• The Journal of Korean Academy of Prosthodontics
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• v.35 no.2
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• pp.357-364
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• 1997

IPS-empress system is one of widely used all ceramic system. The purpose of this study was to determine the pressing accuracy of reused IPS%Empress ingot. 10 specimens were made using new ingot first, and using the rests of the specimen the another group of specimens were made next. finally, the third group of specimens were made with same procedure mentioned above. The results obtained in this study were as follows ; 1. The pressing accuracy of the first group of specimen was 96.1%, that of the second group was 95.4%, and that of the third group was 95.4%. There was no statistical significance among them, that is, the reuse of the IPS-Empress ingot did not influence on the pressing accuracy. 2. the common site of the defect in pressed ingot was central area at the margin opposite of sprue hole.

• Journal of the Korean Society of Mechanical Technology
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• v.13 no.3
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• pp.7-16
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• 2011

This study proposes the compensation method for the mechanical deflection error of a SCARA robot. While most studies on the related subject have dealt with the development of a control algorithm for improvement of robot accuracy, this study presents the control method reflecting the mechanical deflection error which is predicted in advance. The deflection at the end of the gripper of SCARA robot is caused by the self-weights and payloads of Arm 1, Arm 2 and quill. If the deflection is constant even though robot's posture and payload vary, there may not be a big problem on robot accuracy because repetitive accuracy, that is relative accuracy, is more important than absolute accuracy in robot. The deflection in the end of the gripper varies as robot's posture and payload change. That's why the moments $M_x$, $M_y$ and $M_z$ working on every joint of a robot vary with robot's posture and payload size. This study suggests the compensation method which predicts the deflection in advance with the variations in robot's posture and payload using neural network. To do this, I chose the posture of robot and the payloads at random, found the deflections by the FEM analysis, and then on the basis of this data, made compensation possible by predicting deflections in advance successively with the variations in robot's posture and payload through neural network learning.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.6
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• pp.15-22
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• 2023

In this study, We conduct a comparative study of deep learning-based classification of agricultural field attributes using Tagged Image File (TIF) and Enhanced Compression Wavelet (ECW) images. The goal is to interpret and classify the attributes of agricultural fields by analyzing the differences between these two image formats. "FarmMap," initiated by the Ministry of Agriculture, Food and Rural Affairs in 2014, serves as the first digital map of agricultural land in South Korea. It comprises attributes such as paddy, field, orchard, agricultural facility and ginseng cultivation areas. For the purpose of comparing deep learning-based agricultural attribute classification, we consider the location and class information of objects, as well as the attribute information of FarmMap. We utilize the ResNet-50 instance segmentation model, which is suitable for this task, to conduct simulated experiments. The comparison of agricultural attribute classification between the two images is measured in terms of accuracy. The experimental results indicate that the accuracy of TIF images is 90.44%, while that of ECW images is 91.72%. The ECW image model demonstrates approximately 1.28% higher accuracy. However, statistical validation, specifically Wilcoxon rank-sum tests, did not reveal a significant difference in accuracy between the two images.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.1-6
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• 2023

This study developed models using decision forest, support vector machine, and logistic regression methods to predict and prevent suicidal ideation among Korean adolescents. The study sample consisted of 51,407 individuals after removing missing data from the raw data of the 18th (2022) Youth Health Behavior Survey conducted by the Korea Centers for Disease Control and Prevention. Analysis was performed using the MS Azure program with Two-Class Decision Forest, Two-Class Support Vector Machine, and Two-Class Logistic Regression. The results of the study showed that the decision forest model achieved an accuracy of 84.8% and an F1-score of 36.7%. The support vector machine model achieved an accuracy of 86.3% and an F1-score of 24.5%. The logistic regression model achieved an accuracy of 87.2% and an F1-score of 40.1%. Applying the logistic regression model with SMOTE to address data imbalance resulted in an accuracy of 81.7% and an F1-score of 57.7%. Although the accuracy slightly decreased, the recall, precision, and F1-score improved, demonstrating excellent performance. These findings have significant implications for the development of prediction models for suicidal ideation among Korean adolescents and can contribute to the prevention and improvement of youth suicide.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.05a
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• pp.285-288
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• 2007

The use of commercial finite element analysis software to perform the entire process analysis and springback analysis has increased fast for last decade. Pamstamp2G is one of commercial software to be used widely in the world but it has still not been perfected in the springback prediction accuracy. We must select the combination of input parameters for the highest springback prediction accuracy in Pamstamp2G because springback prediction accuracy is sensitive to input parameters. Then we study the affect of input parameters to use member part for acquiring high springback prediction accuracy in Pamstamp2G. First, we choose important four parameters which are adaptive mesh level at drawing stage and cam flange stage, Gauss integration point number through the thickness and cam offset on basis of experiment. Second, we make a orthogonal array table L82[(7)] which is consist of 8 cases to be combined 4 input parameters, compare to tryout result and select main factors after analyzing affect factors of input parameters by Taguchi's method in 6 sigma. Third, we simulate after changing more detail the conditions of parameters to have big affect. At last, we find the best combination of input parameters for the highest springback prediction accuracy in Pamstamp2G. The results of the study provide the selection of input parameters to Pamstamp2G users who want to Increase the springback prediction accuracy.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.7385-7390
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• 2015

Background: Transthoracic fine needle aspiration (FNA) cytology and core needle biopsy (CNB) are two commonly used approaches for the diagnosis of suspected neoplastic intrathoracic lesions. This study compared the diagnostic accuracy of FNA cytology and concurrent CNB in the evaluation of intrathoracic lesions. Materials and Methods: We studied FNA cytology and concurrent CNB specimens of 127 patients retrospectively, using hematoxylin and eosin (H&E), immunohistochemistry, and, on certain occasions cytochemistry. Information regarding additional tissue tests was derived from the electronic archives of the Department of Pathology and Laboratory Medicine as well as patient records. Diagnostic accuracy was calculated for each test. Results: Of 127 cases, 22 were inconclusive and excluded from the study. The remaining 105 were categorized into 73 (69.5%) malignant lesions and 32 (30.5%) benign lesions. FNA and CNB findings were in complete agreement in 63 cases (60%). The accuracy and confidence intervals (CIs) of FNA and CNB for malignant tumors were 86.3% (CI: 79.3-90.7) and 93.2% (CI: 87.3-96.0) respectively. For epithelial malignant neoplasms, a definitive diagnosis was made in 44.8% of cases by FNA and 80.6% by CNB. The diagnostic accuracy of CNB for nonepithelial malignant neoplasms was 83.3% compared with 50% for FNA. Of the 32 benign cases, we made specific diagnoses in 16 with diagnostic accuracy of 81.3% and 6.3% for CNB and FNA, respectively. Conclusions: Our findings suggest that FNA is comparable to CNB in the diagnosis of malignant epithelial lesions whereas diagnostic accuracy of CNB for nonepithlial malignant neoplasms is superior to that for FNA. Further, for histological typing of tumors and examining tumor origin, immunohistochemical work up plays an important role.

• The Journal of Korean Academy of Prosthodontics
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• v.34 no.4
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• pp.850-868
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• 1996

The accuracy and dimensional stability of impression materials are one of the most important factors for successful prosthodontic treatment. The purpose of this study was to evaluate the accuracy of several dental elastomeric impression materials used widely and clinically : Impregum-$F^{(R)},\;Permlastic^{(R)},\;Silascon^{(R)},\;Perfect^{(R)},\;Xantopren^{(R)}$. There have been many studies to evaluate the accuracy of impression materials. But it has not been decided yet, which method was most suitable for the evaluation of the accuracy. In this study, two resin teeth, #15 & 25, were prepared with rounded shoulder margin and 90 degree cavosurface angle. For the polysulfide rubber and polyether, the custom tray was made at least 24 hours prior to impression taking. For the silicone rubber materials, putty/wash impression technique was applied in taking impressions. Marginal openings of the castings on the master dies and prepared resin teeth were measured under stereomicroscope. The results were statistically analyzed and compared between tested impression materials. The results were as follows ; 1. In the overall accuracy of impression materials, polyether was the most accurate one, followed by polysulfide, additional silicone and condensation silicone. 2. On the first model pouring, condensation silicone had the largest discrepancies and there was significant difference compared to the other impression materials. 3. Polysulfide had the least discrepancies in the first model pouring, but showed larger discrepancies in the immediate second pouring than the first pouring. 4. On the immediate second pouring, the discrepancy of polyether was shown to be the smallest, while the largest one was additional silicone. 5. Polyether and polysulfide rubber using custom impression trays showed superior accuracy to silicone rubber, putty/wash impression technique.

• Journal of Biomedical Engineering Research
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• v.43 no.1
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• pp.45-51
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• 2022

The purpose of this study is to improve the classification accuracy compared to the existing InceptionV3 model by proposing a new model modified with the fully connected hierarchical structure of InceptionV3, which showed excellent performance in medical image classification. The data used for model training were trained after data augmentation on a total of 1026 chest X-ray images of patients diagnosed with normal heart and Cardiomegaly at Kyungpook National University Hospital. As a result of the experiment, the learning classification accuracy and loss of the InceptionV3 model were 99.57% and 1.42, and the accuracy and loss of the proposed model were 99.81% and 0.92. As a result of the classification performance evaluation for precision, recall, and F1 score of Inception V3, the precision of the normal heart was 78%, the recall rate was 100%, and the F1 score was 88. The classification accuracy for Cardiomegaly was 100%, the recall rate was 78%, and the F1 score was 88. On the other hand, in the case of the proposed model, the accuracy for a normal heart was 100%, the recall rate was 92%, and the F1 score was 96. The classification accuracy for Cardiomegaly was 95%, the recall rate was 100%, and the F1 score was 97. If the chest X-ray image for normal heart and Cardiomegaly can be classified using the model proposed based on the study results, better classification will be possible and the reliability of classification performance will gradually increase.

• Hip & pelvis
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• v.36 no.2
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• pp.129-134
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• 2024

Purpose: Preoperative planning has become essential in performance of total hip arthroplasty (THA). However, data regarding the effect of the planner's experience on the accuracy of digital preoperative planning is limited. The objective of this study was to assess the accuracy of digital templating in THA based on the surgeon's experience. Materials and Methods: A retrospective study was conducted. An analysis of 98 anteroposterior pelvic radiographs, which were individually templated by four surgeons (two hip surgeons and two orthopaedic residents) using TraumaCad^® digital planning, was performed. A comparison of preoperatively planned sizes with implanted sizes was performed to evaluate the accuracy of predicting component size. The results of preoperative planning performed by hip surgeons and orthopaedic residents were compared for testing of the planner's experience. Results: Femoral stem was precisely predicted in 32.4% of cases, acetabular component in 40.3%, and femoral offset in 76.7%. Prediction of cup size showed greater accuracy than femoral size among all observers. No differences in any variable were observed among the four groups (acetabular cup P=0.07, femoral stem P=0.82, femoral offset P=0.06). All measurements showed good reliability (intraclass correlation coefficient [ICC] acetabular cup: 0.76, ICC femoral stem: 0.79). Conclusion: The results of this study might suggest that even though a surgeon's experience supports improved precision during the planning stage, it should not be restricted only to surgeons with a high level of experience. We consider preoperative planning an essential part of the surgery, which should be included in training for orthopaedics residents.

• Tribology and Lubricants
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• v.15 no.3
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• pp.257-264
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• 1999

Recently as electronics and semi-conductor industry develop, ultra-precision machine tools that use air-spindle with externally pressurized air bearing appear in need of ultra-precision products which demand high precision property. Effects of air compressibility absorbs the vibration of shaft, this is called averaging effect, however, the higher running accuracy is demanded by degrees, the more important factor is machining errors that affect running accuracy of shaft. Actually, it would be very important in the view points of running accuracy to understand effects of machining errors on the running accuracy of the spindle system quantitatively to design and manufacture precision spindle system in the aspect that efficiency in manufacturing spindle system and performance in operation. So fu, there are some researches on the effects that machining error affect running accuracy. However, because these researches deal with one bearing of spindle system, these results aren't enough to explain how much machining errors affect running accuracy in the typical spindle system overall. In this study, we investigate the effects of the perpendicularity error of bearing and shaft on running accuracy of spindle system that consists of journal and thrust bearing theoretically, and suggest design guideline about shape tolerances.