• Journal of the Korean Society for Precision Engineering
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• v.31 no.5
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• pp.423-430
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• 2014

In this study, the five-DOF motion at ultra-precision linear stage under static and dynamic conditions are evaluated through the extending application of ISO 230-2. As the performance factors, the bi-directional accuracy and repeatability of the five-DOF motion are quantitatively evaluated with the measurement uncertainties which are determined using the standard uncertainty of equipment used in experiment. The motion under static condition are analyzed using geometric errors. The five geometric errors except the linear displacement error are measured using optimal measurement system which is designed to enhance the standard uncertainty of geometric errors. In addition, the motion under dynamic conditions are analyzed with respect to the conditions with different feed rate of the stage. The experimental results shows that the feed rate of stage has a significant effect on straightness motions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.434-437
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• 2003

Evaluation or the patterning accuracy of e-beam lithography machines requires a high precision inspection system that is capable of measuring the true xy-locations of fiducial marks generated by the e-beam machine under test. Fiducial marks are fabricated on a single photo mask over the entire working area in the form of equally spaced two-dimensional grids. In performing the evaluation, the principles of self-calibration enable to determine the deviations of fiducial marks from their nominal xy-locations precisely, not being affected by the motion errors of the inspection system itself. It is. however, the fact that only repeatable motion errors can be eliminated, while random motion errors encountered in probing the locations of fiducial marks are not removed. Even worse, a random error occurring from the measurement of a single mark propagates and affects in determining locations of other marks, which phenomenon in fact limits the ultimate calibration accuracy of e-beam machines. In this paper, we describe an uncertainty analysis that has been made to investigate how random errors affect the final result of self-calibration of e-beam machines when one uses an optical inspection system equipped with high-resolution microscope objectives and a precision xy-stages. The guide of uncertainty analysis recommended by the International Organization for Standardization is faithfully followed along with necessary sensitivity analysis. The uncertainty analysis reveals that among the dominant components of the patterning accuracy of e-beam lithography, the rotationally symmetrical component is most significantly affected by random errors, whose propagation becomes more severe in a cascading manner as the number of fiducial marks increases

• Journal of the Korea Computer Graphics Society
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• v.19 no.4
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• pp.13-22
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• 2013

A motion capture system is widely used in movies, computer game, and computer animation industries because it allows for creating realistic human motions efficiently. The inertial motion capture system has several advantages over more popular vision-based systems in terms of the required space and cost. However, it suffers from low accuracy due to the relatively high noise levels of the inertial sensors. In particular, the accelerometer used for measuring gravity direction loses the accuracy when the sensor is moving with non-zero linear acceleration. In this paper, we propose a method to remove the linear acceleration component from the accelerometer data in order to improve the accuracy of measuring gravity direction. In addition, we develop a simple method to calibrate the joint axis of a link to which an inertial sensor belongs as well as the position of a sensor with respect to the link. The calibration enables attaching inertial sensors in an arbitrary position and orientation with respect to a link.

• Journal of Biomedical Engineering Research
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• v.39 no.4
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• pp.153-160
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• 2018

3D ultrasound bladder scanners are getting popular in hospitals for the patients with bladder dysfunction. A current bladder scanner adopts a mechanical scan to acquire 3D images and requires two motors and complicated mechanical devices. In this paper, we propose a new ultrasound bladder scanner using hand-motion scan. Instead of two motors and mechanical devices, it has a motion sensor to record transducer positions during hand-motion scan. The experiments with a bladder phantom and volunteers showed similar measurement accuracy to a conventional 3D ultrasound bladder scanner. We expect that the proposed method will reduce the cost and size of the bladder scanner.

• Journal of Biomedical Engineering Research
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• v.33 no.1
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• pp.8-14
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• 2012

The purpose of this study was to evaluate how much effect to accuracy when measuring abdominal fat by Computed Tomography (CT) under different respiration movements. The study volunteer composed of 66 normal adults ($50.4{\pm}11.2$ years, 33 males, 33 females). We measured their obesity by using Broca index, body mass index (BMI) and CT and have investigated the correlation. The CT scanning for the obesity measurement have done in two ways, one was done in stopping breath after exhaling and the other was holding a breath after inhaling. The results showed no statistically significant difference among the three measuring techniques. And, the error in two ways of inhaling and exhaling was showed 24.2% of volunteers. The two ways of respiration movements made different result in visceral fat area (P = 0.044), subcutaneous fat area (P = 0.636) and abdominal obesity value (P = 0.012). This study demonstrates that the two ways of respiration movements when scanning CT makes change in accuracy in visceral fat area, and in abdominal obesity quantitative measure. Therefore, our study suggests that CT should take twice in two ways while a patient stops breath after exhaling and holds a breath after inhaling when measuring abdominal obesity using CT equipments.

• Advances in nano research
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• v.12 no.2
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• pp.223-239
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• 2022

Object detection has always been to pursue objects with particular properties or representations and to predict details on objects including the positions, sizes and angle of rotation in the current picture. This was a very important subject of computer vision science. While vision-based object tracking strategies for the analysis of competitive videos have been developed, it is still difficult to accurately identify and position a speedy small ball. In this study, deep learning (DP) network was developed to face these obstacles in the study of tennis motion tracking from a complex perspective to understand the performance of athletes. This research has used CNN-VGG 16 to tracking the tennis ball from broadcasting videos while their images are distorted, thin and often invisible not only to identify the image of the ball from a single frame, but also to learn patterns from consecutive frames, then VGG 16 takes images with 640 to 360 sizes to locate the ball and obtain high accuracy in public videos. VGG 16 tests 99.6%, 96.63%, and 99.5%, respectively, of accuracy. In order to avoid overfitting, 9 additional videos and a subset of the previous dataset are partly labelled for the 10-fold cross-validation. The results show that CNN-VGG 16 outperforms the standard approach by a wide margin and provides excellent ball tracking performance.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.982-990
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• 2018

In recent years, many wearable devices and mobile apps related to life care have been developed, and a service for measuring the movement during walking and showing the amount of exercise has been provided. However, they do not measure walking in detail, so there may be errors in the total calorie consumption. If the user's behavior is measured by a multi-axis sensor and learned by a machine learning algorithm to recognize the kind of behavior, the detailed operation of walking can be autonomously distinguished and the total calorie consumption can be calculated more than the conventional method. In order to verify this, we measured activities and created a model using a machine learning algorithm. As a result of the comparison experiment, it was confirmed that the average accuracy was 12.5% or more higher than that of the conventional method. Also, in the measurement of the momentum, the calorie consumption accuracy is more than 49.53% than that of the conventional method. If the activity recognition is performed using the wearable device and the machine learning algorithm, the accuracy can be improved and the energy consumption calculation accuracy can be improved.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.6
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• pp.98-104
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• 1999

As the accuracy of manufactured goods needed high accuracy processing has made the efficiency of NC and measurement technology development, the innovation of machine tools has influence the development of the semi-conductor and optical technology. The movement errors can be expressed in terms of yaw, roll an pitch etc. In the case of expanding the error range, static, dynamic and servo gain errors can be included. Machining center might have twenty-one movement errs including three types of joint errors. Those errors have been measured on the condition of just loading of standard table. Regarding these measuring methods, the mechanical compliance of the structure has not been considered. In this paper, therefor, the influences of the additional load on the positioning accuracy are investigated. The results and the techniques proposed in this study can be considered very effective and useful to compensate more correctly the positioning motion.

• Proceedings of the ESK Conference
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• 1996.10a
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• pp.160-165
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• 1996

This paper presents the development of video-based 3-dimensional tracking system. Measurement of human motion is important in the application of ergonomics. The system uses advanced direct video measurement technology. Passive retro-reflecting markers are attached to a subject and movements of markers are observed by two CCD cameras. Infrared light emitted near the CCD cameras is reflected by the markers and is detected by the cameras. The image ae captured by Samsung MVBO2 board and the center of markers is calculated by DSP program. The position of markers are transferred from MVB02 board to the computer through AT bus. The computer then tracks the position of each marker and saves the data. This system has dynamic accuracy with 1% error and the sampling rate to 6-10 Hz, and can analyse the trajectory and speed of the marker. The results of this study can be used for operators motion analysis, task analysis, and hand movement characteristic analysis.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.4
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• pp.377-386
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• 2015

Joint force/torque estimation by inverse dynamics is a traditional tool in biomechanical studies. Conventionally for this, kinematic data of human body is obtained by motion capture cameras, of which the bulkiness and occlusion problem make it hard to capture a broad range of movement. As an alternative, inertial motion sensing using cheap and small inertial sensors has been studied recently. In this research, the performance of inertial motion sensing especially to calculate inverse dynamics is studied. Kinematic data from inertial motion sensors is used to calculate ground reaction force (GRF), which is compared to the force plate readings (ground truth) and additionally to the estimation result from optical method. The GRF estimation result showed high correlation and low normalized RMSE(R=0.93, normalized RMSE<0.02 of body weight), which performed even better than conventional optical method. This result guarantees enough accuracy of inertial motion sensing to be used in inverse dynamics analysis.