• International Journal of Precision Engineering and Manufacturing
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• v.4 no.4
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• pp.57-63
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• 2003

Machining information such as form accuracy and surface roughness is an important factor for manufacturing precise parts. To this regard, OMM (On the Machine Measurement) has been researched for last several decades to alternate CMM (Coordinate Measurement Machine) process. In this research, the OMM system with a laser displacement sensor was developed for measuring form accuracy and surface roughness of the machined workpiece on the machine tool. The surface roughness was estimated comparing the sensory signal with the reference data measured from master specimen. Also, form accuracy was determined from the moving averaged raw data. In addition, the geometric error map constructed beforehand using the geometric errors of the machine tool was used to compensate the obtained form accuracy. The overall performance was compared with CMM result, and verified the feasibility of the measurement system.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.54-58
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• 2023

In this study, we compared the detection accuracy of the parameters of the scaffold failure detection model. A detection algorithm based on convolutional neural network was used to construct a failure detection model for scaffold. The parameter properties of the model were changed and the results were quantitatively verified. The detection accuracy of the model for each parameter was compared and the parameter with the highest accuracy was identified. We found that the activation function has a significant impact on the detection accuracy, which is 98% for softmax.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.396-409
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• 2023

In the field of autonomous vehicles, where high accuracy and reliability are critical, various satellite navigation augmentation systems have been developed to improve system performance. These systems generate correction and integrity information based on measurements and navigation data collected from ground reference stations, enhancing user positioning accuracy. Thus, the performance of the system heavily relies on the deployment and spacing of reference stations. To construct an effective satellite navigation augmentation system, careful consideration must be given to the installation points of reference stations. This paper presents a user positioning performance modeling formula and proposes a method for selecting the installation points of new reference stations. The proposed method involves selecting a candidate group area that can optimize the user's positioning performance. By utilizing this method, the system's performance can be improved, ensuring high accuracy and reliability for autonomous vehicle applications.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.3
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• pp.16-22
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• 2005

It is difficult to separate those of NC controller from the error of machine tools because the conventional testing methods to inspect the performance are including the errors of moving system, therefore it has been used as the methods that compare the other controllers. Also, it is hard to predict the machine itself errors with the methods assembling the NC controller and moving system on machine because of the variable load conditions. In this study, the performance inspecting system was developed by analyse the $axis\_rotating$ properties of servo system finally outputting from NC controller. The axis torque was controlled by motor dynamometer and the rotating position accuracy was measured by this developed system.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.249-259
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• 2020

China has deployed BDS along with the service of SBAS by 2020. Currently, the correction information for testing BDSBAS is provided through the BDS B1I signal. Many research on SBAS other than BDSBAS has been conducted in Korea. However, studies on BDSBAS are insufficient although Korea is included in both the coverage area of MSAS and BDSBAS. Therefore, it is necessary to continuously analyze the performance of MSAS and BDSBAS. In this paper, the performance of MSAS and BDSBAS in Korea, China, and Japan is analyzed in the aspect of positioning accuracy using the GNSS RINEX data provided by IGS. A Software platform is designed to analyze the performance of GPS-only, BDS-only, GPS/MSAS and BDS/BDSBAS. From the result, it can be concluded that the accuracy enhancement can be hardly seen when using the correction information of MSAS and BDSBAS in Korea

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1040-1043
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• 1996

The accuracy of the servo control in CNC system has a great influence on the duality of machine product. Tracking performance of the servo control is deteriorated mainly by the time delay of the servo system and the inertia of the work table or bed. Contouring errors occur in every interpolation steps by the effect of the tracking performance. In this paper, $H_{\infty}$ two-degree-of-freedom(TDF) controller is designed for improvement to improve the tracking performance. The designed controller is applied 3-axis machining center model and the cutting accuracy is simulated in case of corner cutting, circular and involute interpolation. Simulation results show that $H_{\infty}$ TDF controller designed in this paper has a good effect to improve tracking performance in CNC system.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.50-64
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• 2018

This paper describes a visual object detection approach utilizing ensemble based machine learning. Object detection methods employing 1D features have the benefit of fast calculation speed. However, for real image with complex background, detection accuracy and performance are degraded. In this paper, we propose an ensemble learning algorithm that combines a 1D feature classifier and 2D DNF (Disjunctive Normal Form) classifier to improve the object detection performance in a single input image. Also, to improve the computing efficiency and accuracy, we propose a feature selecting method to reduce the computing time and ensemble algorithm by combining the 1D features and 2D DNF features. In the verification experiments, we selected the Haar-like feature as the 1D image descriptor, and demonstrated the performance of the algorithm on a few datasets such as face and vehicle.

• Structural Engineering and Mechanics
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• v.38 no.5
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• pp.651-674
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• 2011

This paper presents a novel method for predicting the failure probability of structural or mechanical systems subjected to random loads and material properties involving multiple design points. The method involves Multicut High Dimensional Model Representation (Multicut-HDMR) technique in conjunction with moving least squares to approximate the original implicit limit state/performance function with an explicit function. Depending on the order chosen sometimes truncated Cut-HDMR expansion is unable to approximate the original implicit limit state/performance function when multiple design points exist on the limit state/performance function or when the problem domain is large. Multicut-HDMR addresses this problem by using multiple reference points to improve accuracy of the approximate limit state/performance function. Numerical examples show the accuracy and efficiency of the proposed approach in estimating the failure probability.

• 대한예방의학회:학술대회논문집
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• 1994.02b
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• pp.142-149
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• 1994

Substitution of graphic representation for extensive lists of numerical statistical data is highly desirable by both editors and readers of medical journals, faced with an exploding abundance of contemporary medical literature. A novel graphic tool. the 'double-ring diagram', is described herein which permits visual representation of information regarding certain statistical variables used to describe the performance of a test or physical sign in the diagnosis of a disease. The diagram is relatively easy to construct on the basis of a number of primary data such as the prevalence and the true positive, true negative. false positive and false negative test results. These values are reflected in the diagram along with the values of other statistical variables derived from them. such as the sensitivity. specificity, predictive values for positive and negative test result. and accuracy. This diagram may be useful in visualizing a test's performance and facilitating visual comparison of performance of two or more tests.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.241-247
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• 2021

Particulate matter has emerged as a serious global problem, necessitating highly reliable information on the matter. Therefore, various algorithms have been used in studies to predict particulate matter. In this study, we compared the prediction performance of neural network models that have been actively studied for particulate matter prediction. Among the neural network algorithms, a deep neural network (DNN), a recurrent neural network, and long short-term memory were used to design the optimal prediction model using a hyper-parameter search. In the comparative analysis of the prediction performance of each model, the DNN model showed a lower root mean square error (RMSE) than the other algorithms in the performance comparison using the RMSE and the level of accuracy as metrics for evaluation. The stability of the recurrent neural network was slightly lower than that of the other algorithms, although the accuracy was higher.