• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4048-4056
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• 2023

The feasibility of using the Monte Carlo code MCS to generate multigroup cross sections for nodal diffusion simulations RAST-F of liquid metal fast reactors is investigated in this paper. The performance of the MCS/RAST-F code system is assessed using steady-state simulations of the ANTS-100e core. The results show good agreement between MCS/RAST-F and MCS reference solutions, with a keff difference of less than 77 pcm and root-mean-square differences in radial and axial power of less than 0.5% and 0.25%, respectively. Furthermore, the MCS/RAST-F reactivity feedback coefficients are within three standard deviations of the MCS coefficients. To validate the internal thermal-hydraulic (TH) feedback capability in RAST-F code, the coupled neutronic/TH1D simulation of ANTS-100e is performed using the case matrix obtained from MCS branch calculations. The results are compared to those obtained using the MARS-LBE system code and show good agreement with relative temperature differences in fuel and coolant of less than 0.8%. This study demonstrates that the MCS/RAST-F code system can produce accurate results for core steady-state neutronic calculations and for coupled neutronic/TH simulations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.441-452
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• 2017

Drones are increasingly being used to investigate disaster damage because they can quickly capture images in the air. It is necessary to extract the damaged area by registering the drones and the existing ortho-images in order to investigate the disaster damage. In this process, we might be faced the problem of registering two images with different time and spatial resolution. In order to solve this problem, we propose a new methodology that performs initial image transformation using line pairs extracted from images and association matrix, and final registration of images using linear features to refine the initial transformed result. The applicability of the newly proposed methodology in this study was evaluated through experiments using artifacts and the natural terrain areas. Experimental results showed that the root mean square error of artifacts and the natural terrain was 1.29 pixels and 4.12 pixels, respectively, and relatively high accuracy was obtained in the region with artifacts extracted a lot of linear information.

• Journal of Adhesion and Interface
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• v.13 no.1
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• pp.24-30
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• 2012

Morphological changes of polyarylate (PAR) fiber treated with formic acid and ultraviolet (UV) were observed by using a scanning electron microscope (SEM) and an atomic force microscope (AFM). The results were analysed by using root mean square (RMS) roughness. In addition, the chemical changes of surface was investigated using contact angle and the interfacial adhesive strength between PAR fiber and PEN (Polyethylene naphthalate) matrix was calculated using the Pull-out test results. As the acid treatment concentration and UV irradiation time increased, cracks and pores were produced on the PAR fiber surface. Due to the roughness increased, the contact angle was decreased. For this reason, RMS roughness of PAR fiber was increased and the interfacial adhesive strength between the PAR fiber and PEN matrix was improved. The increase of interfacial adhesive strength was responsible for the increase of surface area which have cracks and pores.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1290-1297
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• 2019

The paper proposes a distance estimation technique for ultra-wideband (UWB) systems using convolutional neural network (CNN). To estimate the distance from the transmitter and the receiver in the proposed method, 1 dimensional vector consisted of the magnitudes of the received samples is reshaped into a 2 dimensional matrix, and by using this matrix, the distance is estimated through the CNN regressor. The received signal for CNN training is generated by the UWB channel model in the IEEE 802.15.4a, and the CNN model is trained. Next, the received signal for CNN test is generated by filed experiments in indoor environments, and the distance estimation performance is verified. The proposed technique is also compared with the existing threshold based method. According to the results, the proposed CNN based technique is superior to the conventional method and specifically, the proposed method shows 0.6 m root mean square error (RMSE) at distance 10 m while the conventional technique shows much worse 1.6 m RMSE.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.483-492
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• 2007

Time series of conventional prediction techniques uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to stream data, the rate of prediction accuracy will be decreased. This paper proposes an stream data prediction technique using sliding window and regression. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of stream data prediction experiment are performed by the proposed technique IMQR(Incremental Multiple Quadratic Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• Corrosion Science and Technology
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• v.6 no.4
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• pp.147-153
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• 2007

This study sought to investigate the reaction of Co-binder containing tungsten with molten zinc. Four kinds of Co-W alloys (pure, 10%W, 20%W, 30%W) were prepared using the powder metallurgy method. The specimens were immersion-tested in molten pure zinc baths at $460^{\circ}C$. To evaluate the corrosion property in molten zinc, the weight loss of the specimen was measured after the immersion tests at different immersion times (10~300 min.). Co-10%W alloys, compared with pure cobalt, showed no effect of tungsten addition on the reaction rate in molten zinc. The relationship between the weight loss and the square root of immersion period represents a straight line in both pure cobalt and Co-10%W alloy. The Co-Zn reaction layer in Co- 1O%W alloy consists of $\gamma2$, $\gamma1$, $\gamma$ and ($\beta1$ phases. The rate of weight loss significantly increases and the weight loss behavior is not well accord with the linear relationship as the tungsten content in the Co-W alloy increases. The $\beta1$ layer was not formed on the Co-20%W alloy and neither was a stable Co-Zn intermetallic compound layer found on the Co-30%W alloy. The main cause of increase in reaction rate with increasing tungsten content is related with the instability of the Co-Zn reaction phases as seen on micro-structural analysis.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.155-163
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• 2012

The geometrical and electrical errors of array sensors can severely degrade the performance of array sensor system. Various calibration techniques are developed to alleviate this problem. In this paper, two different calibration methods with respect to location, gain and phase of array sensors are presented. One method applies the first-order Taylor series expansion to approximate the true steering vector from the nominal values of array sensors. Then a set of equations is formed by using the null characteristics of the MUSIC spectrum to estimate errors of location, gain and phase of array sensors. Another method estimates these errors based on the data covariance matrix of pilot sources. From the simulations, it is demonstrated that two calibration algorithms calibrated an array system successfully. In addition to that, Fistas and Manikas's algorithm is more robust against noise than Ng and Lie's one when SNR is from 10dB to 50dB.

• International Journal of Highway Engineering
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• v.18 no.2
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• pp.91-101
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• 2016

PURPOSES : The aim of this study was to compare the performance of the QUEENSOD method and the gravity model in estimating Origin-Destination (O/D) tables for a large-sized microscopic traffic simulation network. METHODS : In this study, an expressway network was simulated using the microscopic traffic simulation model, VISSIM. The gravity model and QUEENSOD method were used to estimate the O/D pairs between internal and between external zones. RESULTS: After obtaining estimations of the O/D table by using both the gravity model and the QUEENSOD method, the value of the root mean square error (RMSE) for O/D pairs between internal zones were compared. For the gravity model and the QUEENSOD method, the RMSE obtained were 386.0 and 241.2, respectively. The O/D tables estimated using both methods were then entered into the VISSIM networks and calibrated with measured travel time. The resulting estimated travel times were then compared. For the gravity model and the QUEENSOD method, the estimated travel times showed 1.16% and 0.45% deviation from the surveyed travel time, respectively. CONCLUSIONS : In building a large-sized microscopic traffic simulation network, an O/D matrix is essential in order to produce reliable analysis results. When link counts from diverse ITS facilities are available, the QUEENSOD method outperforms the gravity model.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.8
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• pp.783-789
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• 2017

In terms of 3D orientation estimation based on nine-axis IMMU(inertial and magnetic measurement unit), there are two disturbance components decreasing estimation accuracy: one is external acceleration disturbing accelerometer's signals and the other is magnetic disturbance related to magnetometer's signals. In order to minimize effects by these two disturbances, two approaches including switching approach and model-based approach have been suggested and further research comparing these two has also been conducted. Nevertheless, effect of disturbance modeling differences on orientation estimation accuracy in model-based approach has not been studied before. This paper compares the recently reported two orientation estimation algorithms that have difference in disturbance models, in order to investigate the effect of disturbance models on accuracy of IMMU-based orientation estimation under various operating conditions. This research shows that the difference in disturbance models leads to difference in process noise covariance matrix. Consequently, this affected the orientation estimation, i.e., the estimation differences between the algorithms were root mean square errors of $1.35^{\circ}$ in average and $3.63^{\circ}$ in yaw estimation.

• Journal of the Korean Chemical Society
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• v.65 no.4
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• pp.254-259
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• 2021

The purpose of this work is the development of a method for an effective, less expensive, rapid, and simultaneous determination of three phenolic compounds (caffeic acid, gallic acid, and quercetin) widely present in food resources and known for their antioxidant powers. The method relies on partial least squares (PLS) calibration of UV-visible spectroscopic data. This model was applied to simultaneously determine, the concentrations of caffeic acid (CA), gallic acid (GA), and quercetin (Q) in six herb infusion extracts: basil, chive, laurel, mint, parsley, and thyme. A wavelength range (250-400) nm, and an experimental calibration matrix with 21 samples of ternary mixtures composed of CA (6.0-21.0 mg/L), GA (10.0-35.2 mg/L), and Q (6.4-17.5 mg/L) were chosen. Spectroscopic data were mean-centered before calibration. Two latent variables were determined using the contiguous block cross-validation procedure after calculating the root mean square error cross-validation RMSECV. Other statistic parameters: RMSEP, R², and Recovery (%) were used to determine the predictive ability of the model. The results obtained demonstrated that UV-visible spectrometry and PLS regression were successfully applied to simultaneously quantify the three phenolic compounds in synthetic ternary mixtures. Moreover, the concentrations of CA, GA and Q in herb infusion extracts were easily predicted and found to be 3.918-18.055, 9.014-23.825, and 9.040-13.350 mg/g of dry sample, respectively.