• Journal of Soil and Groundwater Environment
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• v.12 no.5
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• pp.19-32
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• 2007

In Korea, there have been various methods of estimating groundwater recharge which generally can be subdivided into three types: baseflow separation method by means of groundwater recession curve, water budget analysis based on lumped conceptual model in watershed, and water table fluctuation method (WTF) by using the data from groundwater monitoring wells. However, groundwater recharge rate shows the spatial-temporal variability due to climatic condition, land use and hydrogeological heterogeneity, so these methods have various limits to deal with these characteristics. To overcome these limitations, we present a new method of estimating recharge based on water balance components from the SWAT-MODFLOW which is an integrated surface-ground water model. Groundwater levels in the interest area close to the stream have dynamics similar to stream flow, whereas levels further upslope respond to precipitation with a delay. As these behaviours are related to the physical process of recharge, it is needed to account for the time delay in aquifer recharge once the water exits the soil profile to represent these features. In SWAT, a single linear reservoir storage module with an exponential decay weighting function is used to compute the recharge from soil to aquifer on a given day. However, this module has some limitations expressing recharge variation when the delay time is too long and transient recharge trend does not match to the groundwater table time series, the multi-reservoir storage routing module which represents more realistic time delay through vadose zone is newly suggested in this study. In this module, the parameter related to the delay time should be optimized by checking the correlation between simulated recharge and observed groundwater levels. The final step of this procedure is to compare simulated groundwater table with observed one as well as to compare simulated watershed runoff with observed one. This method is applied to Mihocheon watershed in Korea for the purpose of testing the procedure of proper estimation of spatio-temporal groundwater recharge distribution. As the newly suggested method of estimating recharge has the advantages of effectiveness of watershed model as well as the accuracy of WTF method, the estimated daily recharge rate would be an advanced quantity reflecting the heterogeneity of hydrogeology, climatic condition, land use as well as physical behaviour of water in soil layers and aquifers.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.377-391
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• 2018

In order to efficiently monitor red tide over a wide range, the need for red tide detection using remote sensing is increasing. However, the previous studies focus on the development of red tide detection algorithm for ocean colour sensor. In this study, we propose the use of multi-sensor to improve the inaccuracy for red tide detection and remote sensing data in coastal areas with high turbidity, which are pointed out as limitations of satellite-based red tide monitoring. The study area were selected based on the red tide information provided by National Institute of Fisheries Science, and spatial fusion and spectral-based fusion were attempted using GOCI image as ocean colour sensor and Landsat OLI image as terrestrial sensor. Through spatial fusion of the two images, both the red tide of the coastal area and the outer sea areas, where the quality of Landsat OLI image was low, which were impossible to observe in GOCI images, showed improved detection results. As a result of spectral-based fusion performed by feature-level and rawdata-level, there was no significant difference in red tide distribution patterns derived from the two methods. However, in the feature-level method, the red tide area tends to overestimated as spatial resolution of the image low. As a result of pixel segmentation by linear spectral unmixing method, the difference in the red tide area was found to increase as the number of pixels with low red tide ratio increased. For rawdata-level, Gram-Schmidt sharpening method estimated a somewhat larger area than PC spectral sharpening method, but no significant difference was observed. In this study, it is shown that coastal red tide with high turbidity as well as outer sea areas can be detected through spatial fusion of ocean colour and terrestrial sensor. Also, by presenting various spectral-based fusion methods, more accurate red tide area estimation method is suggested. It is expected that this result will provide more precise detection of red tide around the Korean peninsula and accurate red tide area information needed to determine countermeasure to effectively control red tide.

• Korean Journal of Soil Science and Fertilizer
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• v.34 no.5
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• pp.326-332
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• 2001

To investigate a fertility status of upland soil, the soil were sampled at 854 sites chosen in consideration of areal distribution percent on the basis of topography and were analyzed on pH, organic mater, available phosphorus ($P_2O_5$), exchangeable potassium and calcium and magnesium. The content of soil chemical properties showed pH 5.6, organic mater $24g\;kg^{-1}$, available $P_2O_5$ $577mg\;kg^{-1}$, exchangeable potassium and calcium and magnesium were 0.85, 4.5, $1.4cmol^+kg^{-1}$, respectively. The distribution percent of soil samples within the optimum range for cropping were 13.4% for pH, 46.7% for organic matter. 27.4% for available $P_2O_5$, 10.7, 15.8, 18.3% for exchangeable potassium and calcium and magnesium, respectively. In chronological changes of soil properties, exchangeable calcium and magnesium were ignorable ; pH was slightly decreased ; organic matter was slightly increased ; available $P_2O_5$ and exchangeable potassium were greatly increased.

• Food Science and Preservation
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• v.22 no.6
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• pp.920-925
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• 2015

The microbial composition in Nuruk, a Korean cereal fermentation starter, is a critical factor for the quality and organoleptic properties of traditional alcoholic beverages. This study was aimed at monitoring the compositional change and enzyme activity of culturable lactic acid bacteria (LAB) in two types of Nuruk fermented at different temperatures. All culturable LAB were isolated at various time points (0, 3, 6, 10, 20, and 30 days) and identified by 16S rRNA sequencing. In traditional Nuruk type A (TN-A), which was fermented at $36^{\circ}C$, the population of total culturable LAB during the fermentation period was between $10^4$ and $10^5$ log CFU/mL. On the other hand, the LAB population in traditional Nuruk type B (TN-B) fermented at $45^{\circ}C$ (primary fermentation for 10 days) and $35^{\circ}C$ (secondary fermentation for 20 days) was $10^2$ log CFU/mL; however, these bacteria could not be detected after 6 days. Major LAB strains were identified in both Nuruk types: (1) from the MRS-culture of TN-A, Pediococcus pentosaceus at 3-30 days; (2) from MRS-culture of TN-B, P. pentosaceus at 3 days and Enterococcus hirae at 6 days. The protease activities of the dominant LAB isolated from the TN-A and TN-B cultures were within the ranges of 0.64~1.03 mg/mL and 0.74~0.81 mg/mL (tyrosine content), respectively, whereas the ${\alpha}$-amylase activities were 0.75~0.98 mg/mL and 0.78~0.79 mg/mL (amylose content), respectively.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.6
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• pp.904-911
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• 2015

The objective of this study was to investigate the quality characteristics of traditional wine using an radio-frequency identification (RFID) system annexed to a fermenter. In this study, we proposed an RFID-based data transmission scheme for monitoring fermentation of traditional alcoholic beverages. The pH, total acidity, total sugar, soluble sugar, free sugar, alcohol content, and organic acids of were investigated and subjected to fermentation of traditional alcoholic beverages three times. The pH ranged from 7.98, 7.95, and 7.68 at day 0, decreased drastically to 3.31~2.96 at day 2, and then slowly increased to the end point, finally reaching 3.34 at day 20. Acidity tended to increase quickly with time, especially for all samples after day 2. The fermentation environment induced a sudden increase acidity in reactants and indicated a low pH. The total sugars during fermentation quickly decreased to the range of 20.3, 22.43, and 19.2% at day 2, and the slope of reduction steadily decreased to 5.1, 6.1, and 4.8% at day 10. On the other hand, the alcohol content showed the reverse trend as total sugars. The alcohol content also showed the same pattern as total acids, showing the highest alcohol content of 17.3% (v/v) on day 20. In this study on traditional wine fermentation using an RFID system, we showed that pH, soluble sugar, and alcohol content can be adopted as key indicators for quality control and standardization of traditional wine manufacturing.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.

• Journal of Korean Society of Forest Science
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• v.107 no.4
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• pp.344-350
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• 2018

This study was carried out to improve the forest management method considering the use of high value added timber in the natural broadleaf forests. For this purpose, the criteria for evaluating the quality grade of standing trees were established and applied to the oak stand in the central region of Korea. The evaluation factors of the grade were bending of stem, branch, stem damage, and other defects. If the logs are divided into 2.1 m units and three logs up to 6.3 m are available, they are classified as Grade I (G-I). If two logs are available, they are classified as Grade II (G-II), If only one log is available, it is classified as Grade III (G-III). When any log is not available as timber, it is classified as Grade IV (G-IV). As a result of applying the grade to the oak stand, G-I was 6.7 %, G-II was 28.0 %, G-III was 38.3 %, and G-IV was 27.0 %. The ratio of standing trees by oak species of higher than G-III was 88.2 % for Quercus acutissima, 88.1 % for Q. variabilis, 83.5 % for Q. serrata, 56.3 % for Q. aliena, and 50.3 % for Q. mongolica, respectively. The G-IV ratio for Q. variabilis and Q. mongolica tended to decrease with increasing diameter at breast height. The order of major defect affecting the grading level was bending of stem > branch > stem damage > other defects. Considering the grade level and oak species distribution, it was concluded possible to produce high quality hardwood timber when we concentrate forest tending techniques on Q. acutissima and Q. variabilis stand. In order to improve the accuracy of grading, it is necessary to continuous complement through the monitoring research for evaluation factors.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1827-1836
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• 2022

Disasters that occur unexpectedly are difficult to predict. In addition, the scale and damage are increasing compared to the past. Sometimes one disaster can develop into another disaster. Among the four stages of disaster management, search and rescue are carried out in the response stage when an emergency occurs. Therefore, personnel such as firefighters who are put into the scene are put in at a lot of risk. In this respect, in the initial response process at the disaster site, robots are a technology with high potential to reduce damage to human life and property. In addition, Light Detection And Ranging (LiDAR) can acquire a relatively wide range of 3D information using a laser. Due to its high accuracy and precision, it is a very useful sensor when considering the characteristics of a disaster site. Therefore, in this study, development and experiments were conducted so that the robot could perform real-time monitoring at the disaster site. Multi-sensor module was developed by combining LiDAR, Inertial Measurement Unit (IMU) sensor, and computing board. Then, this module was mounted on the robot, and a customized Simultaneous Localization and Mapping (SLAM) algorithm was developed. A method for stably mounting a multi-sensor module to a robot to maintain optimal accuracy at disaster sites was studied. And to check the performance of the module, SLAM was tested inside the disaster building, and various SLAM algorithms and distance comparisons were performed. As a result, PackSLAM developed in this study showed lower error compared to other algorithms, showing the possibility of application in disaster sites. In the future, in order to further enhance usability at disaster sites, various experiments will be conducted by establishing a rough terrain environment with many obstacles.

• Journal of Appropriate Technology
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• v.7 no.2
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• pp.127-135
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• 2021

To respond to the threat of global warming, countries around the world are promoting the spread of renewable energy and reduction of carbon emissions. In accordance with the United Nation's Sustainable Development Goal to combat climate change and its impacts, global automakers are pushing for a full transition to electric vehicles within the next 10 years. Electric vehicles can be a useful means for reducing carbon emissions, but in order to reduce carbon generated in the stage of producing electricity for charging, a power generation system using eco-friendly renewable energy is required. In this study, we propose a smart electric mobility operating system integrated with off-grid solar power plants established in Tanzania, Africa. By applying smart monitoring and communication functions based on Arduino-based computing devices, information such as remaining battery capacity, battery status, location, speed, altitude, and road conditions of an electric vehicle or electric motorcycle is monitored. In addition, we present a scenario that communicates with the surrounding independent solar power plant infrastructure to predict the drivable distance and optimize the charging schedule and route to the destination. The feasibility of the proposed system was verified through test runs of electric motorcycles. In considering local environmental characteristics in Tanzania for the operation of the electric mobility system, factors such as eco-friendliness, economic feasibility, ease of operation, and compatibility should be weighed. The smart electric mobility operating system proposed in this study can be an important basis for implementing the SDGs' climate change response.

• 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.