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Batri, K.
- Comparative Analysis of Flame Image Features for Combustion Analysis
Abstract Views :152 |
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Authors
Affiliations
1 St. Joseph’s College of Engineering and Technology, Palai, Bharananganam Pravithanam Road, Choondacherry-686579, Kerala, IN
2 Department of Instrumentation, Annamalai University, Rukmani Lakshmipathy Road, Egmore, Chennai-600008, Tamil Nadu, IN
3 PSNA College of Engineering and Technology, Kothandaraman Nagar, National Highway 209, Dindigul – 624622, Tamil Nadu, IN
4 Department of Instrumentation, Annamalai University, Rukmani Lakshmipathy Road, Egmore, Chennai – 600008, Tamil Nadu, IN
1 St. Joseph’s College of Engineering and Technology, Palai, Bharananganam Pravithanam Road, Choondacherry-686579, Kerala, IN
2 Department of Instrumentation, Annamalai University, Rukmani Lakshmipathy Road, Egmore, Chennai-600008, Tamil Nadu, IN
3 PSNA College of Engineering and Technology, Kothandaraman Nagar, National Highway 209, Dindigul – 624622, Tamil Nadu, IN
4 Department of Instrumentation, Annamalai University, Rukmani Lakshmipathy Road, Egmore, Chennai – 600008, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 6 (2016), Pagination:Abstract
Background/ Objective: This article identifies the best feature of the flame video, captured with a camera with frequency response in visible spectrum, from which the flame temperature can be estimated. Methods/Statistical analysis: The flame videos at different air and fuel inlets with different boiler temperatures were recorded from a diesel fired boiler prototype. In the video frames, the flame region was localised by intensity based adaptive thresholding. The correlation between boiler temperature and measures of central tendency and dispersion of different colour channels of the video frames were investigated. Findings: Among the features of the flame video, Standard deviation of blue channel grey levels above 32.95, variance greater than 1293 and mean absolute deviation (MAD) above 30.38 could efficiently represent the region of optimum combustion air supply at which boiler temperature is maximum above 684 degree Celsius. Range of green channel grey levels, interquartile mean, variance and mean absolute deviation of blue channel grey levels are the video features exhibiting maximum correlation (ρ>-0.96) with boiler temperature. Applications/Improvements: The features of the flame video which are correlated with its temperature can be utilised to develop non-intrusive methods of temperature measurement. This will enable efficient control of combustion process.Keywords
Combustion, Flame Image Processing, Flame Temperature Measurement, Image Features, Video ProcessingFull Text
- Exploiting the Local Optima in Genetic Algorithm using Tabu Search
Abstract Views :546 |
PDF Views:0
Authors
Affiliations
1 Department of Information Technology, Lebanese French University, Erbil, Kurdistan Region, IQ
2 Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Kothandaraman Nagar, Dindigul – 624622, Tamil Nadu, IN
1 Department of Information Technology, Lebanese French University, Erbil, Kurdistan Region, IQ
2 Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Kothandaraman Nagar, Dindigul – 624622, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 12, No 1 (2019), Pagination: 1-13Abstract
Objectives: To explores the process of selecting retrieval schemes along with their weights, and fusion function for data fusion in information retrieval. Methods/Statistical Analysis: This has been carried out using the hybrid Genetic Algorithm. The fusion function, retrieval schemes and their weights lead to a tremendous combination. Finding an optimal solution from this great combination is entirely based on the exploration. Findings: We used, odd and even point crossover as an exploration tool. This exploration tool suffers a setback of slow convergence. The convergence rate can be improved by merging Tabu search, a best local search, with the genetic algorithm. This Tabu GA is used to select the retrieval schemes, weights and fusion function. The outcome of the experiments conducted over the test data sets namely: 1. adi, 2. cisi, and 3. cranlooks promising. We achieved 6.89% of improvement in performance, and the significance of the result is tested statistically. The convergence rate is also improved. Application/Improvements: We achieved 6.89% of improvement in performance, and the significance of the result is tested statistically. The convergence rate is also improved.Keywords
Genetic Algorithm, Information Retrieval, Odd and Even Point Crossover, Tabu GA, Tabu SearchFull Text
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