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Neural network journal by Engr. Edgar Carrillo II
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Development of underwater quality and natural gas leak detection system using fuzzy neuro approach image processing

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Developing an underwater quality and natural gas detection system for the future of the Philippine society.

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Development of underwater quality and natural gas leak detection system using fuzzy neuro approach image processing

  1. 1. Development of Underwater Quality and Natural Gas Leak Detection System using Fuzzy Neuro Approach Image Processing By: Edgar Caburatan Carrillo II Thesis Proposal for the degree of Master of Science in Mechanical Engineering De La Salle University Manila, Philippines
  2. 2. Natural Gas Pipeline System
  3. 3. 1. Introduction 1.1. Background of the Study  Worldwide Natural Gas Production  Natural Gas in the Philippines  Problem with Natural Gas leaking  Existing Technologies of Natural Gas  Proposed Solution
  4. 4. 1. Introduction 1.1. Background of the Study  Worldwide Natural Gas Production ¾ of World Energy consumption from Natural gas,liquid and coal by 2040 (USEIA, 2013)  Natural Gas in the Philippines Projects of Philippine government to transport natural through underground and underwater piping networks include: BATMAN 1, SU-MA (Sucat-Malaya), BATMAN 2, ET LOOP and BATCAVE (DOE, 2014)  Problem with Natural Gas leaking economic and environmental risks (TRB, 2004) Existing Technologies of Natural Gas Existing technology need to be improve either by having a leak detection technology that is cheap and accurate (Murvay & Silea, 2012).
  5. 5. Types of Cracks 1.Orifice Crack www.senninger.com 2.Line Crack
  6. 6. Types of Cracks 3.Stress Corrosion Cracking www.met-tech.com 4.Hydrogen Induced Cracking www.masteel.co.uk
  7. 7. Types of Cracks 5.Stress-Oriented Hydrogen Induced Cracking (SOHIC) www.corrosioncontrol.net 6.Laps pmpaspeakingofprecision.com
  8. 8. Types of Cracks 7.Hook Cracks www.china-weldnet.com 8. Fatigue Cracks www.azom.com
  9. 9. Types of Cracks 9. Narrow Axial External Corrosion (NAEC) nainamania.wordpress.com
  10. 10. Flow of Gases in leaks
  11. 11. Turbulent Flow
  12. 12. Laminar Flow
  13. 13. Molecular Flow
  14. 14. Molecular Flow
  15. 15. Molecular Flow
  16. 16. Kinetic Theory As analyzed by Albert Einstein in 1905, this experimental evidence for kinetic theory is generally seen as having confirmed the existence of atoms and molecules.
  17. 17. What is a digital Image? http://people.cs.clemson.edu/~dhouse/courses/405/notes/pixmaps-rgb.pdf
  18. 18. RGB Color Spacehttp://people.cs.clemson.edu/~dhouse/courses/405/notes/pixmaps-rgb.pdf
  19. 19. RGB Color Space
  20. 20. RGB Color Cube
  21. 21. Fuzzy Image Processinghttp://tizhoosh.uwaterloo.ca/Fuzzy_Image_Processing/theory.htm 1. Fuzzy Geometry 2.Measures of Fuzziness and Image Information 3. Fuzzy Inference System 4. Fuzzy Mathematical Morphology 5. Fuzzy Measure Theory 6. Fuzzy Grammars 7. Combined Appoach 8. Extension of Classical methods
  22. 22. Why Fuzzy image processing? 1. Fuzzy techniques are powerful tools for knowledge representation and processing. 2. Fuzzy techniques can manage the vagueness and ambiguity efficiently. In many image processing applications, we have to use expert knowledge to overcome the difficulties (e.g. object recognition, scene analysis). http://tizhoosh.uwaterloo.ca/Fuzzy_Image_Processing/why.htm
  23. 23. Fuzzy Image Processing
  24. 24. Kinds of Image Fuzzification http://tizhoosh.uwaterloo.ca/Fuzzy_Image_Processing/theory.htm Fuzzification- Process of transforming crisp values into grades of membership for linguistic terms of fuzzy sets.
  25. 25. Structure of Fuzzy Image Processing
  26. 26. Fuzzification process (coding of images)
  27. 27. History of Fuzzy Logic
  28. 28. 1.2. Statement of the Problem Existing technology  either expensive (Meng, Yuxing, Wuchang, & Juntao, 2011)  less accurate (Doorhy, 2011)
  29. 29. Proposed solution A natural gas leak detection system that is cheap and accurate by using Fuzzy Neuro Approach. Reasons behind:  Fuzzy neuro approach was used by many researchers in detection of water leaks and the like.
  30. 30. 1.3. Objective of the Study The main purpose of this study is to develop a water quality and natural underwater gas leak detection system using fuzzy-neuro image processing. This study specifically aims: 1.To develop an aquarium prototype for an underwater water quality and gas leak detection experimental set-up, 2. To determine the quality of water using fuzzy logic algorithm, 3. To develop an image processing algorithm to detect water bubbles on both clean and average environment, 4. To develop a neural network algorithm to detect gas leaks in the underwater pipeline system using bubble formation, 5. To verify the accuracy, reliability and robustness of the proposed algorithm in determining gas leaks underwater.
  31. 31. 1.4. Significance of the Study The creation of study will trigger awareness in the stakeholders in the area and create a worldwide impact. These stakeholders include the companies, people occupying in the area, government, investors, and experts. .
  32. 32. 1.5. Scope of the Study Scope:  Natural Gas  Lab scale  PC based model  Contaminant addition
  33. 33. 2.Review of Related Literature 2.1. Properties of Natural Gas
  34. 34. 2.Review of Related Literature 2.2. Leak Detection Method known by Science(Murvay & Silea, 2012)
  35. 35. 2.Review of Related Literature 2.3. Non-conventional Algorithm 2.3.1. Genetic Algorithm(Sivanandan, & Deppa , 2008). 2.3.2. Artificial Neural Network-94.2% (Carvalho et al., 2006) 2.3.3. Fuzzy Logic-90% (Da Silva et al., 2005)
  36. 36. 2.Review of Related Literature 2.4. Image Processing Leak detection in water ( Ekuakille et al., 2014)
  37. 37. 3. Framework 3.1. Conceptual Framework
  38. 38. Clearer View of Prototype Set-up
  39. 39. Fuzzy Logic Structure Determination of Underwater Quality
  40. 40. Detection of Leakage 1. Bubble Formation 2. Pressure Decrease Test 3. Pressure Increase Test 4. Pressure Difference Test http://www.leakdetection-technology.com/science/leak-detection-and-measuring-methods
  41. 41. Detection of Bubbles 1. Classification 2. Feature Extraction 3. Pattern Recognition Techniques can be used in image processing: 1. Pixelation 2. Neural Networks 3.Linear Filtering 4. Principal Component Analysis 5. Hidden Markov Models 6. Anisotropic Diffusion 7. Partial Diffential Equations 8. Self-organizing Maps 9. Wavelets
  42. 42. Pixelation http://en.wikipedia.org/wiki/Pixelation#/media/File:Dithering_example_undithered.png 1. Object Recognition 2. Motion Recognition http://thesisconcepts.com/digital-image-processing
  43. 43. Object Recognition Appearance-based method 1. Edge Matching 2. Divide and Conquer Search 3. Greyscale Matching 4. Gradient Matching 5. Histogram of receptive field responses 6. Large model bases http://en.wikipedia.org/wiki/Outline_of_object_recognition Feature-based method 1. Interpretation trees 2. Hypothesize and test 3. Pose consistency 4. Pose Clustering 5. Invariance 6. Geometric hashing 7. Scale-invariant feature Transform (SIFT) 8. Speed Up Robust Features (SURF) http://en.wikipedia.org/wiki/Outline_of_object_recognition
  44. 44. Motion Detection Motion detection is the process of detecting a change in position of an object relative to its surroundings or the change in the surroundings relative to an object. Motion can be detected by: 1. Infrared (Passive and active sensors) 2. Optics (video and camera systems) 3. Radio Frequency Energy (radar, microwave and tomographic motion detection) 4. Sound (microphones and acoustic sensors) 5. Vibration (triboelectric, seismic, and inertia-switch sensors) 6. Magnetism (magnetic sensors and magnetometers) http://en.wikipedia.org/wiki/Motion_detection
  45. 45. Optical Flow Optical flow or optic flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer (an eye or a camera) and the scene
  46. 46. Neural Network Structure
  47. 47. 4. Methodology
  48. 48. 5. Summary Answering Specific Objectives 1. To develop an aquarium prototype for an underwater water quality and gas leak detection experimental set-up, 2. To determine the quality of water using fuzzy logic algorithm, 3. To develop an image processing algorithm to detect water bubbles on both clean and average environment, 4. To develop a neural network algorithm to detect gas leaks in the underwater pipeline system using bubble formation, 5. To verify the accuracy, reliability and robustness of the proposed algorithm in determining gas leaks underwater.
  49. 49. 1. To develop an aquarium prototype for an underwater water quality and gas leak detection experimental set-up
  50. 50. 2. To determine the quality of water using fuzzy logic algorithm Quality of Water Expected recognition rate Actual recognition rate Clean 80% More than 80% Dirty 80% More than 80%
  51. 51. 3. To develop an image processing algorithm to detect water bubbles on both clean and average environment Quality of Water Previous recognition rate Actual recognition rate Clean 80% More than 80% Dirty 80% More than 80%
  52. 52. 4. To develop a neural network algorithm to detect gas leaks in the underwater pipeline system using bubble formation Quality of Water Previous recognition rate Actual recognition rate Clean 90% More than 90% Dirty 90% More than 90%
  53. 53. 5. To verify the accuracy, reliability and robustness of the proposed algorithm in determining gas leaks underwater. Proposed Algorithm Actual recognition rate Fuzzy Logic Water Detector More than 80% Neural Network gas leak detector More than 90% Image Detector Created More than 90%
  54. 54. 5. Appendix A: Gantt Chart
  55. 55. 5. Appendix B: Costing
  56. 56. Thank You For Listening! The Researcher is now ready to answer questions.
  • LIHUAWANG1

    Feb. 27, 2016

Developing an underwater quality and natural gas detection system for the future of the Philippine society.

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