• Journal of Environmental Science International
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• v.32 no.1
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• pp.47-55
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• 2023

This study evaluated the biochemical methane potential (BMP) of primary sludge, secondary sludge, and food waste in batch anaerobic mono-digestion tests, and investigated the effects of mixture ratio of those organic wastes on methane yield and production rate in batch anaerobic co-digestion tests, that were designed based on a simplex mixture design method. The BMP of primary sludge, secondary sludge and food waste were determined as 234.2, 172.7, and 379.1 mL CH₄/g COD, respectively. The relationships between the mixing ratio of those organic wastes with methane yield and methane production rate were successfully expressed in special cubic models. Both methane yield and methane production rate were estimated as higher when the mixture ratio of food waste was higher. At a mixing ratio of 0.5 and 0.5 for primary sludge and food waste, the methane yield of 297.9 mL CH₄/g COD was expected; this was 19.4% higher than that obtained at a mixing ratio of 0.3333, 0.3333 and 0.3333 for primary sludge, secondary sludge, and food waste (249.5 mL CH₄/g COD). These findings could be useful when designing field-scale anaerobic digersters for mono- and co-digestion of sewage sludges and food waste.

• Electric Engineers Magazine
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• v.182 no.10
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• pp.39-43
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• 1997

에너지수요의 확대 중장기적인 자원제약의 현재화 등에 따라 에너지의 안전공급확보 필요성이 점점 높아지고 있는 한편 에너지 소비의 증대에 의한 지구환경의 오염, 온난화 등이 세계적인 문제로 대두되고 있다. 이에 따라 새로운 에너지절약 및 신에너지 기술개발의 기운이 고조되어가고 있으며 특히 연료전지는 고효율이 고 배기가스가 깨끗한 환경적합성에 우수한 차세대 열병합발전시스템으로서 실용화가 기대되고 있다.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.701-716
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• 2023

Microplastics (<5 mm diameter) in aquatic environments adsorb heavy metals, potentially exposing humans to their toxic effects via food chains. We investigated factors influencing the adsorption of heavy metals on microplastics in aquatic environments, examining their adsorption processes and mechanisms. Adsorption characteristics vary with polymer type, crystallinity, particle size, and environmental conditions (pH, temperature, weathering), and the adsorption capacity for heavy metals increases with weathering and reduction in polymer particle size. However, correlations between environment temperature, polymer crystallinity, and adsorption capacity for heavy metals could not be confirmed. The adsorption behavior of heavy metals can be explained in terms of physicochemical adsorption processes and evaluated through adsorption kinetics and isothermal studies, with multiple mechanisms usually being involved. An understanding of the adsorption of heavy metals by microplastics should aid evaluation of the potential risks of microplastics in aquatic environments.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 1993.07a
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• pp.186-188
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• 1993

90년대에 와서 지구규모의 환경변동을 과학적으로 규명하고 예측하기 위한 해양의 계속적인 관측과 연구에 국제적인 관심을 보이고 있다 지구 환경 변화 연구와 관련하여 1992년 여름 세계해양 관측시스템(GOOS) 계획이 제안되어 이를 여러 전문가의 검토를 거처 보완하여 1993년 봄 UNESCO의 정부간 해양 위원회(IOC) 총회에서 채택 되었고 작년에 환경과 개발에 관한 국제 회의(UNCED)에서도 “21세기를 향한 행동 계획”으로 채택되었다. (중략)

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1829-1836
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• 2021

Cloud detection means determining the presence or absence of clouds in a pixel in a satellite image, and acts as an important factor affecting the utility and accuracy of the satellite image. In this study, among the satellites of various advanced organizations that provide cloud detection data, we intend to perform quantitative and qualitative comparative analysis on the difference between the cloud detection data of GK-2A/AMI, Terra/MODIS, and Suomi-NPP/VIIRS. As a result of quantitative comparison, the Proportion Correct (PC) index values in January were 74.16% for GK-2A & MODIS, 75.39% for GK-2A & VIIRS, and 87.35% for GK-2A & MODIS in April, and GK-2A & VIIRS showed that 87.71% of clouds were detected in April compared to January without much difference by satellite. As for the qualitative comparison results, when compared with RGB images, it was confirmed that the results corresponding to April rather than January detected clouds better than the previous quantitative results. However, if thin clouds or snow cover exist, each satellite were some differences in the cloud detection results.

• Proceedings of the KSEEG Conference
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• 2001.04a
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• pp.396-399
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• 2001

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.493-502
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• 2021

Recently, because of changes in temperature and rising water temperatures due to increased pollution sources, many algae have been produced in the water system. Therefore, there has been a lot of research using satellite images for the generation and monitoring of green algae. However, in prior studies, it is difficult to consider the optical properties of the local water system by using only a single index, and by using medium and low-resolution satellite images to conduct large-scale algae detection, there is a problem of accuracy in narrow, broad rivers. Therefore, in this work, we utilize high-resolution images of Sentinel-2 satellites to perform green algae detection on a single index (NDVI, SEI, FGAI) and development index (NDVI & SEI, FGAI & SEI) that mixes single indices. In this study, POD, FAR, and PC values were utilized to evaluate the accuracy of green algae detection algorithms, and the FGAI & SEI index showed the highest accuracy with 98.29% overall accuracy PC.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.559-566
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• 2021

It is necessary to quickly detect and respond to ship accidents that occur continuously due to the influence of the recently increased maritime traffic. For this purpose, ship detection research is being actively conducted based on satellite images that can be monitored in real time over a wide area. However, there is a possibility that the wake may be falsely detected as a ship because the wake removal is not performed in previous studies that performed ship detection using spectral characteristics. Therefore, in this study, ship detection was performed using SDI (Ship Detection Index) based on the Sentinel-2A satellite image, and the wake was removed by utilizing the difference in the spectral characteristics of the ship and the wake. Probability of detection (POD) and false alarm rate (FAR) indices were used to verify the accuracy of the ship detection algorithm in this study. As a result of the verification, POD was similar and FAR was improved by 6.4% compared to the result of applying only SDI.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1643-1652
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• 2022

Deposited Marine Debris(DMD) can negatively affect marine ecosystems, fishery resources, and maritime safety and is mainly detected by sonar sensors, lifting frames, and divers. Considering the limitation of cost and time, recent efforts are being made by integrating underwater images and artificial intelligence (AI). We conducted a comparative study of You Only Look Once Version 5 (YOLOv5) and You Only Look Once Version 7 (YOLOv7) models to detect DMD from underwater images for more accurate and efficient management of DMD. For the detection of the DMD objects such as glass, metal, fish traps, tires, wood, and plastic, the two models showed a performance of over 0.85 in terms of Mean Average Precision (mAP@0.5). A more objective evaluation and an improvement of the models are expected with the construction of an extensive image database.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1743-1747
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• 2022

Sentinel-2 can be used as proxy data for the Korean Compact Advanced Satellite 500-4 (CAS500-4), also known as Agriculture and Forestry Satellite, in terms of spectral wavelengths and spatial resolution. This letter examined cloud detection for later use in the CAS500-4 based on deep learning technologies. DeepLabV3+, a traditional Convolutional Neural Network (CNN) model, and Shifted Windows (Swin) Transformer, a state-of-the-art (SOTA) Transformer model, were compared using 22,728 images provided by Radiant Earth Foundation (REF). Swin Transformer showed a better performance with a precision of 0.886 and a recall of 0.875, which is a balanced result, unbiased between over- and under-estimation. Deep learning-based cloud detection is expected to be a future operational module for CAS500-4 through optimization for the Korean Peninsula.