• Smart Media Journal
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• v.9 no.4
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• pp.176-186
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• 2020

The size and shape of crops are diverse, and the growing environment is also different. Therefore, when one uses a drone to spray pesticides, the characteristics of each crop must be considered, and flight conditions such as the flight height and forwarding velocity of the drone should be changed. The droplet flow of pesticides is affected by various flight conditions, and a large change occurs in the sprayed area. As a result, an uneven distribution of liquid may be formed at the wake, and the transport efficiency will be decreased as well as there would be a risk of toxic scatter. Therefore, this paper analyzes the degree of distribution of pesticides to the crops through numerical analysis when pesticide is sprayed onto the selected three crops with different characteristics by using agricultural drones with different flight conditions. On the purpose of establishing a guideline for spraying pesticides using a drone in accordance with the characteristics of crops, this paper compares the amount of pesticides distributed in the crops at the wake of nozzle flow using the figure of merit, and the sum of transported liquid rate divided by the root mean square of the probability density function.

• Journal of Food Hygiene and Safety
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• v.35 no.6
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• pp.552-560
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• 2020

Listeria monocytogenes is a highly pathogenic gram-positive bacterium that is easily isolated from cheese, meat, processed meat products, and smoked salmon. A zero-tolerance (n=5, c=0, m=0/25 g) criteria has been applied for L. monocytogenes in cheese meaning that L. monocytogenes must not be detected in any 25 g of samples. However, there was a lack of scientific information behind this criteria. Therefore, in this study, we conducted a risk assessment based on literature reviews to provide scientific information supporting the baseline and to raise public awareness of L. monocytogenes foodborne illness. Quantitative risk assessment of L. monocytogenes for cheese was conducted using the following steps: exposure assessment, hazard characterization, and risk characterization. As a result, the initial contamination level of L. monocytogenes was -4.0 Log CFU/g in cheese. The consumption frequency of cheese was 11.8%, and the appropriate probability distribution for amount of cheese consumed was a Lognormal distribution with an average of 32.5 g. In conclusion, the mean of probabilities of foodborne illness caused by the consumption of cheese was 5.09×10-7 in the healthy population and 4.32×10-6 in the susceptible population. Consumption frequency has the biggest effect on the probability of foodborne illness, but storage and transportation times have also been found to affect the probability of foodborne illness; thus, management of the distribution environment should be considered important. Through this risk assessment, scientific data to support the criteria for L. monocytogenes in cheese could be obtained. In addition, we recommend that further risk assessment studies of L. monocytogenes in various foods be conducted in the future.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.623-631
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• 2021

The increased transport of marine hazardous and noxious substances (HNS) has resulted in frequent HNS spill accidents domestically and internationally. There are about 6,000 species of HNS internationally, and most of them have toxic properties. When an accidental HNS spill occurs, it can destroys the marine ecosystem and can damage life and property due to explosion and fire. Constructing a spectral library of HNS according to wavelength and developing a detection algorithm would help prepare for accidents. In this study, a ground HNS spill experiment was conducted in France. The toluene spectrum was determined through hyperspectral sensor measurements. HNS present in the hyperspectral images were detected by applying the spectral mixture algorithm. Preprocessing principal component analysis (PCA) removed noise and performed dimensional compression. The endmember spectra of toluene and seawater were extracted through the N-FINDR technique. By calculating the abundance fraction of toluene and seawater based on the spectrum, the detection accuracy of HNS in all pixels was presented as a probability. The probability was compared with radiance images at a wavelength of 418.15 nm to select abundance fractions with maximum detection accuracy. The accuracy exceeded 99% at a ratio of approximately 42%. Response to marine spills of HNS are presently impeded by the restricted access to the site because of high risk of exposure to toxic compounds. The present experimental and detection results could help estimate the area of contamination with HNS based on hyperspectral remote sensing.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.133-146
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• 2021

Providing weather information on roads today means that the road weather conditions near weather observation points are presented to road managers and road users. These weather observation points are managed by the Korea Meteorological Administration. However, it is difficult to provide accurate weather information due to physical limitations such as the presence of precipitation collection points, distance to weather information provision roads, and the presence of mountains. Therefore, this study intends to perform a comparative analysis by time zone and administrative dong provided by the Meteorological Administration using the wiper information among the information contained in the PVD(Probe Vehicle Data) collected from the highway C-ITS project. As a result of the analysis it was possible to detect rainfall even in the event of local rainfall and rainfall over a long period of time and the higher the cumulative precipitation per hour, the higher the probability of coincidence. This study is meaningful because it used PVD to solve the limitations of the existing road weather information provision method and suggested utilization plan for PVD.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.205-215
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• 2022

Amidst the global climate crisis, dam operation policies formulated under the stationary climate assumption could lead to unsatisfactory water management. In this work, we assessed status-quo performance of the Yongdam Dam in Korea under various climatic stresses in flood risk reduction and water supply reliability for 2021-2040. To this end, we employed a decision-centric framework equipped with a stochastic weather generator, a conceptual streamflow model, and a machine-learning reservoir operation rule. By imposing 294 climate perturbations to dam release simulations, we found that the current operation rule of the Yongdam dam could redundantly secure water storage, while inefficiently enhancing the supply reliability. On the other hand, flood risks were likely to increase substantially due to rising mean and variability of daily precipitation. Here, we argue that the current operation rules of the Yongdam Dam seem to be overly focused on securing water storage, and thus need to be adjusted to efficiently improve supply reliability and reduce flood risks in downstream areas.

• Journal of Industrial Convergence
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• v.20 no.3
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• pp.81-87
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• 2022

The purpose of this study is to investigate the actual conditions of students before and after acquiring national technical qualifications for nail cosmetologists, and to provide basic data for building systematic data according to students' competency and effective educational methods for acquiring qualifications. The statistical package program SPSS v 18.0 was used for the trend of acquiring national technical qualifications for nail cosmetologists among each variable according to careers in the nail beauty industry for nail beauty students residing in Gwangju. As a result of using the statistical package program SPSS v 18.0, the analysis showed that the proportion of women (80.8%), single (70.8%), 20s (47.7%), college or university graduates (26.2%), and students (42.3%) was high. In addition, as a result of cross-analysis of the period and cost according to gender for obtaining the national technical license of the nail beautician, it was concluded that the Pearson chi-square significant probability (p) was .416 and .899, respectively, and there was a difference between men and women. The field experience of the nail beauty industry was found to have a significant positive (+) effect on the training period, course cost, educational institution, and exam experience (p <.001) for obtaining a certificate. Based on the results of this study, it is necessary to promote the development of nail beauty marketing and to present a constructive vision of the nail beauty industry to be pursued in the future.

• Journal of the Korean Society for information Management
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• v.38 no.4
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• pp.1-23
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• 2021

The purpose of this study is to analyze the effects of the bestseller ranks on the book circulations in public libraries. To achieve this goal, the weekly data sets of 179 books' library circulation and bestseller list from January 1, 2018 to December 29, 2019 were constructed based on the data collected from BigData MarketC and YES24. Three methods for analyzing panel data including linear regression, fixed-effect, and random effect models were compared, and it turned out that fixed-effect model was better than other methods. The results show that the average ranks of bestsellers were associated with their public library circulations visually. Also, the analysis of fixed-effect model showed that the single rank decline of a book on the bestseller list decreases its average circulation of 0.108 while the size of effect varied depending on subject of books. The study empirically demonstrated the impact of a bestseller list on people's book circulation behavior, suggesting that public libraries need to reference sociocultural context as well as bestseller book lists to predict library user needs and to formulate collection development policy.

• Journal of Industrial Convergence
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• v.20 no.6
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• pp.27-35
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• 2022

The purpose of this study is to analyze the metaverse-related issues in the news big data from an educational perspective, explore their characteristics, and provide implications for the educational applicability of the metaverse and future education. To this end, 41,366 cases of metaverse-related data searched on portal sites were collected, and weight values of all extracted keywords were calculated and ranked using TF-IDF, a representative term weight model, and then word cloud visualization analysis was performed. In addition, major topics were analyzed using topic modeling(LDA), a sophisticated probability-based text mining technique. As a result of the study, topics such as platform industry, future talent, and extension in technology were derived as core issues of the metaverse from an educational perspective. In addition, as a result of performing secondary data analysis under three key themes of technology, job, and education, it was found that metaverse has issues related to education platform innovation, future job innovation, and future competency innovation in future education. This study is meaningful in that it analyzes a vast amount of news big data in stages to draw issues from an education perspective and provide implications for future education.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• 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.