• Geophysics and Geophysical Exploration
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• v.23 no.1
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• pp.38-49
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• 2020

Full-waveform inversion (FWI) is an optimization process of fitting observed and modeled data to reconstruct high-resolution subsurface physical models. In acoustic FWI (AFWI), pressure data acquired using a marine streamer has mainly been used to reconstruct the subsurface P-wave velocity models. With recent advances in marine seismic-acquisition techniques, acquiring multi-component data in marine environments have become increasingly common. Thus, AFWI strategies must be developed to effectively use marine multi-component data. Herein, we proposed an AFWI strategy using horizontal and vertical particle-acceleration data. By analyzing the modeled acoustic data and conducting sensitivity kernel analysis, we first investigated the characteristics of each data component using AFWI. Common-shot gathers show that direct, diving, and reflection waves appearing in the pressure data are separated in each component of the particle-acceleration data. Sensitivity kernel analyses show that the horizontal particle-acceleration wavefields typically contribute to the recovery of the long-wavelength structures in the shallow part of the model, and the vertical particle-acceleration wavefields are generally required to reconstruct long- and short-wavelength structures in the deep parts and over the whole area of a given model. Finally, we present a sequential-inversion strategy for using the particle-acceleration wavefields. We believe that this approach can be used to reconstruct a reasonable P-wave velocity model, even when the pressure data is not available.

• Animal Bioscience
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• v.34 no.11
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• pp.1749-1756
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• 2021

Objective: Microminipig (MMP) is a miniature pig with an extra small body size for experimental use. In the present study, the occurrence of stillbirths and their genetic association with swine leukocyte antigen (SLA) class II haplotypes were evaluated in a population of MMPs. Methods: The occurrences of stillbirth and genetic association with SLA class II haplotypes using 483 stillborn and 2,246 live piglets, and their parents were compared among the three groups of newborn piglet litters; an all stillborn (AS) group consisting of only stillborn piglet litters, a partial stillborn (PS) group consisting of stillborn and live piglet litters, and an all alive (AA) group consisting of only live piglet litters. Results: The incidence of stillborn piglets was 483/2,729 (17.7%). Distributions of litter sizes, numbers of stillborn piglets in a litter, parities, and gestation periods were distinct among the three groups. The frequencies of low resolution haplotype (Lr)-0.7 or Lr-0.23 were higher in the AS group than in the PS or AA groups. In sires, the frequency of Lr-0.7 associated with the AS group was significantly higher in the AS group than with the AA group. In dams, the frequency of Lr-0.23 was significantly higher in the AS group than in the PS or AA groups, whereas the frequency of Lr-0.7 was not significantly different. Conclusion: The incidence of stillborn piglets in MMPs appears to be higher than those in other pig breeds. Several traits related with stillbirths such as the number of stillborn piglets and parities of the AS group were different from those of the PS and AA groups. Specific SLA class II haplotypes were associated significantly with a high incidence of stillbirths and could be used as genetic markers to adopt breeding strategies to lower the rate of stillbirth in MMPs.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.243-253
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• 2021

In this study, AHP analysis was conducted on potential residents who are willing or likely to live in a smart city. The purpose is to identify priorities for strategic objectives. In order to establish a hierarchical structure for the vision, core values, and strategic goals of the smart city business, we researched domestic and overseas smart city-related academic papers and research reports, business plans, and institutional websites. After reviewing professors, researchers, experts, and focus groups, 4 2nd tier and 12 3rd tier properties were finally selected and the hierarchical structure was confirmed. As a result of AHP analysis, it was found that residents place the highest importance on quality of life in smart city projects. As a result of the analysis of the lower-level factors, it was found that safety was the most important. The priorities were analyzed in the order of living convenience, eco-friendliness, and social inequality resolution. It is expected that the results of this analysis will be able to suggest strategies to be established when promoting smart city projects in the future.

• The Journal of Korean Association of Computer Education
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• v.22 no.1
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• pp.87-98
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• 2019

It is easy to assume that contradictions are logically incorrect or empty sets that have no solvability. This dilemma, which can not be done, is difficult to solve because it has to solve the contradiction hidden in it. Paradoxically, therefore, contradiction resolution has been viewed as an innovative and creative problem-solving. TRIZ, which analyzes the solution of the problem from the perspective of resolving contradictions, has been used for people rather than computers. The Butterfly model, which analyzes the problem from the perspective of solving the contradiction like TRIZ, analyzed the type of contradiction problem using symbolic logic. In order to apply an appropriate concrete solution strategy for a given contradiction problems, we designed the Butterfly algorithm based on decision making tree. We also developed a visualization tool based on Python tkInter to find concrete solution strategies for given contradiction problems. In order to verify the developed tool, the third grade students of middle school learned the Butterfly algorithm, analyzed the contradiction of the wooden support, and won the grand prize at an invention contest in search of a new solution. The Butterfly algorithm developed in this paper systematically reduces the solution space of contradictory problems in the beginning of problem solving and can help solve contradiction problems without trial and errors.

• Journal of Ecology and Environment
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• v.45 no.3
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• pp.143-151
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• 2021

Background: Plants are able to optimize defense responses induced by various herbivores, which have different feeding strategies. Local and systemic responses within a plant after herbivory are essential to modulate herbivore-specific plant responses. For instance, leaf-chewing herbivores elicit jasmonic acid signaling, which result in the inductions of toxic chemicals in the attacked leaf (tissue-specific responses) and also in the other unattacked parts of the plant (systemic responses). Root herbivory induces toxic metabolites in the attacked root and alters the levels of transcripts and metabolites in the unattacked shoot. However, we have little knowledge of the local and systemic responses against stem-boring herbivores. In this study, we examined the systemic changes in metabolites in the wild tobacco Nicotiana attenuata, when the stem-boring herbivore Trichobaris mucorea attacks. Results: To investigate the systemic responses of T. mucorea attacks, we measured the levels of jasmonic acid (JA), JA-dependent secondary metabolites, soluble sugars, and free amino acids in 7 distinct tissues of N. attenuata: leaf lamina with epidermis (LLE), leaf midrib (LM), stem epidermis (SE), stem pith (SP), stem vascular bundle (SV), root cortex with epidermis (RCE), and root vascular bundle (RV). The levels of JA were increased in all root tissues and in LM by T. mucorea attacks. The levels of chlorogenic acids (CGAs) and nicotine were increased in all stem tissues by T. mucorea. However, CGA was systematically induced in LM, and nicotine was systematically induced in LM and RCE. We further tested the resource allocation by measuring soluble sugars and free amino acids in plant tissues. T. mucorea attacks increased the level of free amino acids in all tissues except in LLE. The levels of soluble sugars were significantly decreased in SE and SP, but increased in RV. Conclusions: The results reveal that plants have local- and systemic-specific responses in response to attack from a stem-boring herbivore. Interestingly, the level of induced secondary metabolites was not consistent with the systemic inductions of JA. Spatiotemporal resolution of plant defense responses against stem herbivory will be required to understand how a plant copes with attack from herbivores from different feeding guilds.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.507-518
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• 2021

Magnetic resonance imaging (MRI) is a key technology that has been seeing increasing use in studying the structural and functional innerworkings of the brain. Analyzing the variability of brain connectome through tractography analysis has been used to increase our understanding of disease pathology in humans. However, there lacks standardization of analysis methods for small animals such as mice, and lacks scientific consensus in regard to accurate preprocessing strategies and atlas-based neuroinformatics for images. In addition, it is difficult to acquire high resolution images for mice due to how significantly smaller a mouse brain is compared to that of humans. In this study, we present an Allen Mouse Brain Atlas-based image data analysis pipeline for structural connectivity analysis involving structural region segmentation using mouse brain structural images and diffusion tensor images. Each analysis method enabled the analysis of mouse brain image data using reliable software that has already been verified with human and mouse image data. In addition, the pipeline presented in this study is optimized for users to efficiently process data by organizing functions necessary for mouse tractography among complex analysis processes and various functions.

• Journal of Korea Entertainment Industry Association
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• v.15 no.5
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• pp.115-128
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• 2021

The purpose of this study is to examine the importance and satisfaction perceived in the purchase of goods and the use of a court in badminton club members. The results will be used for basic data to increase club members and present the methods to activate badminton. The survey on goods, price, programs, facilities, staff, and publicity was conducted. The IPA matrix was applied for data processing. The following conclusions were drawn. First, as a result of analyzing the ranking of importance and satisfaction, the first place of importance was coach's professionalism of staff factors, followed by safety of facility factors and program contents and effects of program factors. The first place of satisfaction was cleanliness and management of facility factors, followed by coach's professionalism of staff factors and staff's kindness of staff factors. Second, as a result of the IPA matrix of importance and satisfaction, Quadrant I included appropriateness of training time and program contents and effect of program factors, parking size and cleanliness and management of facility factors, coach's professionalism and staff's service attitude of staff factors, and customer service and complaint resolution of publicity factors. Quadrant II showed appropriateness of price, value for money, and discount policy of price factors and materials and design of goods factors. Quadrant III included excellent customer service of goods of goods factors, various program construction of program factors, court location and accessibility, and various convenient facilities of facility factors, and various publicity and event programs, website construction, and various publicity strategies of publicity factors. Quadrant IV showed brand value of goods, awareness, and brand specialty of goods of goods factors.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.33-43
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• 2024

This research assessed the applicability of the You Only Look Once (YOLO)v8 and DeepLabv3+ models for the effective detection of compost heaps, identified as a significant source of non-point source pollution. Utilizing high-resolution imagery acquired through Unmanned Aerial Vehicles(UAVs), the study conducted a comprehensive comparison and analysis of the quantitative and qualitative performances. In the quantitative evaluation, the YOLOv8 model demonstrated superior performance across various metrics, particularly in its ability to accurately distinguish the presence or absence of covers on compost heaps. These outcomes imply that the YOLOv8 model is highly effective in the precise detection and classification of compost heaps, thereby providing a novel approach for assessing the management grades of compost heaps and contributing to non-point source pollution management. This study suggests that utilizing UAVs and deep learning technologies for detecting and managing compost heaps can address the constraints linked to traditional field survey methods, thereby facilitating the establishment of accurate and effective non-point source pollution management strategies, and contributing to the safeguarding of aquatic environments.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1679-1692
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• 2023

South Korea is a country that emits a large amount of pollutants as a result of population growth and industrial development and is also severely affected by transboundary air pollution due to its geographical location. As pollutants from both domestic and foreign sources contribute to air pollution in Korea, the location of air pollutant emission sources is crucial for understanding the movement and distribution of pollutants in the atmosphere and establishing national-level air pollution management and response strategies. Based on this background, this study aims to effectively acquire spatial information on domestic and international air pollutant emission sources, which is essential for analyzing air pollution status, by utilizing high-resolution optical satellite images and deep learning-based image segmentation models. In particular, industrial parks and quarries, which have been evaluated as contributing significantly to transboundary air pollution, were selected as the main research subjects, and images of these areas from multi-purpose satellites 3 and 3A were collected, preprocessed, and converted into input and label data for model training. As a result of training the U-Net model using this data, the overall accuracy of 0.8484 and mean Intersection over Union (mIoU) of 0.6490 were achieved, and the predicted maps showed significant results in extracting object boundaries more accurately than the label data created by course annotations.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.125-132
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• 2024

The purpose of this study is to discover a drone utilization model tailored to local characteristics, propose directions for building a drone demonstration city based on demand surveys for drone activation, and suggest ways to utilize and support a drone application system. First, according to the survey results, there was a high understanding of and necessity for drone demonstration projects, particularly in addressing urban issues, which were deemed to have a significant impact. Second, based on the analysis of priorities and short- and long-term approaches, disaster-related tasks were evaluated as a priority, requiring an approach through medium- to long-term strategies. Third, it was noted that budgetary considerations emerged as the most critical issue during project implementation. Practitioners and experts expressed willingness to actively introduce drone-based technologies into their work when budget and technology were ready. Budgetary constraints were identified as the most significant obstacle to proper implementation, emphasizing the need for resolution. Fourth, the necessity of demand surveys during project development was identified in certain areas. Demand surveys were deemed essential for drone-based demonstration city construction, and a survey indicated that public leadership in this regard was also necessary. Fifth, concerning approaches in specific areas, the field of safety and disaster management was highlighted as the most crucial for application.