Trust Measurement Presentation_Part 3
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The document discusses the application of random number generation using linear feedback shift registers in digital circuitry, specifically for measuring trust variables. It explains the methodology for generating unique random numbers, the implementation in Excel, and how data visualization can identify trends in trust levels. Finally, it evaluates the simulation results and discusses error calculations over an extended period.
![DIGITAL CIRCUITRY AND SYSTEM WITH RANDOM
NUMBER GENERATOR:
Weightings on the Variables of Trust
(Trust Measurement_Part 3)
By Gan Chun Chet (Ir., PEng (M‘sia))
23th October, 2023
MSc Operations Management 1997
[Manchester School Management]
University of Manchester Institute of Science and Technology (UMIST),
United Kingdom.
BEng (Hons) Mechanical Engineering 1996
[Simon Building]
University of Manchester, United Kingdom](https://image.slidesharecdn.com/trustmeasurementpresentationpart3-231023100355-240452b7/85/Trust-Measurement-Presentation_Part-3-1-320.jpg)
Trust Measurement Presentation_Part 3
- 1. DIGITAL CIRCUITRY AND SYSTEM WITH RANDOM NUMBER GENERATOR: Weightings on the Variables of Trust (Trust Measurement_Part 3) By Gan Chun Chet (Ir., PEng (M‘sia)) 23th October, 2023 MSc Operations Management 1997 [Manchester School Management] University of Manchester Institute of Science and Technology (UMIST), United Kingdom. BEng (Hons) Mechanical Engineering 1996 [Simon Building] University of Manchester, United Kingdom
- 2. The Initial Part Boolean Algebra Demorgan Theorem Derive a Delay “D” Flip Flop
- 3. Boolean Algebra (1) : AND and OR GATE
- 4. Boolean Algebra (2) : NOT GATE
- 5. Boolean Algebra (3) : NAND (NOT AND) GATE
- 6. Boolean Algebra (4) : NOR (NOT OR) GATE
- 7. We could invert other composite functions like,
- 8. In addition…
- 13. How to Derive a Delay “D” Flip Flop : SR-FF (NAND Implementation) (1)
- 14. How to Derive a Delay “D” Flip Flop : SR-FF (NAND Implementation) (2)
- 15. How to Derive a Delay “D” Flip Flop : SR-FF (NAND Implementation) (3)
- 16. How to Derive a Delay “D” Flip Flop : Master_Slave SR-FF (1)
- 17. How to Derive a Delay “D” Flip Flop : Master_Slave SR-FF (2)
- 18. How to Derive a Delay “D” Flip Flop : Master_Slave JK-FF
- 19. How to Derive a Delay “D” Flip Flop : Delay FF (D-Type) (1)
- 20. How to Derive a Delay “D” Flip Flop : Delay FF (D-Type) (2)
- 21. How to Derive a Delay “D” Flip Flop : Toggle FF (1)
- 22. How to Derive a Delay “D” Flip Flop : Toggle FF (2)
- 23. Implementing a Linear Feedback Shift Register Random Number Generation : 5, 6, 7 & 9 Bit
- 24. Linear Feedback Shift Register (no. of bits, length and tap positions) Item No. of bits Length of loop Tap positions 1 5 31 1 & 4 2 6 63 0 & 5 3 7 127 0 & 6 4 9 511 3 & 8
- 25. The Basic Way to Generate Random Numbers (1) This is a “5-bit Linear Feedback Shift Register” - All numbers generated are unique, and the numbers never repeat
- 26. The Basic Way to Generate Random Numbers (2) This is a “6-bit Linear Feedback Shift Register” - All numbers generated are unique, and the numbers never repeat
- 27. The Basic Way to Generate Random Numbers (3) This is a “7-bit Linear Feedback Shift Register” - All numbers generated are unique, and the numbers never repeat
- 28. The Basic Way to Generate Random Numbers (4) This is a “9-bit Linear Feedback Shift Register” - All numbers generated are unique, and the numbers never repeat
- 29. The Application of RANDOM Number Generation in EXCEL - Weightings on the Variables of Trust Trust Measurement_Part 3 : The Details (based on a simulation).
- 30. The Application of RANDOM Number Generation in EXCEL - Weightings on the Variables of Trust • Ideally, RANDOM Number are generated in EXCEL by the function =RAND() • As the previous slides shows the hardware of generating unique Randomized Numbers (in detail), the subsequent slides shows the application - equation(s) aspect of weighting on the Variables of Trust to obtain a plausible trendline that models the real world (observations – based on random data generation, currently) • What are the future trends to be establish/govern, that could prevent similar occurrence in the past, to ensure foreseeable gains/ and phase-in/depict systemic risks?
- 31. Individually Weightings on the Variables of Trust GENERATED FROM RANDOM NUMBERS =RAND() IN EXCEL WEIGHTINGS ON THE FACTORS TOTAL TO 1.0 SIMULATION RESULT
- 32. Trust Measurement Level – Performance Measurement based on the Components of Trust SIMULATION RESULT
- 33. Trust Measurement Level – Performance Measurement based on the Components of Trust SIMULATION RESULT
- 34. Trust Measurement Level – Performance Measurement based on the Components of Trust SIMULATION RESULT
- 35. The Analogy of “Profitable Trending Transactions” • The analogy of “profitable trending transactions” is similar to the idea of comparing past occurrences with the current situation(s), and to forecasting (or predict) future value figuratively. • Random Number Generation is utilized here to show that the number generated is uniquely generated and when sorting the complete number range - will be incrementally a linear trendline (i.e. lawful within an organized system). It is able to depict (perceptive) that this nature model, in practice, will represent the real world we live in today.
- 36. Example in Excel File_1… SIMULATION RESULT
- 37. Categorical of Trust Level by Chance in Four Quadrants_By Charting (sample)
- 38. Example of Calculation (1) SIMULATION RESULT
- 39. Example in Excel File_2… SIMULATION RESULT
- 40. Example of Calculation (2) SIMULATION RESULT
- 41. Example in Excel File_3… SIMULATION RESULT
- 42. Example of Calculation (3) SIMULATION RESULT AVERAGING THOSE ITEMS THAT ARE “MORE THAN AVERAGE” CURRENT PERFORMANCE
- 43. Example in Excel File_4… SIMULATION RESULT
- 44. Example in Excel File_5… SIMULATION RESULT
- 45. Discussions on the Methods • Probability of a Count of Chance in The Respective Quadrants, most probable in Category 2 -averaging to be between 50 to 80, but averaging to about above 54% • Categorization of Trust Level by Limits (considering the data within the governing limits, “between lower and upper limit” – mean area), average is about 53% to 62% • Moving Average within a defined window of view, shows tremendous improvement, about 64% to 69%
- 46. _Final Error Calculations • Small error detected within the method, as stipulated, during modelling the random numbers, raw data, generated from digital circuitry – preliminary/fundamental derivation depicts, as shown. • Low level of error, roughly 1-2% error is acceptable, over a period of time (about 20 years period cycle, in this case - 20 delivery items – about 4 weeks or 1 month; 20 years on average for 12 data plot within each point).
- 47. Thank you for your attention