1. ™ CuDDI Copper Digital Detection Imaging

2. Reasons for Development One of the last… Remove BIAS from Operator to Operator Digital Recording of Images LIMS Integration Standardization on Light Source Magnification of Sample Strip Strip Size Determination Current Chart – NOT REAL Automatic Measurement A better rating…

3. What Color Is it?

4. Design *Patent Pending

5. Vision Algorithm Define Strip Area – Automatically Inspection Area Determination 8x8 Pixel scan, ~96,000 points total Accumulate Results Calculate Percentage of each Rating Pass values through Support Vector Machine and Algorithm for Categorizing Ability to “Train” and Adopt Model

6. Step 1) Locate Strip and verify dimensions: Must be > Nominal – (Tolerance * 2) e.g. Height = 75mm +/- 5mm, so minimum height must be greater than 65mm. *account for angles Step 2) Location results are used to define the color inspection region. The region is always slightly smaller (5-20 pixels) than the actual strip, to avoid any unwanted shadows and/or round edges. Black rectangle represents the inspection region.

7. Step 3) Segment entire search region into small sections (8 by 8 pixels each), and run each sample through the Classifier. About 46,000 total samples per strip. Results are accumulated. Classifier Determines which rating the sample belongs to. Extract Color from Sample Totalize results

8. Step 4) Calculate percentage of each and determine the most corrosive and the most dominant result using both sides of the strip, and provide analytics: Highest Ranking Most Corrosive Ranking A breakdown (in percentages) of each side of the strip

9. RankHueSaturationIntensityImage 1a29.5 Compare 242.3 Compare 225.7 IgnoreHDR Dark 1b42.8 Compare 252.4 Compare 231.5 IgnoreHDR Dark 2a9.7 Compare 172.8 Compare 141.9 IgnoreHDR Med. 2b9.6 Compare 150.9 Compare 191.5 IgnoreHDR Med. … 4a17.8 Ignore 51.7 Ignore 113.0 CompareHDR Med. 4b18.1 Ignore 76.5 Ignore 61.5 CompareHDR Med. 4c187. 1 Ignore 41.2 Ignore 41.1 CompareHDR Med. The Classifier Given a set of trained models, color samples are compared against Hue, Saturation, and Intensity. Allows multiple samples to be trained. A portable Color Model Library (saved in xml), allows for different color libraries to be used. Result = 1a Saturation 255 0 0 1a Hue 360 1b 2a 2b Color Model Library

12. Data Output PDF Reports Email LIMS Output Raw Images Log to Network Share USB Print

13. Manual1b 4c2c CuDDI1a1b4b3a Dim.13.11 x 73.70 mm 12.76 x 73.24 mm 12.64 x 76.95 mm 11.25 x 71.61 mm 11.78 x 74.96 mm Data0 – 2% 1a - 89% 1b – 7% 2e – 2% 1a - 65% 1b – 26% 2e – 7% 3b – 1% 4a – 2% 4b – 93% 0 – 1% 2a – 10% 2c – 20% 2e – 23% 3a – 34% 4a – 9% 2a – 1% 2b – 6% 2c – 31% 3b – 37% 4a – 23% NotesOver-Rated4a – 4bCommonMissed Green Data

14. Sample ID:Manual CUDDI 11B 23A2B 32A 2A (Highest) 3B (Most Corrosive) 41A 52E 2E (Highest) 4A (Most Corrosive) 64C 72B4A 81B 1B (Highest) 2A (Most Corrosive) 92C 104A 113B 124C4A 132A 142C2C (Highest) 4A (Most Corrosive)

15. Manual RatingCuDDI Method Human EyeDigital CMOS Camera BIASEDRemoves BIAS Variable LightingFixed Standardized Lighting Decision MakingFixed Algorithm* No Digital RecordLogs Results with Images Flat Viewing Tube or HandsInnovative Holder for Strip Differences

16. Lighting Fingerprints Dimensions Proper Polishing Holes in D1838 Water Spots – LPG Samples 2a to 3a Logic Handling 2c to 3b Logic Handling Critical Points

17. Round Robin D130 D1838 D4048 EN & IP Methods Demo Units Available ANNEX in Current Methods? Alternate Procedure? … lighting only? Feedback from Committee Groups Request Plans & Future

18. ™ CuDDI by: Juan J Ayala +1 (312) 622-7520 juan@ayalytical.com THANK YOU! QUESTIONS?