• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1545-1557
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• 2021

Crops show sensitive spectral characteristics according to their species and growth conditions and although frequent observation is required especially in summer, it is difficult to utilize optical satellite images due to the rainy season. To solve this problem, Constrained Cloud-Maximum Normalized difference vegetation index Composite (CC-MNC) algorithm was developed to generate periodic composite images with minimal cloud effect. In thisstudy, using this method, monthly Sentinel-2A/B Normalized Difference Vegetation Index (NDVI) composite images were produced for paddies and high-latitude cabbage fields from 2019 to 2021. In August 2020, which received 200mm more precipitation than other periods, the effect of clouds, was also significant in MODIS NDVI 16-day composite product. Except for this period, the CC-MNC method was able to reduce the cloud ratio of 45.4% of the original daily image to 14.9%. In the case of rice paddy, there was no significant difference between Sentinel-2A/B and MODIS NDVI values. In addition, it was possible to monitor the rice growth cycle well even with a revisit cycle 5 days. In the case of high-latitude cabbage fields, Sentinel-2A/B showed the short growth cycle of cabbage well, but MODIS showed limitations in spatial resolution. In addition, the CC-MNC method showed that cloud pixels were used for compositing at the harvest time, suggesting that the View Zenith Angle (VZA) threshold needsto be adjusted according to the domestic region.

• Journal of Plant Biotechnology
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• v.48 no.3
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• pp.131-138
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• 2021

We reviewed current research trends for discriminating between species of the Angelica genus, a group of important medicinal plants registered in South Korea, China, and Japan. Since the registered species for medicinal purposes differ by country, they are often adulterated as well as mixed in commercial markets. Several DNA technologies have been applied to distinguish between species. However, one of the restrictions is insufficient single-nucleotide polymorphisms (SNPs) within the target DNA fragments; in particular, among closely-related species. Recently, amplification refractory mutation system (ARMS)-PCR and highresolution melting (HRM) curve analysis techniques have been developed to solve such a problem. We applied both technologies, and found they were able to discriminate several lines of Angelica genus, including A. gigas Nakai, A. gigas Jiri, A. sinensis, A. acutiloba Kitag, and Levisticum officinale. Furthermore, although the ITS region differs only by one SNP between A. gigas Nakai and A. gigas Jiri, both HRM and ARMS-PCR techniques were powerful enough to discriminate between them. Since both A. gigas Nakai and A. gigas Jiri are native species to South Korea and are very closely related, they are difficult to discriminate by their morphological characteristics. For practical applications of these technologies, further research is necessary with various materials, such as dried or processed materials (jam, jelly, juice, medicinal decoctions, etc.) in commercial markets.

• Journal of the Korean Society of Earth Science Education
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• v.14 no.2
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• pp.202-211
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• 2021

The purpose of this study is to find out the Learning elements in 'Curriculum Reconstruction' of Elementary Pre-service Teachers in Connection with 'The weather and our daily life'. The pre-service teachers who participated in the study formed a research group of 29 students in 2nd grade who are attending the first semester of A university of education and taking courses in 'teaching research 1'. Participants described the learning topics and contents they would like to add to curriculum 'The weather and our daily life'. Each response was analyzed and classified based on scientific terms related to weather or climate. The results of the study were as follows. First, there were three learning topics related to weather, such as water phenomena in the atmosphere, fine dust and yellow dust phenomena, and light or electricity phenomena, and two topics related to climate such as abnormal climate and global warming. Second, interest in the problem of fine dust and yellow dust in the atmosphere was relatively high. Third, the interest in learning in the knowledge area was relatively higher than in the learning in the function or attitude area. Through these research results, it can be confirmed that it is necessary to develop a climate change or climate crisis education program.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.315-322
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• 2021

This study analyzes the Required Safe Egress Time (RSET), in the event of a fire at a welfare center for the disabled, using the emergency passage according to the floor arrangement of users to evaluate the safety and the difference in RSET for each emergency passage using the Pathfinder simulation program to suggest an efficient evacuation method. As a result of RSET, it was found that there is no problem in evacuation safety for the current state of the facility's personnel allocation by satisfying the standard RSET in case of fire, and evacuation can be completed safely by evacuating through stairs rather than using elevators if possible. It is necessary for employees to be provided sufficient education and training in advance so that they can evacuate effectively with the disabled in case of fire. This study gives significance in saving many precious lives and safely evacuate in case of fire as evacuation routes were secured through the design, construction and operation of facilities for the disabled and the RSET was shortened through regular evacuation practices. It is necessary to discuss the further RSET studies based on the automatic fire shutters open or not when a fire occurs at a specific location following the installation of automatic fire shutter at the entrance of each floor of the facilities.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.55-61
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• 2021

In this paper, we proposed a DeepLabv3+ based encoder-decoder model utilizing an attention mechanism for precise semantic segmentation. The DeepLabv3+ is a semantic segmentation method based on deep learning and is mainly used in applications such as autonomous vehicles, and infrared image analysis. In the conventional DeepLabv3+, there is little use of the encoder's intermediate feature map in the decoder part, resulting in loss in restoration process. Such restoration loss causes a problem of reducing segmentation accuracy. Therefore, the proposed method firstly minimized the restoration loss by additionally using one intermediate feature map. Furthermore, we fused hierarchically from small feature map in order to effectively utilize this. Finally, we applied an attention mechanism to the decoder to maximize the decoder's ability to converge intermediate feature maps. We evaluated the proposed method on the Cityscapes dataset, which is commonly used for street scene image segmentation research. Experiment results showed that our proposed method improved segmentation results compared to the conventional DeepLabv3+. The proposed method can be used in applications that require high accuracy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.103-111
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• 2021

The purpose of this study is to study a method for preventing collisions of multiple drones in controlled airspace. As a result of the study, it was proved that it is appropriate as a method to control drone collisions after setting accurate information on the ROI (Region of Interest) area estimated based on the expected drone path and time in the control system as a method to avoid drone collision. As a result of the empirical analysis, the diameter of the flight path of the operating drone should be selected to reduce the risk of collision, and the change in the departure time and operating speed of the operating drone did not act as an influencing factor in the collision. In addition, it has been demonstrated that providing flight priority is one of the appropriate methods as a countermeasure to avoid collisions. For collision avoidance methods, not only drone sensor-based collision avoidance, but also collision avoidance can be doubled by monitoring and predicting collisions in the control system and performing real-time control. This study is meaningful in that it provided an idea for a method for preventing collisions of multiple drones in controlled airspace and conducted practical tests. This helps to solve the problem of collisions that occur when multiple drones of different types are operating based on the control system. This study will contribute to the development of related industries by preventing accidents caused by drone collisions and providing a safe drone operation environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.100-112
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• 2021

With the advent of big data, traffic prediction has been developed based on historical data analysis methods, but this method deteriorates prediction performance when a traffic incident that has not been observed occurs. This study proposes a method that can compensate for the reduction in traffic prediction accuracy in traffic incidents situations by hybrid approach of machine learning and traffic simulation. The blind spots of the data-driven method are revealed when data patterns that have not been observed in the past are recognized. In this study, we tried to solve the problem by reinforcing historical data using traffic simulation. The proposed method performs machine learning-based traffic prediction and periodically compares the prediction result with real time traffic data to determine whether an incident occurs. When an incident is recognized, prediction is performed using the synthetic traffic data generated through simulation. The method proposed in this study was tested on an actual road section, and as a result of the experiment, it was confirmed that the error in predicting traffic state in incident situations was significantly reduced. The proposed traffic prediction method is expected to become a cornerstone for the advancement of traffic prediction.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.3
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• pp.268-275
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• 2021

The three factors that determine the strength of concrete are the strength of cement paste, aggregate and ITZ(Interfacial Transition Zone) between aggregate and cement paste. Out of these, the strength of ITZ is the most vulnerable. ITZ is formed in 10~50㎛, the ratio of calcium hydroxide is high, and CSH appears low ratio. A high calcium hydroxide ratio causes a decrease in the bond strength of ITZ. ITZ is due to further weak area. The problem of ITZ appears as a more disadvantageous factor when it used lightweight aggregate. The previous study of ITZ properties have measured interfacial toughness, identified influencing factors ITZ, and it progressed SEM and XRD analysis on cement matrix without using coarse aggregates. also it was identified microstructure using EMPA-BSE equipment. However, in previous studies, it is difficult to understand the microstructure and mechanical properties. Therefore, in this study, a method of measuring electrical resistance using EIS(Electrochemical Impedance Spectroscopy) measuring equipment was adopted to identify the ITZ between natural aggregate and lightweight aggregate, and it was tested the change of ITZ by surface coating of lightweight aggregate with ground granulated blast furnace slag. As a result, the compressive strength of natural aggregate and lightweight aggregate appear high strength of natural aggregate with high density, surface coating lightweight aggregate appear strength higher than natural aggregate. The electrical resistivity of ITZ according to the aggregate appeared difference.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.691-704
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• 2021

The development of AI technology has brought about a big change in our lives. The importance of AI and data education is also growing as AI's influence from life to society to the economy grows. In response, the OECD Education Research Report and various domestic information and curriculum studies deal with data literacy and present it as an essential competency. However, the definition of data literacy and the content and scope of the components vary among researchers. Thus, we analyze the semantic similarity of words through Word2Vec deep learning natural language processing methods along with the definitions of key data literacy studies and analysis of word frequency utilized in components, to present objective and comprehensive definition and components. It was revised and supplemented by expert review, and we defined data literacy as the 'basic ability of knowledge construction and communication to collect, analyze, and use data and process it as information for problem solving'. Furthermore we propose the components of each category of knowledge, skills, values and attitudes. We hope that the definition and components of data literacy derived from this study will serve as a good foundation for the systematization and education research of AI education related to students' future competency.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.391-402
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• 2021

Purpose: By categorizing opinions by subject in the safety certification ecosystem, we want to identify weaknesses in system operation and suggest improvement plans so that the safety certification system can have quick resilience against future variability. Method: Through literature research and data analysis, similar domestic and foreign safety certifications and related cases, as well as the current status of international standards and national standards, etc. were confirmed, and a fact-finding survey was conducted for each stakeholder in the safety certification ecosystem, and problem types and improvement measures were established. Result: We conduct a fact-finding survey of the overall system, such as quality satisfaction with safety certification target products, obstacles in the development, manufacturing and use process, and safety certification-related improvements, targeting workplaces that manufacture, import or use safety certification target machines By discovering and categorizing problems and weaknesses in system operation, detailed implementation tasks were derived to establish improvement directions and improve operability. Conclusion: For the advancement and internationalization of the safety certification system, it is necessary to efficiently carry out the detailed promotion tasks derived from this study. In addition, in order to strengthen the resilience to the variability of the safety certification ecosystem, the operating system of a virtuous cycle structure by improving the mutual relationship between each subject construction is considered important.