• Journal of the Korean Society for Marine Environment & Energy
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• v.11 no.4
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• pp.181-190
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• 2008

To response climate change and Kyoto protocol and to reduce greenhouse gas emissions, marine geological storage of $CO_2$ is regarded as one of the most promising option. Marine geological storage of $CO_2$ is to capture $CO_2$ from major point sources(eg. power plant), to transport to the storage sites and to store $CO_2$ into the marine geological structure such as deep sea saline aquifer. To design a reliable $CO_2$ marine geological storage system, it is necessary to perform numerical process simulation using thermodynamic equation of state. The purpose of this paper is to compare and analyse the relevant equations of state including ideal, BWRS, PR, PRBM and SRK equation of state. To evaluate the predictive accuracy of the equation of the state, we compared numerical calculation results with reference experimental data. Ideal and SRK equation of state did not predict the density behavior above $29.85^{\circ}C$, 60 bar. Especially, they showed maximum 100% error in supercritical state. BWRS equation of state did not predict the density behavior between $60{\sim}80\;bar$ and near critical temperature. On the other hand, PR and PRBM equation of state showed good predictive capability in supercritical state. Since the thermodynamic conditions of $CO_2$ reservoir sites correspond to supercritical state(above $31.1^{\circ}C$ and 73.9 bar), we conclude that it is recommended to use PR and PRBM equation of state in designing of $CO_2$ marine geological storage process.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.51-57
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• 2019

In this paper, we analyzed the performance of IoT based smart wearable mine detection device. There are various mine detection methods currently used by the military. Still, in the general field, mine detection is performed by visual detection, probe detection, detector detection, and other detection methods. The detection method by the detector is using a GPR sensor on the detector, which is possible to detect metals, but it is difficult to identify non-metals. It is hard to distinguish whether the area where the detection was performed or not. Also, there is a problem that a lot of human resources and time are wasted, and if the user does not move the sensor at a constant speed or moves too fast, it is difficult to detect landmines accurately. Therefore, we studied the smart wearable mine detection device composed of human body antenna, main microprocessor, smart glasses, body-mounted LCD monitor, wireless data transmission, belt type power supply, black box camera, which is to improve the problem of the error of mine detection using unidirectional ultrasonic sensing signal. Based on the results of this study, we will conduct an experiment to confirm the possibility of detecting underground mines based on the Internet of Things (IoT). This paper consists of an introduction, experimental environment composition, simulation analysis, and conclusion. Introduction introduces the research contents such as mines, mine detectors, and research progress. It consists of large anti-personnel mine, M16A1 fragmented anti-mine, M15 and M19 antitank mines, plastic bottles similar to mines and aluminum cans. Simulation analysis is conducted by using MATLAB to analyze the mine detection device implementation performance, generating and transmitting IoT signals, and analyzing each received signal to verify the detection performance of landmines. Then we will measure the performance through the simulation of IoT-based mine detection algorithm so that we will prove the possibility of IoT-based detection landmine.

• Journal of the Korean Society for Marine Environment & Energy
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• v.13 no.3
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• pp.187-197
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• 2010

Carbon dioxide Capture and Storage(CCS) is regarded as one of the most promising options to response climate change. CCS is a three-stage process consisting of the capture of carbon dioxide($CO_2$), the transport of $CO_2$ to a storage location, and the long term isolation of $CO_2$ from the atmosphere for the purpose of carbon emission mitigation. Up to now, process design for this $CO_2$ marine geological storage has been carried out mainly on pure $CO_2$. Unfortunately the $CO_2$ mixture captured from the power plants and steel making plants contains many impurities such as $N_2$, $O_2$, Ar, $H_2O$, $SO_2$, $H_2S$. A small amount of impurities can change the thermodynamic properties and then significantly affect the compression, purification, transport and injection processes. In order to design a reliable $CO_2$ marine geological storage system, it is necessary to analyze the impact of these impurities on the whole CCS process at initial design stage. The purpose of the present paper is to compare and analyse the relevant physical property models including BWRS, PR, PRBM, RKS and SRK equations of state, and NRTL-RK model which are crucial numerical process simulation tools. To evaluate the predictive accuracy of the equation of the state for $CO_2-SO_2$ mixture, we compared numerical calculation results with reference experimental data. In addition, optimum binary parameter to consider the interaction of $CO_2$ and $SO_2$ molecules was suggested based on the mean absolute percent error. In conclusion, we suggest the most reliable physical property model with optimized binary parameter in designing the $CO_2-SO_2$ mixture marine geological storage process.

• Geophysics and Geophysical Exploration
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• v.10 no.3
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• pp.191-202
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• 2007

To identify the detailed attenuation structure in the southern Korean Peninsula, a numerical test was conducted for the Q tomography inversion to be applied to the accumulated dataset until 2005. In particular, the stochastic pointsource ground-motion model (STGM model; Boore, 2003) was adopted for the 2D Q tomography inversion for direct application to simulating the strong ground-motion. Simultaneous inversion of the STGM model parameters with a regional single Q model was performed to evaluate the source and site effects which were necessary to generate an artificial dataset for the numerical test. The artificial dataset consists of simulated Fourier spectra that resemble the real data in the magnitude-distance-frequency-error distribution except replacement of the regional single Q model with a checkerboard type of high and low values of laterally varying Q models. The total number of Q blocks used for the checkerboard test was 75 (grid size of $35{\times}44km^2$ for Q blocks); Q functional form of $Q_0f^{\eta}$ ($Q_0$=100 or 500, 0.0 < ${\eta}$ < 1.0) was assigned to each Q block for the checkerboard test. The checkerboard test has been implemented in three steps. At the first step, the initial values of Q-values for 75 blocks were estimated. At the second step, the site amplification function was estimated by using the initial guess of A(f) which is the mean site amplification functions (Yun and Suh, 2007) for the site class. The last step is to invert the tomographic Q-values of 75 blocks based on the results of the first and second steps. As a result of the checkerboard test, it was demonstrated that Q-values could be robustly estimated by using the 2D Q tomography inversion method even in the presence of perturbed source and site effects from the true input model.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.5 no.1
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• pp.70-82
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• 1994

Present study was to evaluate the validity and the clinical utility of the Korean version of Luria-Nebraska Neuropsychological Battery for Children(LNNB-C) in various groups including normal, brain damaged attention deficit hyperactivity disordered(ADHD), and psychiatrically disordered. The Korean version of LNNB-C and BGT were administered to clinical groups consisted of 51 patients(19 brain damaged, 16 ADHD. and 16 psychiatric controls), and to normal group composed of 147 children between the age of 8 and It Also KEDI-WISC was administered D clinical groups as a part of comprehensive psychological assessment There were significant differences between the brain damaged and the normals on all scales of LNNB-C, and between the normals and the ADHD on 11 clinical scales and 3 summary scales, which indicate the clinical validity for the scales of the Korean version of LNNB-C. The significant differences between the ADHD and the brain damaged on 3 summary scales were found, suggesting that the summary scales might play an important role id discriminating between two groups. Multiple discriminant analysis showed that the Korean version of LNNB-C significantly discriminates 3 groups - normals, ADHD, and brain damaged. Percentages of correct classification were ranged from 62.5% in the ADHD to 98.6Ta in the normals. For further evaluating the discriminant validity of the LNNB-C, the discriminant power of each items were calculated, and 131 of the 147 items discriminated significantly between the brain damaged and the normals. The scales of LNNB-C significantly correlated with the error scores of BGT and the most of scales of KEDI-WISC. These results put together : strongly support the concurrent and the discriminant validity of the Korean version of LNNB-C in diagnosing brain damage. The limitations of present study and several issues for the luther study were discussed.

• Investigative Magnetic Resonance Imaging
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• v.13 no.1
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• pp.31-39
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• 2009

Purpose : This study proposes the keyhole method in order to improve the time resolution of the proton resonance frequency(PRF) MR temperature monitoring technique. The values of Root Mean Square (RMS) error of measured temperature value and Signal-to-Noise Ratio(SNR) obtained from the keyhole and full phase encoded temperature images were compared. Materials and Methods : The PRF method combined with GRE sequence was used to get MR temperature images using a clinical 1.5T MR scanner. It was conducted on the tissue-mimic 2% agarose gel phantom and swine's hock tissue. A MR compatible coaxial slot antenna driven by microwave power generator at 2.45GHz was used to heat the object in the magnetic bore for 5 minutes followed by a sequential acquisition of MR raw data during 10 minutes of cooling period. The acquired raw data were transferred to PC after then the keyhole images were reconstructed by taking the central part of K-space data with 128, 64, 32 and 16 phase encoding lines while the remaining peripheral parts were taken from the 1st reference raw data. The RMS errors were compared with the 256 full encoded self-reference temperature image while the SNR values were compared with the zero filling images. Results : As phase encoding number at the center part on the keyhole temperature images decreased to 128, 64, 32 and 16, the RMS errors of the measured temperature increased to 0.538, 0.712, 0.768 and 0.845$^{\circ}C$, meanwhile SNR values were maintained as the phase encoding number of keyhole part is reduced. Conclusion : This study shows that the keyhole technique is successfully applied to temperature monitoring procedure to increases the temporal resolution by standardizing the matrix size, thus maintained the SNR values. In future, it is expected to implement the MR real time thermal imaging using keyhole method which is able to reduce the scan time with minimal thermal variations.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2035-2046
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• 2021

Glacier movement speed is the most basic measurement for glacial dynamics research and is a very important indicator in predicting sea level rise due to climate change. In this study, the two-dimensional velocity measurements of Campbell Glacier located in Terra Nova Bay in East Antarctica were observed through the SAR offset tracking technique. For this purpose, domestic KOMPSAT-5 SAR satellite images taken on July 9, 2021 and August 6, 2021 were acquired. The Multi-kernel SAR offset tracking proposed through previous studies is a technique to obtain the optimal result that satisfies both resolution and precision. However, since offset tracking is repeatedly performed according to the size of the kernel, intensive computational power and time are required. Therefore, in this study, we strategically proposed a coarse-to-fine offset tracking approach. Through coarse-to-fine SAR offset tracking, it is possible to obtain a result with improved observation precision (especially, about 4 times in azimuth direction) while maintaining resolution compared to general offset tracking results. Using this proposed technique, a two-dimensional velocity measurements of Campbell Glacier were generated. As a result of analyzing the two-dimensional movement velocity image, it was observed that the grounding line of Campbell Glacier exists at approximately latitude -74.56N. The flow velocity of Campbell Glacier Tongue analyzed in this study (185-237 m/yr) increased compared to that of 1988-1989 (140-240 m/yr). And compared to the flow velocity (181-268 m/yr) in 2010-2012, the movement speed near the ground line was similar, but it was confirmed that the movement speed at the end of the Campbell Glacier Tongue decreased. However, there is a possibility that this is an error that occurs because the study result of this study is an annual rate of glacier movement that occurred for 28 days. For accurate comparison, it will be necessary to expand the data in time series and accurately calculate the annual rate. Through this study, the two-dimensional velocity measurements of the glacier were observed for the first time using the KOMPSAT-5 satellite image, a domestic X-band SAR satellite. It was confirmed that the coarse-to-fine SAR offset tracking approach of the KOMPSAT-5 SAR image is very useful for observing the two-dimensional velocity of glacier movements.

• Journal of Korea Port Economic Association
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• v.37 no.2
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• pp.73-89
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• 2021

The handysize bulk carriers are capable of transporting a variety of cargo that cannot be transported by mid-large size ship, and the spot chartering market is active, and it is a market that is independent of mid-large size market, and is more risky due to market conditions and charterage variability. In this study, Granger causality test, the Impulse Response Function(IRF) and Forecast Error Variance Decomposition(FEVD) were performed using monthly time series data. As a result of Granger causality test, coal price for coke making, Japan steel plate commodity price, hot rolled steel sheet price, fleet volume and bunker price have causality to Baltic Handysize Index(BHSI) and charterage. After confirming the appropriate lag and stability of the Vector Autoregressive model(VAR), IRF and FEVD were analyzed. As a result of IRF, the three variables of coal price for coke making, hot rolled steel sheet price and bunker price were found to have significant at both upper and lower limit of the confidence interval. Among them, the impulse of hot rolled steel sheet price was found to have the most significant effect. As a result of FEVD, the explanatory power that affects BHSI and charterage is the same in the order of hot rolled steel sheet price, coal price for coke making, bunker price, Japan steel plate price, and fleet volume. It was found that it gradually increased, affecting BHSI by 30% and charterage by 26%. In order to differentiate from previous studies and to find out the effect of short term lag, analysis was performed using monthly price data of major cargoes for Handysize bulk carriers, and meaningful results were derived that can predict monthly market conditions. This study can be helpful in predicting the short term market conditions for shipping companies that operate Handysize bulk carriers and concerned parties in the handysize chartering market.

• The Journal of Korean Society for Radiation Therapy
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• v.31 no.1
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• pp.17-24
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• 2019

Purpose: The present study aims to assess the level of coherency and the accuracy of Point dose of the Isocenter of VERO, a linear accelerator developed for the purpose of the Stereotactic Body Radiation Therapy(SBRT). Materials and Method: The study was conducted randomly with 10 treatment plans among SBRT patients in Kyungpook National University Chilgok Hospital, using VERO, a linear accelerator between June and December, 2018. In order to assess the equipment's power stability level, we measured the output constancy by using PTW-LinaCheck, an output detector. We also attempted to measure the level of accuracy of the equipment's Laser, kV(Kilo Voltage) imaging System, and MV(Mega Voltage) Beam by using Tofu Phantom(BrainLab, Germany) to assess the accuracy level of geometrical Isocenter. We conducted a comparative analysis to assess the accuracy level of the dose by using an acrylic Phantom($30{\times}30{\times}20cm$), a calibrated ion chamber CC-01(IBA Dosimetry), and an Electrometer(IBA, Dosimetry). Results: The output uniformity of VERO was calculated to be 0.66 %. As for geometrical Isocenter accuracy, we analyzed the error values of ball Isocenter of inner Phantom, and the results showed a maximum of 0.4 mm, a minimum of 0.0 mm, and an average of 0.28 mm on X-axis, and a maximum of -0.4 mm, a minimum of 0.0 mm, and an average of -0.24 mm on Y-axis. A comparison and evaluation of the treatment plan dose with the actual measured dose resulted in a maximum of 0.97 % and a minimum of 0.08 %. Conclusion: The equipment's average output dose was calculated to be 0.66 %, meeting the ${\pm}3%$ tolerance, which was considered as a much uniform fashion. As for the accuracy assessment of the geometric Isocenter, the results met the recommended criteria of ${\pm}1mm$ tolerance, affirming a high level of reproducibility of the patient's posture. The difference between the treatment plan dose and the actual measurement dose was calculated to be 0.52 % on average, significantly less than the 3 % tolerance, confirming that it obtained predicted does. The current study suggested that VERO equipment is suitable for SBRT, and would result in notable therapeutic effect.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.