• International Journal of Precision Engineering and Manufacturing
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• v.9 no.4
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• pp.26-31
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• 2008

A precision four-degree-of-freedom measurement system has been developed for simultaneous measurement of four motion errors of a linear stage, which include straightness and angular errors, The system employs a retro-reflector to detect the straightness errors and a plane mirror to detect the angular errors. A common-path compensation method for laser beam drift is put forward, and the experimental results show that the influences of beam drift on four motion errors can be reduced simultaneously. In comparison with the API 5D laser measuring system, the accuracy for straightness measurement is about ${\pm}1.5{\mu}m$ within the measuring range of ${\pm}650{\mu}m$, and the accuracy for pitch and yaw measurements is about ${\pm}1.5$ arc-seconds within the range of ${\pm}600$ arc-seconds.

• The Journal of Korean Society for Radiation Therapy
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• v.21 no.1
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• pp.33-39
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• 2009

Purpose: The aim of this study is to compare patient's body posture and its position at the time of simulation with one at the treatment room using On-board Imaging (OBI) and CT (CBCT). The detected offsets are compared with position errors of Rando Phantom that are practically applied. After that, Rando Phantom's position is selected by moving couch based on detected deviations. In addition, the errors between real measured values of Rando Phantom position and theoretical ones is compared. And we will evaluate target position's accuracy of KV X-ray imaging's 2D and CBCT's 3D one. Materials and Methods: Using the Rando Phantom (Alderson Research Laboratories Inc. Stanford. CT, USA) which simulated human body's internal structure, we will set up Rando Phantom on the treatment couch after implementing simulation and RTP according to the same ways as the real radioactive treatment. We tested Rando Phantom that are assumed to have accurate position with different 3 methods. We measured setup errors on the axis of X, Y and Z, and got mean standard deviation errors by repeating tests 10 times on each tests. Results: The difference between mean detection error and standard deviation are as follows; lateral 0.4+/-0.3 mm, longitudinal 0.6+/-0.5 mm, vertical 0.4+/-0.2 mm which all within 0~10 mm. The couch shift variable after positioning that are comparable to residual errors are 0.3+/-0.1, 0.5+/-0.1, and 0.3+/-0.1 mm. The mean detection errors by longitudinal shift between 20~40 mm are 0.4+/-0.3 in lateral, 0.6+/-0.5 in longitudinal, 0.5+/-0.3 in vertical direction. The detection errors are all within range of 0.3~0.5 mm. Residual errors are within 0.2~0.5 mm. Each values are mean values based on 3 tests. Conclusion: Phantom is based on treatment couch shift and error within the average 5mm can be gained by the diminution detected by image registration based on OBI and CBCT. Therefore, the selection of target position which depends on OBI and CBCT could be considered as useful.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.11 s.176
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• pp.135-141
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• 2005

Measurement of five DOF motion errors in a ultra precision feed table was attempted in this study. Yaw and pitch error were measured by using a laser interferometer and roll error was measured by using the reversal method. Linear motion errors in the vertical and horizontal directions were measured by using the sequential two point method. In this case, influence of angular motion errors was compensated by using the previously measured ones by the laser interferometer and the reversal method. The capacitive type sensors and an optical straight edge were used in the reversal method and the sequential two point method. Influence of thermal deformation on sensor jig was investgated and minimized by the periodic measurement according to the variation of room temperature. Deviation of gain between sensors was also compensated using the step response data. 5 DOF motion errors of a hydrostatic table driven by the linear motor werer tested using the measurement method. In the horizontal direction, measuring accuracies for the linear and angular motion were within ${\pm}0.02\;{\mu}m$ and ${\pm}0.04$ arcsec, respectively. In the vertical direction, they were within ${\pm}0.02{\mu}m$ and ${\pm}0.05$ arcsec. From these results, it was found that the introduced measurement method was very effective to measure 5 DOF motion errors of the ultra precision feed tables.

• Journal of radiological science and technology
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• v.25 no.2
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• pp.35-38
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• 2002

Recently, They are usually recording the patient information on the Hospital Information System. In the department of Radiology, For the purpose of assuming patient exposed dose, Authors contrived the mathematical calculation model by use of x-ray out put data on the Excel program, if they in put the exposure factors (kVp, mAs, thickness), the program could automatically calculate the patient Skin dose. The assuming data by three dimensional equation has average errors within ${\pm}5%$, there for We could make good use of clinical field in department of radiology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.413-418
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• 2011

The thermodynamic properties of steam table are obtained by measurement or approximate calculation under appropriate assumptions. Therefore they are supposed to have basic measurement errors. And thermodynamic properties should be modeled through function approximation for using in numerical analysis. In order to make noised thermodynamic properties corresponding to measurement errors, random numbers are generated, adjusted to appropriate magnitudes and added to original thermodynamic properties. Both neural networks and quadratic spline interpolation method are introduced for function approximation of these modified thermodynamic properties in the saturated water based on pressure and temperature. In analysis spline interpolation method gives much less relative errors than neural networks at both ends of data. Excluding the both ends of data, the relative errors of neural networks is generally within ${\pm}0.2%$ and those of spline interpolation method within ${\pm}0.5$~1.5%. This means that the neural networks give smaller relative errors compared with quadratic spline interpolation method within range of use. From this fact it was confirmed that the neural networks trace the original values better than the quadratic interpolation method and neural networks are more appropriate method in modelling the saturated steam table.

• Journal of Ginseng Research
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• v.18 no.1
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• pp.32-38
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• 1994

Male rats aged 15 months, which had been over-trained with 12 arm radial maze up to the criterion of 1 or 2 errors an average per trial, were divided into two groups and 7 months after the over-training and were studied the effect of red ginseng trial saponin fraction (PT) on the spatial memory function. The rats which could be improved the performance of 9.75$\pm$1.26 (within 185$\pm$8.2 1 sec) were classified into "normal group" (n=4) and the ones, which showed R-maze performance of 7.0$\pm$2.87 within 300 sec, "deteriorated memory group" (n=5). PT was dissolved in distilled water and injected into the deteriorated memory group intraperitoneally at 30 min before R-maze for 3 consecutive days. The injected amount of PT on the 1st day was 10mg/rat, 1mg/rat both on the 2nd the 3rd day. As results, the performance was restored to 9.4$\pm$2.0 after stopping ginseng administration, although the lower performance values (1.4$\pm$0.89, 2.8$\pm$0.83, 3.8$\pm$0.84, respectively) were exerted during the three days of PT administration than before PT administration (7.0$\pm$2.87) since 1 day after administratitan of PT. The restored performance values were continuously maintained up to the level of the "normal group". However, any lower performance values were not observed when PT was administered via intraperitoneal route to the normal group in about of 2 mg/rat except 10mg1ra1. The number of errors when the foods in arms were put at only 6 places was clearly reduced during the 9 days of consecutive administration of PT as compared with untreated control. These results indicated that PT administration may enhance their cognitive function after a long lapse of time not only in the memory deteriorated rats but also in normal ones. The reason exerted lower performance values during PT administration in this paper was discussed as compared with the results of T-maze behavior of another 3-month-aged rats. of another 3-month-aged rats.

• Journal of Radiation Protection and Research
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• v.33 no.2
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• pp.47-51
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• 2008

Objective: This study analyzed errors due to rotation or tilt of the magnetic resonance (MR) imaging indicator during image acquisition for a stereotactic radiosurgery. The error correction procedure of a commercially available stereotactic neurosurgery treatment planning program has been verified. Materials and Methods: Software virtual phantoms were built with stereotactic images generated by a commercial programming language, Interactive Data Language (version 5.5). The thickness of an image slice was 0.5 mm, pixel size was $0.5{\times}0.5mm$, field of view was 256 mm, and image resolution was $512{\times}512$. The images were generated under the DICOM 3.0 standard in order to be used with Leksell GammaPlan$^{(R)}$. For the verification of the rotation error correction function of Leksell GammaPlan$^{(R)}$, 45 measurement points were arranged in five axial planes. On each axial plane, there were nine measurement points along a square of length 100 mm. The center of the square was located on the z-axis and a measurement point was on the z-axis, too. Five axial planes were placed at z=-50.0, -30.0, 0.0, 30.0, 50.0 mm, respectively. The virtual phantom was rotated by $3^{\circ}$ around one of x, y, and z-axis. It was also rotated by $3^{\circ}$ around two axes of x, y, and z-axis, and rotated by $3^{\circ}$ along all three axes. The errors in the position of rotated measurement points were measured with Leksell GammaPlan$^{(R)}$ and the correction function was verified. Results: The image registration errors of the virtual phantom images was $0.1{\pm}0.1mm$ and it was within the requirement of stereotactic images. The maximum theoretical errors in position of measurement points were 2.6 mm for a rotation around one axis, 3.7 mm for a rotation around two axes, and 4.5 mm for a rotation around three axes. The measured errors in position was $0.1{\pm}0.1mm$ for a rotation around single axis, $0.2{\pm}0.2mm$ for double and triple axes. These small errors verified that the rotation error correction function of Leksell GammaPlan$^{(R)}$ is working fine. Conclusion: A virtual phantom was built to verify software functions of stereotactic neurosurgery treatment planning program. The error correction function of a commercial treatment planning program worked within nominal error range. The virtual phantom of this study can be applied in many other fields to verify various functions of treatment planning programs.

• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.1
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• pp.43-48
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• 2014

Purpose: In the PET/CT images, The SUV (standardized uptake value) enables the quantitative assessment according to the biological changes of organs as the index of distinction whether lesion is malignant or not. Therefore, It is too important to enter parameters correctly that affect to the SUV. The purpose of this study is to evaluate an allowable error range of SUV as measuring the difference of results according to input errors of Activity, Weight, uptake Time among the parameters. Materials and Methods: Three inserts, Hot, Teflon and Air, were situated in the 1994 NEMA Phantom. Phantom was filled with 27.3 MBq/mL of 18F-FDG. The ratio of hotspot area activity to background area activity was regulated as 4:1. After scanning, Image was re-reconstructed after incurring input errors in Activity, Weight, uptake Time parameters as ${\pm}5%$, 10%, 15%, 30%, 50% from original data. ROIs (region of interests) were set one in the each insert areas and four in the background areas. $SUV_{mean}$ and percentage differences were calculated and compared in each areas. Results: $SUV_{mean}$ of Hot. Teflon, Air and BKG (Background) areas of original images were 4.5, 0.02. 0.1 and 1.0. The min and max value of $SUV_{mean}$ according to change of Activity error were 3.0 and 9.0 in Hot, 0.01 and 0.04 in Teflon, 0.1 and 0.3 in Air, 0.6 and 2.0 in BKG areas. And percentage differences were equally from -33% to 100%. In case of Weight error showed $SUV_{mean}$ as 2.2 and 6.7 in Hot, 0.01 and 0.03 in Tefron, 0.09 and 0.28 in Air, 0.5 and 1.5 in BKG areas. And percentage differences were equally from -50% to 50% except Teflon area's percentage deference that was from -50% to 52%. In case of uptake Time error showed $SUV_{mean}$ as 3.8 and 5.3 in Hot, 0.01 and 0.02 in Teflon, 0.1 and 0.2 in Air, 0.8 and 1.2 in BKG areas. And percentage differences were equally from 17% to -14% in Hot and BKG areas. Teflon area's percentage difference was from -50% to 52% and Air area's one was from -12% to 20%. Conclusion: As shown in the results, It was applied within ${\pm}5%$ of Activity and Weight errors if the allowable error range was configured within 5%. So, The calibration of dose calibrator and weighing machine has to conduct within ${\pm}5%$ error range because they can affect to Activity and Weight rates. In case of Time error, it showed separate error ranges according to the type of inserts. It showed within 5% error when Hot and BKG areas error were within ${\pm}15%$. So we have to consider each time errors if we use more than two clocks included scanner's one during the examinations.

• Journal of Environmental Science International
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• v.27 no.11
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• pp.1155-1167
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• 2018

We developed a small sensor observation system (SSOS) at a relatively low cost to observe the atmospheric boundary layer. The accuracy of the SSOS sensor was compared with that of the automatic weather system (AWS) and meteorological tower at the Korea Meteorological Administration (KMA). Comparisons between SSOS sensors and KMA sensors were carried out by dividing into ground and lower atmosphere. As a result of comparing the raw data of the SSOS sensor with the raw data of AWS and the observation tower by applying the root-mean-square-error to the error, the corresponding values were within the error tolerance range (KMA meteorological reference point: humidity ${\pm}5%$, atmospheric pressure ${\pm}0.5hPa$, temperature ${\pm}0.5^{\circ}C$. In the case of humidity, even if the altitude changed, it tends to be underestimated. In the case of temperature, when the altitude rose to 40 m above the ground, the value changed from underestimation to overestimation. However, it can be confirmed that the errors are within the KMA's permissible range after correction.

• Transactions of Materials Processing
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• v.17 no.6
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• pp.393-400
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• 2008

Springback is one of the most difficult phenomena to analyze and control in sheet forming. Most of traditional springback control methods rely on experiences of skilled workers in industrial fields. This study focuses on prediction and generation of optimum reconfigurable die surfaces to control shape errors originated by springback. For this purpose, a deformation transfer function(DTF) was combined with finite element analysis of the springback in the 2D sheet forming model of elastic-perfectly plastic materials under the condition without blank holder. The results showed shape errors within 1% of the objective shape, which were comparable with analytically predicted errors. In addition to this theoretical analysis, DTF method was also applied to 2D and 3D sheet forming experiments. The experimental results showed ${\pm}0.5$ mm and ${\pm}1.0$ mm shape error distribution respectively, demonstrating that reconfigurable die surfaces were predicted well by the DTF method. Irrespective of material properties and sheet thickness, the DTF method was applicable not only to FEM simulation but also to 2D and 3D elasto-reconfigurable die forming. Consequently, this study shows that springback can be controlled effectively in the elasto-RDF system by using the DTF method.