• Journal of Radiation Protection and Research
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• v.48 no.3
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• pp.131-143
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• 2023

Background: This study examined the detection limit of thyroid screening monitoring conducted at the time of the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident in 2011 using a Monte Carlo simulation. Materials and Methods: We calculated the detection limit of a NaI(Tl) survey meter to measure ¹³¹I accumulation in the thyroid gland of children. Mathematical phantoms of 1- and 5-year-old children were developed in the simulation of the Particle and Heavy Ion Transport code System code. Contamination of the body surface with eight radionuclides found after the FDNPP accident was assumed to have been deposited on the neck and shoulder area. Results and Discussion: The detection limit was calculated as a function of ambient dose rate. In the case of 40 Bq/cm² contamination on the body surface of the neck, the present simulations showed that residual thyroid radioactivity corresponding to thyroid dose of 100 mSv can be detected within 21 days after intake at the ambient dose rate of 0.2 µSv/hr and within 11 days in the case of 2.0 µSv/hr. When a time constant of 10 seconds was used at the dose rate of 0.2 µSv/hr, the estimated survey meter output error was 5%. Evaluation of the effect of individual differences in the location of the thyroid gland confirmed that the measured value would decrease by approximately 6% for a height difference of ±1 cm and increase by approximately 65% for a depth of 1 cm. Conclusion: In the event of a nuclear disaster, simple measurements carried out using a NaI(Tl) scintillation survey meter remain effective for assessing ¹³¹I intake. However, it should be noted that the presence of short-half-life radioactive materials on the body surface affects the detection limit.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.788-797
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• 2023

In this paper, we introduced a test environment to test a vision-aided navigation system, as an alternative navigation system when global positioning system (GPS) is unavailable, for vertical take-off and landing (VTOL) unmanned aerial system. It is efficient to use a virtual environment to test and evaluate the vision-aided navigation system under development, but currently no suitable equipment has been developed in Korea. Thus, the proposed test environment is developed to evaluate the performance of the navigation system by generating input signal modeling and simulating operation environment of the system, and by monitoring output signal. This paper comprehensively describes research procedure from derivation of requirements specifications to hardware/software design according to the requirements, and production of the test environment. This test environment was used for evaluating the vision-aided navigation algorithm which we are developing, and conducting simulation based pre-flight tests.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1037-1051
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• 2020

The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the lead time of 6-12 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 142 TCs which developed in the Northwest Pacific from 2011 to 2016 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of typhoons, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract air and ocean forecasting data. This study suggested two schemes with different input variables to the MTL models. Scheme 1 used only satellite-based input data while scheme 2 used both satellite images and numerical forecast modeling. As a result of real-time TC intensity estimation, Both schemes exhibited similar performance. For TC intensity forecasting with the lead time of 6 and 12 hours, scheme 2 improved the performance by 13% and 16%, respectively, in terms of the root mean squared error (RMSE) when compared to scheme 1. Relative root mean squared errors(rRMSE) for most intensity levels were lessthan 30%. The lower mean absolute error (MAE) and RMSE were found for the lower intensity levels of TCs. In the test results of the typhoon HALONG in 2014, scheme 1 tended to overestimate the intensity by about 20 kts at the early development stage. Scheme 2 slightly reduced the error, resulting in an overestimation by about 5 kts. The MTL models reduced the computational cost about 300% when compared to the single-tasking model, which suggested the feasibility of the rapid production of TC intensity forecasts.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.275-290
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• 2021

Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.

• Journal of Chest Surgery
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• v.24 no.9
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• pp.843-852
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• 1991

We prospectively studied postoperative cardiac arrhythmia after open heart surgery to analyze the types and incidence of cardiac arrhythmia and to predict preoperative risk factors. And also we evaluated the effectiveness of atrial and ventricular epicardial electrodes which were placed during operation Between March 1990 and August 1990, We had operated on in 211 patients and we studied 201 consecutive patients excluding 10 patients. The study group included 99 males and 102 female patients, ages 1 month to 75 years[Mean$\pm$SD=28.0$\pm$21.7 years]. Postoperatively, all patients were regularly seen by the cardiac surgeon and cardiologist, They had continuous electrocardiographic monitoring for the first 3 days, initially in the intensive care unit and were checked routine electrocardiography on the postoperative 7 days, The postoperative cardiac arrhythmia were analyzed and possible associations of this arrhythmia with various pre, intra, and postoperative factors were studied by univariate and multivariate discriminant analysis, The overall incidence of postoperative cardiac arrhythmia except relative sinus bradycardia was 36.8%;[74/201], The incidence of postoperative cardiac arrhythmia in acyanotic congenital heart disease: 19.4%, cyanotic congenital heart disease: 20.8%, cardiac arrhythmia surgery: 33.3%, acquired valvular heart disease: 60.9% and coronary artery occlusive disease: 38.9%. Both univariate and multivariate studies indicated the pre operative symptom duration[p = 0013], the duration of medication[p=0.003], presence of preoperative arrhythmia[p<0.001] and pre-operative left atrial dimension in echocardiography to be the factor promoting postoperative cardiac arrhythmia. Multivariate discriminant analysis showed that the presence of preoperative cardiac arrhythmia, bypass time and the duration of preoperative symptom duration conveyed considerable risk factor on post-operative arrhythmia. The atrial wire electrodes were used diagnostically in 36 and were used therapeutically in 89 among 201 patients. Atrial pacing were used to treat relative sinus bradycardia, accelerated junctional tachycardia or premature atrial or ventricular contractions in 51 patients. Atrioventricular sequential pacing were used in 16 patients and ventricular pacing were used in 20 patients. Hemodynamics were evaluated in 2 patients of relative sinus bradycardia before and after atrial pacing. The atrial pacing increased the amount of cardiac output to 15% more. Because of their great utility in the diagnosis and treatment of arrhythmias, we conclude that routine placement of atrial and ventricular electrodes at the time of operation is indicated regardless of the nature of the open-heart procedure.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.31-44
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• 2008

The 1st phase framework data implementation of National Geographic Information Systems (NGIS) used 1:5,000 digital map with 5 years updating period which is lacking in the latest information. This is a significant factor which hinders the use of framework data. This study proposed the efficient technical method of a location based object data management and system implementation for updating framework data. First, we did an object-oriented data modeling and database design using a location based features identifier(UFID: Unique Feature IDentifier). The second, we developed the system with various functions such as a location based UFID creation, input and output, a spatial and attribute data editing, an object based data processing using UML(Unified Modeling Language). Finally, we applied the system to the study area and got high quality data of 99% accuracy and 35% benefit effect of personnel expenses compare to the previous method. We expect that this study can contribute to the maintenance of national framework data as well as the revitalization of various GIS markets by providing user the latest framework data and that we can develop the methods of a feature-change modeling and monitoring using an object based data management.

• Asian Journal of Atmospheric Environment
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• v.8 no.3
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• pp.162-174
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• 2014

An abnormal decrease in ozonesonde sensor signal occurred during air-pollution study campaigns in November 2011 and March 2012 in Mexico City Metropolitan Area (MCMA). Sharp drops in sensor signal around 5 km above sea level and above were observed in November 2011, and a reduction of signal over a broad range of altitude was observed in the convective boundary layer in March 2012. Circumstantial evidence indicated that $SO_2$ gas interfered with the electrochemical concentration cell (ECC) ozone sensors in the ozonesonde and that this interference was the cause of the reduced sensor signal output. The sharp drops in November 2011 were attributed to the $SO_2$ plume from Popocat$\acute{e}$petl volcano southeast of MCMA. Experiments on the response of the ECC sensor to representative atmospheric trace gases showed that only $SO_2$ could cause the observed abrupt drops in sensor signal. The vertical profile of the plume reproduced by a Lagrangian particle diffusion simulation supported this finding. A near-ground reduction in the sensor signal in March 2012 was attributed to an $SO_2$ plume from the Tula industrial complex north-west of MCMA. Before and at the time of ozonesonde launch, intermittent high $SO_2$ concentrations were recorded at ground-level monitoring stations north of MCMA. The difference between the $O_3$ concentration measured by the ozonesonde and that recorded by a UV-based $O_3$ monitor was consistent with the $SO_2$ concentration recorded by a UV-based monitor on the ground. The vertical profiles of the plumes estimated by Lagrangian particle diffusion simulation agreed fairly well with the observed profile. Statistical analysis of the wind field in MCMA revealed that the effect Popocat$\acute{e}$petl was most likely to have occurred from June to October, whereas the effect of the industries north of MCMA, including the Tula complex, was predicted to occur throughout the year.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.127-137
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• 2011

We have developed a wearable device that can convert sensor data into real-time step counts and activity levels. Sensor data on gait were acquired using a triaxial accelerometer. A test was performed according to a test protocol for different walking speeds, e.g., slow walking, walking, fast walking, slow running, running, and fast running. Each test was carried out for 36 min on a treadmill with the participant wearing a portable gas analyzer (K4B2), an Actical device, and the device developed in this study. The signal vector magnitude (SVM) was used to process the X, Y, and Z values output by the triaxial accelerometer into one representative value. In addition, for accurate step-count detection, we used three algorithms: an heuristic algorithm (HA), the adaptive threshold algorithm (ATA), and the adaptive locking period algorithm (ALPA). A regression equation estimating the energy expenditure (EE) was derived by using data from the accelerometer and information on the participants. The recognition rate of our algorithm was 97.34%, and the performance of the activity conversion algorithm was better than that of the Actical device by 1.61%.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.73-81
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• 2013

Ordinary least squares (OLS) regression is the primary statistical method in previous studies for vegetation distribution patterns in relation to landform. However, this global regression lacks the ability to uncover some local-specific relationships and spatial autocorrelation in model residuals. This study employed geographically weighted regression (GWR) to examine the spatially varying relationships between NDVI (Normalized Difference Vegetation Index) patterns and changing trends of landform (elevation, slope) and solar intensity (insolation and duration of sunshine) in Mt Geum-gang of North-Korea. Results denoted that GWR was more powerful than OLS in interpreting relationships between NDVI patterns and landform/solar intensity, since GWR was characterized by higher adjusted R2, and reduced spatial autocorrelations in model residuals. Unlike OLS regression, GWR allowed the coefficients of explanatory variables to differ by locality by giving relatively more weight to NDVI patterns which are affected by local landform and solar factors. The strength of the regression relationships in the GWR increased significantly, by showing regression coefficient of higher than 70% (0.744) in the southern ridge of the experimental area. It is anticipated that this research output will serve to increase the scientific and objective vegetation monitoring in relation to landform and solar intensity by overcoming serious constraints suffered from the past non-GWR-based approach.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.137-143
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• 2013

The high resolution satellite images are used in many fields such as weather observation, remote sensing, military facilities monitoring, cultural properties protection etc. Although satellite images are obtained in same satellite imaging system, the satellite images are degraded depending on the condition of hardware(optical device, satellite operation altitude, image sensor, etc.). Due to the fact that changing the hardware of satellite imaging system is impossible for resolution enhancement of these degraded satellite after launching a satellite, therefore the method of resolution enhancement with satellite images is necessary. In this paper the resolution is enhances by using a Super Resolution(SR) algorithm. The SR algorithm is an algorithm to enhance the resolution of an image by uniting many low resolution images, so an output image has higher resolution than using other interpolation methods. But It is difficult to obtain many images of the same area. Therefore, to solve this problem, we applied SR after by applying the affine and projection transform. As a results, we found that the images applied SR after affine and projection transform have higher resolution than the images only applied SR.