• Journal of Apiculture
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• v.32 no.3
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• pp.181-189
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• 2017

Lysinibacillus fusiformis has been suspected to be a pathogen of Bombus terrestris in Korea since 2008. In this study, we developed the rapid detection method for the L. fusiformis by utilizing the Ultra-rapid PCR. After optimizing of L. fusiformis-specific Ultra-rapid PCR, it can detect the existence of $1.0{\times}10^8$ L. fusiformis-specific DNA molecules in 4 minute and 22 seconds. Even, only 10 molecules could be detected quantitatively using this method. In addition, for the first time, in our knowledge, L. fusiformis was detected using proposed method from bumblebee produced commercially in Korea. Not only in the laboratory but also in the field, L. fusiformis-specific Ultra-rapid PCR would be applied and might be expected as convenient tools at production of bumblebee or inspection for the import and export of bumblebee.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.1-13
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• 2020

In the event of fire, it is necessary to put out the fire within a golden time to minimize personal and property damages. To this end, it is necessary for fire engines to arrive at the site quickly. This study established a fire engine travel time estimation model to secure the golden time by identifying road and environmental factors that influence fire engine travel time in the case of fire by examining data on fire occurrence with GIS DB. The study model for the estimation of fire engine travel time (model 1) covers variables by applying correlation analysis and regression analysis with dummy variables and predicts travel time for different types of places where fire may occur (models 2, 3, 4). Analysis results showed that 17 siginificant independent variables are derived in model 1 and the fire engine travel time differs depending on the types of places where fire occurs. Key variables(travel distance, number of lane, type of road) that are included commonly in the 4 models were identified. Variables identified in this study can be utilized as indicators for research related to travel time of emergency vehicles and contribute to securing the golden time for emergency vehicles.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.88-96
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• 2021

When oil tankers, large passenger ships and cargo ships sink under the sea owing to various maritime accidents, the residual cargo oil or fuel oil in the such vessels could pose direct risk to factor for the marine environment and it require safe and rapid removal. Although domestic and foreign salvage companies are adopting various recovery methods and technologies with customizations according to each site during recovery operations studies on reasonable assessment modules for the operation process are relatively insufficient. In this study, the data from trapped-oil recovery operations performed at different site conditions were collected and analyzed in order to designed an operation assessment module, define the operational process steps in terms of preparation, implementation and completion, and derive key factors for each detailed process. Subsequently, the module was designed in such a way as to construct performance indicators to assess these key factors. In order to exclude subjective opinions from the assessment as much as possible, the assessment each item was constructed with indicators based on data that could be evaluated quantitatively and its usefulness was verified by applying the module to the trapped-oil recovery operation cases. We expect this the method and the technology assessment module for the trapped-oil recovery operation on sunken vessels will help to verify the adequacy of the trapped-oil recovery such operation before or after. Furthermore, it is expected that the continuous accumulation of assessment data and feedback from past or future operation cases will contribute toward enhancing the overall safety, efficiency and field applicability of trapped-oil recovery operation.

• The Korea Journal of Herbology
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• v.36 no.1
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• pp.9-17
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• 2021

Objectives : Tetrapanacis Medulla and Akebiae Caulis are one of the most frequently adulterated herbal medicines because of their confusability of terms in the ancient writings and the similarity of morphological features of dried herbal products. The major adulterant is Aristolochia manshuriensis (Guanmutong) which has a serious safety concern with its toxicity. To ensure the safety and quality of the two herbal medicines, it is necessary to discriminate the toxic adulterant from authentic species. The aim of this study is to develop SCAR markers and to establish the multiplex-SCAR assay for discrimination of four plant species related to Tetrapanacis Medulla and Akebiae Caulis. Methods : ITS regions of fifteen samples of four species (Tetrapanax papyrifer, Fatsia japonica, Aristolochia manshuriensis, and Akebia quinata) collected from different sites were amplified and sequenced. Fifteen obtained ITS sequences were aligned and analysed for the detection of species-specific sequence variations. The SCAR markers were designed based on the sequence alignments and then, multiplex-SCAR assay enhancing rapidity was optimized. Results : ITS sequences clearly distinguished the four species at the species level. The developed SCAR markers and multiplex-SCAR assay were successfully discriminated four species and detected the adulteration of commercial product samples by comparison of the amplified DNA fragment sizes. Conclusions : These SCAR markers and multiplex-SCAR assay are a rapid, simple, and reliable method to identify the authentic Tetrapanacis Medulla and Akebiae Caulis from adulterants. These genetic tools will be useful to ensure the safety and to standardize the quality of the two herbal medicines.

• Journal of the Korean Society for information Management
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• v.38 no.1
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• pp.243-262
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• 2021

The present study aims to identify the factors that affect the continuous use and discontinuance of the hospital information seeking applications(hospital apps thereafter) by employing the post acceptance model. The surveys were conducted with people who used the hospital apps from October 11 to 18, 2019. Researchers collected 125 valid data and analyzed them by using the structural equation model. The study found that the satisfaction and confirmation of expectation for the hospital apps users had significant effects on intention for continuous use and perceived usefulness, respectively. However, the perceived usefulness did not have a significant effect on the intention for continue use. The present study has identified the variables that influence the continuous use of these innovative technologies. The findings of the study confirmed the post acceptance model by observing the adoption and use of the hospital apps and extended the literature of the post acceptance model by discussing the unique characteristics of the hospital apps that satisfy the urgent help-seekers under emergency situations or the information needs emphasizing promptness. In addition, based on the benefits and limitations of hospital apps reported by consumers, the study provided practical implications for designing more user-friendly apps to hospital app developers or managers.

• Economic and Environmental Geology
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• v.55 no.3
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• pp.309-316
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• 2022

In order to minimize the human and time consumption required for rock classification, research on rock classification using artificial intelligence (AI) has recently developed. In this study, basic volcanic rocks were subdivided by using polarizing microscope thin section images. A convolutional neural network (CNN) model based on Tensorflow and Keras libraries was self-producted for rock classification. A total of 720 images of olivine basalt, basaltic andesite, olivine tholeiite, trachytic olivine basalt reference specimens were mounted with open nicol, cross nicol, and adding gypsum plates, and trained at the training : test = 7 : 3 ratio. As a result of machine learning, the classification accuracy was over 80-90%. When we confirmed the classification accuracy of each AI model, it is expected that the rock classification method of this model will not be much different from the rock classification process of a geologist. Furthermore, if not only this model but also models that subdivide more diverse rock types are produced and integrated, the AI model that satisfies both the speed of data classification and the accessibility of non-experts can be developed, thereby providing a new framework for basic petrology research.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.769-777
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• 2021

Purpose: Earthquake disasters are frequently occur unpredictable situations due to various variables and unexpected situations. As a result, the work process itself is not uniform, making it difficult for public officials in the disaster safety department to familiarize themselves with the earthquake field manual. This paper is specifically and accurately grasp the current work situation conducted by the Disaster and Safety Countermeasures Headquarters of the Gyeongsangbuk-do Office and present a plan for designing serious game scenarios necessary for field manual learning. Method: In this study, scenarios were designed based on the GBS(Goal Based Scenario) model, and in the process of assigning missions and roles based on the Gyeongsangbuk-do earthquake field manual, public officials related to earthquakes were able to acquire knowledge and skills to solve practical tasks. Result: Scenario data of the proposed technique was implemented as a systematic procedure by processing various earthquake-related information into logical data and simplifying and abstracting it for game expression for earthquake situation training. Conclusion: In the event of an earthquake due to learning through serious games, related public officials of Gyeongsangbuk-do provincial are expected to be able to respond quickly to various earthquake disasters.

• Food Science and Industry
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• v.55 no.1
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• pp.58-73
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• 2022

Due to the recent demand for low-calorie foods, consumers are looking for alternative sweeteners that can control blood sugar, low risk of tooth decay and low calories. Regulations for permitted sweeteners in food vary from every country, and it is important for the government and the food industry to monitor products containing these sweeteners to ensure global compliance. Therefore, rapid, precise, and accurate analysis for food matrices should be applied to quality control, market surveillance, monitoring, and evaluation of food additive intake in the food industry. To analyze sweeteners simultaneously, it is essential to develop an efficient and rapid analytical method and to perform appropriate pretreatment steps such as solvent extraction and purification. This study presented the recent analysis trends about the suitable extraction method for food matrices focusing on non-nutritive sweeteners. Additionally, techniques for multi-compounds analysis using HPLC and LC-MS/MS and non-destructive analysis techniques using FT-IR were comprehensively described.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.1-11
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• 2021

The purpose of our research is to efficiently deform a 3D models which is composed of a triangular mesh and a skeleton. We designed a novel inverse kinematics (IK) solver that calculates the updated positions of mesh vertices with fewer computing operations. Through our user interface, one or more markers are selected on the surface of the model and their target positions are set, then the system updates the positions of surface vertices to construct a deformed model. The IK solving process for updating vertex positions includes many computations for obtaining transformations of the markers, their affecting joints, and their parent joints. Many of these computations are often redundant. We precompute those redundant terms in advance so that the 3-nested loop computation structure was improved to a 2-nested loop structure, and thus the computation time for a deformation is greatly reduced. This novel IK solver can be adopted for efficient performance in various research fields, such as handling 3D models implemented by LBS method, or object tracking without any markers.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.239-247
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• 2022

Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process.