• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.213-218
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• 1997

Thermal deformation of machine origin of machine tools due to internal and external heat sources has been the most important problem to fabricate products with higher accuracy and performance. In order to solve this problem, GMDH models were constructed to estimate thermal deformation of machine origin for a vertical machining ceneter through measurement of temperature data of specific points on the machine tool. These models are nonlinear equations with high-order polynomials and implemented in a multilayered perceptron type network structure. Input variables and orders are automatically selected by correlation and optimization procedure. Sensors with small influence are deleted automatically in this algorithm. It was shown that the points of temperature measurement can be reduced without sacrificing the estimation accuracy of $\pm$5${\mu}{\textrm}{m}$. From the experimental result, it was confirmed that GMDH methodology was superior to least square models to estimate the thermal behavior of machine tools.

• The korean journal of orthodontics
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• v.54 no.4
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• pp.257-263
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• 2024

Objective: Lingual fixed retainers, made from 0.0175-inch 3-strand twisted stainless steel wire (TW) and 0.016 × 0.022-inch straight rectangular wire (RW), are generally used in clinical practice. This study aimed to calculate their accuracy by comparing the discrepancy between computer-aided customized retainers made from these two types of wires. Methods: Eleven orthodontic patients were selected, resulting in 22 maxillary and mandibular three-dimensional printing dental models. Two types of lingual fixed retainers were bonded from canine to canine. To determine the accuracy, five points were chosen for each model, resulting in 110 selected points. The absolute values of the distances on the x-, y-, and z-axes were measured to compare the accuracy of the two types of computer-aided retainers. Results: The accuracy of the two types of retainers did not differ significantly in the x- and z-axes, but only in the y-axis (P < 0.01), where RW-fixed retainers exhibited a slightly but significantly increased distance compared to the TW. Conclusions: Both types of retainers showed high accuracy; however, RW had a slight but statistically significant difference along the y-axis compared with TW. This type of computer-aided design/computer-aided manufacturing bending machine is limited to two dimensions, and the dental arch is curved. Therefore, RW may require slight manual adjustment by the practitioner after manufacturing.

• Journal of Periodontal and Implant Science
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• v.53 no.1
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• pp.38-53
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• 2023

Purpose: The current Classification of Periodontal and Peri-Implant Diseases and Conditions, published and disseminated in 2018, involves some difficulties and causes diagnostic conflicts due to its criteria, especially for inexperienced clinicians. The aim of this study was to design a decision system based on machine learning algorithms by using clinical measurements and radiographic images in order to determine and facilitate the staging and grading of periodontitis. Methods: In the first part of this study, machine learning models were created using the Python programming language based on clinical data from 144 individuals who presented to the Department of Periodontology, Faculty of Dentistry, Süleyman Demirel University. In the second part, panoramic radiographic images were processed and classification was carried out with deep learning algorithms. Results: Using clinical data, the accuracy of staging with the tree algorithm reached 97.2%, while the random forest and k-nearest neighbor algorithms reached 98.6% accuracy. The best staging accuracy for processing panoramic radiographic images was provided by a hybrid network model algorithm combining the proposed ResNet50 architecture and the support vector machine algorithm. For this, the images were preprocessed, and high success was obtained, with a classification accuracy of 88.2% for staging. However, in general, it was observed that the radiographic images provided a low level of success, in terms of accuracy, for modeling the grading of periodontitis. Conclusions: The machine learning-based decision system presented herein can facilitate periodontal diagnoses despite its current limitations. Further studies are planned to optimize the algorithm and improve the results.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.3
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• pp.16-23
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• 2004

Thermally induced errors of machine tools have been recognized as one of the most important issues in precision machining. This is probably the most formidable obstacle to obtain high level of machining accuracy. To this regard, the experimental compensation methodologies such as software-based method or origin shift of machine tool axes have been suggested. In this research, to cope with thermal deformation, a model based correction was carried out with the function of an external machine coordinate shift. Models with multi-linear regression or neural network were investigated to selected a good one for thermal compensation. Consequently, multi-linear regression model combined with origin shift was verified good enough form the machining of dot matrices of plate with ball end milling.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.866-869
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• 2000

The finishing process for die is an important process because it has influence on final quality of products. And it is difficult to automatize finishing process so that the process has depended on expert's skill until now. However, recently a study on development of die automatic finishing machine has been progressed, and actually this machine is applied to fabrication of die. But die automatic finishing machine has the problems such as low supply rate and high machine price. In this paper 3-axis machine was applied to the die finishing. And to improve form accuracy of die finishing path was regenerated. The finishing path considered tilting of finishing tool. and variation of machining force with contacting point between finishing and workpiece.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.309-312
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• 2005

DTM (Diamond Turning Machine) is using for ultra precision manufacturing such as, plastic lens die or aspherical optics. This study is on a design of precision 2-axis stage for DTM. We designed and manufactured a back lash free stage using different weights and measured the positioning accuracy using Interferometer. Also, the 2-D moving accuracy is measured using the high magnification CCD technique. Then, the stage is tested with the machining of spherical and aspherical lens in a DTM with air bearing spindle. It was shown that the back lash free stage is effective for improving the positioning accuracy. Also, positioning control errors in motion control board were able to be found using the proposed stages system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1324-1330
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• 2007

In this paper, we propose pattern recognition algorithm for activities of daily living by adopting extreme learning machine based on single layer feedforward networks(SLFNs) to the signal from bidirectional accelerometer. For activity classification, 20 persons are participated and we acquire 6, types of signals at standing, walking, running, sitting, lying, and falling. Then, we design input vector using reduced model for ELM input. In ELM classification results, we can find accuracy change by increasing the number of hidden neurons. As a result, we find the accuracy is increased by increasing the number of hidden neuron. ELM is able to classify more than 80 % accuracy for experimental data set when the number of hidden is more than 20.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.681-686
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• 2000

Machining information such as machined form and surface roughness accuracy is an important factor for manufacturing precise parts. To this regard, OMM(On the Machine Measurement) has been issued for last several decades to alternate with CMM. In this research, measuring system consisting of a laser probe is developed for machined form and surface roughness measurement on the machine tool. The obtained machined form accuracy is compared with reference one defined in CAD model. The measured surface roughness data is compared with measured master surface beforehand. Furthermore, using the pre-defined volumetric error map approach compensates the geometric accuracy of the machine tool. The overall performance is compared with CMM, and verified the feasibility of the measurement system.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.7
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• pp.232-240
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• 2000

Machining information such as machined form and surface roughness accuracy is an important factor for manufacturing precise parts. To this regard, OMM(On the Machine Measurement) has been issued for last several decades to alternate with CMM. In this research, measuring system consisting of a laser probe is developed for machined form and surface roughness measurement on the machine tool. The obtained machined form accuracy is compared with reference one defined in CAD model. The measured surface roughness data is compared with measured master surface beforehand. Furthermore, using the pre-defined volumetric error map approach compensates the geometric accuracy of the machine tool. The overall performance is compared with CMM, and verified the feasibility of the measurement system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.