• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.3
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• pp.51-56
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• 2002

In order to achieve high precision machine tools, Performance enhancement of feed drive systems is required. One of the important technical issues is how to decrease thermal expansion of ball screw in proportion to the increase of machining speed. When measuring force of stretch of ball screw, since not only actual expansion and the value of bending have to be considered, it is impossible to define the exact value of expansion. In addition, support bearings of ball screw gain considerable force in axial direction. It also generates thermal expansion on the ball screw, and deteriorates the performances of the hearings. In conclusion, it is impossible to give the pretension enough to absorb all the elongation due to thermal expansion generated during machine is running. If given bed column and saddle are all bent to chance machine accuracy, and the support bearings of ball screw is damaged.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.1
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• pp.98-107
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• 1996

The use of composite hyperpatch model is proposed to predict a machine tool positional error over the entire work space. This is an appropriate representation of the distorted work space. This model is valid for any configuration of 3-axis machine tool. Tool position, which is given NC data or CL data, contains error vector in actual work space. In this study, off-line compensation scheme was investigated for tool position error due to inaccuracy in machine tool structure. The error vector in actual work space is corrected by the error model using Newton-Raphson method. The proposed error compensation method shows the possibility of improving machine accuracy at a low cost.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.10
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• pp.172-179
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• 1998

To quickly determine the effect of the substitute component on the machine's performance is very important in the design and manufacturing processes. And minimizing machine cost and maximizing machine quality mandate predictability of machine accuracy. In this study, in order to evaluate the effects of the component's geometric errors and dimensions on the machining accuracy of gantry-type 5-axis machining centers, a geometric error analysis and virtual manufacturing system are developed based on the mathematical model for the shape generation motion of machine tool considering the component's geometric errors and dimensions, the solid modeling techniques and so on.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.9
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• pp.127-134
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• 1998

This paper describes the design of a 6-component force/moment calibration machine with having the maximum capacities of 500 N in forces and 50 Nm in moments. To be used for the characteristic of a multi-component load cell. this machine consists of a body, a fixture, a force generating system, a moment generating system and weights. We have also evaluated the accuracy of the calibration machine. Test results show that the expanded relative uncertainty for force components $\pmFx,\;\pmFy\;and\;moment\;components\;\pmMx,\;\pmMy\;are\;less\; than\;8.6\times10^{-4}$, and force components +Fz, -Fz and moment components $\pmMz\;is\;less\;than\;1.6\times10^{-3},\;2.0\times10^{-5},\;1.7\times10^{-3}$ respectively.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.5
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• pp.52-59
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• 2008

The accuracy of the machine tools is degraded because of thermal error of structure due to thermal variation. To improve the accuracy of a machine tools, measurement and prediction of thermal error is very important. The main part of thermal source is spindle due to high speed with friction. The thermal error of spindle is very important because it is over 10% in total thermals errors. In this paper, the suitable thermal error prediction technology for machine tools with open architecture controller is developed and implemented to machine tools. Two thermal error prediction technologies, neural network and multi-linear regression, are investigated in several methods. The multi-linear regression method is more effective for implementation to CNC. The developed thermal error prediction technology is implemented on the internal function of CNC.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.6
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• pp.627-635
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• 2019

In this study, we developed for processing complex features using CAM software that satisfies precision for example practice and related qualification tests suiTable for CNC training purposes. In addition, functions such as location control, speed control, and processing path generation, which are the main functions of CNC machining machines, were constructed using small equipment parts, servo motors, inverters, general purpose PCs, and commercial NC software and researched with the goal of developing low-cost education equipment. In the static accuracy inspection, the degree of machine when measuring the parallelism of the X, Y and Z axes and the vibration of the main shaft did not reach the allowable value. However, we have obtained a finished product that satisfies the CNC machine book sample shape machining, detailed functions of the position control function of the CNC machine tool, linear interpolation function, circular interpolation function, and tool offset function. In the qualification test shape processing, a shape with a degree of 1/100 mm was processed to obtain position accuracy that satisfied the tolerance.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.165-171
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• 2021

The current study investigated cyber learners' use and perceptions of online machine translation (MT) tools. The results show that learners use several MT tools frequently and extensively for various second language learning (L2) purposes according to their needs. The learners' overall perceptions of using MT for English learning were generally positive. The learners reported several advantages of machine translation: ease of use, helpful feedback, effective revision, and facilitation of self-directed learning. At the same time, a considerable number of learners were aware of MT's drawbacks, such as awkward sentences, inaccurate grammar, and inappropriate words, and thus held a negative or skeptical view on the quality and accuracy of MT. These findings have important pedagogical implications for using MT in the context of a cyber university. For successful integration of MT in English classes, teachers need to provide appropriate guidelines and training that will help learners use MT effectively.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.258-270
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• 2022

Cancer has become a common disease for the past two decades throughout the globe and there is significant increase of cancer among women. Breast cancer and ovarian cancers are more prevalent among women. Majority of the patients approach the physicians only during their final stage of the disease. Early diagnosis of cancer remains a great challenge for the researchers. Although several drugs are being synthesized very often, their multi-benefits are less investigated. With millions of drugs synthesized and their data are accessible through open repositories. Drug repurposing can be done using machine learning techniques. We propose a feature selection technique in this paper, which is novel that generates multiple populations for the grey wolf algorithm and classifies breast cancer drugs efficiently. Leukemia drug dataset is also investigated and Multilayer perceptron achieved 96% prediction accuracy. Three supervised machine learning algorithms namely Random Forest classifier, Multilayer Perceptron and Support Vector Machine models were applied and Multilayer perceptron had higher accuracy rate of 97.7% for breast cancer drug classification.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.149-155
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• 2023

In urban areas it can be difficult to utilize global navigation satellite systems (GNSS) due to signal reflections and blockages. It is thus crucial to detect reflected or blocked signals because they lead to significant degradation of GNSS positioning accuracy. In a previous study, a classifier for global positioning system (GPS) signal reception conditions was developed using three features and the support vector machine (SVM) algorithm. However, this classifier had limitations in its classification performance. Therefore, in this study, we developed an improved machine learning based method of classifying GPS signal reception conditions by including an additional feature with the existing features. Furthermore, we applied various machine learning classification algorithms. As a result, when tested with datasets collected in different environments than the training environment, the classification accuracy improved by nine percentage points compared to the existing method, reaching up to 58%.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.281-286
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• 2023

Diagnosing reading disorders involves complex procedures to evaluate complex cognitive processes. For an accurate diagnosis, a series of tests and evaluations by human experts are required. In this study, we propose a quantitative tool to diagnose reading disorders based on natural reading behaviors using minimal human input. The eye movements of the third- and fourth-grade students were recorded while they read a text at their own pace. Seven machine learning models were used to evaluate the gaze patterns of the words in the presented text and classify the students as normal or having a reading disorder. The accuracy of the machine learning-based diagnosis was measured using the diagnosis by human experts as the ground truth. The highest accuracy of 0.8 was achieved by the support vector machine and random forest classifiers. This result demonstrated that machine learning-based automated diagnosis could substitute for the traditional diagnosis of reading disorders and enable large-scale screening for students at an early age.