• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.601-610
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• 2018

The national accuracy of global land cover (GLC) products is of great importance to ecological and environmental research. However, GLC products that are derived from different satellite sensors, with differing spatial resolutions, classification methods, and classification schemes are certain to show some discrepancies. The goal of this study is to assess the accuracy of four commonly used GLC datasets in South Korea, GLC2000, GlobCover2009, MCD12Q1, and GlobeLand30. First, we compared the area of seven classes between four GLC datasets and a reference dataset. Then, we calculated the accuracy of the four GLC datasets based on an aggregated classification scheme containing seven classes, using overall, producer's and user's accuracies, and kappa coefficient. GlobeLand30 had the highest overall accuracy (77.59%). The overall accuracies of MCD12Q1, GLC2000, and GlobCover2009 were 75.51%, 68.38%, and 57.99%, respectively. These results indicate that GlobeLand30 is the most suitable dataset to support a variety of national scientific endeavors in South Korea.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.53-63
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• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.

• Journal of Forest and Environmental Science
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• v.34 no.2
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• pp.142-144
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• 2018

This paper discusses the development of semi-automatic search program for crown delineation in stand level. The crown of an individual tree was delineated by applying the Watershed (WS) and Valley Following (VF) algorithms. Unmanned Aerial Vehicle (UAV) images were used in the semi-automatic search program to delineate the crown area. The overall accuracy and Khat were used in accuracy assessment. WS algorithm's model showed the overall accuracy and Khat index of 0.80 and 0.59, respectively, in Plot 1. However, the overall accuracy and Khat of VF algorithm's model were 0.78 and 0.51, respectively, in Plot 2.

• Journal of Radiation Protection and Research
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• v.36 no.3
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• pp.107-118
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• 2011

Conventional (SRS) and fractionated (FSRS) stereotactic radiosurgery necessarily require stringent overall target point accuracy and precision. We determine three-dimensional intracranial target point deviations (TPDs) in a whole treatment procedure using magnetic resonance image (MRI)-based polymer-gel dosimetry, and suggest a technique for overall system tests. TPDs were measured using a custom-made head phantom and gel dosimetry. We calculated TPDs using a treatment planning system. Then, we compared TPDs using mid bi-plane and three-dimensional volume methods with spherical and elliptical targets to determine their inherent analysis errors; finally, we analyzed regional TPDs using the latter method. Average and maximum additive errors for ellipses were 0.62 and 0.69 mm, respectively. Total displacements were 0.92 ${\pm}$ 0.25 and 0.77 ${\pm}$ 0.15 mm for virtual SRS and FSRS, respectively. Average TPDtotal at peripheral regions was greater than that at central regions for both. Overall system accuracy was similar to that reported previously. Our technique could be used as an overall system accuracy test that considers the real radiation field shape.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1551-1565
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• 2020

On April 4, 2019, a forest fire started in Goseong County and lasted for three days, burning the neighboring areas of Sokcho. The strong winds moved the blaze from one region to another region and declared the worst wildfire in South Korea in years. More than 1,880 facilities, including 400 homes, were burnt down. The fire burned a total area of 529 hectares (1,307 acres), which involved 13,000 rescuers and 16,500 military troops to control the fire occurrence. Thousands of people were evacuated, and two people are dead. This study generated post-wildfire maps to provide necessary data for evacuation and mitigation planning to respond to this destructive wildfire, also prevent further damage and restore the area affected by the wildfire. This study used KOMPSAT-3A and Sentinel-2 imagery to map the post-wildfire condition. The SVM showed higher accuracy (overall accuracy 95.29%) compared with ANN (overall accuracy of 94.61%) for the KOMPSAT-3A. Moreover, for Sentinel-2, the SVM attained a higher accuracy (overall accuracy of 91.52%) than the ANN algorithm (overall accuracy 90.11%). In total, four post-wildfire burned area maps were generated; these results can be used to assess the area affected by the Sokcho wildfire and wildfire mitigation planning in the future.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.521-528
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• 2007

The overall objective of this research was to investigate various combination of segmentation parameters and to improve classification accuracy of object-oriented classification. This research presents a method for evaluation of segmentation parameters by calculating Moran's I and Intrasegment Variance. This research used Landsat-7/ETM image of $11{\times}14$ Km developed area in Ansung, Korea. Segmented images are generated by 75 combinations of parameter. Selecting 7 combinations of high, middle and low grade expected classification accuracy was based on calculated Moran's I and Intrasegment Variance. Selected segmentation images are classified 4 classes and analyzed classification accuracy according to method of objected-oriented classification. The research result proved that classification accuracy is related to segmentation parameters. The case of high grade of expected classification accuracy showed more than 85% overall accuracy. On the other hand, low ado showed around 50% overall accuracy.

• Animal Bioscience
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• v.37 no.4
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• pp.555-566
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• 2024

Objective: This study aimed to assess the genetic parameters and accuracy of genomic predictions for twenty-four linear body conformation traits and overall conformation scores in Korean Holstein dairy cows. Methods: A dataset of 2,206 Korean Holsteins was collected, and genotyping was performed using the Illumina Bovine 50K single nucleotide polymorphism (SNP) chip. The traits investigated included body traits (stature, height at front end, chest width, body depth, angularity, body condition score, and locomotion), rump traits (rump angle, rump width, and loin strength), feet and leg traits (rear leg set, rear leg rear view, foot angle, heel depth, and bone quality), udder traits (udder depth, udder texture, udder support, fore udder attachment, front teat placement, front teat length, rear udder height, rear udder width, and rear teat placement), and overall conformation score. Accuracy of genomic predictions was assessed using the single-trait animal model genomic best linear unbiased prediction method implemented in the ASReml-SA v4.2 software. Results: Heritability estimates ranged from 0.10 to 0.50 for body traits, 0.21 to 0.35 for rump traits, 0.13 to 0.29 for feet and leg traits, and 0.05 to 0.46 for udder traits. Rump traits exhibited the highest average heritability (0.29), while feet and leg traits had the lowest estimates (0.21). Accuracy of genomic predictions varied among the twenty-four linear body conformation traits, ranging from 0.26 to 0.49. The heritability and prediction accuracy of genomic estimated breeding value (GEBV) for the overall conformation score were 0.45 and 0.46, respectively. The GEBVs for body conformation traits in Korean Holstein cows had low accuracy, falling below the 50% threshold. Conclusion: The limited response to selection for body conformation traits in Korean Holsteins may be attributed to both the low heritability of these traits and the lower accuracy estimates for GEBVs. Further research is needed to enhance the accuracy of GEBVs and improve the selection response for these traits.

• Advances in pediatric surgery
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• v.20 no.2
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• pp.33-37
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• 2014

Purpose: The purpose of this study was to compare the diagnostic accuracy of the non-invasive diagnostic methods and rectal suction biopsy for the detection of Hirschsprung disease (HD). Methods: We reviewed diagnostic methods and results retrospectively in patients who underwent anorectal manometry, barium enema and rectal suction biopsy for the diagnosis of HD at Asan Medical Center from January 2000 to December 2012. Results: There were 97 patients (59 neonates and 38 infants) in the study period. The overall accuracy of anorectal manometry for the diagnosis of HD was 71.1% and its sensitivity was 51.4% (48.1% in neonate and 62.5% in infant, respectively) and its overall specificity was 82.3% (81.3% in neonate and 83.3% in infant, respectively). The Overall accuracy of barium enema was 66.0% (72.8% in neonate and 55.3% in infant, respectively) and specificity of barium enema was 53.2% (56.3% in neonate and 50.0% in infant, respectively). These results were lower than those of anorectal manometry. The overall sensitivity of barium enema was 88.6% (92.6% in neonate and 75.0% in infant, respectively) and it was higher than the sensitivity of anorectal manometry. Histological studies confirmed HD in 35 patients, in one of whom the suction biopsy showed negative finding. Conclusion: Accuracy of non-invasive methods for diagnosis of HD in our study is lower than those in previous study, so we need to improve the quality of diagnostic tools in our hospital. We conclude that the rectal suction biopsy is the most accurate test for diagnosing HD, so the biopsy to confirm the diagnosis of the HD is very important.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.6 no.1
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• pp.109-124
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• 1996

This study was performed to evaluate the performance of three passive samplers made in U.S.A. Three passive samplers selected for this study included products made by 3M, Gilian, and SKC in U.S.A. Three organic solvents, such as toluene, trichloroethylene, and n-hexane which are used frequently in Korean industry were selected for the study. Conclusions obtained from this study are summarized as follows. 1. For toluene and trichloroethylene, the overall accuracy of the results from all of three products was within 25 %, which is the criteria recommended by the U.S. National Institute for Occupational Safety and Health (NIOSH). For n-hexane, the overall accuracy of the results from two products except 3M was exceeding 25 %. Thus 3M product showed the best accuracy among three products. 2. When passive samplers collected organic vapors were exposed to clean air for two hours, there were 12 - 16 % loss of organic vapors due to reverse diffusion in Gilian products. There was no significant loss in results from other two products. 3. Air velocity affected greatly on the performance of passive samplers which did not have permeation membrane. At high velocity, 100 cm/sec, accuracies of results from Gilian and SKC were 57 - 108 and 128 - 164 %, respectively. However, the results from 3M samplers, which contain permeation membrane, indicated accuracy below 25 %. 4. When passive samplers collected organic vapors for eight hours, the accuracy was reduced. Thus, it is recommended that passive samplers be used for less than four hours.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.4 no.2
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• pp.208-223
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• 1994

The purpose of this study was to evaluate the efficiency of diffusive(or passive) sampler in measuring airbone organic solvents. Diffusive samplers are generally simple in construction and do not require power for operation. The efficiency of the diffusive samplers has not sufficiently been investigated in Korea. Three types of samplers were studied in this study. The sampling and analytical results by passive samplers were compared with results by charcoal tube method recommended by NIOSH(National Institute for Occupational Safty and Health). The following characteristics are identified and studied as critical to the performance passive monitors; recovery, reverse diffusion, storage stability, accuracy and precision, face velocity and humidity, n-Hexane, TCE(trichloroethylene) and toluene were used as test vapors. A dynamic vapor exposure system consisting of organic vapor generator and sampling chamber for evaluating diffusive samplers are made. The results of the study are summarized as follows. 1. NIOSH recommands that the overall accuracy of a sampling method in the range of 0.5 to 2.0 times the occupational health standard should be ${\pm}25$ percent for 95 percent confidence level. Among three types of diffusive samplers, sampler A has permeation membrane and samplers Band C have diffusive areas, samplers A and B met the criterion that overall accuracy for 95% confidence level of the samplers were within ${\pm}25$ percent of the reference value. Sampler C had overall accuracy ${\pm}9.6%$ and ${\pm}11.8%$ in hexane and TCE, respectively. The concentration of toluene was overestimated in sampler C with overall accuracy of ${\pm}43.9%$. 2. The desorption efficiencies of diffusive samplers were 96-107%. 3. There was no significant sampe loss during four weeks of storage both with and without refrigeration. 4. There was no significant reverse diffusion, when the samplers were exposure to clean air for 2 hours after sampling for 2 hours at the level of 2 TLY. 5. In case of 8 hours sampling, relative differences(RD) of concentrations between charcoal tube method and diffusive method were 15-39%, 13-46%, and 4-35% for sampler A, B and C, respectively. The performance was poor in 8 hours sampling for multiple substance monitors. 6. At high velocity(100 cm/sec), samplers B and C overestimated the concentrations of organic vapors, and sampler A with permeation membrance gave better results. 7. At 80% relative humidity, samplers showed no siginificant effect. Low humidity also did not affect the diffusive samplers.