• Imaging Science in Dentistry
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• v.39 no.3
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• pp.149-156
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• 2009

Purpose : The purpose of this study was to develop a stent-based image guided surgery system and to apply it to oral and maxillofacial surgeries for anatomically complex sites. Materials and Methods : We devised a patient-specific stent for patient-to-image registration and navigation. Three-dimensional positions of the reference probe and the tool probe were tracked by an optical camera system and the relative position of the handpiece drill tip to the reference probe was monitored continuously on the monitor of a PC. Using 8 landmarks for measuring accuracy, the spatial discrepancy between CT image coordinate and physical coordinate was calculated for testing the normality. Results : The accuracy over 8 anatomical landmarks showed an overall mean of $0.56{\pm}0.16\;mm$. The developed system was applied to a surgery for a vertical alveolar bone augmentation in right mandibular posterior area and possible interior alveolar nerve injury case of an impacted third molar. The developed system provided continuous monitoring of invisible anatomical structures during operation and 3D information for operation sites. The clinical challenge showed sufficient accuracy and availability of anatomically complex operation sites. Conclusion : The developed system showed sufficient accuracy and availability in oral and maxillofacial surgeries for anatomically complex sites.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.25-25
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• 2022

Drought becomes frequent and severe because of continuous global warming, leading to a significant loss of crop yield. In soybean (Glycine max [L.]), most of quantitative trait loci (QTLs) analyses for drought tolerance have conducted by investigating yield changes under water-restricted conditions at the reproductive stages. More recently, the necessity of QTL studies to use physiological indices responding to drought at the early growth stages besides the reproductive ones has arisen due to the unpredictable and prevalent occurrence of drought throughout the soybean growing season. In this study, we thus identified QTLs conferring wilting scores and moisture contents of soybean subjected to drought stress in the early vegetative stage using an recombinant inbred line (RIL) population derived from a cross between Taekwang (drought-sensitive) and SS2-2 (drought-tolerant). For the two traits, the same major QTL was located on chromosome 10, accounting for up to 11.5% of phenotypic variance explained with LOD score of 12.5. This QTL overlaps with a reported QTL for the limited transpiration trait in soybean and harbors an ortholog of the Arabidopsis ABA and drought-induced RING-D UF1117 gene. Meanwhile, one of important features of plant drought tolerance is their ability to limit transpiration rates under high vapor pressure deficiency in response to mitigate water loss. However, monitoring their transpiration rates is time-consuming and laborious. Therefore, only a few population-level studies regarding transpiration rates under the drought condition have been reported so far. Via employing an Arduino-based platform, for the reasons addressed, we are measuring and recording total pot weights of soybean plants every hour from the 1^st day after water restriction to the days when the half of the RILs exhibited permanent tissue damage in at least one trifoliate. Gradual decrease in moisture of soil in pots as time passes refers increase in the severity of drought stress. By tracking changes in the total pot weights of soybean plants, we will infer transpiration rates of the mapping parents and their RILs according to different levels of VPD and drought stress. The profile of transpiration rates from different levels of severity in the stresses facilitates a better understanding of relationship between transpiration-related features, such as limited maximum transpiration rates, to water saving performances, as well as those to other drought-responsive phenotypes. Our findings will provide primary insights on drought tolerance mechanisms in soybean and useful resources for improvement of soybean varieties tolerant to drought stress.

• Annals of Clinical Neurophysiology
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• v.11 no.2
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• pp.54-58
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• 2009

Background: Obstructive sleep apnea (OSA) is associated with several cardiovascular diseases. However, the mechanisms are not completely understood. The measure of common carotid artery intima-media thickness (IMT) has been extensively used as an early marker of atherosclerosis. The aim of this study was to test the hypothesis that early signs of atherosclerosis are present in patients with OSA and correlate with OSA severity Methods: Eleven male patients with OSA were studied by using full standard overnight polysomnography and high-definition echo-tracking device to measure intima-media thickness and carotid artery diameter. Eight healthy volunteers matched for age and sex were studied by portable respiratory monitoring device. All participants were free of hypertension, diabetes, and were not on any medications. Patients with OSA were naive to treatment. Results: All patients and normal controls were male. There was no significant difference of age between patients and controls ($48.4{\pm}8.85$ and $48.0{\pm}9.77$). Significant differences existed between control subjects and patients with mild to moderate and severe OSA (apnea-hypopnea index, $1.51{\pm}1.15$ and $38.51{\pm}19.13$ respectively) in intima-media thickness ($0.59{\pm}0.064$ and $0.93{\pm}0.16$; P=0.0023), and carotid diameter ($5.79{\pm}0.44$ and $6.47{\pm}0.51$; P=0.0227). Multivariate analyses showed that the apnea-hypopnea index correlated independently with intima-media thickness and carotid diameter (r=0.79, P=0.0008, and r=0.47, P=0.0482). Conclusions: Middle-aged patients with OSA who are free of overt cardiovascular diseases have early signs of atherosclerosis, which further supports the hypothesis that OSA plays an independent role in atherosclerosis progression.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• Analytical Science and Technology
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• v.22 no.3
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• pp.201-209
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• 2009

Four heavy metals (Pb, Cd, As, and Hg) in 38 species (total 325 samples) of oriental animality medicines were monitored by inductively coupled plasma-mass spectrometry (ICP-MS) and automatic mercury analyzer (AMA). The detected concentration range of Pb, Cd, As was presented as $0.02{\mu}gkg^{-1}$ $(D.L){\sim}11.29mgkg^{-1}$, $0.01{\mu}gkg^{-1}$ $(D.L){\sim}2.50 mgkg^{-1}$, $0.12{\mu}gkg^{-1}$ $(D.L){\sim}5.27mgkg^{-1}$, respectively. In case of Hg, it the concentration range was $0.01{\sim}77.11mgkg^{-1}$ except one sample which exceeded detection limit. In all samples of Amydae Carapax and Gallnut, it was not detected over the maximum residue limits of metals. Pb is in charge of the greatest portion of contamination in 22 species of animality medicines, and in case of Hg, 54.46% of total samples were over the maximum residue limits. Therefore, environmental levels of Pb and Hg are needed to continue the researches and the studies for tracking pollution source are required.

• Journal of Environmental Health Sciences
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• v.34 no.1
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• pp.86-94
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• 2008

Lake Shiwha, an artificial lake located near metropolitan Seoul, offers a unique water environment and has been suspected to have high levels of chemical and microbiological contaminations. Lake Shiwha was originally connected to the sea but currently has four major surface water inputs from agricultural, municipal, industrial areas and in addition an occasional inflow from the sea. The objectives of this study are to investigate the relative contribution of microbial contaminants from each of the inflowing surface waters and to identify appropriate microbial indicator organisms in this unique water environment. We measured the levels of microbial contaminations in the four inflowing surface waters. A number of microbial indicator organisms including total coliform (TC), fecal coliform (FC), E. coli, Enterococci, somatic and male-specific coliphages were analyzed. Bacterial indicator microorganisms were detected and quantified by the $Colilert^{(R)},\;Enterolert^{(R)}$ kit. Surface water (50 l) was sampled by $ViroCap^{TM}\;5"$ cartridge filters and analyzed by the single agar layer method for detecting coliphages. The concentrations of TC, FC, E. coli, and Enterococci were 1543 CFU/100 ml${\sim}1.99{\times}10^6$ CFU/100 ml, 0 CFU/100 ml${\sim}202$ CFU/100ml, 0 CFU/100 ml${\sim}1.80{\sim}10^5$ CFU/100ml, 74 CFU/100 ml${\sim}3408$ CFU/100 ml, respectively. The male-specific and somatic coliphages were detected in three different inflowing surface waters. Isolated E. coli and Enterococci strains were further analyzed by 16s rDNA amplification and subsequent phylogenetic analysis from Jungwang-chun, Ansan-chun, Banwol-chun and penstock of inflowing surface water. Our results indicated that the concentrations of different fecal indicator microorganisms might not be highly correlated with each other. Multiple microbial indicator organisms should be used for monitoring microbial contamination and microbial source tracking methods.

• Journal of the Korean Society for Marine Environment & Energy
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• v.17 no.3
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• pp.247-256
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• 2014

Recently, camera and RADAR are used for more effective and accurate observation of the bird migration. In recent years, many researches on the bird migration using RADAR are undertaking and in active, thus causes the advent of "RADAR ornithology" as a new academic field. Due to the lack of accessibility, economic feasibility and mobility of weather RADAR, airport searching RADAR and tracking RADAR, Nowadays, a marine RADAR is widely used for a bird observation. In this paper, we deals with a study on development of a remote bird observation system using marine FMCW RADAR, which monitors, records and analyzes bird movement by RADAR image processing and target recognition technology. Also, we conduct first test and second test for availability of the developed system, and verify the system to apply in bird observation domain. Consequently, we figured problems out, and correct the problems to improve the system. The developed system can apply in other domains such as environment evaluation. In the future, the system needs to improve accuracy of statistics and to track migration route of bird.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.9
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• pp.661-674
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• 2009

Recently, there has been much research and many attempts to enhance converged information technology services using new technology such as ubiquitous sensor networks (USN) in medical, environmental, industrial, and logistic areas. There has also been much research and various attempts to apply this new technology to agricultural areas. However, applications to the agricultural areas should be considered differently against the same areas such as medical, environmental, industrial, and logistics. Therefore, this paper suggests that an agricultural cultivating management and traceability system. This system is a unified system that supports the processes sowing seeds through selling agricultural products to consumers. Farmers can be provided with an effective calendar for cultivation and weather information in real time as well as the monitoring of the growth of farm products on the farm in real time using this system. Farmers can also control all equipment installed on the farm directly or remotely and the equipment can be controlled automatically when the measured values such as temperature and humidity deviate from the decent criteria which are set by farmers or this system. Additionally, the reliability for and the better quality of the agricultural products can be improved because farmers can use this unified system to cover all processes from sowing seeds to selling to consumers.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.245-257
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• 2023

The representative crop in the Republic of Korea, rice, is cultivated over extensive areas every year, which resulting in reduced resistance to pests and diseases. One of the major rice diseases, rice blast disease, can lead to a significant decrease in yields when it occurs on a large scale, necessitating early detection and effective control of rice blast disease. Drone-based crop monitoring techniques are valuable for detecting abnormal growth, but frequent image capture for potential rice blast disease occurrences can consume significant labor and resources. The purpose of this study is to early detect rice blast disease using remote sensing data, such as drone and satellite images, along with weather data. Satellite images was helpful in identifying rice cultivation fields. Effective detection of paddy fields was achieved by utilizing vegetation and water indices. Subsequently, air temperature, relative humidity, and number of rainy days were used to calculate the risk of rice blast disease occurrence. An increase in the risk of disease occurrence implies a higher likelihood of disease development, and drone measurements perform at this time. Spectral reflectance changes in the red and near-infrared wavelength regions were observed at the locations where rice blast disease occurred. Clusters with low vegetation index values were observed at locations where rice blast disease occurred, and the time series data for drone images allowed for tracking the spread of the disease from these points. Finally, drone images captured before harvesting was used to generate spatial information on the incidence of rice blast disease in each field.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.667-674
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• 2023

In the event of a disaster occurring within a building, the prompt and efficient evacuation and rescue of occupants within the building becomes the foremost priority to minimize casualties. For the purpose of such rescue operations, it is essential to ascertain the distribution of individuals within the building. Nevertheless, there is a primary dependence on accounts provided by pertinent individuals like building proprietors or security staff, alongside fundamental data encompassing floor dimensions and maximum capacity. Consequently, accurate determination of the number of occupants within the building holds paramount significance in reducing uncertainties at the site and facilitating effective rescue activities during the golden hour. This research introduces a methodology employing computer vision algorithms to count the number of occupants within distinct building locations based on images captured by installed multiple CCTV cameras. The counting methodology consists of three stages: (1) establishing virtual Lines of Interest (LOI) for each camera to construct a multi-camera network environment, (2) detecting and tracking people within the monitoring area using deep learning, and (3) aggregating counts across the multi-camera network. The proposed methodology was validated through experiments conducted in a five-story building with the average accurary of 89.9% and the average MAE of 0.178 and RMSE of 0.339, and the advantages of using multiple cameras for occupant counting were explained. This paper showed the potential of the proposed methodology for more effective and timely disaster management through common surveillance systems by providing prompt occupancy information.