• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.413-413
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• 2023

Wetlands are a critical component of the global carbon cycle and are essential in mitigating climate change. Accurately quantifying wetland carbon emissions is crucial for understanding and predicting the impact of wetlands on the global carbon budget. The uncertainty quantifying carbon in wetlands may comes from the ecosystem's hydrological, biochemical, and microbiological variability. The Community Land Model is a sophisticated and flexible land surface model that offers several configuration options such as energy and water fluxes, vegetation dynamics, and biogeochemical cycling, necessitating careful consideration for the alternative configurations before model implementation to develop a practical model framework. We conducted a systematic literature review, analyzing the alternatives, focusing on the carbon stock pools configurations and the parameters with significant sensitivity for carbon quantification in wetlands. In addition, we evaluated the feasibility and availability of in situ observation data necessary for validating the different alternatives. This analysis identified the most suitable option for our study site, the Binbong Wetland, in Korea.

• Proceedings of the Microbiological Society of Korea Conference
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• 2007.05a
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• pp.120-122
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• 2007

All living organisms use numerous signal-transduction pathways to sense and respond to their environments and thereby survive and proliferate in a range of biological niches. Molecular dissection of these signalling networks has increased our understanding of these communication processes and provides a platform for therapeutic intervention when these pathways malfunction in disease states, including infection. Owing to the expanding availability of sequenced genomes, a wealth of genetic and molecular tools and the conservation of signalling networks, members of the fungal kingdom serve as excellent model systems for more complex, multicellular organisms. Here, we employed Cryptococcus neoformans as a model system to understand how fungal-signalling circuits operate at the molecular level to sense and respond to a plethora of environmental stresses, including osmoticshock, UV, high temperature, oxidative stress and toxic drugs/metabolites. The stress-activated p38/Hog1 MAPK pathway is structurally conserved in many organisms as diverse as yeast and mammals, but its regulation is uniquely specialized in a majority of clinical Cryptococcus neoformans serotype A and D strains to control differentiation and virulence factor regulation. C. neoformans Hog1 MAPK is controlled by Pbs2 MAPK kinase (MAPKK). The Pbs2-Hog1 MAPK cascade is controlled by the fungal "two-component" system that is composed of a response regulator, Ssk1, and multiple sensor kinases, including two-component.like (Tco) 1 and Tco2. Tco1 and Tco2 play shared and distinct roles in stress responses and drug sensitivity through the Hog1 MAPK system. Furthermore, each sensor kinase mediates unique cellular functions for virulence and morphological differentiation. We also identified and characterized the Ssk2 MAPKKK upstream of the MAPKK Pbs2 and the MAPK Hog1 in C. neoformans. The SSK2 gene was identified as a potential component responsible for differential Hog1 regulation between the serotype D sibling f1 strains B3501 and B3502 through comparative analysis of their meiotic map with the meiotic segregation of Hog1-dependent sensitivity to the fungicide fludioxonil. Ssk2 is the only polymorphic component in the Hog1 MAPK module, including two coding sequence changes between the SSK2 alleles in B3501 and B3502 strains. To further support this finding, the SSK2 allele exchange completely swapped Hog1-related phenotypes between B3501 and B3502 strains. In the serotype A strain H99, disruption of the SSK2 gene dramatically enhanced capsule biosynthesis and mating efficiency, similar to pbs2 and hog1 mutations. Furthermore, ssk2, pbs2, and hog1 mutants are all hypersensitive to a variety of stresses and completely resistant to fludioxonil. Taken together, these findings indicate that Ssk2 is the critical interface protein connecting the two-component system and the Pbs2-Hog1 pathway in C. neoformans.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.27 no.4
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• pp.110-120
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• 2014

Objectives : We performed a clinical study to investigate autonomic conditions in persistent allergic rhinitis depending on various pattern identifications and the availability of heart rate variability (HRV) as a pattern identification diagnostic tool. Methods : 32 patients with persistent allergic rhinitis were asked to interview with doctor of Korean Medicine and perform the four pattern questionnaires (Cold-Heat Pattern, Phlegm Pattern, Yin Deficiency pattern, bloodstasis pattern). Then, they were examined their autonomic conditions with heart rate variability test. Results : Patients were classified as three pattern groups (Lung-stomach heat, Lung qi deficiency cold, Lung-spleen qi deficiency) by doctor. In the Lung qi deficiency cold group, Total power of the HRV (TP) and the power of the low frequency component (LF) significantly higher than in the Lung-stomach heat or Lung-spleen qi deficiency group (P < 0.05). Also, Patients were classified as 8 pattern groups (Cold/Heat, Phlegm/Non-phlegm, Yin deficiency/Non-yin deficiency, Bloodstasis/Non-bloodstasis) by four pattern questionnaires. Only in the Yin deficiency group, the power of the low frequency component (LF) significantly lower than in the Non-yin deficiency group (P < 0.05). There were not any significant differences in the rest groups. Conclusions : The result may provide that HRV doesn't reflect well the differences in the various pattern groups, and the HRV's availability is low. Continuous studies are needed to develop the objective and standardized pattern identification diagnostic tool for allergic rhinitis.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.131-146
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• 1999

The satellite visible data have been successfully applied to study the ocean color. Another ocean color sensor, the Ocean Scanning Multi-spectral Imager (OSMI) on the Korea Multi-Purpose Satellite (KOMPSAT) will be launched in 1999. In order to understand the characteristics of future OSMI images, we have first discussed the simulation models and procedures in detail, and produced typical patterns of radiances at visible bands by using radiative transfer models. The various simulated images of full satellite passes and Korean local areas for different seasons, water types, and the satellite crossing equator time (CET) are presented to illustrate the distribution of each component of radiance (i.e., aerosol scattering, Rayleigh scattering, sun glitter, water-leaving radiance, and total radiance). A method to evaluate the image quality and availability is then developed by using the characteristics of image defined as the Complex Signal Noise Ratio (CSNR). Meanwhile, a series of CSNR images are generated from the simulated radiance components for different cases, which can be used to evaluate the quality and availability of OSMI images before the KOMPSAT will be placed in orbit. Finally, the quality and availability of OSMI images are quantitatively analyzed by the simulated CSNR image. It is hoped that the results would be useful to all scientists who are in charge of OSMI mission and to those who plan to use the data from OSMI.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.1
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• pp.35-42
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• 2016

Modern aircraft is facing the increase of power demands and thermal challenges. In accordance with the application of more electric technology and advanced mission requirement, aircraft system requires increase of power generation and it cause increase of internal heat generation. Simultaneously, restrictions have significantly been imposed to the thermal management system. Modern aircraft must maintain low radar observability and infra-red signature. In addition, new composite aircraft skins have reduced the amount of heat that can be rejected to the environment. The combination of these characteristics has increased the challenges faced by thermal management. In order to mitigate the thermal challenges, the concept of system level thermal management should be applied and new modeling and simulation tools need to be developed. To develop and utilize system level thermal management technology, three key points are considered. Firstly, the performance changes of subsystems and components must be assessed at an integrated thermal system. It is because that each subsystem and component interacts with other subsystems or components and it can directly effects on overall system performance. Secondly, system level thermal management requirements and solutions must be evaluated early in conceptual design process as vehicle and propulsion system configuration decisions are being made. Finally, new component level thermal management technologies must focus on reducing heat generation and increasing the availability of heat sinks.

• The Korean Journal of Food And Nutrition
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• v.32 no.4
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• pp.364-370
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• 2019

This study was conducted to select cultivars and determine the harvest period suited for the availability of biological activities in unripen apple. To analyze effective the components in the apple (Malus domestica), three cultivars, 'Summerking', 'Hongro', 'Fuji' were harvested from 40~50 days after full bloom to harvest time. Soluble solid content increased gradually by ripening but titratable acidity decreased with ripening regardless of the cultivars. The total phenol content significantly reduced with ripening from May 30 to July 30 (p<0.05). Substantially, the total phenol content of 'Hongro' in May 30 was four times higher than that of 'Summerking' in the same period and ten times higher than that of 'Hongro' in August 30. The total flavonoid content reduced with ripening regardless of cultivars (p<0.05) and that of 'Hongro' in May 30 was significantly highest (p<0.05). The ascorbic acid content was the highest in 'Hongro' in May 30 (p<0.05). The contents of tannin and ursolic acid significantly reduced with ripening from May 30 to July 30 (p<0.05), while no significant difference was observed between Hogro and Fuji after July 30. Therefore, 'Hongro' harvested in May 30 was considered to be best in the utilization of the effective components of immature apple.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.4
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• pp.59-68
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• 2014

Climate change influences multiple environmental aspects, certain of which are specifically related to agricultural water resources such as water supply, water management, droughts and floods. Understanding the impact of climate change on reservoirs in relation to the passage of time is an important component of water resource management for stable water supply maintenance. Changes on rainfall and hydrologic patterns due to climate change can increases the occurrence of reservoir water shortage and affect the future availability of agricultural water resources. It is a main concern for sustainable development in agricultural water resources management to evaluate adaptation capability of water supply under the future climate conditions. The purpose of this study is to predict the sustainability of agricultural water demand and supply under future climate change by applying an irrigation vulnerability assessment model to investigate evidence of climate change occurrences at a local scale with respect to potential water supply capacity and irrigation water requirement. Thus, it is a recommended practice in the development of water supply management strategies on reservoir operation under climate change.

• Journal of Forest and Environmental Science
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• v.29 no.1
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• pp.38-48
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• 2013

Decomposition of plant material is an important component in the study of forest ecosystem because of its critical role in nutrient cycling. Different tree species has different nutrient release patterns, which are related to leaf litter quantitative traits and seasonal environmental factors. The quantitative traits of leaf litter are important predictors of decomposition and decomposition rates increase with greater nutrient availability in the forest ecosystems. At the ecosystem level, litter quantitative traits are most often related to the physical and chemical characteristics of the litter, for example, leaf toughness and leaf mass per unit area, and lignin content tannin and total phenolics. Thus, the analysis of litter quantitative traits and decomposition are highly important for the understanding of nutrient cycling in forest ecosystems. By studying the role of litter quantitative traits on decomposition and nutrient cycling in forest ecosystems will provide a valuable insight to how quantitative traits influence ecosystem nutrient dynamics. Such knowledge will contribute to future forest management and conservation practices.

• Journal of Korean Society for Quality Management
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• v.21 no.2
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• pp.1-16
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• 1993

Early generations of products had little to no inherent capability to test themselves. The technologies involved often required only visual inspection and limited probing to troubleshoot the system once it was turned over to maintenance personnel. However, as the complexity of military and commercial systems grew, symptoms of failure became less noticeable to the operator. Therefore, the procedure to access, inspect, repair and replace a component became complicated, the requirements for personnel skill and testing equipment increased. and it took too long of a time to maintain a system. Meanwhile, the need for availability became more mission-critical and maintenance become very expensive. The obvious solution was to design in-system circuits or devices to self-test the primary system, the Built-In-Test(BIT) was born. This approach has continued right on up through present systems and is an integral part of systems now being designed. The object of this paper is to present a state-of-the-art research for filtering out the BIT diagnosis mistakes using Bayesian analysis and develop the algorithm for Redundant systems with BIT to improve BIT diagnosis.