• Journal of the Institute of Convergence Signal Processing
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• v.22 no.1
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• pp.14-19
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• 2021

This study has attempted to use ozone for the treatment of skin diseases as research results that ozonated olive oil has an excellent therapeutic effect on skin diseases are known. However, there is hardly any accurate data in Korea. Usually, animal tests related to cosmetics are not performed, but toxicity tests were conducted because they were absolutely necessary. In general, there are not many cases of measuring actual data through animal tests for the purpose of confirming the performance of cosmetics, but in the case of toxicity tests, it is recommended to accurately measure skin reactions, so this experiment was conducted. In this experiment, in order to evaluate the skin irritation of ozonated oil (high concentration) on the rabbit, the test substance was applied to the back of the rabbit for 24 hours, and then mortality, general symptoms and skin irritation were evaluated. Experimental Results As a result of evaluating the treatment site of the test substance after a certain period of time, no skin irritation was observed in all animals.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.491-498
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• 2021

In this study, the effective dose was measured using the PCXMC v2.0 program by examining the conditions used to set the diagnostic reference level for intraoral imaging recommended by the government, and the effect of the Al additive filter was confirmed. In oral imaging, the largest effective dose was calculated from the oral mucosa among 11 organs. The effect of the Al additive filter showed an excellent radiation reduction effect at 2mm rather than 1mm. In the case of children aged 5 years, the overall effective dose was calculated to be high in all 11 organs because they are more sensitive to radiation than adults. And as a result of evaluating the image quality according to the use of an additional filter during intraoral imaging, there was no significant difference in SNR and CNR changes compared to before the additional filter was used. Based on this study, it is thought that additional filter settings can be recommended for intraoral imaging.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.184-193
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• 2021

The system for managing spatial data updates the existing information by extracting only the information that is different from the existing information for the newly obtained spatial information file to update the stored information. In order to extract only objects that have changed from existing information, it is necessary to compare whether there is any difference from existing information for all objects included in the newly obtained spatial information file. This study was conducted to improve this total inspection method in a situation where the amount of spatial information that is frequently updated increases and data update is required at the national level. In this study, before inspecting individual objects in a new acquisition space information file, a method of determining whether individual space objects have been changed only by the information in the file was considered. Spatial data files have structured data characteristics different from general image or text document files, so it is possible to determine whether to change the file unit in a simpler way compared to the existing method of creating and managing file hash. By reducing the number of target files that require full inspection, it is expected to improve the use of resources in the system by saving the overall data quality inspection time and saving data extraction time.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.193-198
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• 2021

Recently, interests in the 4th industry have increased the level of demand for pest control by farmers in the field of rice farming, and the interests and use of agricultural pest control drones. Therefore, the diversification of agricultural control drones that spray high-concentration pesticides and the increase of agricultural exterminators due to the acquisition of national drone certifications are rapidly developing the agricultural sector in the drone industry. In addition, as detailed projects, an effective platform is required to construct large-scale big data due to pesticide management, exterminator management, precise spraying, pest control work volume classification, settlement, soil management, prediction and monitoring of damages by pests, etc. and to process the data. However, studies in South Korea and other countries on development of models and programs to integrate and process the big data such as data analysis algorithms, image analysis algorithms, growth management algorithms, AI algorithms, etc. are insufficient. This paper proposed an online drone pest control management information platform to meet the needs of managers and farmers in the agricultural field and to realize precise AI pest control based on the agricultural drone pest control processor using drones and presented foundation for development of a comprehensive management system through empirical experiments.

• Journal of The Korean Association For Science Education
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• v.42 no.2
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• pp.253-264
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• 2022

The purpose of this study is to examine the practice of scientists from the perspective of Ian Hacking's 'creation of phenomena'. Scientific phenomena, according to Hacking, are regular and do not exist in nature without the intervention of scientists or experimental tools. This study tries to derive scientific educational meaning by analyzing the thoughts and episodes of the 'Sontanda (inter-individual variability)' phenomenon experienced by four life scientists. The Sontanda phenomenon is a common term used by scientists to describe phenomena in which findings do not appear consistently even when studies are carried out using the same experimental procedure and materials. The following four educational implications were discovered as a result of the research. First, we confirmed the importance of embodied knowledge, or non-verbal knowledge, which solves issues by making appropriate judgments and reactions at all times, rather than simply becoming accustomed to the experimental method. This argues that propositional knowledge and non-verbal knowledge should be handled equally in order to provide students with a practical scientific inquiry. Second, we tried to reconsider the picture of the experiment. The phenomenon revealed in the interviews of scientists is rare, and it takes a long time to stabilize the phenomenon. On the other hand, the image of school experiments is always positive and consistent, necessitating a shift in perspective. Third, the precise meaning of scientific practice could be confirmed. This study confirms that scientists use their knowledge effectively in line with the circumstances, and we examined strategies to apply scientific practice to school instruction based on this. Finally, by provoking uncertainty, the Sontanda phenomena may give students with an opportunity to engage in meaningful scientific involvement. By breaking away from the cookbook experiment, this study expects school experimental education to help in efforts to experience scientific practice.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.378-390
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• 2022

With the recent development of deep composite multiplication neural network learning, deep learning techniques applied to single-image super-resolution have shown good results, and the strong expression ability of deep networks has enabled complex nonlinear mapping between low-resolution and high-resolution images. However, there are limitations in applying it to real-time or low-power devices with increasing parameters and computational amounts due to excessive use of composite multiplication neural networks. This paper uses blocks that extract hierarchical characteristics little by little using information distillation and suggests the Recursive Distillation Super Resolution Network (RDSRN), a lightweight network that improves performance by making more accurate high frequency components through high frequency residual purification blocks. It was confirmed that the proposed network restores images of similar quality compared to RDN, restores images 3.5 times faster with about 32 times fewer parameters and about 10 times less computation, and produces 0.16 dB better performance with about 2.2 times less parameters and 1.8 times faster processing time than the existing lightweight network CARN.

• Industry Promotion Research
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• v.7 no.1
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• pp.59-74
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• 2022

There are three main ways to interpret Zhouyi(周易). The first is to interpret it as a number, the second is to interpret it as an image symbolized by the Gwae(卦), and the third is to interpret it as the moral reason contained in it. Although YulRyeo(律呂) is not as widely known as Zhouyi, its use in ancient times was the same as that of the main character. First, the mathematical analysis method using the three-pronged method for tuning musical instruments, second, the symbolic interpretation using the musical meaning symbolized by YulRyeo, and third, the applied interpretation method that expands to the moral reason contained in YulRyeo. The purpose of this thesis is to organize the dictionary meaning of YulRyeo and various meanings of ancient YulRyeo. In addition, by studying ancient literature on the meaning of YulRyeo's magic spell mechanics(術數易學) and Naepeum and Five Elements(納音五行), which is the origin of Gobeop Myongriology, I classify and interpret them in detail. and to find ways to apply it to Myongriology. It is hoped that this study will give a more in-depth understanding of YulRyeo and will be of little help to related studies such as the mechanics of magic and Myongriology studies in the future.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.461-469
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• 2022

This paper presents a novel training data extraction approach using semi-supervised learning (SSL)-based classification without the analyst intervention for time-series land-cover mapping. The SSL-based approach first performs initial classification using initial training data obtained from past images including land-cover characteristics similar to the image to be classified. Reliable training data from the initial classification result are then extracted from SSL-based iterative classification using classification uncertainty information and class labels of neighboring pixels as constraints. The potential of the SSL-based training data extraction approach was evaluated from a classification experiment using unmanned aerial vehicle images in croplands. The use of new training data automatically extracted by the proposed SSL approach could significantly alleviate the misclassification in the initial classification result. In particular, isolated pixels were substantially reduced by considering spatial contextual information from adjacent pixels. Consequently, the classification accuracy of the proposed approach was similar to that of classification using manually extracted training data. These results indicate that the SSL-based iterative classification presented in this study could be effectively applied to automatically extract reliable training data for time-series land-cover mapping.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.661-672
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• 2022

After the outbreak of the SARS-CoV2 virus that causes COVID-19, it spreads around the world with the number of infections and deaths rising rapidly caused a shortage of medical resources. As a way to solve this problem, chest X-ray diagnosis using Artificial Intelligence(AI) received attention as a primary diagnostic method. The purpose of this study is to comprehensively analyze the detection of COVID-19 via AI. To achieve this purpose, 292 studies were collected through a series of Classification methods. Based on these data, performance measurement information including Accuracy, Precision, Area Under Cover(AUC), Sensitivity, Specificity, F1-score, Recall, K-fold, Architecture and Class were analyzed. As a result, the average Accuracy, Precision, AUC, Sensitivity and Specificity were achieved as 95.2%, 94.81%, 94.01%, 93.5%, and 93.92%, respectively. Although the performance measurement information on a year-on-year basis gradually increased, furthermore, we conducted a study on the rate of change according to the number of Class and image data, the ratio of use of Architecture and about the K-fold. Currently, diagnosis of COVID-19 using AI has several problems to be used independently, however, it is expected that it will be sufficient to be used as a doctor's assistant.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.57-64
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• 2022

If the inside of a building collapses due to a disaster such as fire, collapse, or natural disaster, the physical security inside the building is likely to become ineffective. Here, physical security is needed to minimize the human casualties and physical damages in the collapsed building. Therefore, this paper proposes an algorithm to minimize the damage in a disaster situation by fusing existing research that detects obstacles and collapsed areas in the building and a deep learning-based object detection algorithm that minimizes human casualties. The existing research uses a single camera to determine whether the corridor environment in which the robot is currently located has collapsed and detects obstacles that interfere with the search and rescue operation. Here, objects inside the collapsed building have irregular shapes due to the debris or collapse of the building, and they are classified and detected as obstacles. We also propose a method to detect rescue requesters-the most important resource in the disaster situation-and minimize human casualties. To this end, we collected open-source disaster images and image data of disaster situations and calculated the accuracy of detecting rescue requesters in disaster situations through various deep learning-based object detection algorithms. In this study, as a result of analyzing the algorithms that detect rescue requesters in disaster situations, we have found that the YOLOv4 algorithm has an accuracy of 0.94, proving that it is most suitable for use in actual disaster situations. This paper will be helpful for performing efficient search and rescue in disaster situations and achieving a high level of physical security, even in collapsed buildings.