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# Simulation of Natural Gas leak detection system using AI

This powerpoint presentation talks about natural gas leak detection system using AI. The AI involve here includes fuzzy logic, genetic algorithm and neural networks

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### Simulation of Natural Gas leak detection system using AI

1. 1. Natural Gas Leak Detection System using AI: Fuzzy Logic, Neural Network,GA By: Edgar Caburatan Carrillo II Master of Science in Mechanical Engineering De La Salle University Manila, Philippines
2. 2. Natural Gas Industry
3. 3. Natural Gas Production
4. 4. Natural Gas Pipeline System
5. 5. Gulf of Mexico 2010 BP Gulf of Mexico
6. 6. Oil companies
7. 7. Oil Companies
8. 8. Oil Companies
9. 9. 1. Fuzzy Logic Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. Compared to traditional binary sets, fuzzy logic variables may have a truth value that ranges in degree between 0 and 1.
10. 10. 2. Neural Networks In computer science and related fields, artificial neural networks are computational models inspired by an animal's central nervous systems which is capable of machine learning as well as pattern recognition.
11. 11. 3. Genetic Algorithm In the computer science field of artificial intelligence, genetic algorithm is a search heuristic that mimics the process of natural selection. This heuristic is routinely used to generate useful solutions to optimization and search problems
12. 12. When GA is applied to a problem: Following must be addressed: 1. How to represent the individual? ( Genetic Algorithm Structure) 2. What is the fitness function? ( to measure the performance of each individual) 3.What is the criterion for selection? ( best or fittest individuals in the population) 4. How to end the search? ( loop termination condition)
13. 13. Genetic Algorithm Structure
14. 14. EC Clone Genetic Algorithm
15. 15. Conclusion In our Discussion, we are able to learn: 1.Natural gas pipeline system 2. Fuzzy logic 3. Neural Networks 4. Genetic Algorithm 5. GAApplication
16. 16. 7. References [1] http://en.wikipedia.org/wiki/Imperial_College_London [2]http://www.nytimes.com/imagepages/2012/10/25/world/asia/25iht- sreducemerging25-graphic.html?ref=asia [3] http://www3.imperial.ac.uk/aboutimperial [4] http://www.ee.ic.ac.uk/gelenbe/ [5] http://en.wikipedia.org/wiki/Random_neural_network [6] https://www.academia.edu/3875250/Analysis_of_single_and_networked_auctions [7] http://rspa.royalsocietypublishing.org/content/464/2096/2219.full [8] E. Gelenbe, Random neural networks with negative and positive signals and product form solution, Neural Computation, vol. 1, no. 4, pp. 502–511, 1989. [9] E. Gelenbe, Stability of the random neural network model, Neural Computation, vol. 2, no. 2, pp. 239–247, 1990. E. Gelenbe, A. Stafylopatis, and A. Likas, Associative memory operation of the random network model, in Proc. Int. Conf. Artificial Neural Networks, Helsinki, pp. 307–312, 1991. [10] E. Gelenbe, F. Batty, Minimum cost graph covering with the random neural network, Computer Science and Operations Research, O. Balci (ed.), New York, Pergamon, pp. 139–147, 1992.
17. 17. References [11] Gelenbe, Erol (Sep., 1993). "G-Networks with Triggered Customer Movement". Journal of Applied Probability 30 (3): 742–748. doi:10.2307/3214781. JSTOR 3214781. [12] Gelenbe, Erol; Fourneau, Jean-Michel (2002). "G-networks with resets". Performance Evaluation 49 (1/4): 179–191. doi:10.1016/S0166-5316(02)00127-X. [13] Harrison, Peter (2009). "Turning Back Time – What Impact on Performance?". The Computer Journal 53 (6): 860. doi:10.1093/comjnl/bxp021. [14] Gelenbe, Erol (1991). "Product-form queueing networks with negative and positive customers". Journal of Applied Probability 28 (3): 656–663. doi:10.2307/3214499. JSTOR 3214499. [15] Gelenbe, Erol (1993). "G-Networks with signals and batch removal". Probability in the Engineering and Informational Sciences 7: 353–342. [16] Artalejo, J.R. (Oct., 2000). "G-networks: A versatile approach for work removal in queueing networks". European Journal of Operational Research 126 (2): 233–249. doi:10.1016/S0377- 2217(99)00476-2. [17] Gelenbe, Erol; Mao, Zhi-Hong; Da Li, Yan (1999). "Function approximation with spiked random networks". IEEE Transactions on Neural Networks 10 (1): 3–9. [18] Harrison, P. G. Pitel, E. (1993). "Sojourn Times in Single-Server Queues with Negative Customers". Journal of Applied Probability 30 (4): 943–963. doi:10.2307/3214524. JSTOR 3214524. [19] Harrison, Peter G.. "Response times in G-nets". 13th International Symposium on Computer and Information Sciences (ISCIS 1998). pp. 9–16. ISBN 9051994052. [20] Lakshmi N. Chakrapani, Bilge E. S. Akgul, Suresh Cheemalavagu, Pinar Korkmaz, Krishna V. Palem and Balasubramanian Seshasayee. "Ultra Efficient Embedded SOC Architectures based on Probabilistic CMOS (PCMOS) Technology". Design Automation and Test in Europe Conference (DATE), 2006.

This powerpoint presentation talks about natural gas leak detection system using AI. The AI involve here includes fuzzy logic, genetic algorithm and neural networks

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