• 한국조경학회:학술대회논문집
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• 한국조경학회 2022년도 추계학술대회 논문집
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• pp.108-110
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• 2022

• 한국조경학회:학술대회논문집
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• 한국조경학회 2022년도 추계학술대회 논문집
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• pp.26-28
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• 2022

• 한국조경학회:학술대회논문집
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• 한국조경학회 2022년도 추계학술대회 논문집
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• pp.59-61
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• 2022

• 한국조경학회:학술대회논문집
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• 한국조경학회 2022년도 추계학술대회 논문집
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• pp.62-64
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• 2022

• 한국조경학회:학술대회논문집
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• 한국조경학회 2023년도 추계학술대회 논문집
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• pp.120-121
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• 2023

• 한국조경학회:학술대회논문집
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• 한국조경학회 2023년도 추계학술대회 논문집
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• pp.66-67
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• 2023

• 물과 미래
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• 제57권1호
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• pp.16-22
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• 2024

• 한국환경과학회지
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• 제31권12호
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• pp.1051-1059
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• 2022

This study investigated microbial communities and their diversity in a full-scale mesophilic anaerobic digester treating sewage sludge. Influent sewage sludge and anaerobic digester samples collected from a wastewater treatment plant in Busan were analyzed using high-throughput sequencing. It was found that the microbial community structure and diversity in the anaerobic digester could be affected by inoculation effect with influent sewage sludge. Nevertheless, distinct microbial communities were identified as the dominant microbial communities in the anaerobic digester. Twelve genera were identified as abundant bacterial communities, which included several groups of syntrophic bacteria communities, such as Candidatus Cloacimonas, Cloacimonadaceae W5, Smithella, which are (potential) syntrophic-propionate-oxidizing bacteria and Mesotoga and Thermovigra, which are (potential) syntrophic-acetate-oxidizing bacteria. Lentimicrobium, the most abundant genus in the anaerobic digester, may contribute to the decomposition of carbohydrates and the production of volatile fatty acids during the anaerobic digestion of sewage sludge. Of the methanogens identified, Methanollinea, Candidatus Methanofastidiosum, Methanospirillum, and Methanoculleus were the dominant hydrogenotrophic methanogens, and Methanosaeta was the dominant aceticlastic methanogens. The findings may be used as a reference for developing microbial indicators to evaluate the process stability and process efficiency of the anaerobic digestion of sewage sludge.

• 한국기계가공학회지
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• 제20권7호
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• pp.80-88
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• 2021

The behavior of the injection system was determined through FFT and PSD analysis of the pressure data of the common rail, and when the diesel fuel is mixed with water, the pressure data of the common rail, depending on the water content and engine rotation speed, represent a different frequency component distribution. Recently, a theory has been suggested that mixing diesel fuel with water controls engine overheating, fuel efficiency, NOx, CO, etc., but if water content exceeds 10%, it can have a fatal adverse effect on the engine's injection system. In the future, it is necessary to promote fault diagnosis and prediction studies of diesel engines using FFT and PSD results from common rail pressure data.

• 한국기계가공학회지
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• 제20권3호
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• pp.1-7
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• 2021

With the advent of industry 4.0, big data and machine learning techniques are being widely adopted in the maintenance domain. Inverters are widely used in many engineering applications. However, overloading and complex operation conditions may lead to various failures in inverters. In this study, failure mode effect analysis was performed on inverters and voltages collected to investigate the over-voltage effect on capacitors. Several features were extracted from the collected sensor data, which indicated the health state of the inverter. Based on this correlation, the best features were selected for classification. Moreover, random forest classifiers were used to classify the healthy and faulty states of inverters. Different performance metrics were computed, and the classifiers' performance was evaluated in terms of various health features.