Methane Gas Emission Detection using Deep Learning and Hyperspectral Imagery

2021 ◽  
Author(s):  
Richard Gu
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imagery ◽  
Gas Emission ◽  
Methane Gas ◽  
Emission Detection

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

Ground food waste discharge to sewer enhances methane gas emission: A lab-scale investigation

2020 ◽  
Vol 174 ◽  
pp. 115616 ◽  
Author(s):  
Feixiang Zan ◽  
Ji Dai ◽  
Feng Jiang ◽  
George A. Ekama ◽  
Guanghao Chen
Keyword(s):  
Food Waste ◽  
Gas Emission ◽  
Methane Gas ◽  
Waste Discharge

scholarly journals Effect of standing water levels on methane gas emission and yield performance of transplanted Aman rice (Oryza sativa L. cv. BRRI dhan51)

2020 ◽  
Vol 5 (3) ◽  
pp. 299-305
Author(s):  
Biddut Kumar Paul ◽  
Muhammad Aslam Ali ◽  
Kallyan Kanty Saha ◽  
Md. Badiuzzaman Khan
Keyword(s):  
Water Level ◽  
Paddy Soil ◽  
Oryza Sativa L ◽  
Static Condition ◽  
Water Levels ◽  
Ch4 Emission ◽  
Gas Emission ◽  
Standing Water ◽  
Methane Gas ◽  
Emission Yield

An experiment was conducted to investigate the effect of standing water levels on methane gas emission and yield of transplanted Aman rice (cv. BRRI dhan51) during July to December 2015 at medium low-lying area of Mohangonj upazila under Netrokona district. The experiment comprised five standing water levels on surface paddy soil viz., 5 cm, 10 cm, 15 cm, 20 cm and 25 cm. Methane (CH4) gas emission, yield components and yield of transplanted Aman rice were significantly affected by standing water levels on surface paddy soil. CH4 emission was gradually increased with rising standing water levels and remained static condition at 20-25 cm water level.  The highest CH4 emission was observed at 20 cm standing water level and the lowest CH4 emission was recorded at 5 cm water level. The highest CH4   peak recorded at 85 days after transplanting (DAT). The highest CH4 flux (36.59 mg/m2/h) was observed in treatment 20 cm water level whereas the lowest CH4flux (21.17 mg/m2/h) was observed in 5 cm water level.  Finally, the CH4 emission dropped at 108 DAT. On and average, the CH4 emission rate during rice cultivation followed 20 cm > 25 cm> 15 cm > 10 cm > 5 cm water level. Soil Eh gradually decreased with progress of time and plant growth and at 85 DAT highly reduced condition developed in all treatments. The maximum reduced condition was observed (-238.67 mV) in treatment 20 cm water level and minimum one (- 214.667 mV) was found in 5 cm water level. The highest grain (5260 kg ha-1) and straw (6725.0 kg ha-1) yields were obtained at 10 cm standing water level while the lowest grain (4191.6 kg ha-1) and straw (5050.0 kg ha-1) yields were recorded in 5 cm and 15 cm water level, respectively. It may be concluded that 10 cm standing water level is beneficial for transplant Aman rice (cv. BRRI dhan51) cultivation in low lying area in respect of grain yield and environmental issues.

scholarly journals Enhancement of Methane Generation from the Sewage by the Addition of Fish Waste ​

2021 ◽  
Author(s):  
ANAND KUMAR VARMA S ◽  
SUVALAKSHMI A
Keyword(s):  
Anaerobic Digestion ◽  
Correlation Coefficient ◽  
Strong Correlation ◽  
Fishing Industry ◽  
Gas Emission ◽  
Fish Waste ◽  
Methane Generation ◽  
Methane Gas ◽  
Experimental Part ◽  
Predicted Values

Abstract Purpose-Fish waste affects the area surrounding and can change a broad oceanfront zone at unlike environment levels by its effluent. In order to reduce the environmental impacts of improper disposal of both fish waste and sewage in the vicinity of the fishing industry in the coastal zone, an attempt has been made to convert the mixture of fish waste and sewage into energy.Findings-The by-product wastes mainly head, bones, skin, gut, tail and sometimes full whole fish waste. The main composition of fish waste is Protein 65%, fat 18% and minerals 17%. So fish waste having a large amount of biodegradable matter when compared to fresh sewage and also by adding higher amount of fish waste we can able to generate higher methane emissions. Methodology-The ultimate aim of the project is to find the optimum methane gas emission from fish waste mixed with fresh sewage using the anaerobic digestion for the experimental part. And for the stoichiometry combustion equation for the theoretical part.Value-Using stoichiometries equations methane gas generated has been calculated from the mixture of sewage and fish waste. Using the Orsat’s apparatus methane gas generated hasbeen measured from the mixture of sewage and fish waste. The correlation coefficient - R2 value is 0.9906 indicating a strong correlation for the predicted values and the measured values.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery

2021 ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background: Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods: We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results: The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion: IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

scholarly journals Analysis of Reduce Potential Methane Gas Emission by Methanotrofic Bacteria from Rice Field in Gowa

2016 ◽  
Vol 10 (7) ◽  
pp. 183
Author(s):  
Maymuna Nontji ◽  
Baharuddin Patenjengi ◽  
Burhanuddin Rasyid ◽  
Pirman Pirman
Keyword(s):  
Energy Source ◽  
Incubation Period ◽  
Rice Field ◽  
Rice Fields ◽  
Gas Emission ◽  
Gas Emissions ◽  
Gas Concentration ◽  
Methane Gas ◽  
Rice Plants ◽  
Needed Information

<p>The Increase of temperature in atmosphere caused by increasing concentrations of methane in rice field affects to metabolism of rice plants, it can reduce productivity of rice. <em>Methanotrofic</em> bacteria are one of the organisms that can reduce methane gas emissions, because the bacteria use methane as an energy source. Based on the fact, needed information about the reduce potential of methane gas by the bacteria. The aim of this study was to analyze reduce potential of <em>methanotrofic</em> bacteria have been previously isolated that from rice fields in Gowa. The Analysis was done by measuring concentration of methane gas using chromatography gas techniques. Observations of remaining gas concentration were done four times during 13-days incubation period. All isolates were able to reduce methane with varies potential. The highest reduction shown by isolates GMP 2 with the reduction about 88%. The Lowest reduction shown by GMV 3 with the reduction about 51.9%.</p><p><strong>Keywords</strong>: emission, methane, <em>methanotrofic</em></p>

scholarly journals Hard X-Ray Emission Detection Using Deep Learning Analysis of the Radiated UHF Electromagnetic Signal From a Plasma Focus Discharge

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 74899-74908 ◽  
Author(s):  
Gonzalo Avaria ◽  
Jorge Ardila-Rey ◽  
Sergio Davis ◽  
Luis Orellana ◽  
Benjamin Cevallos ◽  
...  
Keyword(s):  
Deep Learning ◽  
Plasma Focus ◽  
Electromagnetic Signal ◽  
X Ray ◽  
Emission Detection ◽  
Learning Analysis ◽  
Plasma Focus Discharge
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