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RISS 인기검색어
- 검색키워드
- 저자 : Amos T. Kabo‑Bah (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Oleyiblo Oloche James,Jia-Shun Cao,Amos T Kabo-Bah,Gan Wang 대한토목학회 2015 KSCE JOURNAL OF CIVIL ENGINEERING Vol.19 No.5
The State Point Analysis (SPA) tool implemented in BioWin software was used to study the impact of Solids Retention Time (SRT) on the secondary clarifier as it relate to effluent quality in a full-scale treatment plant. SRT is the operating parameter which replaces loading factor as the key design parameter in the activated sludge design. It influences a number of factors, such as; growth rate of microorganisms, nitrification, biomass stabilization, degradation of slowly biodegradable organics, selection of microbial composition of the mixed liquor and its settling and treatment characteristics. SRT is the most difficult parameter to manipulate, its control is paramount to ensure effective waste water treatment. Studies have shown best operating SRT’s, nevertheless, the clarifier working conditions in those studies were not considered. SPA is a practical tool developed for the purposes of assessing clarifier performance under different operating scenarios. This study shows the usefulness of SPA in determining optimum operating SRT while maintaining clarification and thickening without compromising clarifier operation. The SPA results revealed that the clarifier is overloaded at the present operating SRT (16 days). However, it was found that the treatment plant performs better at 12 days SRT without violating effluent concentrations and without being critically loaded.
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Deep learning-based framework for vegetation hazard monitoring near powerlines
Nana Ekow Nkwa Sey,Mark Amo‑Boateng,Martin Kyereh Domfeh,Amos T. Kabo‑Bah,Prince Antwi‑Agyei 대한공간정보학회 2023 Spatial Information Research Vol.31 No.5
The increasing popularity of drones has led to their adoption by electric utility companies to monitor intrusive vegetation near powerlines. The study proposes a deep learning-based detection framework compatible with drones for monitoring vegetation encroachment near powerlines which estimates vegetation health and detects powerlines. Aerial image pairs from a drone camera and a commercial-grade multispectral sensor were captured and processed into training and validation datasets which were used to train a Generative Adversarial Network (Pix2Pix model) and a Convolutional Neural Network (YoLov5 model). The Pix2Pix model generated satisfactory synthetic image translations from coloured images to Look-Up Table (LUT) maps whiles the YoLov5 obtained good performance for detecting powerlines in aerial images with precision, recall, mean Average Precision (mAP) @0.5, and mAP0.5:0.95 values are 0.82, 0.76, 0.79 and 0.56 respectively. The proposed vegetation detection framework was able to detect locations of powerlines and generate NDVI estimates represented as LUT maps directly from RGB images captured from aerial images which could serve as a preliminary and affordable alternative to relatively expensive multispectral sensors which are not readily available in developing countries for monitoring and managing the presence and health of trees and dense vegetation within powerline corridors.
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