• Journal of the Korean Society for Precision Engineering
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• v.22 no.11 s.176
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• pp.142-150
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• 2005

Design and performance evaluation of a spindle system which was composed of a grinding spindle and a regulating spindle for the centerless grinding of ferrule were performed in this paper. Layout and details of spindle system were designed and hydrostatic bearings for spindles were also designed. Prototype of spindle system was developed and its availabilities to machine the ferrule were discussed using the experimental results on the spindle stiffness of each spindle, loop stiffness, rotational accuracy and thermal characteristics. Loop stiffness of the spindle system was $130\;N/{\mu}m$, which was enough to machine the ferrule. Rotational accuracies of each spindle were about $0.2{\mu}m$ at the primary speed of 2,300 rpm(grinding spindle) and 300 rpm(regulating spindle). Temperature rises at the same speed were about $4.4\~4.7^{\circ}C$ in the case of grinding spindle and $1.8^{\circ}C$ in the case of regulating spindle, which agreed well with the designed value. From these results, it was estimated that the prototype of spindle system had enough performances for the centerless grinding machine to machine the ferrule.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.1
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• pp.59-63
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• 2014

Elliptical gears are used in the oval flowmeter and oval flow meter inspects volume of water thanks to space by the elliptical shape. The purpose of this study is to judge accuracy of processing of the elliptical gear and develop inspection system using machine vision. Demand of machine vision is increasing while the factory automation is spreading and principle factor in-process inspection. But, gear inspection using the machine vision rarely used because of complex shape of gear. In this study, it seems possible that elliptical gear is inspected by inspection software using machine vision and inspection program can judge accuracy of processing of the elliptical gear designed this study.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3431-3437
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• 2021

In this study, one-dimensional gamma ray source positions are estimated using a plastic scintillating optical fiber, two photon counters and via data processing with a machine learning algorithm. A nonlinear regression algorithm is used to construct a machine learning model for the position estimation of radioactive sources. The position estimation results of radioactive sources using machine learning are compared with the theoretical position estimation results based on the same measured data. Various tests at the source positions are conducted to determine the improvement in the accuracy of source position estimation. In addition, an evaluation is performed to compare the change in accuracy when varying the number of training datasets. The proposed one-dimensional gamma ray source position estimation system with plastic scintillating fiber using machine learning algorithm can be used as radioactive leakage scanners at disposal sites.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.41-49
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• 2022

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and machine learning in lightweight embedded system. The inverse perspective transformation method is presented for obtaining a bird's-eye view of the scene from a perspective image to remove perspective effects. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, machine learning algorithm that has passed the first classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve the detection speed. The first classifier is applied in the bird's-eye view image to determine lane regions. A lane region passed the first classifier is detected more accurately through machine learning. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed method works well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about 3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.1
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• pp.82-88
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• 1998

It is very important to test linear cycle positioning accuracy of NC lathes as it affect all other machines machined by them in industries. For example, if the linear positioning accuracy of x or z-axis directions is bad, the size of works will be wrong and the change-ability will be bad in the assembly of machine parts. In this paper , measuring systems are organized to measure linear displacement of ATC(Automatic tool changer) of NC lathe using laser interferometer, magnescale and tick pulses coming out from computer in order to get data at constant time intervals from the sensors, And each set of data gotten from test is expressed to a plots by computer treatment and the results of linear positioning error motion is estimated to numerics by statistical treatments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.399-404
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• 2002

Improvement of measuring accuracy is an essential part of quality control manufacturing process. The OMM is less than the CMM in measure accuracy but the OMM system is more efficient, easier to use than other measurement system. About 40~70% of the machine tool errors are induced by the thermal errors. Therefore, a key requirement for improving the measuring accuracy is to reduce the geometric and thermal errors. Thermal errors are measured by a ball bar system and predicted by the thermal error modeling. Furthermore, using the pre-defined thermal error map approach compensates the geometric accuracy of the OMM. Appropriate experiments are performed using ball-bar system, temperature measuring devices and touch-type probe. Compensated results are compared with those obtained using CMM to verify the proposed methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.546-550
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• 2002

This paper proposes an error compensation method that improves accuracy with geometry information of injection molding parts. Geometric information can give an improved accuracy in reverse engineering. Measuring data can not lead to get accurate geometric model, including errors of physical parts and measuring machines. Measuring data include errors which can be classified into two types. One is molding error in product, the other is measuring error. Measuring error includes optical error of laser scanner, deformation by probe forces of CMM and machine error. It is important to compensate these in reverse engineering. Least square method(LSM) provides the cloud data with a geometry compensation, improving accuracy of geometry. Also, the functional shape of a part and design concept can be reconstructed by error compensation using geometry information.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1991.11a
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• pp.116-120
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• 1991

Technique of length measurement error is widely used in the accuracy assessment of CMMS(Coordinate measuring machines) and machine tools, as it is simple and direct measurement within the working volume of a machine. In this paper, a new method is proposed for the evaluation of the length measurement error in relation to the volumetric accuracy. lD, 2D, and 3D measuring lines are considered for recpective length measurement error: 1D, 2D, and 3D length measurement uncertainties are evaluated from volumetric accuracy. The relationship between the volumetric accuracy md length measurement error to is discussed. PC based system for length measurement error evaluation and simulation is developed.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.11-13
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• 2007

The appearance inspection of various electronic products and parts has been executed by the eyesight of human. But inspection by eyesight can't bring about uniform inspection result. Because the appearance inspection result by eyesight of human is changed by condition of physical and spirit of the checker. So machine vision inspection system is currently used to many appearance inspection fields instead of the checker. However the inspection result of machine vision is changed by the illumination of workplace. Therefore we have used a power-law transformation in this paper. for improvement of vision inspection accuracy and could increase inspection accuracy of vision system. Also this system has been developed only using PC, CCD Camera and Visual C++ for universal workplace.