David Prendergast - Innovative Physics - From AI to Fukushima - Isle of Wight Cafe Sci - Jan 2019
•
1 like•299 views
David Prendergast from Shanklin-based Innovative Physics presented the talk 'From AI to Fukushima' to the Isle of Wight Cafe Scientifique on 21 Jan 2019.
Recommended
More Related Content
What's hot
What's hot (9)
Similar to David Prendergast - Innovative Physics - From AI to Fukushima - Isle of Wight Cafe Sci - Jan 2019
Similar to David Prendergast - Innovative Physics - From AI to Fukushima - Isle of Wight Cafe Sci - Jan 2019 (20)
More from onthewight
More from onthewight (20)
Recently uploaded
Recently uploaded (20)
David Prendergast - Innovative Physics - From AI to Fukushima - Isle of Wight Cafe Sci - Jan 2019
- 1. Jan 2019 Café Scientifique AI to Fukushima
- 2. Quick Summary •Founded in 2008 to commercialise IP portfolio of radiation detection technologies •Privately owned and funded •UK grown company; trading office on the Isle of Wight •Representatives in BeNeLux, China, Japan, USA and Singapore •Markets including, Civil Nuclear, Homeland Security and Defence, Environmental Protection, CBRNe and Medical 2
- 3. IPL Capabilities – Devices & Systems Sensors & hardware Software and Services Cloud Based Support & Sofware as a service Sensors Device Fab, ASICs, PCB, Material OEM Support FPGA, Firmware, Analogue, Digital circuit design High-level sofware, Reconstructon, AI (machine and deep learning) AWS API REST,JSON Support and high levels processing
- 5. 5
- 8. 8 Why?
- 10. What are they cleaning up? • Radiaton Roadside Fukushima City 10
- 12. 12 Gamma Radia
- 13. HSL Spectral- Feature Enhancements HSL core spec…. - Energy Binning - Spectral Analysis - Multi-isotope identification: 50keV to 3MeV - User defined colour coding for ease of identification - Intuitive User Interface 13
- 14. Gamma camera…
- 15. Pinhole 15
- 17. Computer Vision v AI Feature extraction Data Acquisition Preprocessing Segmentation Good/ bad Classification computer vision deep learning patern recogniton
- 18. 18 Classic Statstcal Patern Recogniton Dimensional Reducton 0 200 400 600 800 1000 1200 0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 channel probability Ra226 plus poisson noise -0.08 -0.06 -0.04 -0.02 0 -0.01 0 0.01 0.02 0.03 -0.02 -0.015 -0.01 -0.005 0 0.005 0.01 PCA ba133 and ra226 ba133 ra226
- 19. AI/Computer vision support Artfcial Intelligence Machine Learning Deep Learning Computer Vision
- 20. 20
- 21. Deep-learning – feature extraction
- 24. HSL Spectral- Software ** Prototype design - Individual isotopes can be assigned a colour. Colours can be assigned automatically or via menu selection. - Example shows Cs137 as grey and Co60 as blue Cs137 Co60
- 25. HSL Spectral- Software 25 ** Prototype design - Hotspots can be selected on from an isotope list which is filled in automatically. - Example shows a Cs137 Isotopic ID selected
- 27. Problem Definition 27 To spot a radioactve signature on a person or luggage at standof distances.
- 28. Difficulties: Inverse square law 28 The intensity of the radiaton observer varies with the inverse square of the distance. 33kBq Cs137 source results in approximately 0.1nSv/h at 5m. Such low levels of radiaton are extremely difficult to observe.
- 29. 3d Silhouette (live video tracking example) 29
- 30. Tracking the person 30 This allows use to focus the image reconstructon algorithms on the areas where the moving objects are and ignore the background
- 32. Lung cancer
- 33. Some details: Convolutional neural networks. 33
- 35. Nodule finding – typical results
- 36. Lung cancer detection (Animated image)
- 37. Prostate cancer
- 40. AI 2d segmentation (deep UNET) Seismic data
- 41. Thank You Please visit our website www.inphys.com Please contact us for more information Sam Teale sam.teale@inphys.com +44 7421 302854
- 42. AI Backup Slides
- 43. Some details: GAN’s 43 • Fraudster detectve approach in competton • The fraudster create fake versions of the hit data • Discriminator learns how to recognise fakes • Our main aim is to provide robust recogniton results.
- 44. Deep learning to generate spectrums
- 45. GAN’s interesting examples • Trained on celebrity database. • Changing the input parameters can generate realistc face images. 45
- 46. Training the Gan’s 46 • Training GANs
- 47. Lyrebird • Fake faces in real tme • Fake voices. 47
- 49. Pinhole Pinhole system with source at distance B, focal length L, pinhole diameter d, and detector position resolution RD. 49
- 50. Pinhole • Pinhole gamma cameras operate in a very similar fashion to the Anger camera except the photographic paper is replaced with a sensor to read and record the data. • The advantage of a pinhole gamma camera is the hole only lets through a small amount of radiation, they are usually able to reach very high dose rate levels • The disadvantage is that because of the one hole, they do not have a wide field of view , taking a long time to do a capture, also they aren’t very sensitive. • A typical pinhole gamma camera will use either a scintillator or a semiconductor detection system 50
- 51. Coded Aperture • Coded aperture is essentially multiple pinholes. • It works similar to a pinhole camera except has multiple holes, there multiple images are produced. • These images are then run through an algorithm that matched the specific coded aperture used which will be the final reconstructed images. 51
- 53. Coded Aperture The pattern of merged pinholes is chosen to facilitate reconstruction. 53
- 54. Coded Aperture MURA HURA Modified Uniformly Redundant Array Hexagonal Uniformly Redundant Array 54
- 55. Coded Aperture • When the reconstructed image is produced, the result will be a ring of possible locations the count could have come from. • This is worked out from the way the sensor has detected the gamma ray. • As the sensor receives more and more counts, the maximum likelihood of the original source is determined. 55
- 56. Clean up
- 57. 57 Road Hotspot Locator Toshiba Unit
- 58. 58 The Clean Up • Top soil removal • The karaoke bar
- 59. 59 The Clean Up • Top soil removal • Before and afer
- 62. IPL Capabilities – Devices & Systems Sensors & hardware Software and Services Cloud Based Support & Sofware as a service Sensors Device Fab, ASICs, PCB, Material OEM Support FPGA, Firmware, Analogue, Digital circuit design High-level sofware, Reconstructon, AI (machine and deep learning) AWS API REST,JSON Support and high levels processing
- 63. Areas where IPL can add value Cloud based customer suppport AI Support Modular Read Chain IoT/ Network Support Reconstruction Algorithms ComputerVision
- 64. Reconstruction Algorithms Reconstruction Algorithms • Classic laminography • Computed laminography • Multiple images captures • Addition with different shifts allows different focal planes.
- 66. Computed tomography • Filtered back- projection • MLEM maximum likelihood expectation maximum
- 67. AI/Computer vision support Artfcial Intelligence Machine Learning Deep Learning Computer Vision AI Support ComputerVision
- 68. Computer Vision v AI Feature extraction Data Acquisition Preprocessing Segmentation Good/ bad Classification computer vision deep learning
- 69. Computer Vision/Machine learning • High throughput TDI inline detection • Noise reduction • Motion artifact removal • High value – low throughput • Continuous calibration/SNR estimation • Feature detection and extraction • Multi-spectral imaging • Combined optical and x-ray recognition algorithms. ComputerVision
- 70. Image calibration and artifact removal
- 71. AI Image Interpretation • Supervised and unsupervised deep-learning • 2D Inspection • Automatic fault finding • PCB inspection • BGA void detection • Intelligent template matching • Back scatter density estimation • 3D Inspection (3d convolutional neural nets) • Casting inspection (Magnesium castings) identification of voids and inclusions. • Weld inspection • Complex aero-structures • Counterfeit IC detection AI Support
- 72. AI – outlier detection • Can identify components which deviate from expectations • Doesn’t require hard-rules • Can be parameterized with a single parameter, representing multi- parametric difference i.e. a simple slider.
- 73. AI 2d segmentation (deep UNET) Seismic data
- 74. AI 3D Segmentation (deep RESNET) • 3d segmentation • MRI and CAT
- 75. AI CT 3d lung cancer Nodule finding – typical results
- 76. Lung cancer detection (Animated image)
- 78. Hardware added value Modular Read Chain • X-ray tube • Carbon nano-tube • Excellent MTBF • Less variation over lifetime of tube • Lightweight • Simplifies cooling solution • X-ray sensors • Digital x-ray sensors • Area and line-scan • High-speed • Excellent • Spatial resolution • MTF • DQE
- 79. IPL industrial inspection software framework OEM Support Framework OEM System Support OEM Manufacturing Support Cloud based support & software as a service
- 80. Factory based OEM support • Sensor calibration • Flat field • MTF • SNR • Intelligent monitoring • Constantly monitors system characteristics to provide system health estimate. • Includes trend analysis and failure prediction. • Can automatically schedule maintenance. Sensor Calibraton OEM Manufacturing Support Intelligent Monitoring
- 81. Real-time intelligent monitor cont… • Trained to understand tube v image SNR characteristics, using machine learning. • Reads KeVp and current from API. • Real-time assessment of SNR, using image analysis. • Trend analysis and comparison to peer machined in factory. • Using cloud services can provide a comparison service across entire portfolio.
- 82. Image Stitching
- 83. Image stitching
- 84. 360 deg imaging
- 85. Feature matching
- 88. Computer vision
- 89. Deep learning
- 91. Contamination mapping 1500 1000 Dose rate mapping room 26 x cm 500 0 600 400 200 y cm 0 -10 10 20 30 0
- 92. Contamination mapping… 5 10 15 5 10 15 20 25 30 35 40 45 50 Test rig feld of view 5 10 15 5 10 15 5 10 15 20 25 30 5 10 15 20 25 30
- 93. Calibration
- 94. Calibration routines… (Energy and Dose calibration)