• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.1-8
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• 2021

In the political field of news articles, there are polarized and biased characteristics such as conservative and liberal, which is called political bias. We constructed keyword-based dataset to classify bias of news articles. Most embedding researches represent a sentence with sequence of morphemes. In our work, we expect that the number of unknown tokens will be reduced if the sentences are constituted by subwords that are segmented by the language model. We propose a document embedding model with subword tokenization and apply this model to SVM and feedforward neural network structure to classify the political bias. As a result of comparing the performance of the document embedding model with morphological analysis, the document embedding model with subwords showed the highest accuracy at 78.22%. It was confirmed that the number of unknown tokens was reduced by subword tokenization. Using the best performance embedding model in our bias classification task, we extract the keywords based on politicians. The bias of keywords was verified by the average similarity with the vector of politicians from each political tendency.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.45-50
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• 2021

In this paper, we propose the development of a smart space security device that can be opened and closed remotely using IoT. Existing space security devices can control opening and closing by breaking hardware or only using button devices or replicated keys. The recent COVID-19 crisis has created several applications for non-contact devices. In this study, we propose the development of a small space security device that has the function of unlocking through an app without touching the device. By transferring the control authority to a smartphone, device that cannot be opened or closed by only operating hardware at the user's option. It is convenient and hygienic because it can be opened and closed using an app without touching the locking device. Multiple security is possible because security can be released using an app after user authentication by fingerprint recognition and pattern input on a smartphone. If the user wishes, after using the app security, the security is released by directly touching a button installed in the safe or space or opening it with a key. In addition, by adding an inactive function to the app, it is designed so that the door of the safe cannot be opened when the key is lost or the small safe is lost. This study is expected to be able to effectively expand the security system by applying variously to objects that require security.

• Tunnel and Underground Space
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• v.30 no.6
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• pp.509-518
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• 2020

This study constructed a new simulation platform, including mesh generation process, numerical simulation, and post-processing for results analysis based on exploration data to perform real-scale numerical analysis considering the actual geological structure efficiently. To build the simulation platform, we applied for open-source programs. The source code is open to be available for code modification according to the researcher's needs and compatibility with various numerical simulation programs. First, a three-dimensional model(3D) is acquired based on the exploration data obtained using a drone. Then, the domain's mesh density was adjusted to an interpretable level using Blender, the free and open-source 3D creation suite. The next step is to create a 3D numerical model by creating a tetrahedral volume mesh inside the domain using Gmsh, a finite element mesh generation program. To use the mesh information obtained through Gmsh in a numerical simulation program, a converting process to conform to the program's mesh creation protocol is required. We applied a Python code for the procedure. After we completed the stability analysis, we have created various visualization of the study using ParaView, another open-source visualization and data analysis program. We successfully performed a preliminary stability analysis on the full-scale Dokdo model based on drone-acquired data to confirm the usefulness of the proposed platform. The proposed simulation platform in this study can be of various analysis processes in future research.

• Research in Plant Disease
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• v.26 no.4
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• pp.229-238
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• 2020

Field validation of PBcast, an infection risk model for Phytophthora blight of pepper, was conducted through a designed field experiment in 2012 and 2013. Conduciveness of weather conditions at 26 locations in Korea in 2014-2017 was also evaluated using PBcast. The PBcast estimated daily infection risk (IR) of Phytophthora capsici based on weather and soil texture data. In the designed filed experiment, four treatments including routine sprays at 7-day intervals (RTN7), forecast-based sprays when IR reached 200 (IR200) and 224 (IR224), and no spray (CTRL) were compared in terms of disease incidence and number of sprays recommended for disease control. In 2012, IR had reached over 200 twice, but never reached 224. In 2013, IR had reached over 200 three times and once higher than 224. The RTN7 plots were sprayed 17 and 18 times in 2012 and 2013, respectively. Weather conditions throughout the country were generally conducive for Phytophthora blight and 3-4 times of fungicide sprays would have been reduced if the PBcast forecast information was adopted in the decision-making for fungicide sprays. In conclusion, the PBcast forecast would be useful to reduce fungicide applications without losing the disease control efficacy to protect pepper crop from Phytophthora blight.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.67-74
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• 2021

Ground Operations Command (GOC) has studied various methods using artificial intelligence (AI) in order to accomplish ground missions more effectively and to strongly respond to variable strategic situations with advancements in fourth industrial revolution technology. As the result of various literature reviews, PEST-7S-SWOT is considered the most appropriate methodology for promoting strategies and for task development. These procedures consist of three stages. Phase 1 is analysis of external environmental factors from applying PEST procedures. We analyzed external environmental factors to determine opportunities and risk factors. Phase 2 is the analysis of internal environmental factors from applying 7S strategies. We analyzed the current state of an organization to find strengths and weaknesses. Phase 3 is SWOT analysis. It is based on the opportunities and risk factors from Phase 1 and the strength and weakness factors from Phase 2. We derive promotional strategies and tasks through SWOT analysis. In this study, four strategies and 11 tasks were derived for GOC AI systems. Those are promotion of policies and systems, reinforcing organizations, building an AI base, increasing expertise and capabilities, and validating PEST-7S-SWOT methodologies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.332-340
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• 2021

The purpose of this study was to present a method for the application of drones through analysis after surveying Jeollanam-do firefighters regarding the recognition, operation, field of application, necessary field of work, and the need for education on fire drones. As a result of the survey, 80.29% of respondents were found to be willing to operate drones, and the fields of work for which drones were considered the most necessary were in the order of rescue, fire suppression, life safety, first aid, and others. Besides, 77.38% of respondents thought that drones could contribute to the prevention of safety accidents for firefighters, and 70.13% of respondents thought that it would be appropriate to recruit firefighting drone operators through changing positions, and respondents chose firefighters in their 40s as the most suitable age group for firefighting drone operation. Also, 82.84% of respondents said they would participate in drone training, and they recognized that the use of drones could contribute to solving the physical problems caused by the aging of firefighters, and that drone training would also help firefighters manage their retirement. The fields where firefighting drones are used were investigated in the order of searching for requestors, checking on-site information, and checking on-site prior risk. In this study, a difference analysis for each group was performed according to the drone operation experience. There was a statistically significant difference in the items of safety measures for requestors. The results of variance analysis by work experience confirmed that there were statistically significant differences in a total of eight items, including four items related to the field of use of drones, and the age group of the drone operating crew, and whether or not to help retirement management.

• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.62-69
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• 2011

Six commonly used non-linear growth functions were fitted to individual tree height-dbh data of eight major tree species measured by the $5^{th}$ National Forest Inventory in Chungcheong province. A total of 2,681 trees were collected from permanent sample plots across Chungcheong province. The available data for each species were randomly splitted into two sets: the majority (90%) was used to estimate model parameters and the remaining data (10%) were reserved to validate the models. The performance of the models was compared and evaluated by $R^2$, RMSE, mean difference (MD), absolute mean difference (AMD) and mean difference(MD) for diameter classes. The combined data (100%) were used for final model fitting. The results showed that these six sigmoidal models were able to capture the height-diameter relationships and fit the data equally well, but produced different asymptote estimates. Sigmoidal growth models such as Chapman-Richards, Weibull functions provided the most satisfactory height predictions. The effect of model performance on stem volume estimation was also investigated. Tree volumes of different species were computed by the Forest Resources Evaluation and Prediction Program using observed range of diameter and the predicted tree total height from the six models. For trees with diameter less than 30 cm, the six height-dbh models produced very similar results for all species, while more differentiation among the models was observed for large-sized trees.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1201-1208
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• 2020

A mass of seawater with similar properties in the ocean is called a water mass, and the front is a sea area where two masses of different properties meet. The gradient algorithm is a method of extracting where the sea water temperature pixel changes rapidly assuming that the slope is large, and the place with the large slope is assumed to be a front. This method is able to process large amounts of satellite data at once. Therefore, in this study, we tried to find the front lines in the sea area around the Korean Peninsula by using a gradient algorithm. The study data used gridded sea surface temperature satellite data. The resolution was 1/4°, and the monthly average data from January 1993 to December 2018 were used. There were major five fronts representatively, China Coastal Front, South Sea Coastal Front, Kuroshio Front/ Kuroshio Extension Front, Subpolar Front and the Subarctic Front. As a result of comparing the distribution of front by season, more types of front were distributed in winter and spring than in summer and autumn, and the distribution range was wider.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.49-60
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• 2020

The number of traffic control equipment installed to prevent traffic accidents increases every year due to continuous installation by the National Police Agency and local governments. However, it is installed based on qualitative judgment rather than engineering analysis results. The purpose of this study was to present additional installations in the future by presenting the installation criteria considering the severity of accidents for each road type and calculating the appropriate number of installations. ARI indicators that can indicate the severity of traffic accidents were developed, and road types were classified through analysis of variance and cluster analysis, and accident information by road type was analyzed to derive ARI of clusters with high traffic accident severity. The ARI values required to determine the installation of equipment for each road type were presented, and 5,244 additional installation points were analyzed.