• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.2
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• pp.73-78
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• 2022

This experiment was conducted to establish the technology for artificial hay preparation in Korea. Using far-infrared heater, a device that can control temperature, airflow, and far-infrared radiation was produced and conducted on the fourth harvested alfalfa. The drying conditions were carried out by selecting a total of four conditions. For each condition, the radiation rate was set to around 40% (33-42%), and the temperature was set at 58~65℃, and the speed of the airflow was fixed at 60m/s. The overall drying time was set to 30 min in the single and 60 min (30-30 min) and 90 min (30-30-30 min) in the complex condition, and the radiation rate and temperature were changed by time period. In the case of drying condition 1, the final dry matter (DM) content was 46.26%, which did not reach a DM suitable for hay. However, all of the alfalfa corresponding to the remaining drying conditions 2 to 7 showed a DM content of 80% or more, resulting in optimal alfalfa hay production. In power consumption according to the drying conditions, the second drying condition showed the lowest at 4.7 KW, and the remaining drying conditions were as high as 6.5 to 7.1 KW. The crude protein content was found to be high at an average of 25.91% and it showed the highest content in the 5th drying condition (26.93%) and the lowest value in the 6th drying condition (25.16%). The digestibility showed a high value with an average of 84.90%, and there was no significant difference among treatments (p>0.05). Considering the above results, it was judged that drying condition 2 was the most advantageous.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.411-420
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• 2022

Recently, research using remote sensing has been active in various fields such as environment, science, and society. The results of research using remote sensing are not only numerical results, but also play an important role in solving and preventing social and scientific problems. The purpose of this thesis is to tell the correlation between the data provided and each data by using remote sensing technology for the tidal flat environment. The purpose of this study is to obtain high-resolution data using artificial satellites during remote sensing to find out information on tidal flat currents. Tidal flats created by erosion, sedimentation, low tide, and high tide contain information about the tidal flat slope and information about the ecosystem. Therefore, it can be considered as one of the very important studies to analyze the overall tidal flow channel. This paper creates a DEM (Digital Elevation Model) through TanDEM-X, and DEM is used as the most basic data to create a tidal channel. The research area is a tidal flat located in the middle of the west coast of Ganghwado tidal flat. By analyzing the tidal channel created, various information such as the slope direction of Ganghwado tidal flat and the shape of the tidal channel can be grasped. It is expected that the results of this study will increase the importance and necessity of using DEM data for tidal flat research in the future, and that high-quality results can be obtained.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.707-723
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• 2022

Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R² of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.

• Korean Journal of Ecology and Environment
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• v.54 no.4
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• pp.346-362
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• 2021

A sediment control dam is an artificial structure built to prolong sedimentation in the main dam by reducing the inflow of suspended solids. These dams can affect changes in dissolved organic matter (DOM) in the water body by changing the river flow regime. The main DOM component for Yeongju Dam sediment control of the Naeseongcheon River was analyzed through 3D excitation-emission matrix (EEM) and parallel factor (PARAFAC) analyses. As a result, four humic-like components (C1~C3, C5), and three proteins, tryptophan-like components (C2, C6~C7) were detected. Among DOM components, humic-like components (autochthonous: C1, allochthonous: C2~C3) were found to be dominant during the sampling period. The total amount of DOM components and the composition ratio of each component did not show a difference for each depth according to the amount of available light (100%, 12%, and 1%). Throughout the study period, the allochthonous organic matter was continuously decomposing and converting into autochthonous organic matter; the DOM indices (fluorescence index, humification index, and freshness index) indicated the dominance of autochthonous organic matter in the river. Considering the relative abundance of cyanobacteria and that the number of bacteria cells and rotifers increased as autochthonous organic matter increased, it was suggested that the algal bloom and consequent activation of the microbial food web was affected by the composition of DOM in the water body. Research on DOM characteristics is important not only for water quality management but also for understanding the cycling of matter through microbial food web activity.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.29 no.4
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• pp.366-375
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• 1984

In order to confirm the effect of climatic consumption index (C C I) on the number of spikelets and yield of rice, 3 levels of shading rates such as 0, 25, 50% of full light were treated during the tillering stage, reproductive growth stage and ripening period, respectively, in a phytotron controlled with day/ night temperature of 20/10$^{\circ}C$ and 30/20$^{\circ}C$, and field at Crop Experiment Station, Suwon, Korea. The results are as follows: 1. As solar radiation decreased during the reproductive growth stage in 30/20$^{\circ}C$ or field condition, the number of spikelets per panicle was decreased due to the decrease of the number of differentiated secondary rachis branches and spikelets as well as the increase of the number of degenerated secondary rachis branches and spikelets. 2. Our results showed slight negative correlation between C C I of the reproductive growth stage and number of panicles per square meter and number of differentiated secondary rachis branches. On the other hand, there was highly significant positive correlation between C C I of the reproductive growth stage and the number of degenerated secondary rachis branches and spikelets, and negative correlation in number of differentiated spikelets. 3. The shading during the reproductive growth stage did not affect on the percentage of ripened grains and 1000 grains weight of hulled rice, whereas those were decreased with shading during the ripened period. 4. Influence of shading in each growing stage on the yield was severe in the order of ripened period, reproductive growth stage, tillering stage. 5. Respiration rate in Jinheung was higher than that of Tongil at low temperature, but reversed above 30$^{\circ}C$. Respiratory coefficients (Q$\sub$10/) of Tongil and Jinheung were 2.74 and 1.96, respectively. Respiration/ photosynthesis ratio in Jinheung was higher than that of Tongil at low temperature, while higher in Tongil above 32$^{\circ}C$. 6. Transportation of $\^$14/C was restricted at 20/10$^{\circ}C$ in Tongil, however, there was no differences at 30/20$^{\circ}C$ in both Tongil and Jinheung. The influence of shading on the transportation of $\^$14/C did not affect at 20/10$^{\circ}C$, but it was hampered with shading at 30/20$^{\circ}C$ in both varieties.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.40 no.5
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• pp.662-669
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• 1995

To clarify the effect of temperature during ripening on grain quality, rice plant which was grown under normal natural conditions untill heading stage was subjected various temperature regimes during grain filling stage. Three varieties, Odaebyeo, Hwaseongbyeo and Dongjinbyeo were used. Grains on primary and secondary-rachis branches were harvested seperately and measured specific gravity, grain size and weight. The optimum daily temperature for grain filling of rice during ripening period was about 22$^{\circ}C$ and grain filling under alternating temperature(26/18$^{\circ}C$, day /night) was better than constant temperature (22 /22$^{\circ}C$) based on 1, 000-grain weight and yield. Grain size based on volume per grain was smallest under the constant temperature of 22 /22$^{\circ}C$. The size of secondary-rachis branches was smaller than primary-rachis branch. The difference in size between primary and secondary-rachis branches was biggest in Hwaseongbyeo, followed by Odaebyeo and Dongjinbyeo. The temperature regimes treated did not influence the grain size difference between branches so much. The 1000-grain weight was lighter under high temperature than low temperature in Odaebyeo, but reversed tendency was observed in Dongjinbyeo of secondary rachis-branches.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.spc
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• pp.30-36
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• 2022

A sensor is needed to continuously and automatically measure the change in HNS concentration in industrial facilities that directly discharge to the sea after water treatment. The basic function of the sensor is to be able to detect ppb levels even at room temperature. Therefore, a method for increasing the sensitivity of the existing sensor is proposed. First, a method for increasing the conductivity of a film using a conductive carbon-based additive in a nanoparticle thin film and a method for increasing ion adsorption on the surface using a catalyst metal were studied.. To improve conductivity, carbon black was selected as an additive in the film using ITO nanoparticles, and the performance change of the sensor according to the content of the additive was observed. As a result, the change in resistance and response time due to the increase in conductivity at a CB content of 5 wt% could be observed, and notably, the lower limit of detection was lowered to about 250 ppb in an experiment with organic solvents. In addition, to increase the degree of ion adsorption in the liquid, an experiment was conducted using a sample in which a surface catalyst layer was formed by sputtering Au. Notably, the response of the sensor increased by more than 20% and the average lower limit of detection was lowered to 61 ppm. This result confirmed that the chemical resistance sensor using metal oxide nanoparticles could detect HNS of several tens of ppb even at room temperature.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.281-298
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• 2023

Continuous research efforts are being devoted to unmanned mobile platforms for lunar exploration. There is an ongoing demand for real-time information processing to accurately determine the positioning and mapping of areas of interest on the lunar surface. To apply deep learning processing and analysis techniques to practical rovers, research on software integration and optimization is imperative. In this study, a foundational investigation has been conducted on real-time analysis of virtual lunar base construction site images, aimed at automatically quantifying spatial information of key objects. This study involved transitioning from an existing region-based object recognition algorithm to a boundary box-based algorithm, thus enhancing object recognition accuracy and inference speed. To facilitate extensive data-based object matching training, the Batch Hard Triplet Mining technique was introduced, and research was conducted to optimize both training and inference processes. Furthermore, an improved software system for object recognition and identical object matching was integrated, accompanied by the development of visualization software for the automatic matching of identical objects within input images. Leveraging satellite simulative captured video data for training objects and moving object-captured video data for inference, training and inference for identical object matching were successfully executed. The outcomes of this research suggest the feasibility of implementing 3D spatial information based on continuous-capture video data of mobile platforms and utilizing it for positioning objects within regions of interest. As a result, these findings are expected to contribute to the integration of an automated on-site system for video-based construction monitoring and control of significant target objects within future lunar base construction sites.

• Journal of the korean academy of Pediatric Dentistry
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• v.50 no.1
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• pp.113-120
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• 2023

Restoring composite resins with the optimal shades for all primary teeth is a great challenge for pediatric dentists. A newly developed single-shade composite resin can exhibit a color similar to that of the surrounding tooth structure based on the structural color phenomenon. This study aims to evaluate the color adjustment potential (CAP) of a single-shade composite resin compared to conventional multi-shade composite resins in primary teeth. A single-shade composite resin and two conventional multi-shade composite resins were included in this study. Two types of specimens, a single specimen and a dual specimen, were evaluated. For single specimens, duplications of the primary second molar denture teeth were made using experimental composite resins. For dual specimens, cavities were prepared on the buccal surfaces of extracted primary second molars and restored with experimental composite resins. The L^*, a^*, and b^* values were measured using a colorimeter for the extracted teeth and specimens. The mean ΔE_ab^* values for single and dual specimens and CAP were calculated. Bonferroni post-hoc analysis was performed to confirm the statistical significance between the ΔE_ab^* and CAP values of the experimental resins. Among the single specimens, the single-shade composite resin showed significantly higher ΔE_Single compared to other composite resins (p < 0.0167). There was no significant difference between ΔE_Dual for all experimental resins. The single-shade composite resin showed highest CAP compared to other multi-shade composite resins. A single-shade composite resin exhibited the most prominent color adaptability compared to other conventional multi-shade composite resins for primary second molars. A single-shade composite resin can simplify shade matching and provide esthetic outcomes for the restoration of primary second molars.