• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.333-339
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• 2022

In this study, we study the effects of note-taking skills on students' academic performance, satisfaction, and concentration, and immersiveness when students are taking online classes. The Cornell note format was used for the note-taking skills. The survey result shows that note-taking skills in online class increase students' diligence, participation, and concentration. We find a strong positive correlation between the number of Cornell note submission and academic performance, and we show that the association between two is a statistically significant by using simple/multiple regression analysis. The multiple regression result shows that one unit increase in the Cornell note submission is associated with the increase in 0.253 midterm score on average. In addition, one unit increase in the Cornell note submission is associated with increase in 0.287 final exam score on average. Further, we conduct bootstrapping regression as a robustness test and show that the results are consistent with the simple/multiple regression results. These analyses show that Cornell note taking skills in online classes can be beneficial for students to improve the quality of their learning.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.109-117
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• 2023

In this study, the effect of the shape of the specimen and the feedrate of the dental ultrasonic surgical instrument on the cutting force and surface roughness of the specimen is analyzed. Experimental specimens were made of SAWBONES artificial bone materials in square and spherical specimens. In addition, the cutting feedrate of the surgical instrument was controlled through the developed moving system. The cutting force generated when cutting the specimen was measured through a force sensor. After the experiment, the cutting surface of the specimen was observed through a three-dimensional optical microscope and the surface roughness was measured. Through one-way ANOVA, the effect of each specimen shape and feed rate on surface roughness was analyzed. As a result of the experiment, the cutting force increased proportionally in the initial feed rate increase stage, but the increase in cutting force decreased as the feed rate continued to increase. Also, the cutting force showed a difference according to the shape of the specimen. The spherical specimen with a relatively small cutting surface area had less cutting force than the square specimen. However, as a result of one-way ANOVA, it was found that the specimen shape and feed rate did not affect the surface roughness. In future studies, it is expected to be used for comparative analysis of ultrasonic surgical instruments and correlation analysis between cutting factors.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.34-34
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• 2023

The Chah-Nimeh reservoirs, which are a sort of natural lakes located in the border of Iran and Afghanistan, are the main drinking and agricultural water resources of Sistan arid region. Considering the occurrence of intense seasonal wind, locally known as levar wind, this study aims to explore the possibility to provide a TSM (Total Suspended Matter) monitoring model of Chah-Nimeh reservoirs using multi-temporal satellite images and in-situ wind speed data. The results show that a strong correlation between TSM concentration and wind speed are present. The developed empirical model indicated high performance in retrieving spatiotemporal distribution of the TSM concentration with R²=0.98 and RMSE=0.92g/m³. Following this observation, we also consider a machine learning-based model to predicts the average TSM using only wind speed. We connect our in-situ wind speed data to the TSM data generated from the inversion of multi-temporal satellite imagery to train a neural network based mode l(Wind2TSM-Net). Examining Wind2TSM-Net model indicates this model can retrieve the TSM accurately utilizing only wind speed (R²=0.88 and RMSE=1.97g/m³). Moreover, this results of this study show tha the TSM concentration can be estimated using only in situ wind speed data independent of the satellite images. Specifically, such model can supply a temporally persistent means of monitoring TSM that is not limited by the temporal resolution of imagery or the cloud cover problem in the optical remote sensing.

• Journal of the Korean institute of surface engineering
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• v.34 no.5
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• pp.403-408
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• 2001

In this work, the cyclic perfluorinated ether (c-C$_4$F$_{8}$O) with very high destructive removal efficiency (DRE) than other alternative gases, such as $C_3$F$_{8}$, c-C$_4$F$_{8}$ and NF$_3$ was used as an alternative process chemical. The plasma cleaning of silicon nitride using gas mixtures of c-C$_4$F$_{8}$O/O$_2$ and c-C$_4$F$_{8}$O/O$_2$+ $N_2$ was investigated in order to evaluate the effects of adding $N_2$ to c-C$_4$F$_{8}$O/O$_2$ on the global warming effects. Under optimum condition, the emitted net perfluorocompounds (PFCs) during cleaning of silicon nitride were quantified and then the effects of additive $N_2$ by obtaining the destructive removal efficiency (DRE) and the million metric tons of carbon equivalent (MMT-CE) were calculated. DRE and MMTCE were obtained by evaluating the volumetric emission using. Fourier transform-infrared spectroscopy (FT-IR). During the cleaning using c-C$_4$F$_{8}$O/O$_2$+$N_2$, DRE values as high as (equation omitted) 98% were obtained and MMTCE values were reduced by as high as 70% compared to the case of $C_2$F$_{6}$O$_2$. Recombination characteristics were indirectly investigated by combining the measurements of species in the chamber using optical emission spectroscopy (OES), before and after the cleaning, in order to understand any correlation between plasma and emission characteristics as well as cleaning rate of silicon nitride.silicon nitride.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.41.2-41.2
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• 2021

In this study, we generate He I 1083 nm images from Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images using a novel deep learning method (pix2pixHD) based on conditional Generative Adversarial Networks (cGAN). He I 1083 nm images from National Solar Observatory (NSO)/Synoptic Optical Long-term Investigations of the Sun (SOLIS) are used as target data. We make three models: single input SDO/AIA 19.3 nm image for Model I, single input 30.4 nm image for Model II, and double input (19.3 and 30.4 nm) images for Model III. We use data from 2010 October to 2015 July except for June and December for training and the remaining one for test. Major results of our study are as follows. First, the models successfully generate He I 1083 nm images with high correlations. Second, the model with two input images shows better results than those with one input image in terms of metrics such as correlation coefficient (CC) and root mean squared error (RMSE). CC and RMSE between real and AI-generated ones for the model III with 4 by 4 binnings are 0.84 and 11.80, respectively. Third, AI-generated images show well observational features such as active regions, filaments, and coronal holes. This work is meaningful in that our model can produce He I 1083 nm images with higher cadence without data gaps, which would be useful for studying the time evolution of chromosphere and coronal holes.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.45.1-45.1
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• 2021

The polarization degree as a function of phase angle (the Sun-target-observer's angle), so-called the polarimetric phase curves (PPC), have provided priceless information on asteroids' albedos since B. Lyot (1929). Succeeding experimental works in 1970s have confirmed the Umow law: There is a universal and strong correlation between the albedo and the PPC slope (slope of the tangential line at the zero of the PPC at phase angle ~ 20 degrees). Experiments in 1990s (ref [1]), on the other hand, have demonstrated that the negative branch of PPC is dependent on the size parameter (X ~ π * particle-size / wavelength), especially when X <~5. The change in particle size changed the minimum polarization degree, location of the minimum, and the width of the negative branch (called the inversion angle). From polarimetry[2] and spectroscopy[3], large asteroids are expected to be covered with fine (<~ 10 ㎛ size) particles due to the gravity. The size parameters are X ~ 30 at the optical wavelength (λ ~ 0.5 ㎛) and X ~ 10 in near-infrared (J, H, Ks bands; λ ~ 1.2-2.2 ㎛), if the representative particle size of 5 ㎛ is considered. Accordingly, the near-infrared polarimetry has a great potential to validate the idea in ref[1]. We conducted near-infrared photopolarimetry of the large asteroid (4) Vesta using the Nishiharima Infrared Camera (NIC) at Nishi-Harima Astronomical Observatory (NHAO). NIC allows simultaneous polarimetric measurements in J, H, and Ks bands, and thus the change of PPC is obtained for three different size parameters. As a result, we found a signature of the change in the negative branch in the PPC of asteroid (4) Vesta. We will introduce our observation and the results and give an interpretation of the regolith on Vesta.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1707-1720
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• 2023

With the increase in satellite time series data, the utility of remote sensing data is growing. In the analysis of time series data, the relative positional accuracy between images has a significant impact on the results, making image registration essential for correction. In recent years, research on image registration has been increasing by applying deep learning, which outperforms existing image registration algorithms. To train deep learning-based registration models, a large number of image pairs are required. Additionally, creating a correlation map between the data of existing deep learning models and applying additional computations to extract registration points is inefficient. To overcome these drawbacks, this study developed a data augmentation technique for training image registration models and applied it to OffsetNet, a registration model that predicts the offset amount itself, to perform image registration for KOMSAT-2, -3, and -3A. The results of the model training showed that OffsetNet accurately predicted the offset amount for the test data, enabling effective registration of the master and slave images.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1791-1799
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• 2023

The Korea Aerospace Research Institute is responsible for supplying and supporting the utilization of imagery data from the Arirang satellite series for organizations affiliated with the Government Satellite Information Application Consultation. Most of them primarily utilize optical imagery, and there is a relative lack of utilization of Synthetic Aperture Radar (SAR) imagery. In this paper, as part of supporting the use of SAR images, we investigated SAR intensity-based change detection algorithms and their use cases that have been researched to determine SAR intensity-based change detection algorithms to be developed in the future. As a result of the research, we found that various algorithms utilizing intensity difference, correlation coefficients, histograms, or polarimetric information have been researched by numerous researchers to detect and analyze change pixels and the applications of change detection algorithms have been studied in various fields such as a city, flood, forest fire, and vegetation. This study will serve as a reference for the development of SAR change detection algorithms, intended for utilization in the Government Satellite Information Application Consultation.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.6
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• pp.435-441
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• 2007

Ground-based optical sensing over the crop canopy provides information on the mass of plant body which reflects the light, as well as crop nitrogen content which is closely related to the greenness of plant leaves. This method has the merits of being non-destructive real-time based, and thus can be conveniently used for decision making on application of nitrogen fertilizers for crops standing in fields. In the present study relationships among leaf nitrogen content of rice canopy, crop growth status, and Normalized Difference Vegetation Index (NDVI) values were investigated. We measured Green normalized difference vegetation index($gNDVI=({\rho}0.80{\mu}m-{\rho}0.55{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.55{\mu}m)$) and NDVI($({\rho}0.80{\mu}m-{\rho}0.68{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.68{\mu}m)$) were measured by using two different active sensors (Greenseeker, NTech Inc. USA). The study was conducted in the years 2005-06 during the rice growing season at the experimental plots of National Institute of Agricultural Science and Technology located at Suwon, Korea. The experiments carried out with randomized complete block design with the application of four levels of nitrogen fertilizers (0, 70, 100, 130kg N/ha) and same amount of phosphorous and potassium content of the fertilizers. gNDVI and rNDVI increased as growth advanced and reached to maximum values at around early August, G(NDVI) were a decrease in values of observed with the crop maturation. gNDVI values and leaf nitrogen content were highly correlated at early July in 2005 and 2006. On the basis of this finding we attempted to estimate the leaf N contents using gNDVI data obtained in 2005 and 2006. The determination coefficients of the linear model by gNDVI in the years 2005 and 2006 were 0.88 and 0.94, respectively. The measured and estimated leaf N contents using gNDVI values showed good agreement ($R^2=0.86^{***}$). Results from this study show that gNDVI values represent a significant positive correlation with leaf N contents and can be used to estimate leaf N before the panicle formation stage. gNDVI appeared to be a very effective parameter to estimate leaf N content the rice canopy.