• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.139-142
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• 2020

NB-IoT(Narrow Band Internet of Things) is an emerging LPWAN(Low Power Wide Area Network) radio technology. NB-IoT has many advantages like low power, low cost, and high coverage. However low bandwidth and low sampling rates also lead to poor positioning accuracy. This paper proposed a solution to optimize positioning accuracy under the OTDOA(Observed Time Difference of Arrival) approach by utilizing MLR(Multiple Linear Regression) models. Through the MLR model to predict the influence degree of weather(temperature, humidity, light intensity and air pressure) on the arrival time of signal transmission to improve the measurement accuracy. The improvement of measurement accuracy can greatly improve IoT applications based on NB-IoT.

• Journal of Environmental Science International
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• v.26 no.12
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• pp.1307-1319
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• 2017

In this study, we investigated the impact of different initial data on atmospheric modeling results using the Weather Research and Forecast (WRF) model. Four WRF simulations were conducted with different initialization in March 2015, which showed the highest monthly mean $PM_{10}$ concentration in the recent ten years (2006-2015). The results of WRF simulations using NCEP-FNL and ERA-Interim were compared with observed surface temperature and wind speed data, and the difference of grid nudging effect on WRF simulation between the two data were also analyzed. The FNL simulation showed better accuracy in the simulated temperature and wind speed than the Interim simulation, and the difference was clear in the coastal area. The grid nudging effect on the Interim simulation was larger than that of the FNL simulation. Despite of the higher spatial resolution of ERA-Interim data compared to NCEP-FNL data, the Interim simulation showed slightly worse accuracy than those of the FNL simulation. It was due to uncertainties associated with the Sea Surface Temperature (SST) field in the ERA-Interim data. The results from the Interim simulation with different SST data showed significantly improved accuracy than the standard Interim simulation. It means that the SST field in the ERA-Interim data need to be optimized for the better WRF simulation. In conclusion, although the WRF simulation with ERA-Interim data does not show reasonable accuracy compared to those with NCEP-FNL data, it would be able to be Improved by optimizing the SST variable.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.6
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• pp.477-482
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• 2017

When using the FDM to create a 3D object, a thermoplastic filament is heated to its melting point and then extruded layer by layer. Although the FDM printing process has many advantages, its accuracy, and surface finish are not satisfactory. In recent years, much research has been devoted to improving the accuracy of the FDM printing process. The temperature difference between the nozzle and the interior of the chamber of a 3D printer is one of the important parameters affecting the printing process. In this study, we propose a methodology to reduce this temperature difference through design improvement. In addition, we elucidate how this design improvement affects product quality. The FDM printing process is conventionally carried out in a closed chamber. However, in this study, an open heating system is used to reduce the temperature. The FDM printing processes were simulated using FEM analysis.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.609-620
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• 2023

In this study, we used Sentinel-1 C-band synthetic aperture radar to calculate the surface state factor (SSF) for distinguishing the frozen-thawed state of soil. The accuracy of SSF classification was analyzed through comparison with air temperature (AT), grass temperature (GT), and underground temperature (UT). For the analysis, 116 Sentinel-1B Descending nodes observed over a period of 4 years from 2017 to 2020 were established for the central region of South Korea. AT, GT, and UT data were obtained from 23 soil moisture observation points of the Rural Development Administration during the same period, and analyzed using the 06:00 am data adjacent to the shooting time of the Sentinel-1B images. The average accuracy and F1-score for all stations were 0.63 and 0.47 for AT, 0.63 and 0.48 for GT, and 0.57 and 0.21 for UT, respectively. For winter (December-February) data, the average accuracy and F1-score were 0.66 and 0.76 for AT, 0.67 and 0.76 for GT, and 0.47 and 0.44 for UT, respectively. The increase in accuracy during winter data may be attributed to the fact that errors occurring in other seasons are not included.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.265-282
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• 2022

Sea surface temperature (SST) is a factor that greatly influences ocean circulation and ecosystems in the Earth system. As global warming causes changes in the SST near the Korean Peninsula, abnormal water temperature phenomena (high water temperature, low water temperature) occurs, causing continuous damage to the marine ecosystem and the fishery industry. Therefore, this study proposes a methodology to predict the SST near the Korean Peninsula and prevent damage by predicting abnormal water temperature phenomena. The study area was set near the Korean Peninsula, and ERA5 data from the European Center for Medium-Range Weather Forecasts (ECMWF) was used to utilize SST data at the same time period. As a research method, Long Short-Term Memory (LSTM) algorithm specialized for time series data prediction among deep learning models was used in consideration of the time series characteristics of SST data. The prediction model predicts the SST near the Korean Peninsula after 1- to 7-days and predicts the high water temperature or low water temperature phenomenon. To evaluate the accuracy of SST prediction, Coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) indicators were used. The summer (JAS) 1-day prediction result of the prediction model, R²=0.996, RMSE=0.119℃, MAPE=0.352% and the winter (JFM) 1-day prediction result is R²=0.999, RMSE=0.063℃, MAPE=0.646%. Using the predicted SST, the accuracy of abnormal sea surface temperature prediction was evaluated with an F1 Score (F1 Score=0.98 for high water temperature prediction in summer (2021/08/05), F1 Score=1.0 for low water temperature prediction in winter (2021/02/19)). As the prediction period increased, the prediction model showed a tendency to underestimate the SST, which also reduced the accuracy of the abnormal water temperature prediction. Therefore, it is judged that it is necessary to analyze the cause of underestimation of the predictive model in the future and study to improve the prediction accuracy.

• Korean Journal of Optics and Photonics
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• v.7 no.4
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• pp.348-356
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• 1996

A mobile CARS spectrometer is constructed to measure the instantaneous temperature of gases, of which software include the quick fit methods and a least square fitting method to obtain temperatures from the spectra. Two quick-fit-methods give smaller variance of temperatures than the least square fitting method even though they consume much shorter time to yield temperatures. The precision and accuracy of CARS temperature is measured in the graphite tube blackbody furnace in reference to a radiation pyrometer. The accuracy of the CARS temperature is $\pm$2% from 1000K to 2400K and the precision is $\pm$35K at 1600K with the most accurate quick-fit-method. As a demonstration of the instantaneous measurement, the spectrometer is applied for measurement of the turbulent combustion at a certain condition. eograms(HS) are made using a relatively small number of synthesized 2D images. The influence of aliasing artifacts caused by insufficient or improper sampling is presented, and a new sampling theory is proposed, which is used to making holographic stereograms. Also, the optical system for extension of viewing distance and viewing zone is proposed. Results of this analysis can be applied to design normal holographic stereograms and computer based holographic stereograms.

• Korean Journal of Optics and Photonics
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• v.17 no.3
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• pp.216-222
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• 2006

We propose the use of the Gaussian-curve fitting algorithm for the improvement of measurement accuracy in wavelengthscanned Fabry-Perot filter based demodulation systems. The peak locations of FBG sensors were calculated from the fitted curves rather than from distorted PD profiles, resulting in much better measurement accuracy than that of the highest-peak search algorithm. Also, the algorithm was proved to minimize measurement uncertainty of spectrally-distorted grating sensors. From our experimental results, a temperature resolution as small as ${\sim}0.3^{\circ}C$ was readily achieved by use of the Gaussian-curve fitting algorithm whereas the highest-peak search algorithm gave a temperature resolution larger than ${\sim}4^{\circ}C$.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.3
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• pp.222-226
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• 2012

When ship blocks are erected or pre-erected, most blocks will be at outdoors where they are not protected from weather and exposed to ray of the sun. A deck plate compared to those in radiation heat transfer from the sun will have higher temperature than it of ambient air, and will expand more than lower laying structures whose temperatures are similar with air. But deck plates and under-structures are connected, so the deck plate will be under out-of-plane deformation rather than expand in length. In this study, we considered the temperature difference between air and plate as a major parameter of out-of-plane deformation, and analyzed how much additional deformation would take place. In addition, when a deformation could take place was also analyzed based on the initial deformed shape of deck plate. Because the accuracy inspections of deck plate will be done during daytime, conventional accuracy check results on sunny day could make us feel unfair. Thus resonable datum about momentary additional out-of-plane deformation due to environmental effects have been determined. The real deformation values can be specified even under enlarged deformations by radiation-expansion.

• Journal of Environmental Science International
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• v.29 no.1
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• pp.33-44
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• 2020

Recently, a variety of modeling studies have been conducted to examine the air quality over South Korea during the Korea - United States Air Quality (KORUS-AQ) campaign period (May 1 to June 10, 2016). This study investigates the impact of different meteorological initializations on atmospheric modeling results. We conduct several simulations during the KORUS-AQ period using the Weather Research and Forecasting (WRF) model with two different initial datasets, which is FNL of NCEP and ERA5 of ECMWF. Comparing the raw initial data, ERA5 showed better accuracy in the temperature, wind speed, and mixing ratio fields than those of NCEP-FNL. On the other hand, the results of WRF simulations with ERA5 showed better accuracy in the simulated temperature and mixing ratio than those with FNL, except for wind speed. Comparing the nudging efficiency of temperature and wind speed fields, the grid nudging effect on the FNL simulation was larger than that on the ERA5 simulation, but the results of mixing ratio field was the opposite. Overall, WRF simulation with ERA5 data showed a better performance for temperature and mixing ratio simulations than that with FNL data. For wind speed simulation, however, WRF simulation with FNL data indicated more accurate results compared to that with ERA5 data.

• Structural Monitoring and Maintenance
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• v.7 no.1
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• pp.1-12
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• 2020

Railway tracks are the direct supporting structures of the trains, which are vulnerable to produce large deformation under the temperature stress or subgrade settlement. The health status of track is critical, and the track should be routinely monitored to improve safety, lower the risk of excess deformation and provide reliable maintenance strategy. In this paper, the distributed optical fiber sensor was proposed to monitor the continuous deformation of the track. In order to validate the feasibility of the monitoring method, two deformation monitoring tests on one steel rail model in laboratory and on one real railway tack in outdoor were conducted respectively. In the model test, the working conditions of simply supported beam and continuous beam in the rail model under several concentrated loads were set to simulate different stress conditions of the real rail, respectively. In order to evaluate the monitoring accuracy, one distributed optical fiber sensor and one fiber Bragg grating (FBG) sensor were installed on the lower surface of the rail model, the strain measured by FBG sensor and the strain calculated from FEA were taken as measurement references. The model test results show that the strain measured by distributed optical fiber sensor has a good agreement with those measured by FBG sensor and FEA. In the outdoor test, the real track suffered from displacement and temperature loads. The distributed optical fiber sensor installed on the rail can monitor the corresponding strain and temperature with a good accuracy.