, which is the main task of the cabin crew, and this can cause confusion among evacuating passengers in the event of an emergency. In addition, if one of the minimum cabin crew becomes incapacitated due to an injury or the like, it will become a serious impediment in performing emergency evacuation duties. Even in the normal situation, since it will be violating the Act prescription on the minimum cabin crew complement, passengers will have to move to another available airline flights, encountering extreme inconvenience. Annex 6 to the Convention on International Civil Aviation specifies international standards for the determination of the minimum number of cabin crew shall be based only on the number of passenger seats or passengers on board for safe and expeditious emergency evacuation. Thereby in order to enhance the safety of the passengers and the crew on board, it is necessary to consider the cabin crew's fatigue that may occur in the various job characteristics (service, safety, security, first aid)and floor level emergency exit seating in calculating the minimum number of cabin crew. And it is also deemed necessary for the government's regulatory body to enhance the cabin safety for passengers and crew when determining the number of minimum cabin crew by reflecting the cabin crew's workload leading to their fatigue and unavailability to be seated in a floor level emergency exit on low cost carriers.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Korean Journal of Environmental Agriculture
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• v.38 no.1
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• pp.54-60
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• 2019

BACKGROUND: Hydrogen fluoride is one of the 97 accident preparedness substances regulated by the Ministry of Environment (Republic of Korea) and chemical accidents should be managed centrally due to continual occurrence. Especially, hydrogen fluoride has a characteristic of rapid diffusion and very toxic when leaking into the environment. Therefore, it is important to predict the impact range quickly and to evaluate the residual contamination immediately to minimize the human and environmental damages. METHODS AND RESULTS: In order to estimate the accident impact range, the off-site consequence analysis (OCA) was performed to the worst and alternative scenarios. Also, in order to evaluate the residual contamination of hydrogen fluoride in crop, the samples in accident site were collected from 15-divided regions (East direction from accident sites based on the main wind direction), and the concentration was measured by fluoride ($F^-$) ion-selective electrode potentiometer (ISE). As a result of the OCA, the affected distance by the worst scenario was estimated to be >10 km from the accident site and the range by the alternative scenario was estimated to be about 1.9 km. The residual contamination of hydrogen fluoride was highest in the samples near the site of the accident (E-1, 276.82 mg/kg) and tended to decrease as it moved eastward. Meanwhile, the concentrations from SE and NE (4.96~28.98 mg/kg) tended to be lower than the samples near the accident site. As a result, the concentration of hydrogen fluoride was reduced to a low concentration within 2 km from the accident site (<5 mg/kg), and the actual damage range was estimated to be around 2.2 km. Therefore, it is suggested that the results are similar to those of alternative accident scenarios calculated by OCA (about 1.9 km). CONCLUSION: It is difficult to estimate the chemical accident-affecting range/region by the OCA evaluation, because it is not possible to input all physicochemical parameters. However simultaneous measurement of the residual contamination in the environment will be very helpful in determining the diffusion range of actual chemical accident.

• Journal of Bio-Environment Control
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• v.28 no.4
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• pp.366-375
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• 2019

This experiment aims to determine the proper irrigation scheduling based on a whole-substrate capacitance using a newly developed device (SCMD) by comparing with the integrated solar radiation automated irrigation system (ISR) and sap flow sensor automated irrigation system (SF) for the cultivation of tomato (Solanum lycopersicum L. 'Hoyong' 'Super Doterang') during spring to winter season. For the SCMD system, irrigation was conducted every 10 minutes after the first irrigation was started until the first run-off was occurred, of which the substrate capacitance was considered to be 100%. When the capacitance threshold (CT) was reached to the target point, irrigation was re-conducted. After that, when the target drain volume (TDV) was occurred, the irrigation stopped. The irrigation volume per event for the SCMD was set to 50, 75, or 100 mL at CT 0.9 and TDV 100 mL during the spring to summer cultivation, and the CT was set to 0.65, 0.75, 0.80, or 0.90 in the winter cultivation. When the irrigation volume per event was set to 50, 75, or 100 mL, the irrigation frequency in a day was 39, 29, and 19, respectively, and the drain rate was 3.04, 9.25, and 20.18%, respectively. When the CT was set to 0.65, 0.75, or 0.90 in winter, the irrigation frequency was about 6, 7, 15 times, respectively and the drain rate was 9.9, 10.8, 35.3% respectively. The signal of stem sap flow at the beginning of irrigation starting time did not correspond to that of solar irradiance when the irrigation volume per event was set to 50 or 75 mL, compared to that of 100 mL. In winter cultivation, the stem sap flow rate and substrate volumetric water content at the CT 0.65 treatment were very low, while they were very high at CT 0.90 was high. All the integrated data suggest that the proper range of irrigation volume per event is from 75 to 100 mL under at CT 0.9 and TDV 100 mL during the spring to summer cultivation, and the proper CT seems to be higher than 0.75 and lower than 0.90 under at 75 mL of the irrigation volume per event and TDV 70 mL during the winter cultivation. It is going to be necessary to investigate the relationship between capacitance value and substrate volumetric water content by determining the correction coefficient.