• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.115-127
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• 2024

This research aimed to construct models with various structures based on the Transformer module and to perform land cover classification, thereby examining the applicability of the Transformer module. For the classification of land cover, the Unet model, which has a CNN structure, was selected as the base model, and a total of four deep learning models were constructed by combining both the encoder and decoder parts with the Transformer module. During the training process of the deep learning models, the training was repeated 10 times under the same conditions to evaluate the generalization performance. The evaluation of the classification accuracy of the deep learning models showed that the Model D, which utilized the Transformer module in both the encoder and decoder structures, achieved the highest overall accuracy with an average of approximately 89.4% and a Kappa coefficient average of about 73.2%. In terms of training time, models based on CNN were the most efficient. however, the use of Transformer-based models resulted in an average improvement of 0.5% in classification accuracy based on the Kappa coefficient. It is considered necessary to refine the model by considering various variables such as adjusting hyperparameters and image patch sizes during the integration process with CNN models. A common issue identified in all models during the land cover classification process was the difficulty in detecting small-scale objects. To improve this misclassification phenomenon, it is deemed necessary to explore the use of high-resolution input data and integrate multidimensional data that includes terrain and texture information.

• Tuberculosis and Respiratory Diseases
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• v.79 no.4
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• pp.282-288
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• 2016

Background: Tuberculosis (TB) is a major health problem, and accurate and rapid diagnosis of multidrug-resistant (MDR) and extended drug-resistant (XDR) TB is important for appropriate treatment. In this study, performances of solid and liquid culture methods were compared with respect to MDR- and XDR-TB isolate recovery and drug susceptibility testing. Methods: Sputum specimens from 304 patients were stained with Ziehl-Neelsen method. Mycobacterium tuberculosis (Mtb) isolates were tested for recovery on $L{\ddot{o}wenstein$-Jensen (LJ) medium and the BacT Alert 3D system. For drug susceptibility testing of Mtb, isolates were evaluated on M-KIT plates and the BacT Alert 3D system. Results: The recovery rates were 94.9% (206/217) and 98.2% (213/217) for LJ medium and the BacT Alert 3D system, respectively (kappa coefficient, 0.884). The rate of drug resistance was 13.4% for at least one or more drugs, 6.0% for MDR-TB and 2.3% for XDR-TB. M-KIT plate and BacT 3D Alert 3D system were comparable in drug susceptibility testing for isoniazid (97.7%; kappa coefficient, 0.905) and rifampin (98.6%; kappa coefficient, 0.907). Antibiotic resistance was observed using M-KIT plates for 24 of the total 29 Mtb isolates (82.8%). Conclusion: The liquid culture system showed greater reduction in the culture period, as compared with LJ medium; however, drug susceptibility testing using M-KIT plates was advantageous for simultaneous testing against multiple drug targets.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.2168-2173
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• 2003

Pressure balancing valve is one of important control devices, which is fully automatic and no manual controls, regulating or adjustments are needed. It is typically used to maintain constant temperature of working fluid in power and chemical plants and domestic water supply systems. Pressure balancing valve is composed of body, cylinder and balancing piston. Therefore, the balancing piston shapes are important design parameters for a pressure balancing valve. In this study, numerical and experimental analyses are carried out with two different balancing piston shapes. Especially, the distribution of static pressure is investigated to calculate the flow coefficient($C_v$). The governing equations are derived from making using of three-dimensional Navier-Stokes equations with standard ${\kappa}-{\varepsilon}$ turbulence model and SIMPLE algorithm. Using commercial code, PHOEIC, the pressure and flow fields in pressure balancing valve are depicted.

• Imaging Science in Dentistry
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• v.47 no.1
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• pp.25-31
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• 2017

Purpose: The aim of this study was to compare the diagnostic accuracy of previously trained endodontists in the detection of artificially created periapical lesions using cone-beam computed tomography (CBCT) and digital periapical radiography (DPR). Materials and Methods: An ex vivo model using dry skulls was used, in which simulated apical lesions were created and then progressively enlarged using #1/2, #2, #4, and #6 round burs. A total of 11 teeth were included in the study, and 110 images were obtained with CBCT and with an intraoral digital periapical radiographic sensor (Instrumentarium dental, Tuusula, Finland) initially and after each bur was used. Specificity and sensitivity were calculated. All images were evaluated by 10 previously trained, certified endodontists. Agreement was calculated using the kappa coefficient. The accuracy of each method in detecting apical lesions was calculated using the chisquare test. Results: The kappa coefficient between examiners showed low agreement (range, 0.17-0.64). No statistical difference was found between CBCT and DPR in teeth without apical lesions (P=.15). The accuracy for CBCT was significantly higher than for DPR in all corresponding simulated lesions(P<.001). The correct diagnostic rate for CBCT ranged between 56.9% and 73.6%. The greatest difference between CBCT and DPR was seen in the maxillary teeth (CBCT, 71.4%; DPR, 28.6%; P<.01) and multi-rooted teeth (CBCT, 83.3%; DPR, 33.3%; P<.01). Conclusion: CBCT allowed higher accuracy than DPR in detecting simulated lesions for all simulated lesions tested. Endodontists need to be properly trained in interpreting CBCT scans to achieve higher diagnostic accuracy.

• Journal of Korean Society of Forest Science
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• v.81 no.4
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• pp.346-356
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• 1992

The extensive Snake River floodplain in Northwest United States has experienced major changes in water channels and vegetation types due to floodings. To detect the change of wetland cover-types for the period of 1985 and 1988, post-classification comparison and image differencing change detection techniques were evaluated using Landsat-5 TM digital data. Differenced infrared-band images indicated better accuracy indices than any visible-band images. A thresholding technique was applied to identify the change and no change categories from the transformed images produced by image differencing. The problems in using different accuracy indices, including the Kappa coefficient of agreement, overall accuracy, producer's accuracy, user's accuracy, and average accuracy(based on both the producer's and user's accuracy approaches) in determining an optimal threshold level, were examined.

• Journal of Physiology & Pathology in Korean Medicine
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• v.32 no.3
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• pp.171-177
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• 2018

The purpose of this study was to evaluate the reliability and the validity of Deficiency and Excess Pattern Identification Questionnaire. The number of subjects enrolled in this study was 248, from July 2015 to March. 2016. The surveys was conducted two times with 3 month interval. The Cronbach's ${\alpha}$ analysis for internal reliability, Pearson Correlation Coefficient analysis for test-retest reliability were conducted. Factor analysis with varimax rotation for construct validity was used. Kappa analysis for diagnostic reliability were used. The significant p-value was < .05. The Cronbach's ${\alpha}$ was .929 in Deficiency and .932 in Excess questionnaire. The reliabilities between test and retest Intra Correlation Coefficient (ICC) was .71-.762 in the Deficiency, and .58-.786 in Excess questionnaire, respectively. Deficiency was divided by five factors, and Excess four factors. The factor convergence was 72.54% in the Deficiency and 67.5% in Excess questionnaire. The test-retest agreement of four pattern was 68.5% and Kappa was .530. This study reveals that Deficiency and Excess Pattern Identification Questionnaire is a reliable and valid. However, further study to validate the questionnaire is needed.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.2
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• pp.491-495
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• 2016

Background: Small B-cell non-Hodgkins lymphoma (NHL) is difficult to be distinguished from non-neoplastic reactive processes using conventional haematoxylin-eosin (HE) staining due to different interpretations among pathologists with diagnosis based on morphologic features. Ancillary examinations such as immunohistochemical (IHC) staining are essential. However, negative or doubtful results are still sometimes obtained due to unsatisfactory tissue processing or IHC technique. The polymerase chain reaction (PCR) as a molecular diagnostic technique is very sensitive and specific. Clonality detection of heavy chain immunoglobulin (IgH) gene rearrangement has been widely used to establish diagnosis of B-cell NHL. Aims: To elaborate interobserver variation in small B-cell NHL diagnosis based on morphologic features only and to confirm sensitivity and specificity of the PCR technique as an ancillary method. Materials and Methods: A toptal of 28 samples of small B cell NHL and suspicious lymphoma were interpreted by 3 pathologists in Sardjito General Hospital based on their morphology only. The reliability of assessment and the coefficient of interobserver agreement were calculated by Fleiss kappa statistics. Interpretation results were confirmed with IHC staining (CD20, CD3, Bcl2). PCR was performed to analyze the clonality of IgH gene rearrangement. Results: Interobserver agreement in morphologic evalution of small B cell NHL and chronic lymphadenitis revealed kappa coefficient 0.69 included in the substantial agreement category. The cases were divided into 3 groups based on morphology and IHC results; lymphoma, reactive process and undetermined group. PCR analysis showed 90% sensitivity and 60% specificity. Conclusions: The present study revealed a substantial agreement among pathologists in small B-cell NHL diagnosis. For difficult cases, PCR is useful as complementary method to morphologic and IHC examinations to establish definitive diagnosis.

• Korean Journal of Child Studies
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• v.26 no.2
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• pp.27-41
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• 2005

The purpose of this study was to test the reliability and validity of the Infant Montessori Performance Assessment Scales developed by the authors. Subjects of the study consisted of 132 Montessori teachers and 50 infants who were attending Montessori home school throughout the whole country, using census method. Infant's age ranged from 6 to 24 months. Data were analysed by using SAS 8.2 PC program. Statistical methods employed were frequency, Cronbach's alpha, Kappa coefficient, test-retest correlation, construct validity, and concurrent validity. The Cronbach's alpha of 6 sub-scales included physical, creativity, practical life, sense, language, and cognitive education, which ranged from .70 to .86. And the! correlation of the test/re-test was .72. The correlation between the 6 sub-scales of Infant-Montessori Performance Assessment Scales and the total scores of 6 items ranged from .06 to .84. Therefore, the construct validity of Infant Montessori Performance Assessment Scales was verified. The Kappa coefficient of inter-rater reliability was .76. The correlation between the Infant Montessori Performance Assessment Scales and the Standardized Korean Creativity Traits Checklist(K-CTC) and the Korean Child Social Maturity Scales showed non-significant levels of .16 and .12 respectively. Conclusively, Infant Montessori Performance Assessment Scales developed by the authors were verified through the above reliability and validity tests. Specifically the Infant Montessori Performance Assessment Scales showed the relationship of the convergent and divergent validity with the Korean Child Social Maturity Scales and the Standardized Korean Creativity Traits Checklist, respectively.

• Journal of Periodontal and Implant Science
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• v.48 no.5
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• pp.317-325
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• 2018

Purpose: We investigated correlations between the findings of oral examinations and panoramic radiography in order to determine the efficacy of using panoramic radiographs in screening examinations. Methods: This study included patients who visited dental clinics at National Health Insurance Service (NHIS) Ilsan Hospital for checkups during 2009-2015 and underwent panoramic radiographic examinations within 1 year prior to the oral examinations. Among the 48,006 patients who received checkups, 1,091 were included in this study. The data were evaluated using the Cohen kappa and interrater agreement coefficients. Accuracy, sensitivity, and specificity were calculated using data from the panoramic radiographs as true positive diagnoses. Results: The interrater agreement coefficient for occlusal caries was 28.8%, and the Cohen kappa coefficient was 0.043 between the oral and panoramic radiographic examinations. Root caries and subgingival calculus were only found on the radiographs, while gingival inflammation was found only by the oral examinations. The oral examinations had a specificity for detecting occlusal dental caries of 100%, while their sensitivity for proximal dental caries and supragingival calculus was extremely low (14.0% and 18.3%, respectively) compared to the panoramic radiographic examinations. The oral examinations showed a relatively low sensitivity of 66.2% and a specificity of 43.7% in detecting tooth loss compared with panoramic radiography. Conclusions: Panoramic radiography can provide information that is difficult to obtain in oral examinations, such as root caries, furcation involvement, and subgingival calculus, which are factors that can directly affect the survival rate of teeth. It therefore seems reasonable and necessary to add panoramic radiography to large-scale health checkup programs such as that provided by the NHIS.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2011-2025
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• 2021

In high-resolution satellite image classification, when the color values of pixels belonging to one class are different, such as buildings with various colors, it is difficult to determine the color information representing the class. In this paper, to solve the problem of determining the representative color information of a class, we propose a method to divide the color channel of HSV (Hue Saturation Value) and perform object-based classification. To this end, after transforming the input image of the RGB color space into the components of the HSV color space, the Hue component is divided into subchannels at regular intervals. The minimum distance-based image classification is performed for each hue subchannel, and the classification result is combined with the image segmentation result. As a result of applying the proposed method to KOMPSAT-3A imagery, the overall accuracy was 84.97% and the kappa coefficient was 77.56%, and the classification accuracy was improved by more than 10% compared to a commercial software.