• Korean Journal of Soil Science and Fertilizer
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• v.40 no.1
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• pp.25-30
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• 2007

Objective of this experiment was to investigate the growth effects of Chinese cabbage and soil salinity to alternative irrigation waters for drought periods. The treatments were consisted of the discharge water from industrial wastewater treatment plant (DIWT), the discharge water from municipal wastewater treatment plant (DMWT) and ground water as the control. For the chemical compositions of alternative water, it appeared that concentrations of the $Ni^+$ and SAR values in DIWT were over the reuse criteria of other countries for irrigation, but CODcr concentration in DMWT was higher than the reuse criteria for agricultural irrigation. According to classification of water by $EC_i$ value, DIWT and DMWT are ranged from 0.7 to $2.0dS\;m^{-1}$, slight salinity. Average harvest indexes were 0.64 for DIWT and 0.63 for DMWT as compared to 0.61 of the control regardless of irrigation periods. SAR value in soil was increased with prolonging the irrigation periods at head forming stage, but not much difference except for 30 days of irrigation period at harvesting time for DIWT. However, it was not much difference along with irrigation periods through the growth stages for DMWT as compared with the groundwater. At harvesting time, average $EC_e$ for the soil irrigated with alternative agricultural waters was $0.017dS\;m^{-1}$ for its DIMT and $0.036dS\;m^{-1}$ for its DMWT as compared to $0.013dS\;m^{-1}$ of its groundwater as the control. For $NH_4-N$ concentrations, it observed that there were no differences among the treatments with different irrigation periods at head forming stage in soil after irrigation. Also, $NO_3-N$ concentration in soil was increased up to 20 days after irrigation, and then decreased at 30 days after irrigation with DMWT at head forming stage. The $Ni^+$ concentration in upper layer soil (0-15 cm) irrigated with DIWT was increased with prolonging the irrigation period at head forming stage, but it was dramatically decreased and almost constant in all the treatments at harvesting time. Therefore, it might be concluded that there was potentially safe to irrigate the discharge water from municipal wastewater treatment plant for 20 days after transplanting to drought periods with cultivating the Chinese cabbage.

• Ocean and Polar Research
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• v.33 no.1
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• pp.1-11
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• 2011

This study describes measurements of fast ice recorded on May 23, 2009, in Kongsfjorden (translated as 'Kongs Fjord'), an inlet on the west coast of Spitsbergen in the Svalbard Archipelago. Seasonal fast ice is an important feature for Svalbard fjords, both in relation to their physical environment and also the local ecosystem, since it grows seaward from the coast and remains in place throughout the winter. Ice thickness, snow, ice properties, and wind speed were measured, while SAR (Synthetic Aperture Radar) data was observed simultaneously observed two times from ALOS-PALSAR (L-band). Measured ice thickness was about 25-35 cm while the thickness of ice floe broken from fast ice was measured as 10-15 cm. Average salinity was 1.9-2.0 ppt during the melting period. Polarimetric data was used to extract H/A/alpha-angle parameters of fast ice, ice floe, snow and glacier, which was classified into 18 classes based on these parameters. It was established that the area of fast ice represents surface scattering which indicates low and medium entropy surface scatters such as Bragg and random surfaces, while fast ice covered with snow belongs to a zone of low entropy surface scattering similar to snow-covered land surfaces. The results of this study will contribute to various interpretations of interrelationships between H/A/alpha parameters and the wave scattering Phenomenon of sea ice.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1923-1929
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• 2021

Earth observation satellites are being used in various field and are being developed in many countries due to their high utility and marketability. Korea is developing various Earth observation satellites according to National Space Development Plan. Among them, the Korea Multi-Purpose Satellite(KOMPSAT) series is the most representative low-orbit satellite. So far, a total of five KOMPSAT have been launched to meet the national image demand and have been used in various fields, including national institutions. This special issue introduces research related to data processing, analysis, and utilization using various image data from the KOMPSAT series. Meanwhile, for the uninterrupted utilization of the subsequent KOMPSAT image data, data processing and utilization research suitable for high-resolution images must be continued, and related research contents will be continuously shared through a special issue.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1478-1499
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• 2024

Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. COVID-19 become an active epidemic disease due to its spread around the globe. The main causes of the spread are through interaction and transmission of the droplets through coughing and sneezing. The spread can be minimized by isolating the susceptible patients. However, it necessitates remote monitoring to check the breathing issues of the patient remotely to minimize the interactions for spread minimization. Thus, in this article, we offer a wearable-IoTs-centered framework for remote monitoring and recognition of the breathing pattern and abnormal breath detection for timely providing the proper oxygen level required. We propose wearable sensors accelerometer and gyroscope-based breathing time-series data acquisition, temporal features extraction, and machine learning algorithms for pattern detection and abnormality identification. The sensors provide the data through Bluetooth and receive it at the server for further processing and recognition. We collect the six breathing patterns from the twenty subjects and each pattern is recorded for about five minutes. We match prediction accuracies of all machine learning models under study (i.e. Random forest, Gradient boosting tree, Decision tree, and K-nearest neighbor. Our results show that normal breathing and Bradypnea are the most correctly recognized breathing patterns. However, in some cases, algorithm recognizes kussmaul well also. Collectively, the classification outcomes of Random Forest and Gradient Boost Trees are better than the other two algorithms.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1871-1877
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• 2022

In the past, satellite development required enormous budget and time, so only some developed countries possessed satellites. However, with the recent emergence of low-budget satellites such as micro-satellites, many countries around the world are participating in satellite development. Low-orbit and geostationary-orbit satellites are used in various fields such as environment and weather monitoring, precise change detection, and disasters. Recently, it has been actively used for monitoring through deep learning-based object-of-interest detection. Until now, Korea has developed satellites for national demand according to the space development plan, and the satellite image obtained through this is used for various purpose in the public and private sectors. Interest in satellite image is continuously increasing in Korea, and various contests are being held to discover ideas for satellite image application and promote technology development. In this special issue, we would like to introduce the topics that participated in the recently held 2022 Satellite Information Application Contest and research on the processing and utilization of KOMPSAT image data.