• Animal Bioscience
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• v.35 no.5
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• pp.778-788
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• 2022

Objective: The objective of the study was to understand and document socio-economic characteristics, production parameters, challenges and management practices used by Fijian households which keep indigenous chickens. Methods: A survey involving 200 households was carried out in coastal and inland communities of Fiji's wet and semi-dry ecoregions. Data on the influence of ecoregion and location of households relative to the sea on management practices, challenges and productivity of indigenous chickens were analyzed using logistic regression and general linear model of SAS software. Results: Irrespective of location relative to the sea and ecoregion, households indicated that they kept indigenous chickens for food and income generation. The Welsummer was the most (p>0.05) preferred breed. Households in the semi-dry inland communities had the largest (p<0.05) flocks compared to those in semi-dry coastal communities and the wet region. Chickens in the semi-dry region performed better (p<0.05) than those in the wet region in terms of number of clutches per year and mature live weight. Predators and feed shortages were the biggest challenges faced by households in all areas. The mongoose was ranked as the most (p>0.05) common predator followed by domestic dogs. Most households in the wet ecoregion's coastal communities housed their chickens at night, whereas communities in semi-dry ecoregion housed their chickens most of the time (p<0.05). In all regions, no households sold their chickens to commercial markets (p>0.05). Households in semi-dry ecoregion were more likely (p>0.05) to sell their chickens at the local market place. Conclusion: The productivity of local chickens in Fiji is low because of feed shortage, predators such as the mongoose and lack of market linkages.

• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.11-19
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• 2022

Recently, various researches on medical image generation have been suggested, and it becomes crucial to accurately evaluate the quality and diversity of the generated medical images. For this purpose, the expert's visual turing test, feature distribution visualization, and quantitative evaluation through IS and FID are evaluated. However, there are few methods for quantitatively evaluating medical images in terms of fidelity and diversity. In this paper, images are generated by learning a chest CT dataset of non-small cell lung cancer patients through DCGAN and PGGAN generative models, and the performance of the two generative models are evaluated in terms of fidelity and diversity. The performance is quantitatively evaluated through IS and FID, which are one-dimensional score-based evaluation methods, and Precision and Recall, Improved Precision and Recall, which are two-dimensional score-based evaluation methods, and the characteristics and limitations of each evaluation method are also analyzed in medical imaging.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.487-498
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• 2022

Depth map is an image that contains distance information in 3D space on a 2D plane and is used in various 3D vision tasks. Many existing depth estimation studies mainly use narrow FoV images, in which a significant portion of the entire scene is lost. In this paper, we propose a technique for generating 360° omnidirectional RGBD images from a sparse set of narrow FoV images. The proposed generative adversarial network based image generation model estimates the relative FoV for the entire panoramic image from a small number of non-overlapping images and produces a 360° RGB and depth image simultaneously. In addition, it shows improved performance by configuring a network reflecting the spherical characteristics of the 360° image.

• Journal of Veterinary Science
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• v.24 no.1
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• pp.2.1-2.14
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• 2023

Background: Hypothermia is a crucial environmental factor that elevates the risk of cardiovascular disease, but the underlying effect is unclear. Objectives: This study examined the role of cold stress (CS) in cardiac injury and its underlying mechanisms. Methods: In this study, a chronic CS-induced myocardial injury model was used; mice were subjected to chronic CS (4℃) for three hours per day for three weeks. Results: CS could result in myocardial injury by inducing the levels of heat shock proteins 70 (HSP70), enhancing the generation of creatine phosphokinase-isoenzyme (CKMB) and malondialdehyde (MDA), increasing the contents of tumor necrosis factor-α (TNF-α), high mobility group box 1 (HMGB1) interleukin1b (IL-1β), IL-18, IL-6, and triggering the depletion of superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH). Multiple signaling pathways were activated by cold exposure, including pyroptosis-associated NOD-like receptor 3 (NLRP3)-regulated caspase-1-dependent/Gasdermin D (GSDMD), inflammation-related toll-like receptor 4 (TLR4)/myeloid differentiation factor 88 (MyD88)-mediated nuclear factor kappa B (NF-κB), and mitogen-activated protein kinase (MAPK), as well as oxidative stressinvolved thioredoxin-1/thioredoxin-interacting protein (Txnip) signaling pathways, which play a pivotal role in myocardial injury resulting from hypothermia. Conclusions: These findings provide new insights into the increased risk of cardiovascular disease at extremely low temperatures.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2004.03a
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• pp.369-375
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• 2004

Gas turbine engine simulation in terms of transient, steady state performance and operational characteristics is complex work at the various engineering functions of aero engine manufacturers. Especially, efficiency of control system design and development in terms of cost, development period and technical relevance implies controlling diverse simulation and identification activities. The previous engine simulation has been accomplished within a limited analysis area such as fan, compressor, combustor, turbine, controller, etc. and this has resulted in improper engine performance and control characteristics because of limited interaction between analysis areas. In this paper, we propose a new simulation methodology for gas turbine engine performance analysis as well as its digital controller to solve difficulties as mentioned above. The novel method has particularities of (ⅰ) resulting in the integrated control simulation using almost every component/module analysis, (ⅱ) providing automated math model generation process of engine itself, various engine subsystems and control compensators/regulators, (ⅲ) presenting total sophisticated output results and easy understandable graphic display for a final user. We call this simulation system GT3GS (Gas Turbine 3D Graphic Simulator). GT3GS was built on both software and hardware technology for total simulation capable of high calculation flexibility as well as interface with real engine controller. All components in the simulator were implemented using COTS (Commercial Off the Shelf) modules. In addition, described here includes GT3GS main features and future works for better gas turbine engine simulation.

• Human Ecology Research
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• v.60 no.4
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• pp.625-643
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• 2022

The purpose of this study was to explore the trends and core keywords of the secondary school home economics teacher selection test using content analysis and text network analysis. The sample comprised texts of the secondary school home economics teacher 1st selection test for the 2017-2022 school years. Determination of frequency of occurrence, generation of word clouds, centrality analysis, and topic modeling were performed using NetMiner 4.4. The key results were as follows. First, content analysis revealed that the number of questions and scores for each subject (field) has remained constant since 2020, unlike before 2020. In terms of subjects, most questions focused on 'theory of home economics education', and among the evaluation content elements, the highest percentage of questions asked was for 'home economics teaching·learning methods and practice'. Second, the network of the secondary school home economics teacher selection test covering the 2017-2022 school years has an extremely weak density. For the 2017-2019 school years, 'learning', 'evaluation', 'instruction', and 'method' appeared as important keywords, and 7 topics were extracted. For the 2020-2022 school years, 'evaluation', 'class', 'learning', 'cycle', and 'model' were influential keywords, and five topics were extracted. This study is meaningful in that it attempted a new research method combining content analysis and text network analysis and prepared basic data for the revision of the evaluation area and evaluation content elements of the secondary school home economics teacher selection test.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.125-125
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• 2023

Estuarine dams are dams constructed in estuaries for reasons such as securing freshwater resources, controlling water levels, and hydroelectric power generation. These estuarine dams alter the flow of freshwater to the coastal ocean and the tidal properties of the estuaries which has implications for the estuaries' circulation and sediment transport. A previous study has analyzed the effect of estuarine dams on 1D (along-channel) circulation and sediment transport. However, the effect of estuarine dams on the transverse variability of along-channel and across-channel circulation and sediment transport has not been studied and is not known. In this study, a coupled hydrodynamic-sediment dynamic numerical model (COAWST) was used to analyze the transverse variability of along-channel and across-channel flow and sediment transport in estuaries with estuarine dams. The estuarine dam was found to change the 3D structure of circulation and sediment transport, and the result was found to depend on the estuarine type (i.e., strongly stratified (SS) or well-mixed (WM) estuary). The SS estuary had inflow in the channel and outflow over the shoals, consistent with estuarine circulation. Longer discharge interval reduced the estuarine circulation. The WM estuary had inflow over the shoals and outflow in the channel, consistent with tide-induced circulation. As the estuarine dam was located nearer to the estuary mouth, the tide-induced circulation was reduced and replaced with estuarine circulation. The lateral circualtion was the greatest in the tide-dominated estuaries. It was reduced and changed direction due to differential advection change as the dam was located nearer the mouth. Overall, the WM estuary transverse flow structure changed the most. Lateral sediment flux was important in all estuaries, particularly for transporting sediments to the tidal flats.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.2
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• pp.127-137
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• 2010

A Widget platform is acknowledged to be a next generation intelligent platform that is well suited to Web 2.0 and mobile convergence environments. With prospects of growth, examining users' perceptions of current widgets can be a valuable source of information in setting directions for Widget's future development. This study identifies user interface factors that affect widget usability and investigates a strategic approach to promoting the use of widgets by analyzing user's "intention to use" in connection with the identified interface factors. The experimental results show the consistency, intuition, minimal action, and personalization have a positive(+) effect on perceived ease of use and that personalization and design have a causal effect on perceived enjoyment. Inaddition, perceived ease of use has an influence on perceived enjoyment that, inturn, has a direct influence on intention to use. On the other hand, the hypothesis that perceived ease of use has a direct effect on intention to use was rejected.

• Journal of Hydrogen and New Energy
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• v.34 no.4
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• pp.358-368
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• 2023

The fuel cell market is expected to grow rapidly. Therefore, it is necessary to scale up fuel cells for buildings, power generation, and ships. A multi-stack system can be an effective way to expand the capacity of a fuel cell. Multi-stack fuel cell systems are better than single-stack systems in terms of efficiency, reliability, durability and maintenance. In this research, we developed a residential fuel cell stack and system model that generates electricity using the fuel cell-photovoltaic hybrid system. The efficiency and hydrogen consumption of the fuel cell system were calculated according to the three proposed power distribution methods (equivalent, Daisy-chain, and optimal method). As a result, the optimal power distribution method increases the efficiency of the fuel cell system and reduces hydrogen consumption. The more frequently the multi-stack fuel cell system is exposed to lower power levels, the greater the effectiveness of the optimal power distribution method.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.