• Journal of the Korean Society of Radiology
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• v.7 no.3
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• pp.251-257
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• 2013

Spatial dose rates of high dose $^{131}I$ therapy patients were Measured Three dimensional (X, Y, Z) distributions. I have constructed geometrical an aluminum support structure for spatial dose meters placed in 5 different heights, 8 different azimuthal angles, 6 different time interval and distance 100 cm from High dose$^{131}I$ therapy patients. when the height of vertical plane Spatial dose distribution is 100 cm, the Spatial dose rates is max and the error range is low. the vertical plane Spatial dose rates was found to be 71.85 ${\mu}Sv/h$ on the average at a distance of 100 cm, height 100 cm, from the patients 24 hours after $^{131}I$ oral administration. I divided 12 patients into two groups. I have analysed group A (drinking 5 L water) and group B (drinking 3 L water) in order to measure decrease spatial dose rates. I have found the spatial distributions of patient dose rates is $44.9{\pm}7.2$ ${\mu}Sv/h$ in group A and $100.3{\pm}8.1$ ${\mu}Sv/h$ in group B by 24 after $^{131}I$ oral administration. the reduction factor was found to be approximately 54 % through drinking 5 L water during 24 hours.

• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.227-244
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• 2015

Synthetic Aperture Radar is able to provide images of wide coverage in day, night, and all-weather conditions. Recently, as the SAR image resolution improves up to the sub-meter level, their applications are rapidly expanding accordingly. Especially there is a growing interest in the use of geographic information of high resolution SAR images and the change detection will be one of the most important technique for their applications. In this paper, an automatic threshold tracking and change detection algorithm is proposed applicable to high-resolution SAR images. To detect changes within SAR image, a reference image is generated using log-ratio operator and its amplitude distribution is estimated through K-S test. Assuming SAR image has a non-gaussian amplitude distribution, a generalized thresholding technique is applied using Kittler and Illingworth minimum-error estimation. Also, MoLC parametric estimation method is adopted to improve the algorithm performance on rough ground target. The implemented algorithm is tested and verified on the simulated SAR raw data. Then, it is applied to the spaceborne high-resolution SAR images taken by Cosmo-Skymed and KOMPSAT-5 and the performances are analyzed and compared.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1179-1194
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• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.19-33
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• 2008

Linear regression method, proposed by Haseman and Elston(1972), for detecting linkage to a quantitative trait of sib pairs is a linkage testing method for a single locus and a single trait. However, multivariate methods for detecting linkage are needed, when information from each of several traits that are affected by the same major gene are available on each individual. Amos et al. (1990) extended the regression method of Haseman and Elston(1972) to incorporate observations of two or more traits by estimating the principal component linear function that results in the strongest correlation between the squared pair differences in the trait measurements and identity by descent at a marker locus. But, it is impossible to control the probability of type I errors with this method at present, since the exact distribution of the statistic that they use is yet unknown. In this paper, we propose a multivariate nonparametric trend test for detecting linkage to multiple traits. We compared with a simulation study the efficiencies of multivariate nonparametric trend test with those of the method developed by Amos et al. (1990) for quantitative traits data. For multivariate nonparametric trend test, the results of the simulation study reveal that the Type I error rates are close to the predetermined significance levels, and have in general high powers.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.183-196
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• 2016

Ground control points are needed for precision geometric correction of satellite images, and the coordinates of a high-quality ground control point can be obtained from the GPS measurement. However, considering the GPS measurement requires an excessive amount o f t ime a nd e fforts, there is a need for coming up with an alternative solution to replace it. Therefore, we examined the possibility of replacing the existing GPS measurement with coordinates available at online maps to acquire the coordinates of ground control points. To this end, we examined error amounts between the coordinates of ground control points obtained through Daum Map API, and them compared the accuracies between three types of coordinate transformation equations which were used for geometric correction of satellite images. In addition, we used the coordinate transformation equation with the highest accuracy, the coordinates of ground control point obtained through the GPS measurement and those acquired through D aum M ap A PI, and conducted geometric correction on them to compare their accuracy and evaluate their effectiveness. According to the results, the 3rd order polynomial transformation equation showed the highest accuracy among three types of coordinates transformation equations. In the case of using mid-resolution satellite images such as those taken by Landsat-8, it seems that it is possible to use geometrically corrected images that have been obtained after acquiring the coordinates of ground control points through Daum Map API.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1141-1141
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• 2001

The aim of the study was to evaluate the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural network (ANN) on the prediction of chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with information relative to moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected with 10 different Foss NIR Systems instruments, which were either standardized or not standardized to one master instrument. The spectra were trimmed to a wavelength range between 1100 and 2498 nm. Two data sets, one standardized (IVAL) and the other not standardized (SVAL) were used as independent validation sets, but 10% of both sets were omitted and kept for later expansion of the calibration database. The remaining samples were combined into one database (n=21,696), which was split into 75% calibration (CALBASE) and 25% validation (VALBASE). The chemical components in the 3 validation data sets were predicted with each model derived from CALBASE using the calibration database before and after it was expanded with 10% of the samples from IVAL and SVAL data sets. Calibration performance was evaluated using standard error of prediction corrected for bias (SEP(C)), bias, slope and R2. None of the models appeared to be consistently better across all validation sets. VALBASE was predicted well by all models, with smaller SEP(C) and bias values than for IVAL and SVAL. This was not surprising as VALBASE was selected from the calibration database and it had a sample population similar to CALBASE, whereas IVAL and SVAL were completely independent validation sets. In most cases, Local and ANN models, but not modified PLS, showed considerable improvement in the prediction of IVAL and SVAL after the calibration database had been expanded with the 10% samples of IVAL and SVAL reserved for calibration expansion. The effects of sample processing, instrument standardization and differences in reference procedure were partially confounded in the validation sets, so it was not possible to determine which factors were most important. Further work on the development of large databases must address the problems of standardization of instruments, harmonization and standardization of laboratory procedures and even more importantly, the definition of the database population.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.7 no.2
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• pp.190-202
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• 1996

This study assessed psychiatrically referred 5-to 13-year-old children who presented inattention or hyperactivity as chief complaints. Demographic characteristics, primary diagnosis, and comorbid psychiatric conditions of them were identified, and they were assessed using questionnaires and neuropsychological tests. Primary diagnoses included ADHD, anxiety disorder, mental retardation, depression, oppositional defiant disorder, developmental language disorder and others. functional enuresis, conduct disorder, and developmental language disorder were among the secondarily diagnosed disorders. In patients diagnosed as ADHD, overall comorbidity rate was 55.3%. The disorders that frequently co-occured with ADHD were specific developmental disorder, conduct disorder, oppositional defiant disorder, anxiety disorder and other. ADHD groups with or without comorbidity differed in performance IQ and CPT scores. ADHD group differed from externalizing disorders group in the information subscore of IQ, MFFT, and CPT scores, and differed in teachers rating scales, the uncommunication factor of CBCL, and CPT card error compared with internalizing disorders group. The authors concluded that inattentive or hyperactive children should be assessed using various instruments to differentiate other disorders and to identify possible presence of comorbid conditions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.5 no.4
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• pp.247-254
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• 2003

This study was conducted to develop site index equations by main species grown in Gyunggi and Chungcheong provinces using environmental factors obtained from a digital forest site map. For this, 28 environmental factors were regressed on site index by species. Four to five environmental factors by species were selected as independent variables in the best site index equations (coefficients of determination greater than 0.91). For these site index equations, three evaluation statistics, mean difference, standard deviation of difference, and standard error of difference, were applied to the data set. Site index equations by species relationships developed in this study effectively estimate forest productivity in the study area. However, the site index equation of Larix leptolepis showed a larger than expected bias between the estimated and the measured site index. The reason is not clear in this situation, but might be because of the small sample set. It will be necessary, therefore, to conduct more studies to determine the exact reason. It is also expected that the site index equations with a few environmental factors as independent variables could provide valuable information about species well suited to given site conditions. Site index equations for other species should be developed to establish a rational policy about the selection of best species for site conditions.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1249-1256
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• 2019

In this paper, we propose a new algorithm to remove acoustic feedback and noise in hearing aids. Instead of using the conventional FIR structure, this algorithm is a deep learning algorithm using neural network adaptive prediction filter to improve the feedback and noise reduction performance. The feedback canceller first removes the feedback signal from the microphone signal and then removes the noise using the Wiener filter technique. Noise elimination is to estimate the speech from the speech signal containing noise using the linear prediction model according to the periodicity of the speech signal. In order to ensure stable convergence of two adaptive systems in a loop, coefficient updates of the feedback canceller and noise canceller are separated and converged using the residual error signal generated after the cancellation. In order to verify the performance of the feedback and noise canceller proposed in this study, a simulation program was written and simulated. Experimental results show that the proposed deep learning algorithm improves the signal to feedback ratio(: SFR) of about 10 dB in the feedback canceller and the signal to noise ratio enhancement(: SNRE) of about 3 dB in the noise canceller than the conventional FIR structure.

• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.170-179
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• 2014

Recently the U-Healthcare system has been rapidly advanced to manage emergence rescue for seniors. We can access emergency rescue systems with high quality services anytime, anywhere under ubiquitous healthcare systems. The more the various systems develop, the more software security systems become important. Therefore, the safety-critical system has been widely spread to the world by advancement of the information and communication technologies. There are a lot kind of fault analysis methods to evaluate software security systems. However due to characteristics of software that is not applied by human error, it can be prevented the enormous damages and losses from improving the safety of safety-critical system. So this paper proposes an integration method of FTA and Forward and Backward FMECA. This method has each strength of FTA and FMECA which is visual and numeric in normalization. First, by use of FTA, we can redraw FTA with Forward FMECA and Backward FMECA in consideration of occurrence, severity, detection, correctness, robustness, and security. Also according to value of NRVP at each event, we can modify FTA diagrams as shown critical paths given by severity and occurrence. Also, we propose the improved emergency rescue service platform of ubiquitous healthcare systems through identifying priorities of the criticality according to normalized risk priority values (NRPV).