• Nuclear Engineering and Technology
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• v.50 no.7
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• pp.1154-1159
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• 2018

Precise prediction of the radiation interaction position in scintillators plays an important role in medical and industrial imaging systems. In this research, the incident position of the gamma rays was predicted precisely in a plastic rod scintillator by using attenuation technique and multilayer perceptron (MLP) neural network, for the first time. Also, this procedure was performed using nonlinear regression (NLR) method. The experimental setup is comprised of a plastic rod scintillator (BC400) coupled with two PMTs at two sides, a $^{60}Co$ gamma source and two counters that record count rates. Using two proposed techniques (ANN and NLR), the radiation interaction position was predicted in a plastic rod scintillator with a mean relative error percentage less than 4.6% and 14.6%, respectively. The mean absolute error was measured less than 2.5 and 5.5. The correlation coefficient was calculated 0.998 and 0.984, respectively. Also, the ANN technique was confirmed by leave-one-out (LOO) method with 1% error. These results presented the superiority of the ANN method in comparison with NLR and the other methods. The technique and set up used are simpler and faster than other the previous position sensitive detectors. Thus, the time, cost and shielding and electronics requirements are minimized and optimized.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.4
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• pp.448-457
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• 2008

A principal components analysis (PCA) of the median-plane head-related impulse responses (HRIRs) in the CIPIC HRTF database reveals that the individual HRIRs in the median plane can be adequately reconstructed by a linear combination of 12 orthonormal basis functions. These basis functions can be used to model arbitrary median-plane HRIRs, which are not included in the process to obtain the basis functions. Memory size can be reduced up to 5-fold depending on the number of HRIRs to be modeled. To clarify whether these basis functions can be used to model other set of arbitrary median plane HRIRs, a numerical error analysis for modeling and a series of subjective listening tests were carried out using the measured and modeled HRIRs. The results showed that the set of individual HRIRs in the median plane, which were measured in our lab using different measurement conditions, techniques, and source positions, can be modeled with reasonable accuracy. All subjects, involved in the subjective listening test, reported not only the accurate vertical perception but also the front-back discrimination with the modeled HRIRs based on 12 basis functions.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• The Journal of Industrial Distribution & Business
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• v.11 no.1
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• pp.39-47
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• 2020

Purpose: This study investigated the effects of valuation errors on the capital market through the earnings forecasting errors of financial analysts. As a follow-up to Jensen (2005)'s study, which argued of agency cost of overvaluation, it was intended to analyze the effect of valuation errors on the earnings forecasting behavior of financial analysts. We hypothesized that if the manager tried to explain to the market that their firms are overvalued, the analysts' earnings forecasting errors would decrease. Research design, data and methodology: To this end, the analysis period was set from 2011 to 2018 of KOSPI and KOSDAQ-listed markets. For overvaluation, the study methodology of Rhodes-Kropf, Robinson, and Viswanathan (2005) was measured. The earnings forecasting errors of the financial analyst was measured by the accuracy and bias. Results: Empirical analysis shows that the accuracy and bias of analysts' forecasting errors decrease as overvaluation increase. Second, the negative relationship showed no difference, depending on the size of the auditor. Third, the results have not changed sensitively according to the listed market. Conclusions: Our results indicated that the valuation error lowered the financial analyst earnings forecasting errors. Considering that the greater overvaluation, the higher the compensation and reputation of the manager, it can be interpreted that an active explanation of the market can promote the accuracy of the financial analyst's earnings forecasts. This study has the following contributions when compared to prior research. First, the impact of valuation errors on the capital market was analyzed for the domestic capital market. Second, while there has been no research between valuation error and earnings forecasting by financial analysts, the results of the study suggested that valuation errors reduce financial analyst's earnings forecasting errors. Third, valuation error induced lower the earnings forecasting error of the financial analyst. The greater the valuation error, the greater the management's effort to explain the market more actively. Considering that the greater the error in valuation, the higher the compensation and reputation of the manager, it can be interpreted that an active explanation of the market can promote the accuracy of the financial analyst's earnings forecasts.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.2
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• pp.121-127
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• 2017

From the perspective of commercializing rotating machines equipped with magnetic bearings, maintaining the error between the mechanical center and the magnetic center within an acceptable level is crucial. The existing method of measuring the center error is to adjust the position references that minimize the current imbalance present in levitation control outputs. However, this method can be applied only after all the components of the system are operational. In this paper, we present a new method of estimating the center error by using only the position sensors and a current source. A force model that relates the position of the rotor with the coil currents is set up. Using this model, the center error is estimated by minimizing the difference between the force angles and the contact angles measured in a pull test. The feasibility of the method is numerically and experimentally validated.

• Journal of the Korean Society of Radiology
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• v.3 no.3
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• pp.5-11
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• 2009

When using Image Guided Radiation Therapy, the patient is placed using skin marker first and after confirming anatomical location using OBI, the couch is moved to correct the set up. Evaluation for the error made at that moment was done. Through comparing $0^{\circ}$ and $270^{\circ}$ direction DRR image and OBI image with 2D-2D matching when therapy planning, comparison between patient's therapy plan setup and actual treatment setup was made to observe the error. Treatment confirmation on important organs such as head, neck and spinal cord was done every time through OBI setup and other organs such as chest, abdomen and pelvis was done 2 ~ 3 times a week. But corrections were all recorded on OIS so that evaluation on accuracy could be made through using skin index which was divided into head, neck, chest and abdomen-pelvis on 160 patients. Average setup error for head and neck patient on each AP, SI, RL direction was $0.2{\pm}0.2cm$, $-0.1{\pm}0.1cm$, $-0.2{\pm}0.0cm$, chest patient was $-0.5{\pm}0.1cm$, $0.3{\pm}0.3cm$, $0.4{\pm}0.2cm$, and abdomen was $0.4{\pm}0.4cm$, $-0.5{\pm}0.1cm$, $-0.4{\pm}0.1cm$. In case of pelvis, it was $0.5{\pm}0.3cm$, $0.8{\pm}0.4cm$, $-0.3{\pm}0.2cm$. In rigid body parts such as head and neck showed lesser setup error compared to chest and abdomen. Error was greater on chest in horizontal axis and in AP direction, abdomen-pelvis showed greater error. Error was greater on chest in horizontal axis because of the curve in patient's body when the setup is made. Error was greater on abdomen in AP direction because of the change in front and back location due to breathing of patient. There was no systematic error on patient setup system. Since OBI confirms the anatomical location, when focus is located on the skin, it is more precise to use skin marker to setup. When compared with 3D-3D conformation, although 2D-2D conformation can't find out the rolling error, it has lesser radiation exposure and shorter setup confirmation time. Therefore, on actual clinic, 2D-2D conformation is more appropriate.

• The Journal of the Korea Contents Association
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• v.11 no.7
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• pp.234-238
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• 2011

This paper will evaluate the usefulness of 3D target of CBCT by comparing human body's posture and position when simulated treatment is being carried out as well as human phantom posture and position using CBCT which is applying OBI. From the Rando Phantom which is located in the datum point moved in parallel and rotationary direction using CBCT. Then the mean and standard deviation difference on images location difference that are acquired were compared with real the Rando Phantom' moved distance. To make a plan of simulated treatment with the same procedure of real radiation therapy, we are going to setup the Rando Phantom. With an assumption that the position is set in accurate place, we measured the setup errors accroding to the change of the translation and rotation. Tests are repeated 10 times to get the standard deviation of the error values. The variability in couch shift after positioning equivalent to average residual error showed lateral $0.2{\pm}0.2$mm, longitudinal $0.4{\pm}0.3$mm, vertical $-0.4{\pm}0.1$mm. The average rotation erroes target localization after simulated $0.4{\pm}0.2$ mm, $0.3{\pm}0.3$ mm, and $0.3{\pm}0.4$ mm. The detection error by rotation is $0{\sim}0.6^{\circ}$ CBCT 3D/3D matching using the Rando Phantom minimized the errors by realizing accurate matching during simulated treatment and patient caring.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.10
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• pp.52-60
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• 2010

It is generally important to get a precise position information for autonomous unmanned vehicle(AUV) to run safely. For getting the position of AUV, the GPS has been using to navigation in a vehicle. Though it is useful to finding a position, it is difficult to precisely control a trajectory of the AUV due to large measuring error which may reach over 10 meters. Therefore to apply AUV it needs to compensate for the error. This paper proposes a method to more precisely localize AUV using three low-cost differential global positioning systems (DGPS). The distance errors between each DGPS are minimized as using the least square method (LSM) and the Kalman filter to eliminate a Gaussian white noise. The selected DGPS is cheaper and easier to set up than the RTK-GPS. It is also more precise than the general GPS. The proposed method can compensate the relatively position error according to stationary and moving distance of the AUV. For evaluating the algorithm by simulation, the DGPS signal with the Gaussian white noise to any points is generated by the AR model and compared with the measurement signal. It is confirmed that the proposed method can effectively compensate the position error as comparing with the measurement signal. The compensated position signal can be used to localize and control the AUV in the road.

• Journal of the Korean Institute of Gas
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• v.18 no.4
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• pp.68-75
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• 2014

It is necessary to control and evaluate human factors to reduce economic loss by major accident in toxic gas facilities. Conventional works to evaluate hazards have been focused on mechanical and systematic failure, while only a little works have been studied on managing human errors. In this work, a classification system of performance shaping factor (PSF) was suggested to consist human error in managing accident in the toxic gas facilities. Four types of PSFs (human, system, task characteristics, and task environment) were collected, reviewed, and analyzed to be categorized selected according their characteristics of situational, task, and environmental parameters. The PSFs were further modified to set up PSF systems adequate to evaluate human error, and the proposed system to consist PSFs to evaluate human error was further studied through accident analysis in toxic gas facilities.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.1
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• pp.52-63
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• 2008

The side collision reconstruction algorithm is developed using three dimensional car crash analysis. Medium size passenger car is modeled for finite element analysis. Total 24 side collision configurations, four different speed and six different angle, are set up for making side collision database. Deformation index and degree index are built up for each collision case. Deformation index is a kind of deformation estimate averaging displacement of side door of crashed car from finite element analysis result. Angle index is constructed measuring deformed angle of crashing car. There are two kinds of angle index, one is measured at driver's side and the other is measured at passenger's side. Also a collision analysis information in side of cars is used for giving a basis for scientific and practical reason in a reconstruction of the car accident. The analysis program, LS-DYNA3D is utilized for finite element analysis program for a collision analysis. Those database are used for side collision reconstruction. Side collision reconstruction algorithm is developed, and applied to find the collision conditions before the accident occurs. Three example collision cases are tried to check the effectiveness of the algorithm. Deformation index and angle index is extracted for the case from the analysis result. Deformation index is compared to the established database, and estimated collision speed and angle are introduced by interpolation function. Angle index is used to select a specific collision condition from the several available conditions. The collision condition found by reconstruction algorithm shows good match with original condition within 10% error for speed and angle. As a result, the calculation from the reconstruction of the situation is reproducing the situation well. The performance in this study can be used in many ways for practical field using deformation index and degree index. Other different collision situations may be set up for extending the scope of this study in the future.