• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.61-68
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• 2010

A magnetotelluric (MT) survey has been performed to delineate deeply extended fracture systems at the geothermal field in Seokmo Island, Korea. To assist interpretation of the MT data, geological surveying and well logging of existing wells were also performed. The surface geology of the island shows Cretaceous and Jurassic granite in the north and Precambrian schist in the south. The geothermal regime has been found along the boundary between the schist and Cretaceous granite. Because of the deep circulation along the fracture system, geothermal gradient of the target area exceeds $45^{\circ}C/km$, which is much higher than the average geothermal gradient in Korea. 2D and 3D inversions of MT data clearly showed a very conductive anomaly, which is interpreted as a fracture system bearing saline water that extends at least down to 1.5 km depth and is inclined eastwards. After drilling down to the depth of 1280 m, more than 4000 tons/day of geothermal water overflowed with temperature higher than $70^{\circ}C$. This water showed very similar chemical composition and temperature to those from another existing well, so that they can be considered to have the same origin; i.e. from the same fracture system. A new geothermal project for combined heat and power generation was launched in 2009 in Seokmo Island, based on the survey. Additional geophysical investigations including MT surveys to cover a wider area, seismic reflection surveys, borehole surveys, and well logging of more than 20 existing boreholes will be conducted.

• Safety and Health at Work
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• v.10 no.4
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• pp.452-460
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• 2019

Background: Monitoring and control of PM_2.5 are being recognized as key to address health issues attributed to PM_2.5. Availability of low-cost PM_2.5 sensors made it possible to introduce a number of portable PM_2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scatteringe-based PM_2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM_2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy. Methods: This study discussed the calibration of a low-cost PM_2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM_2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM_2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference. Results: Based on the performance of ML algorithms used, regression of the output of the PMD to PM_2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R²) of 0.78 and standard error of 5.0 ㎍/㎥, corresponding to 8% increase in R² and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol. Conclusions: Calibration of a low-cost PMD, which is based on construction of PM_2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.12
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• pp.85-96
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• 1999

In a number of predictive video compression standards, the motion is compensated by the block-based motion compensation (BMC). The effective motion field used for the prediction by the BMC is obviously discontinuous since one motion vector is used for the entire macro-block. The usage of discontinuous motion field for the prediction causes the blocky artifacts and one obvious approach for eliminating such artifacts is to use a smoothed motion field. The optimal procedure will depend on the type of motion within the video. In this paper, several procedures for the motion vectors are considered. For any interpolation or approaches, however, the motion vectors as provided by the block matching algorithm(BMA) are no longer optimal. The optimum motion vectors(still one per macro-block) must minimize the of the displaced frame difference (DFD). We propose a unified algorithm that computes the optimum motion vectors to minimize the of the DFD using a conjugate gradient search. The proposed algorithm has been implemented and tested for the affine transformation based motion compensation (ATMC), the bilinear transformation based motion compensation (BTMC) and our own filtered motion compensation(FMC). The performance of these different approaches will be compared against the BMC.

• Progress in Medical Physics
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• v.18 no.2
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• pp.65-72
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• 2007

An echo planar imaging (EPI)-based spin-echo sequence Is often used to obtain diffusion tensor imaging (DTI) data on most of the clinical MRI systems, However, this sequence is confounded with the susceptibility artifacts, especially on the temporal lobe in the human brain. Therefore, the objective of this study was to design a pulse sequence that relatively immunizes the susceptibility artifacts, but can map diffusion tensor components in a single-shot mode. A multi-slice multi-echo pulsed-gradient spin-echo (MePGSE) sequence with eight echoes wasdeveloped with selective refocusing pulses for all slices to map the full tensor. The first seven echoes in the train were diffusion-weighted allowing for the observation of diffusion in several different directions in a single experiment and the last echo was for crusher of the residual magnetization. All components of diffusion tensor were measured by a single shot experiment. The sequence was applied in diffusive phantoms. The preliminary experimental verification of the sequence was illustrated by measuring the apparent diffusion coefficient (ADC) for tap water and by measuring diffusion tensor components for watermelon. The ADC values in the series of the water phantom were reliable. The MePGSE sequence, therefore, may be useful in human brain studies.

• Journal of Radiation Protection and Research
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• v.40 no.1
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• pp.55-64
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• 2015

Dose calculations which are a crucial requirement for radiotherapy treatment planning systems require accuracy and rapid calculations. The conventional radiotherapy treatment planning dose algorithms are rapid but lack precision. Monte Carlo methods are time consuming but the most accurate. The new combined system that Monte Carlo methods calculate part of interesting domain and the rest is calculated by neural can calculate the dose distribution rapidly and accurately. The preliminary study showed that neural networks can map functions which contain discontinuous points and inflection points which the dose distributions in inhomogeneous media also have. Performance results between scaled conjugated gradient algorithm and Levenberg-Marquardt algorithm which are used for training the neural network with a different number of neurons were compared. Finally, the dose distributions of homogeneous phantom calculated by a commercialized treatment planning system were used as training data of the neural network. In the case of homogeneous phantom;the mean squared error of percent depth dose was 0.00214. Further works are programmed to develop the neural network model for 3-dimensinal dose calculations in homogeneous phantoms and inhomogeneous phantoms.

• Journal of Soil and Groundwater Environment
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• v.17 no.5
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• pp.49-55
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• 2012

A 1.28 L-batch reactor and continuous-flow stirred tank reactor (CFSTR) fed with formate and trichloroethene (TCE) were operated for 120 days and 56 days, respectively, to study the effect of formate as electron donor on anaerobic reductive dechlorination (ARD) of TCE to cis-1,2-dichloroethylene (c-DCE), vinyl chloride (VC), and ethylene (ETH). In batch reactor, injected 60 ${\mu}mol$ TCE was completely degraded in the presence of 20% hydrogen gas ($H_2$) in less than 8 days by anaerobic dechlorination mixed-culture (300 mg-soluble protein), Evanite Culture with ability to completely degrade tetrachloroethene (PCE) and -TCE to ETH under anaerobic conditions. Once the formate was used as electron donor instead of hydrogen gas in batch or chemostat system, the TCE-dechlorination rate decreased and acetate production rate increased. It indicates that the concentration of hydrogen produced in both systems is possibly more close to threshold for homoacetogenesis process. Soluble protein concentration of Evanite culture during the batch test increased from 300 mg to 688 mg for 120 days. Through the protein monitoring, we confirmed an increase of microbial population during the reactor operation. In CFSTR test, TCE was fed continuously at 9.9 ppm (75.38 ${\mu}mol/L$) and the influent formate feed concentration increased stepwise from 1.3 mmol/L to 14.3 mmol/L. Injected TCE was accumulated at 18 days of HRT, but TCE was completely degraded at 36 days of HRT without accumulation of the injected-TCE during the left of experiment period, getting $H_2$ from fermentative hydrogen production of injected formate. Although c-DCE was also accumulated for 23 days after beginning of CFSTR operation, it reached steady-state in the presence of excessive formate. We also evaluated microbial dynamic of the culture at different chemical state in the reactor by DGGE (denaturing gradient gel electrophoresis).

• Investigative Magnetic Resonance Imaging
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• v.23 no.3
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• pp.179-201
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• 2019

Portable low-cost magnetic resonance imaging (MRI) systems have the potential to enable "point-of-care" and timely MRI diagnosis, and to make this imaging modality available to routine scans and to people in underdeveloped countries and areas. With simplicity, no maintenance, no power consumption, and low cost, permanent magnets/magnet arrays/magnet assemblies are attractive to be used as a source of static magnetic field to realize the portability and to lower the cost for an MRI scanner. However, when taking the canonical Fourier imaging approach and using linear gradient fields, homogeneous fields are required in a scanner, resulting in the facts that either a bulky magnet/magnet array is needed, or the imaging volume is too small to image an organ if the magnet/magnet array is scaled down to a portable size. Recently, with the progress on image reconstruction based on non-linear gradient field, static field patterns without spatial linearity can be used as spatial encoding magnetic fields (SEMs) to encode MRI signals for imaging. As a result, the requirements for the homogeneity of the static field can be relaxed, which allows permanent magnets/magnet arrays with reduced sizes, reduced weight to image a bigger volume covering organs such as a head. It offers opportunities of constructing a truly portable low-cost MRI scanner. For this exciting potential application, permanent magnets/magnet arrays have attracted increased attention recently. A magnet/magnet array is strongly associated with the imaging volume of an MRI scanner, image reconstruction methods, and RF excitation and RF coils, etc. through field patterns and field homogeneity. This paper offers a review of permanent magnets and magnet arrays of different kinds, especially those that can be used for spatial encoding towards the development of a portable and low-cost MRI system. It is aimed to familiarize the readers with relevant knowledge, literature, and the latest updates of the development on permanent magnets and magnet arrays for MRI. Perspectives on and challenges of using a permanent magnet/magnet array to supply a patterned static magnetic field, which does not have spatial linearity nor high field homogeneity, for image reconstruction in a portable setup are discussed.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.119-133
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• 2022

One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.

• Korean Journal of Optics and Photonics
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• v.34 no.1
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• pp.1-12
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• 2023

In this study, we propose a hybrid phase-control algorithm for multi-channel coherent beam combining (CBC) system by combining the covariant matrix adaption evolution strategy (CMA-ES) and stochastic parallel gradient descent (SPGD) algorithms and analyze its operational performance. The proposed hybrid CMA-ES/SPGD algorithm is a sequential process which initially runs the CMA-ES algorithm until the combined final output intensity reaches a preset interim value, and then switches to running the SPGD algorithm to the end of the whole process. For ideal 7-channel and 19-channel all-fiber-based CBC systems, we have found that the mean convergence time can be reduced by about 10% in comparison with the case when the SPGD algorithm is implemented alone. Furthermore, we analyzed a more realistic situation in which some additional phase noise was introduced in the same CBC system. As a result, it is shown that the proposed algorithm reduces the mean convergence time by about 17% for a 7-channel CBC system and 16-27% for a 19-channel system compared to the existing SPGD alone algorithm. We expect that for implementing a CBC system in a real outdoor environment where phase noise cannot be ignored, the hybrid CMA-ES/SPGD algorithm proposed in this study will be exploited very usefully.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.10
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• pp.299-308
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• 2023

As the spectrum shortage problem has accelerated by the emergence of various services, New Radio-Unlicensed (NR-U) has appeared, allowing users who communicated in licensed bands to communicate in unlicensed bands. However, NR-U network users reduce the performance of Wi-Fi network users who communicate in the same unlicensed band. In this paper, we aim to simultaneously maximize the fairness and throughput of the unlicensed band, where the NR-U network users and the WiFi network users coexist. First, we propose an optimal power allocation scheme based on Monte Carlo Policy Gradient of reinforcement learning to maximize the sum of rates of NR-U networks utilizing rate-splitting multiple access in unlicensed bands. Then, we propose a channel occupancy time division algorithm based on sequential Raiffa bargaining solution of game theory that can simultaneously maximize system throughput and fairness for the coexistence of NR-U and WiFi networks in the same unlicensed band. Simulation results show that the rate splitting multiple access shows better performance than the conventional multiple access technology by comparing the sum-rate when the result value is finally converged under the same transmission power. In addition, we compare the data transfer amount and fairness of NR-U network users, WiFi network users, and total system, and prove that the channel occupancy time division algorithm based on sequential Raiffa bargaining solution of this paper satisfies throughput and fairness at the same time than other algorithms.