• The Journal of Korean Medicine
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• v.20 no.1 s.37
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• pp.60-65
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• 1999

We have conducted a study of the interobserver reliabilities of Modified Barthel Index and Motor Assessment Scale with 30 patients hospitalized with strokes in the department of circulatory internal medicine, Sang Ji University Oriental Hospital. The observations were performed by two staff and residents in the circulatory internal medicine department. Raters were assigned in random pairs to individual patients. Evaluations were performed independently by the two observers. In order to minimize the impact of fluctuations in the patients' clinical status, the second set of observations immediately followed the first. Each patient was used for only one pair of evaluations. The results were as follows. 1. Mean kappa value of 13 items in Modified Barthel Index(MBD was 0.742, which indicated excellent interobserver reliability. The kappa values indicated almost $perfect({\kappa}:\;0.81-1.00)$ for 4, substantial for $9({\kappa}:\;0.61-0.80)$, and moderate for $2({\kappa}:\;0.41-0.60)$ of 13 items. All items. except Grooming item, showed statistically significant interobserver agreement(p<0.01) 2. Mean kappa value of 8 items. except General tonus, in Motor Assessment Scale(MAS) was 0.823, which indicated excellent interobserver reliability. and this value of Motor Assessment Scale was more high than MBI' s value. 0.81. The kappa values indicated almost perfect for 5, substantial for 3 of 8 items. All items showed statistically significant interobserver agreement(P<0.01).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.135-144
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• 2022

Since vegetation provides humans with various ecological spaces and is also very important in terms of water resources and climatic environment, many vegetation monitoring studies using vegetation indexes based on near infrared sensors have been conducted. Therefore, if the near infrared sensor is not provided, the vegetation monitoring study has a practical problem. In this study, to improve this problem, the NDVI (Normalized Difference Vegetation Index) was used as a reference to evaluate the accuracy of the vegetation index based on the optical sensor. First, the Kappa coefficient was calculated by overlapping the vegetation survey point surveyed in the field with the NDVI. As a result, the vegetation area with a threshold value of 0.6 or higher, which has the highest Kappa coefficient of 0.930, was evaluated based on optical sensor based vegetation index accuracy. It could be selected as standard data. As a result of selecting NDVI as reference data and comparing with vegetation index based on optical sensor, the Kappa coefficients at the threshold values of 0.04, 0.08, and 0.30 or higher were the highest, 0.713, 0.713, and 0.828, respectively. In particular, in the case of the RGBVI (Red Green Red Vegetation Index), the Kappa coefficient was high at 0.828. Therefore, it was found that the vegetation monitoring study using the optical sensor is possible even in environments where the near infrared sensor is not available.

• Journal of dental hygiene science
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• v.15 no.3
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• pp.377-382
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• 2015

The purpose of this study was to evaluate the validity and reliability of plaque scoring system using new Qraycam (All in One Bio, Korea) device which enables plaque score without tooth disclosing. This study measured Quigley-Hein index and plaque control record by both Qraycam and disclosing agent on 64 elderly people and checked degree of congruence between the two methods. Reliability was evaluated with the mean of measured values, kappa index and intraclass correlation coefficient statistical analysis. The analysis of the plaque scores showed a high agreement between the measured values according to the method of measurement and the measured part. The mean of plaque index of anterior labial were not significantly different according to measurement method. The kappa index was higher by Qraycam and tooth disclosing method of plaque index. Therefore, it was verified that Qraycam has sufficient reliability as screening tool for plaque scoring system.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.327-340
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• 2009

To achieve the noise reduction effect in boundary image matching, we use the moving average transform of time-series matching. Our motivation is based on an intuition that using the moving average transform we may exploit the noise reduction effect in boundary image matching as in time-series matching. To confirm this simple intuition, we first propose $\kappa$-order image matching, which applies the moving average transform to boundary image matching. A boundary image can be represented as a sequence in the time-series domain, and our $\kappa$-order image matching identifies similar images in this time-series domain by comparing the $\kappa$-moving average transformed sequences. Next, we propose an index-based matching method that efficiently performs $\kappa$-order image matching on a large volume of image databases, and formally prove the correctness of the index-based method. Moreover, we formally analyze the relationship between an order $\kappa$ and its matching result, and present a systematic way of controlling the noise reduction effect by changing the order $\kappa$. Experimental results show that our $\kappa$-order image matching exploits the noise reduction effect, and our index-based matching method outperforms the sequential scan by one or two orders of magnitude.

• Journal of the Korean Society of Radiology
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• v.16 no.7
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• pp.927-934
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• 2022

This study attempted to find out the degree of agreement between ultrasound image findings along with analysis of attenuation index and scatter distribution index values within tissues through quantitative measurement analysis using liver ultrasound images. From August 2022 to October 2022, liver ultrasound was performed on 45 patients who were suspected of having fatty liver and who received a prescription for liver ultrasound. As a result of the study, as a result of analyzing the agreement between the ultrasound image findings and the tissue attenuation index, the Kappa value was 0.82 (p<0.05), showing a very high agreement between the two examination methods. In addition, as a result of the agreement analysis between the ultrasound image findings and the scatter distribution index in the tissue, the Kappa value was 0.642 (p<0.05), showing high agreement between the two examination methods. At the time of fat liver prediction, the use of liver ultrasound findings and quantitative ultrasonography techniques, such as intra-tissue attenuation index and intra-tissue scatter distribution index, may be helpful in determining the degree of progression of fatty liver patients.

• Journal of the Korean Society of Physical Medicine
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• v.16 no.1
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• pp.1-7
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• 2021

PURPOSE: The purpose of this study is to conduct inter-rater and intra-rater reliability tests in patients with low back pain (LBP) using the prone instability test (PIT) and side-lying instability test (SIT). We have analyzed the Korean version Oswestry disability index (K-ODI) correlations and radiograph finding (RF) for validity. METHODS: Individuals (n = 51) (mean age of 40.27 ± 13.28) with LBP for at least over a week were recruited, together with two participating physical therapist examiners. The measurement consisted of PIT, PST, K-ODI, and RF. Sensitivity (Sn), specificity (Sp), positive predictive value, negative predictive value, prevalence index, agreement %, Cohen's kappa, and prevalence-adjusted bias-adjusted kappa (PABAK) were calculated. The PIT and SIT were compared with RF for validity analysis, while PIT, SIT, K-ODI, and RF were calculated for the correlation analysis. RESULTS: The intra-rater reliability test measured for the PIT (kappa = .79, PABAK = .88) and SIT (kappa = .73, PABAK = .84), and inter-rater reliability test measured for the SIT (kappa = .80, PABAK = .88) showed good agreements. The PIT (Sn = .65, Sp = .63) and SIT validities (Sn = .68, Sp = .70) were compared with RF, showing a significant correlation in PIT and RF (r = .69), SIT and RF (r = .73), and PIT and K-ODI (r = .53). CONCLUSION: The SIT is a more comfortable position test than the PIT in patients. Both PIT and SIT have acceptable reliability and validity.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.671-679
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• 2020

In order to increase the food self-sufficiency rate, monitoring and analysis of crop conditions in the cultivated area is important, and the existing measurement methods in which agricultural personnel perform measurement and sampling analysis in the field are time-consuming and labor-intensive for this reason inefficient. In order to overcome this limitation, it is necessary to develop an efficient method for monitoring crop information in a small area where many exist. In this study, RGB images acquired from unmanned aerial vehicles and vegetation index calculated using RGB image were applied as deep learning input data to classify complex upland crops in small farmland. As a result of each input data classification, the classification using RGB images showed an overall accuracy of 80.23% and a Kappa coefficient of 0.65, In the case of using the RGB image and vegetation index, the additional data of 3 vegetation indices (ExG, ExR, VDVI) were total accuracy 89.51%, Kappa coefficient was 0.80, and 6 vegetation indices (ExG, ExR, VDVI, RGRI, NRGDI, ExGR) showed 90.35% and Kappa coefficient of 0.82. As a result, the accuracy of the data to which the vegetation index was added was relatively high compared to the method using only RGB images, and the data to which the vegetation index was added showed a significant improvement in accuracy in classifying complex crops.

• Journal of the Korean Mathematical Society
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• v.52 no.5
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• pp.991-1001
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• 2015

In this work we obtain conditions for nonspecial line bundles on general ${\kappa}$-gonal curves failing to be normally generated. Let L be a nonspecial very ample line bundle on a general ${\kappa}$-gonal curve X with ${\kappa}{\geq}4$ and $deg\mathcal{L}{\geq}{\frac{3}{2}}g+{\frac{g-2}{{\kappa}}}+1$. If L fails to be normally generated, then L is isomorphic to $\mathcal{K}_X-(ng^1_{\kappa}+B)+R$ for some $n{\geq}1$, B and R satisfying (1) $h^0(R)=h^0(B)=1$, (2) $n+3{\leq}degR{\leq}2n+2$, (3) $deg(R{\cap}F){\leq}1$ for any $F{\in}g^1_k $. Its converse also holds under some additional restrictions. As a corollary, a very ample line bundle $\mathcal{L}{\simeq}\mathcal{K}_X-g^0_d+{\xi}^0_e$ is normally generated if $g^0_d{\in}X^{(d)}$ and ${\xi}^0_e{\in}X^{(e)}$ satisfy $d{\leq}{\frac{g}{2}}-{\frac{g-2}{\kappa}}-3$, supp$(g^0_d{\cap}{\xi}^0_e)={\phi}$ and deg$(g^0_d{\cap}F){\leq}{\kappa}-2$ for any $F{\in}g^1_k$.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.201-214
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• 2020

This study aimed to early detect damaged trees by pine wilt disease using the vegetation indices of UAV images. The location data of 193 pine wilt disease trees were constructed through field surveys and vegetation index analyses of NDVI, GNDVI, NDRE and SAVI were performed using multi-spectral UAV images at the same time. K-Means algorithm was adopted to classify damaged trees and confusion matrix was used to compare and analyze the classification accuracy. The results of the study are summarized as follows. First, the overall accuracy of the classification was analyzed in order of NDVI (88.04%, Kappa coefficient 0.76) > GNDVI (86.01%, Kappa coefficient 0.72) > NDRE (77.35%, Kappa coefficient 0.55) > SAVI (76.84%, Kappa coefficient 0.54) and showed the highest accuracy of NDVI. Second, K-Means unsupervised classification method using NDVI or GNDVI is possible to some extent to find out the damaged trees. In particular, this technique is to help early detection of damaged trees due to its intensive operation, low user intervention and relatively simple analysis process. In the future, it is expected that the utilization of time series images or the application of deep learning techniques will increase the accuracy of classification.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.837-847
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• 2023

In this study, we explored a method for assessing the extent of damage caused by chemical substances at an accident site through the use of a vegetation index. Data collection involved the deployment of two different drone types, and the damaged area was determined using photogrammetry technology from the 3D point cloud data. To create a vegetation index image, we utilized spectral band data from a multi-spectral sensor to generate an orthoimage. Subsequently, we conducted statistical analyses of the accident site with respect to the damaged area using a predefined threshold value. The Kappa values for the vegetation index, based on the near-infrared band and the green band, were found to be 0.79 and 0.76, respectively. These results suggest that the vegetation index-based approach for analyzing damage areas can be effectively applied in investigations of chemical accidents.