• Journal of Korean Association for Spatial Structures
• /
• v.10 no.2
• /
• pp.95-103
• /
• 2010

This paper study on the accuracy improvement of nonlinear stiffness equation. The large structure must have thin thickness for build the large space structure there fore structure instability review is important when we do structural design. The structure instability of the shelled structure is accept it sensitively by varied conditions. This come to a nonlinear problem with be concomitant large deformation. Accuracy of nonlinear stiffness equation must improve to examine structure instability. In this study, space truss is analysis model Among tangent stiffness equation and nonlinear stiffness equation is using nonlinearity analysis program. The study compares an analysis result to investigate accuracy and convergence properties improvement of nonlinear stiffness equation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.89-93
• /
• 2009

Embedded systems are becoming more popular as many embedded platforms have become more affordable. It offers a compact solution for many different problems including computer vision applications. Texture classification can be used to solve various problems, and implementing it in embedded platforms will help in deploying these applications into the market. This paper proposes to deploy the texture classification algorithms onto the embedded computer vision (ECV) platform. Two algorithms are compared; grey level co-occurrence matrices (GLCM) and Gabor filters. Experimental results show that raw GLCM on MATLAB could achieves 50ms, being the fastest algorithm on the PC platform. Classification speed achieved on PC and ECV platform, in C, is 43ms and 3708ms respectively. Raw GLCM could achieve only 90.86% accuracy compared to the combination feature (GLCM and Gabor filters) at 91.06% accuracy. Overall, evaluating all results in terms of classification speed and accuracy, raw GLCM is more suitable to be implemented onto the ECV platform.

• Korean Journal of Head & Neck Oncology
• /
• v.4 no.1
• /
• pp.5-11
• /
• 1988

CT examination has been used in the preoperative examination of patients with head and neck cancer. The accuracy of CT in detecting nodal metastases has not been well established. We studied 35 patients (41 neck specimens) with head and neck cancer who underwent neck dissection. Surgical pathologic findings were compared with preoperative CT scan to establish the diagnostic accuracy for cervical lymph node metastases. The results of physical examination, CT scans of neck and histologic examinations were compared each other. The overall diagnostic accuracy of CT was 83.3%. Comparison with clinical accuracy shows the CT scan to be superior to the clinical examination in spite of careful physical examination, particularly in detecting occult metastases.

• Journal of Korean Society for Geospatial Information Science
• /
• v.8 no.2 s.16
• /
• pp.23-33
• /
• 2000

On this study, Among the classification methods of land cover using satellite imagery, we compared the classification accuracy of Neural Network Classifier and that of Maximum Likelihood Classifier which has the characteristics of parametric and non-parametric classification method. In the assessment of classification accuracy, we analyzed the classification accuracy about testing area as well as training area that many analysts use generally when assess the classification accuracy. As a result, Neural Network Classifier is superior to Maximum Likelihood Classifier as much as 3% in the classification of training data. When ground reference data is used, we could get poor result from both of classification methods, but we could reach conclusion that the classification result of Neural Network Classifier is superior to the classification result of Maximum Likelihood Classifier as much as 10%.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1996.03a
• /
• pp.183-187
• /
• 1996

On the machine measuring system composed of touch trigger probes, a DNC module, a CMM module, an analysis module and a man-machine interface unit was developed. Measuring accuracy is affected by working accuracy of the on the machine measuring system. The working accuracy of the system is due to geometric errors of th machine tool, servo errors of feed drives and positioning errors of probes. In order to compensate for the measuring errors due to the working accuracy, a calibration module was developed. The measuring automation system was realized with the on the machine measuring system and an IBM-PC on the machine center through a RS-232C. It turns the machining machine (CMM). The system is used for dimensional checking of machined components. initial job setup, part identification, identification of machining errors due to deflection and wear of tools. cutter run out, and calibration of machine tools. A horizontal machining center equipped with FANUC OMC wre used for verification of the system. The validity and reliability of the system. The validity and reliability of the system were confirmed through a series of experiments with gage blocks, ring gages, comparison measurement with a commercial CMM, and so on.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.31 no.4
• /
• pp.490-495
• /
• 2007

This research aims to examine accuracy and efficiency of the first four moments corresponding to mean, standard deviation, skewness, and kurtosis, which are estimated by a function approximation moment method (FAMM). In FAMM, the moments are estimated from an approximating quadratic function of a system response function. The function approximation is performed on a specially selected experimental region for accuracy, and the number of function evaluations is taken equal to that of the unknown coefficients for efficiency. For this purpose, three error-minimizing conditions are utilized and corresponding canonical experimental regions constructed accordingly. An interpolation function is then obtained using a D-optimal design and then the first four moments of it are obtained as the estimates for the system response function. In order to verify accuracy and efficiency of FAMM, several non-linear examples are considered including a polynomial of order 4, an exponential function, and a rational function. The moments calculated from various coefficients of variation show very good accuracy and efficiency in comparison with those from analytic integration or the Monte Carlo simulation and the experimental design technique proposed by Taguchi and updated by D'Errico and Zaino.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.1
• /
• pp.177-183
• /
• 2013

In this paper, Cohen's kappa and weighted kappa are applied to measuring classification accuracy when performing classification in data minig. Cohen's kappa compensates for classifications that may be due to chance and is used for the data with nominal or ordinal scales. Especially, for the ordinal data, weighted kappa which measures the classification accuracy by quantifying the classification errors as weights is used. We used two weights (linear weight, quadratic weight) for calculations of weighted kappa. Also for the calculation and comparison of kappa and weighted kappa we used a real data set, fat-liver data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.17 no.3
• /
• pp.283-292
• /
• 1999

Aerotriangulation for the large scale mapping(photo-scale l/5,000) was studied with the projection center determined by kinematic DGPS positioning. For the feasibility study, the accuracy and error was analyzed with the comparison between a projection center from the conventional model adjustment and the projection center determined by the kinematic DGPS positioning. Kinematic DGPS-supported Bundle adjustment was also performed. The accuracy of projection center, determined by L1 phase data observed within 30 km from base station, was stable, and the planimetric accuracy(RMS) is 13 cm and the vertical accuracy(RMS) is 15 cm with 4 ground control points, which satisfies the national standard of digital mapping. Thus, this study shows that GPS-assisted aerotriangulation can be used for economic digital mapping.

• Korean Journal of Remote Sensing
• /
• v.35 no.1
• /
• pp.83-92
• /
• 2019

Land cover (LC) is an important factor in socioeconomic and environmental studies. According to various studies, a number of LC maps, including global land cover (GLC) datasets, are made using polar orbit satellite data. Due to the insufficiencies of reference datasets in Northeast Asia, several LC maps display discrepancies in that region. In this paper, we performed a feasibility assessment of LC mapping using Geostationary Ocean Color Imager (GOCI) data over Northeast Asia. To produce the LC map, the GOCI normalized difference vegetation index (NDVI) was used as an input dataset and a level-2 LC map of South Korea was used as a reference dataset to evaluate the LC map. In this paper, 7 LC types(urban, croplands, forest, grasslands, wetlands, barren, and water) were defined to reflect Northeast Asian LC. The LC map was produced via principal component analysis (PCA) with K-means clustering, and a sensitivity analysis was performed. The overall accuracy was calculated to be 77.94%. Furthermore, to assess the accuracy of the LC map not only in South Korea but also in Northeast Asia, 6 GLC datasets (IGBP, UMD, GLC2000, GlobCover2009, MCD12Q1, GlobeLand30) were used as comparison datasets. The accuracy scores for the 6 GLC datasets were calculated to be 59.41%, 56.82%, 60.97%, 51.71%, 70.24%, and 72.80%, respectively. Therefore, the first attempt to produce the LC map using geostationary satellite data is considered to be acceptable.

• Korean Journal of Artificial Intelligence
• /
• v.11 no.1
• /
• pp.1-7
• /
• 2023

In this paper, a study was conducted to compare the prediction model of cardiovascular disease occurrence. It is the No.1 disease that accounts for 1/3 of the world's causes of death, and it is also the No. 2 cause of death in Korea. Primary prevention is the most important factor in preventing cardiovascular diseases before they occur. Early diagnosis and treatment are also more important, as they play a role in reducing mortality and morbidity. The Results of an experiment using Azure ML, Logistic Regression showed 88.6% accuracy, Decision Tree showed 86.4% accuracy, and Support Vector Machine (SVM) showed 83.7% accuracy. In addition to the accuracy of the ROC curve, AUC is 94.5%, 93%, and 92.4%, indicating that the performance of the machine learning algorithm model is suitable, and among them, the results of applying the logistic regression algorithm model are the most accurate. Through this paper, visualization by comparing the algorithms can serve as an objective assistant for diagnosis and guide the direction of diagnosis made by doctors in the actual medical field.