Lecture-2-System-Modelling-Simulation-and-Human-factors.pdf

System Modelling, Simulation and Human factors Dr. K D Sandaruwan Computer Science: Focus: Computer science primarily deals with the study of algorithms, data structures, programming languages, software development, and the theoretical foundations of computing. It covers a broad range of topics suc...

System Modelling, Simulation and Human factors Dr. K D Sandaruwan Computer Science: Focus: Computer science primarily deals with the study of algorithms, data structures, programming languages, software development, and the theoretical foundations of computing. It covers a broad range of topics such as artificial intelligence, machine learning, computer graphics, computer networks, databases, and more. Computational Science: Focus: Computational science, on the other hand, focuses on using computational methods, algorithms, and simulations to solve complex scientific and engineering problems. It involves applying computational techniques to model, simulate, and analyze systems and phenomena in various scientific domains such as physics, chemistry, biology, engineering, and more. Computational Science Computational science is an interdisciplinary field that uses computational methods and models to solve complex scientific problems. It involves the use of computer simulations, data analysis, and visualization techniques to understand and predict the behavior of natural and man-made systems. Applications of computational science can be found in a wide range of fields such as physics, chemistry, biology, engineering, and finance. System Modelling and Simulation Since the 1960s, numerical weather prediction has revolutionized forecasting. “Since then, forecasting has improved side by side with the evolution of computing technology, and advances in computing continue to drive better forecasting as weather researchers develop improved numerical models” A Weather Research and Forecasting (WRF) Model was released in 2000 System Modelling and Simulation Scientists at Los Alamos National Laboratory and the University of Minnesota wrote, “Mathematical modeling has impacted our understanding of HIV pathogenesis” 2000-2002. o Pathogenesis is the process by which a disease or disorder develops. System Modelling and Simulation System Modelling and Simulation University of Tennessee USA: Institute for Environmental Modeling is using computational ecology to study complex options for ecological management of the Everglades. computational technology, coupled with mathematics and ecology, will play an ever-increasing role in generating vital information society needs to make tough decisions about its surrounding System Modelling and Simulation System Modelling and Simulation In physics, computational modeling can be used to simulate the behavior of particles in a plasma, the motion of celestial bodies, or the properties of materials. In chemistry, computational modeling can be used to predict the properties of molecules and chemical reactions, or to design new materials with specific properties. In biology, computational modeling can be used to simulate the behavior of cells, predict protein structures, or study the dynamics of ecological systems. In engineering, computational modeling can be used to design and optimize mechanical systems, predict the behavior of structures under load, or simulate the performance of electrical circuits. In finance, computational modeling can be used to predict stock prices, model the risk of financial investments, or study the behavior of markets. Computational University of Tokyo, Japan, use high-performance computation with sophisticated models to simulate Modeling earthquakes, making quantitative predictions of infrastructural damages, response, and recovery to help minimize damage, death, and injury Computational Harvard School of Public Health, uses computational and statistical tools to better understand the genetic variation in Modeling complex human diseases, such as dyslipidemia, cancer, and type 2 diabetes (Liang) Computational Modeling Human-Computer Interaction Institute, Carnegie-Mellon University, develop computer models of student reasoning and learning to aid in the design of educational software and to guide teaching practices. System Modelling and Simulation Modeling Modeling Simulation Simulation Why modelling?  Why modelling? Impossible/expensive to do certain experiments Time consuming Cost effective Reduce Risk Known and believed can be different from the actual Can bring new insights The real system may not be accessible Complex behavior makes difficult to analyze Sometimes (even simple) models can easily capture complex behaviour What can we do with models?  Investigate future/past states Investigate various responses Investigate critical events What can we Predictions Comparison what is known, believed and do with actual Investigate what could be happening (Real models? system may not be observable) Example Model 1: A model of survival: Game of Life Take a square grid. Color some cells in black. These are live cells. Any live cell with fewer than two live neighbors dies, as if by under population. Any live cell with two or three live neighbors lives on to the next generation. Any live cell with more than three live neighbors dies, as if by overpopulation. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction. Evolution is determined by ????? Example Model 1: A model of survival: Game of Life Take a square grid. Color some cells in black. These are live cells. Any live cell with fewer than two live neighbors dies, as if by under population. Any live cell with two or three live neighbors lives on to the next generation. Any live cell with more than three live neighbors dies, as if by overpopulation. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction. Evolution is determined by its initial state https://conwaylife.com/ https://playgameoflife.com/ Example Model: A model of survival: Game of Life A simple systems which exhibit self-organisation and complex patterns. Types of behavior: Stable solutions Periodic solutions Chaotic solutions What about other variants? Example Model: A model of survival: Game of Life A simple systems which exhibit self-organisation and complex patterns. Types of behavior: Stable solutions Periodic solutions Chaotic solutions What about other variants? Different grids? Different rules? Deterministic vs Probabilistic What if transition rules are probabilistic. Example Model 3: Example Model 3: Steps of the Modeling Process Steps of the Modeling Process 1. Analyze the problem. Problem identification 2. Formulate a model. Forming an abstraction of the system Data Simplifying assumptions Variables and units Relationship amount variables and sub models Equations and functions 3. Solve the model Steps of the Modeling Process 4. Verify and interpret the solution Verification: determines if the solution works correctly Solving the problem correctly Validation: establishes whether the system satisfies the problem requirements. Solving the correct problem Repeat the cycle if needed Steps of the Modeling Process 5. Report on the model Analysis of the problem Must clearly explain the problem and the objectives of the study. Model design Diagrams Model solution Techniques for solving the problem and the solution. Results and conclusions 6. Maintain the model Steps of the Modeling Process Objectives/Problem type Analyze the problem. Transform the problem into a mathematical form Steps of the Modeling Process Analyze the problem. Position, Orientation RCin1, RCin2, RCin3, RCin4 Steps of the Modeling Process Formulate a model.  Steps of the Modeling Process Formulate a model. (T1, P1) (T2, P2) (T3, P3) Real world Scenario Existing Condition Prediction (T2, P’2) Simulated Scenario Steps of the Modeling Process Formulate a model. Steps of the Modeling Process Solve the model. Steps of the Modeling Process Solve the model. Steps of the Modeling Process Verify and interpret the solution  Steps of the Modeling Process Verify and interpret the solution 6 Speed (m/s) 5 Simulated Total speed 4 Observed Total speed 3 2 8 Speed (m/s) 1 7 Time (s) 6 Simulated Total 0 speed 0 500 1000 1500 2000 5 Observed Total 4 speed 3 2 1 0 Time (s) 0 500 1000 1500 2000 Steps of the Modeling Process Verify and interpret the solution Physically-Based Modeling and Simulation Physically-Based Modeling and Simulation Physically-Based Modeling Simulation and Visualization Challenges and Issues Solutions Physically-Based Modeling and Simulation 44 Physically-Based Modeling Attempts to map a natural phenomena to a computer simulation program Mathematical modeling: Concerns the description of natural phenomena by mathematical equations Numerical solution: Involves computing an efficient and accurate solution of the mathematical equations Approximate the mathematical models with simple procedures Physically-Based Modeling and Simulation 45 Natural Phenomena T1 T T2 S1 S2 Current state Next state Physically-Based Modeling and Simulation 46 Physically-Based Model T1 t T2 S1 S2 Current state Next state Physically-Based Modeling and Simulation 47 Natural Phenomena and Simulation 01 02 None Real-time simulation Physically-Based Modeling and Simulation 48 Simulation with Visualization Real-time Simulation with Visualization Physically-Based Modeling and Simulation 49 50 Simulation with Visualization Real-time simulation with Visualization 25-30 FPS (1/30)s Physically-Based Modeling and Simulation 51  Challenges and Issues Simulate Specific Real-world Scenario Realism of the Simulated Scenario Evaluation of the Simulated Scenario Physically-Based Modeling and Simulation 52  What is Simulation ? (T2) (T1, P1) (T2, P2) (T3, P3)  What is Simulation ? (T1, P1) (T2, P2) (T3, P3) Real world Scenario Existing Condition Prediction (T2, P’2) Simulated Scenario  What is Simulation ? Simulated Scenario Real world Scenario Real-time Simulation Frame Rate 25-30 FPS Real-time Visualization 1/25 s 0.04 s  Simulation Real-time Non Real-time  Naval training (Ship Handling Simulators)  Ship hull designing  Simulating military scenes  Waterway designs  Entertainment activities such as computer games  Three Realism Indexes (T2) Real world Scenario Simulated Scenario Three Realism Indexes (T2)  Three Realism Indexes Behavioral realism Environmental realism (Determined by the visual system and 3D stereo sound system) Physical realism Y. Yin, et al., "Application of Virtual Reality in Marine Search and Rescue Simulator," The International Journal of Virtual Reality, vol. IX, no. 3, pp. 19-26, Sep. 2010.  Three Realism Indexes (T2) Real world Scenario Simulated Scenario  Three Realism Indexes Real world Scenario Simulated Scenario Three Realism Indexes Three Realism Indexes Physical Realism Physical Realism Practical Aspects of Visualization Real Virtual  Practical Aspects of Visualization Practical goal: Visualization - to generate images (usually of recognizable subjects) that are useful in some way. Ideal goal: Photorealism - to produce images indistinguishable from photographs.  Practical Aspects of Visualization Practical goal: Visualization - to generate images (usually recognizable subjects) that are useful in some way. Ideal goal: Photorealism - to produce images indistinguishable from photographs.  Practical Aspects of Visualization Practical goal: Ideal goal: Human factors & VR Human Eye- FOV Depth Cues  Depth Cues Motion Parallax Cue Perspective ….. Monocular Depth Cues- Motion Parallax Cue Motion parallax is a type of monocular cue. Monocular cues are depth perception cues that can be perceived through the use of one eye. Motion parallax occurs when objects that are at different distances from us appear to move at rates that are different while we are moving. We judge an object's distance based on how quickly an object moves. The closer an object is to us, the quicker it appears to move. The further an object is from us, the slower it appears to move. Monocular Depth Cues- Perspective When looking down a straight level road we see the parallel sides of the road meet in the horizon. This effect is often visible in photos and it is an important depth cue. It is called linear perspective. Binocular Depth Cues- Convergence Convergence is the movement of the eyes to bring an object into the same location of the retina of each eyes. The orbital muscle movement used for convergence provide information to the brain on the distance of the object in view. Stereopsis & Stereoscopic vision ◆Stereopsis is the process responsible for this reconstruction of the depth ◆Stereoscopic vision describes the three dimensional sensation of depth associated when seeing with two eyes. Stereopsis is derived from the parallax between the different images received by the retina in each eye (binocular disparity). The stereoscopic image depth cue depends on parallax, which is the apparent displacement of objects viewed from different locations. Stereospsis is particularly effective for objects within 5 m. It is especially useful when manipulating objects within arms reach The Eye Total Power: 60 Dioptres Power of Lens:20 Dioptres Oscillating Accommodation The Eye Accommodation & Vergence Accommodation & Vergence The Pupil Fusion Frequency Critical Fusion Frequency Image size Brightness ✗ ✓ Flicker The Retina Human Ear 20 0C 344 ms-1 20 Hz-20000 Hz Human Ear Localize the Sound Source Localize the Sound Source 1. Interaural time difference (ITD) 2. Interaural level difference (ILD) 3. Head-related transfer function (HRTF) Localize the Sound Source Localize the Sound Source 2. Interaural level difference (ILD) Difference in sound pressure level reaching the two ears Localize the Sound Source 2. Head-related transfer function (HRTF) The pinna and head affect the intensities of