According to the capacity design concept which forms the basis of the current steel seismic codes, the braces in concentrically braced frames (CBFs) should dissipate seismic energy through cyclic tension yielding and cyclic compression buckling while the beams and the columns should remain elastic. Brace buckling in inverted V-braced frames induces unbalanced vertical forces which, in turn, impose the additional beam moments and column axial forces. However, due to difficulty in predicting the location of buckling stories, the most conservative approach implied in the design code is to estimate the column axial forces by adding all the unbalanced vertical forces in the upper stories. One alternative approach, less conservative and recommended by the current code, is to estimate the column axial forces based on the amplified seismic load expected at the mechanism-level response. Both are either too conservative or lacking technical foundation. In this paper, three combination rules for a rational estimation of the column axial forces were proposed. The idea central to the three methods is to detect the stories of high buckling potential based on pushover analysis and dynamic behavior. The unbalanced vertical forces in the stories detected as high buckling potential are summed in a linear manner while those in other stories are combined by following the SRSS(square root of sum of squares) rule. The accuracy and design advantage of the three methods were validated by comparing extensive inelastic dynamic analysis results. The mode-shape based method(MSBM), which is both simple and accurate, is recommended as the method of choice for practicing engineers among the three.
Purpose: We tested a sample of nuclear medicine workers at Korean healthcare institutions for internal contamination with radioactive isotopes, measuring concentrations and evaluating doses of individual exposure. Materials and Methods: The detection and measurement was performed on urine samples collected from 25 nuclear medicine workers at three large hospitals located in Seoul. Urine samples were collected once a week, 100~200 mL samples were gathered up to 6~10 times weekly. A high-purity germanium detector was used to measure gamma radiations in urine samples for the presence of radioactive isotopes. Based on the detection results, we estimated the amounts of intake and committed effective doses using IMBA software. In cases where committed effective doses could not be adequately evaluated with IMBA software, we estimated individual committed effective doses for radionuclides with a very short half life such as $^{99m}Tc$ and $^{123}I$, using the methods recommended by International Atomic Energy Agency. Results: Radionuclides detected through the analysis of urine samples included $^{99m}Tc$, $^{123}I$, $^{131}I$ and $^{201}Tl$, as well as $^{18}F$, a nuclide used in Positron Emission Tomography examinations. The committed effective doses, calculated based on the radionuclide concentrations in urine samples, ranged from 0 to 5 mSv, but were, in the majority of cases, less than 1 mSv. The committed effective dose exceeded 1 mSv in three of the samples, and all three were workers directly handling radioactive sources. No nurses were found to have a committed effective dose in excess of 1 mSv. Conclusions: To improve the accuracy of results, it may be necessary to conduct a long-term study, performed over a time span wide enough to allow the clear determination of the influence of seasonal factors. A larger sample should also help increase the reliability of results. However, as most Korean nuclear medicine workers are currently not necessary to monitored routinely for internal contamination with radionuclides. Notwithstanding, a continuous effort is recommended to reduce any unnecessary exposure to radioactive substances, even if in inconsequential amounts, by regularly surveying workplace environments and frequently monitoring atmospheric concentrations of radionuclides.
In order to examine the relative accuracy of satellite observations and model reanalyses about lower stratospheric temperature trends, two satellite-observed Microwave Sounding Unit (MSU) channel 4 (Ch 4) brightness temperature data and two GCM (ECMWF and GEOS) reanalyses during 1981${\sim}$1993 have been intercompared with the regression analysis of time series. The satellite data for the period of 1980${\sim}$1999 are MSU4 at nadir direction and SC4 at multiple scans, respectively, derived in this study and Spencer and Christy (1993). The MSU4 temperature over the globe during the above period shows the cooling trend of -0.35 K/decade, and the cooling over the global ocean is 1.2 times as much as that over the land. Lower stratospheric temperatures during the common period (1981${\sim}$1993) globally show the cooling in MSU4 (-0.14 K/decade), SC4 (-0.42 K/decade) and GEOS (-0.15 K/decade) which have strong annual cycles. However, ECMWF shows a little warming and weak annual cycle. The 95% confidence intervals of the lower stratospheric temperature trends are greater than those of midtropospheric (channel 2) trends, indicating less confidence in Ch 4. The lapse rate in the trend between the above two atmospheric layers is largest over the northern hemispheric land. MSU4 has low correlation with ECMWF over the globe, and high value with GEOS near the Korean peninsula. Lower correlations (r < 0.6) between MSU4 and SC4 (or ECMWF) occur over $30^{\circ}$N latitude belt, where subtropical jet stream passes. Temporal correlation among them over the globe is generally high (r > 0.6). Four kinds of lower stratospheric temperature data near the Korean peninsula commonly show cooling trends, of which the SC4 values (-0.82 K/decade) is the largest.
Intercomparisons between four kinds of data have been done to estimate the accuracy of satellite observations and model reanalysis for middle and lower tropospheric thermal state over regional oceans. The data include the Microwave Sounding Units (MSU) Channel 2 (Ch2) brightness temperatures of NOAA satellites and the vertically weighted corresponding temperature of ECMWF GCM (1980-93). The satellite data for midtropospheric temperatures are MSU2 (1980-98) in nadir direction and SC2 (1980-97) in multiple scans, and for lower tropospheric temperature SC2R (1980-97). MSU2 was derived in this study while SC2 and SC2R were described in Spencer and Christy (1992a, 1992b). Temporal correlations between the above data were high (r${\ge}$0.90) in the middle and high latitudes, but low(r${\sim}$0.65) over the low latitude and more convective regions. Their values with SC2R which included the noises due to hydrometeors and surface emission were conspicuously low. The reanalysis shows higher correlation with SC2 than with MSU2 partially because of the hydrometeors screening. SC2R in monthly climatological anomalies was more sensitive to surface thermal condition in northern hemisphere than MSU2 or SC2. The first EOF mode for the monthly mean data of MSU and ECMWF shows annual cycle over most regions except the tropics. The mode in MSU2 over the Pacific suggests the east-west dipole due to the Walker circulation, but this tendency is not clear in other data. In the first and second modes for the Ch2 anomalies over most regions, the MSU and ECMWF data commonly indicate interannual variability due to El Ni${\tilde{n}$o and La Ni${\tilde{n}$a. The substantial disagreement between observations and model reanalysis occurs over the equatorial upwelling region of the western Pacific, suggesting uncertainties in the model parameterization of atmosphere-ocean interaction.
Over the past decades, daily sea surface temperature (SST) composite data have been produced using periodically and extensively observed satellite SST data, and have been used for a variety of purposes, including climate change monitoring and oceanic and atmospheric forecasting. In this study, we evaluated the accuracy and analyzed the error characteristic of the SST composite data in the sea around the Korean Peninsula for optimal utilization in the regional seas. We evaluated the four types of multi-satellite SST composite data including OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis), OISST (Optimum Interpolation Sea Surface Temperature), CMC (Canadian Meteorological Centre) SST, and MURSST (Multi-scale Ultra-high Resolution Sea Surface Temperature) collected from January 2016 to December 2016 by using in-situ temperature data measured from the Ieodo Ocean Research Station (IORS). Each SST composite data showed biases of the minimum of 0.12℃ (OISST) and the maximum of 0.55℃ (MURSST) and root mean square errors (RMSE) of the minimum of 0.77℃ (CMC SST) and the maximum of 0.96℃ (MURSST) for the in-situ temperature measurements from the IORS. Inter-comparison between the SST composite fields exhibited biases of -0.38-0.38℃ and RMSE of 0.55-0.82℃. The OSTIA and CMC SST data showed the smallest error while the OISST and MURSST data showed the most obvious error. The results of comparing time series by extracting the SST data at the closest point to the IORS showed that there was an apparent seasonal variation not only in the in-situ temperature from the IORS but also in all the SST composite data. In spring, however, SST composite data tended to be overestimated compared to the in-situ temperature observed from the IORS.
Urbanization causes urban floods and urban heat island in the summer, so it is necessary to understanding the changes of the thermal environment through urban climate and energy balance. This can be explained by the energy balance, but in urban areas, unlike the typical energy balance, the storage heat flux saved in the building or artificial land cover should be considered. Since the environment of each city is different, there is a difficulty in applying the method of retrieving the storage heat flux of the previous research. Especially, most of the previous studies are focused on the overseas cities, so it is necessary to study the storage heat retrieval suitable for various land cover and building characteristics of the urban areas in Korea. Therefore, the object of this study, it is to derive the regression formula which can quantitatively retrieve the storage heat using the data of the area where various surface types exist. To this end, nonlinear regression analysis was performed using net radiation and surface temperature data as independent variables and flux tower based storage heat estimates as dependent variables. The retrieved regression coefficients were applied to each independent variable to derive the storage heat retrieval regression formula. As a result of time series analysis with flux tower based storage heat estimates, it was well simulated high peak at day time and the value at night. Moreover storage heat retrieved in this study was possible continuous retrieval than flux tower based storage heat estimates. As a result of scatter plot analysis, accuracy of retrieved storage heat was found to be significant at $50.14Wm^{-2}$ and bias $-0.94Wm^{-2}$.
Cho, Yoonhae;Seol, Bitna;Min, Kyoung Ok;Kim, Wan Suk;Lee, Junbae;Lee, Soohyung
Journal of Korean Society of Environmental Engineers
/
제38권1호
/
pp.42-46
/
2016
The radioactive iodine ($^{131}I$) presents in the environment through the excrete process of nuclear medicine patients. In the detecting of low level of $^{131}I$ in the public water, the counting uncertainty has an effect on the accuracy and reliability of detecting $^{131}I$ radioactivity concentration. In this study, the contribution of sample amount, radioactivity concentration and counting time to the uncertainty was investigated in the case of public water sample. Sampling points are public water and the effluents of a sewage treatment plant at Sapkyocheon stream, Geumgang river. In each point, 1, 10 and 20 L of liquid samples were collected and prepared by evaporation method. The HPGe (High Purity Germanium) detector was used to detect and analyze emitted gamma-ray from samples. The radioactivity concentration of $^{131}I$ were in the range of 0.03 to 1.8 Bq/L. The comparison of the counting uncertainty of the sample amount, 1 L sample is unable to verify the existence of the $^{131}I$ under 0.5 Bq/L radioactivity concentration. Considering the short half-life of $^{131}I$ (8.03 days), a method for measuring 1 L sample was used. However comparing the detecting and preparing time of 1, 10 L respectively, detecting 10 L sample would be an appropriate method to distinguish $^{131}I$ concentration in the public water.
SAR ground moving target indicator (GMTI) has long been an important issue for SAR advanced applications. As spatial resolution of space-borne SAR system has been significantly improved recently, the GMTI becomes a very useful tool. Various GMTI techniques have been developed particularly using multi-channel SAR systems. It is, however, still problematic to detect ground moving targets within single channel SAR images while it is not practical to access high resolution multi-channel space-borne SAR systems. Once a ground moving target is detected, it is possible to retrieve twodimensional velocities of the target from single channel space-borne SAR with an accuracy of about 5 % if moving faster than 3 m/s. This paper presents a quick-and-dirty method for detecting ground moving targets from single channel SAR single-look complex (SLC) images by differentiation. Since the signal powers of derivatives present Doppler centroid and rate, it is very efficient and effective for detection of non-stationary targets. The derivatives correlate well with velocities retrieved by a precise method with a correlation coefficient $R^2$ of 0.62, which is well enough to detect the ground moving targets. While the approach is theoretically straightforward, it is necessary to remove the effects of residual Doppler rate before finalizing the ground moving target candidates. The confidence level of results largely depends on the efficiency and effectiveness of the residual Doppler rate removal method. Application results using TerraSAR-X and truck-mounted corner reflectors validated the efficiency of the method. While the derivatives of moving targets remain easily detectable, the signal energy of stationary corner reflectors was suppressed by about 18.5 dB. It results in an easy detection of ground targets moving faster than 8.8 km/h. The proposed method is applicable to any high resolution single channel SAR systems including KOMPSAT-5.
Purpose: Alvarado scoring system was evaluated regarding its usefulness for the early diagnosis of acute appendicitis in adult and in reduction of the incidence of negative appendicectomies. To evaluate the accuracy of diagnosing appendicitis using the Alvarado score in children. Methods: Prospectively, we surveyed 122 patients (male 67, female 55) suffering from abdominal pain, who had visited to the emergency department of Chosun University Hospital from June 2002 to May 2003. The Alvarado score has been computed from the white blood cell count, neutrophil count, body temperature, resistance in the right lower quadrant, length of symptoms, nausea and vomiting. Each patient was evaluated by a pediatric resident and then by a general surgeon independently. Results: Out of 170 total children who visited to the emergency department due to abdominal pain, 122 patients were associated with appendicitis. A total of 122 patients (67 male and 55 female) were visited to the emergency room with suspected appendicitis. From 105 operated patients, 92 (87.6%) were diagnosed acute appendicitis and erronous diagnostic rate was 12.4%, pathologically. Mean alvarado score of appendicitis group was $5.40{\pm}1.24$ whereas those of non-appendicitis group was $3.73{\pm}1.82$ (p<0.05). From 6 Alvarado score high sensitivity (86.4%) and high specificity (80.0%) were observed. Sensitivity of ultrasonography or computed tomography was 92.5%. Conclusion: We found that Alvarado score system is a noninvasive, safe diagnostic method, which is simple, reliable and repeatable. Alvarado score is useful system for a first, rapid and economic evaluation for the appendicitis in children.
Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.
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