• The Journal of Advanced Prosthodontics
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• v.14 no.4
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• pp.236-249
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• 2022

PURPOSE. The present study compared the accuracy between digital and conventional implant impressions. MATERIALS AND METHODS. The experimental models were divided into six groups depending on the implant location and the scanning span. Digital impressions were captured using the intraoral optical scanner TRIOS (3Shape, Copenhagen, Denmark). Conventional impressions were taken with the monophase impression material based on addition-cured silicones, Honigum-Mono (DMG, Hamburg, Germany). A high-precision laboratory scanner D900 (3Shape, Copenhagen, Denmark) was used to obtain digital data of resin models and stone casts. Surface tessellation language (STL) datasets from scanner were imported into the analysis software Geomagic Qualify 14 (3D Systems, Rock Hill, SC, USA), and scan body deviations were determined through two-dimensional and three-dimensional analyses. Each scan body was measured five times. The Sidak t test was used to analyze the experimental data. RESULTS. Implant position and scanning distance affected the impression accuracy. For a unilateral arch implant and the mandible models with two implants, no significant difference was observed in the accuracy between the digital and conventional implant impressions on scan bodies; however, the corresponding differences for trans-arch implants and mandible with six implants were extremely significant (P<.001). CONCLUSION. For short-span scanning, the accuracy of digital and conventional implant impressions did not differ significantly. For long-span scanning, the precision of digital impressions was significantly inferior to that of the traditional impressions.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1096-1100
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• 2010

In this paper, we propose an automatic segmentation of the meniscus based on active shape model using interpolated shape information in MR images. First, the statistical shape model of meniscus is constructed to reflect the shape variation in the training set. Second, the generation technique of interpolated shape information by using the weight according to shape similarity is proposed to robustly segment the meniscus with large variation. Finally, the automatic meniscus segmentation is performed through the active shape model fitting. For the evaluation of our method, we performed the visual inspection, accuracy measure and processing time. For accuracy evaluation, the average distance difference between automatic segmentation and semi-automatic segmentation are calculated and visualized by color-coded mapping. Experimental results show that the average distance difference was $0.54{\pm}0.16mm$ in medial meniscus and $0.73{\pm}0.39mm$ in lateral meniscus. The total processing time was 4.87 seconds on average.

• The Research Journal of the Costume Culture
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• v.14 no.5
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• pp.840-849
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• 2006

Breast volume has been approximately estimated under the assumption that the shape of breast is a corn. However, women's breast is more like a bulged bag in reality. In this paper, three methods of breast volume estimation were compared to find out the more accurate method. The shape of the breast is assumed as a hemisphere in the first estimation method and a corn in the second one. In the third method, arc along the cross sectional shape of breast was utilized in the calculation. Comparisons among the methods were made using the actual 3D volume measurement of thirty seven women's breast. As results, the third method was the best one for the normal breast type, especially for the lower part of the breast ($R^2=0.74$) which is the crucial design parameter of the brassiere. Assumption of the shape of breast as a corn was reasonably acceptable when the breast is sagged. It was expected that when women wore brassiere, the accuracy of the third method would increase more, since the shape of breast becomes more symmetrical.

• Transactions of Materials Processing
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• v.27 no.3
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• pp.154-159
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• 2018

In the rectangular bar multi-stage drawing process, the cross-section dimensional accuracy of the rectangular bar varies depending on the aspect ratio and process conditions. It is very important to predict the dimensional error of the cross-section occurring in the multi-stage drawing process according to the aspect ratio of the rectangular bar and the half die angle of each pass. In this study, a process map for improving the dimensional accuracy according to the aspect ratio was derived in the drawing process of a rectangular bar. FE-simulation of the multi-stage shape drawing process was carried out with four types of rectangular bar. The results of the FE-simulation were trained to the nonlinear relationship between the shape parameters using an Artificial Neural Network (ANN), and the process maps were derived from them. The optimum half die angles were determined from the process maps on the dimensional accuracy. The validity of the suggested process map for aspect ratios 1.25~2:1 were verified through FE-simulation and experimentation.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.5
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• pp.800-805
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• 2013

Incremental forming of sheet metal is a modern method of forming sheet metal, where parts can be formed without the use of dedicated dies. Existing experimental configurations for incremental forming can be broadly classified into two categories, i.e., negative and positive forming. In this paper, forming qualities such as shape accuracy and surface roughness of Al 1050 material were discussed for different forming methods. The formed and the corresponding opposing surfaces were measured with a three-dimensional scanner and a surface roughness tester. It was found that in terms of shape accuracy, the best opposing surface was obtained with positive forming, whereas the worst formed surface was obtained with negative forming; furthermore, the opposing surface is always better than the formed surface, regardless of the forming method used.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.4
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• pp.20-26
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• 2010

Any relative deformation between the cutting tool and the workpiece at the machining point, results directly in form and dimensional errors. The source of relative deformations between the cutting tool and the workpiece at the contact point may be due to thermal, weight, and cutting forces. This paper presents an investigation into dry and fluid machining with the objective of evaluating shape accuracy effect for the turning process of Al6061. The thermal distribution of cutting tool and cutting force was predicted using finite element method after measuring the temperature of the tool holder. To reach this goal, shape accuracy turning experiments are carried out according to cutting conditions with dry and fluid machining methods. The variable cutting conditions are cutting speed, depth of cutting and feed rate.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.864-875
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• 2018

In this paper, we propose a measurement method for height of white light scanning interferometer using deep learning. In order to measure the fine surface shape, a three-dimensional surface shape measurement technique is required. A typical example is a white light scanning interferometer. In order to calculate the surface shape from the measurement image of the white light scanning interferometer, the height of each pixel must be calculated. In this paper, we propose a neural network for height calculation and use virtual data generation method to train this neural network. The accuracy was measured by inputting 57 actual data to the neural network which had completed the learning. We propose two new functions for accuracy measurement. We have analyzed the cases where there are many errors among the accuracy calculation values, and it is confirmed that there are many errors when there is no interference fringe or outside the learned range. We confirmed that the proposed neural network works correctly in most cases. We expect better results if we improve the way we generate learning data.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.99-103
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• 2021

Warpage defect detecting of scaffold is very important in biosensor production. Because warpaged scaffold cause problem in cell culture. Currently, there is no detection equipment to warpaged scaffold. In this paper, we produced detection model for shape warpage detection using deep learning based CNN. We confirmed the shape of the scaffold that is widely used in cell culture. We produced scaffold specimens, which are widely used in biosensor fabrications. Then, the scaffold specimens were photographed to collect image data necessary for model manufacturing. We produced the detecting model of scaffold warpage defect using Densenet among CNN models. We evaluated the accuracy of the defect detection model with mAP, which evaluates the detection accuracy of deep learning. As a result of model evaluating, it was confirmed that the defect detection accuracy of the scaffold was more than 95%.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• Transactions of Materials Processing
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• v.19 no.4
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• pp.242-247
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• 2010

In the multi-pass shape drawing process, it is important to design the intermediate dies for producing sound products. Up to now, the design of the intermediate dies is mainly carried out by the industrial experts based on their experience. In this study, a design program was developed to design the intermediate dies for multi-pass shape drawing process. The program was programmed by using VisualLISP. In this program the intermediate dies can be designed by using the initial material shape and the final product shape. In order to verify the effectiveness, the program was applied to design the intermediate dies of multi-pass shape drawing for producing four teeth spline and gun slide. Finally, FE analysis and shape drawing experiment were performed to verify the effectiveness of the designed intermediate dies. As a result, it was possible to produce the drawn products with the required dimensional accuracy.