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Ontonix: Engineering - Healthcare applications


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Detailed presentation outlining Ontonix approach and (Ontospace) applications, specifically in Engineering and in Healthcare

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Ontonix: Engineering - Healthcare applications

  2. 2. Disclaimer<br />The concepts and methods presented in this document are for illustrative purposes only, and are not intended to be exhaustive. Ontonix assumes no liability or responsibility to any person or company for direct or indirect damages resulting from the use of any information contained herein.<br />Any reproduction or distribution of this document, in whole or in part, without the prior written consent of Ontonix is prohibited.<br />Reverse-engineering of the concepts, methods or ideas contained in this document is strictly forbidden. The methods described in the present document are protected by US patents.<br />OntoSpace is a trademark of Ontonix All other trademarks are the property of their respective owners. <br />Copyright 2010, Ontonix S.r.l. All Rights Reserved.<br />
  3. 3. CONTENTS<br />Why Manage Complexity<br />What is Complexity?<br />Applications of Complexity in Engineering and Manufacturing<br />Back-up Information<br />
  4. 4. Why Complexity Management <br />Complexity is rapidly increasing in all spheres of social life. This leads to multiple converging stresses and increases the levels of turbulence of the global economy as well as of the society. Doing business is increasingly difficult. Complexity must be managed before it reaches dangerous levels and threatens sustainability.<br />
  5. 5. What is Complexity?<br />Complexity is a function of structureanduncertainty(entropy). It quantifies the degree of sophistication and the “amount of chaos” within a system. It is a fundamental property of dynamical systems, just like energy.<br />Structure<br />(Topology of information flow)<br />Uncertainty<br />(Noise content in information)<br />COMPLEXITY<br />
  6. 6. Complex or Complicated?<br />A system may be complicated, but have very low complexity.<br />A large number of parts doesn’t generally imply high complexity. It does, in general, imply a complicated system.<br />Complexity implies capacity to surprise, to deliver unexpected behaviour.<br />In order to measure the amount of complexity it is necessary to take uncertainty into account, not just the number of parts.<br />
  7. 7. Principle of Incompatibility<br />High precision is incompatible with high complexity.<br /> L. Zadeh, UCLA<br />High complexity<br />System is: <br /><ul><li>Unpredictable
  8. 8. Difficult to understand
  9. 9. Cannot be described </li></ul> precisely<br /><ul><li>Difficult to control
  10. 10. Can surprise
  11. 11. Entropy dominated
  12. 12. Vulnerable</li></ul>Low complexity<br />System is: <br /><ul><li>Predictable
  13. 13. Easy to understand
  14. 14. Can be described with precision
  15. 15. Easy to control
  16. 16. Unable to surprise
  17. 17. Structure dominated
  18. 18. Robust</li></li></ul><li>Representing Structure<br />
  19. 19. Representing Structure<br />Conventional Map:<br />Difficult to analyze when number of nodes <br />and links is large (“spaghetti effect”).<br />Hubs are difficult to identify.<br />Ontonix Complexity Map:<br />Easy to analyze even when the number of <br />nodes and links is very large.<br />Hubs are easy to identify.<br />Link<br />Node<br />Node<br />Link<br />
  20. 20. Examples of Complexity Maps <br />
  21. 21. Variable 1 Variable 2 Variable 3 Variable 4 Variable 5 Variable 6 Variable 7 Variable 8 ........<br />Sample 1<br />Sample 2<br />Sample 3<br />.<br />.<br />.<br />.<br />Variable 1<br />Identifying Structure In Data<br />Steps:<br />For all combinations of variables:<br /><ul><li>Build (x;y) scatter plot
  22. 22. Transform plot to 2D image
  23. 23. Analyze image and measure its </li></ul> information content and “amount <br /> of structure”<br /><ul><li>If image contains structure, create a</li></ul> link in the map – this corresponds to a<br /> relationship between two variables<br />Variable 3<br />Variable 2<br />Variable 5<br />
  24. 24. From Data to Images and Structure<br />y<br />Image with little or no structure:<br />no information is exchanged <br />between x and y.<br />Image with evident structure:<br />much information is exchanged<br />between x and y.<br />Image with evident structure:<br />much information is exchanged<br />between x and y.<br />x<br />y<br />x<br />y<br />x<br />
  25. 25. Critical Complexity<br />Complexity cannot grow indefinitely and has <br /> a maximum. Close to this maximum, called <br />critical complexity, a given system becomes <br /> fragile and vulnerable.<br />In the proximity of critical complexity systems <br /> possess numerous modes of behaviour and<br /> can switch from one mode to another <br /> sponaneously.<br />Modes represent (behavioural) attractors.<br />Critically complex systems are very difficult to <br /> manage and can easily run out of hand.<br />After critical complexity decay begins unless<br /> structural changes are made to the system.<br />Mode 2<br />Mode 1<br />Mode 3<br />Mode 4<br />
  26. 26. Complexity and Robustness<br />It is not convenient to function in the proximity of critical complexity – behaviour becomes unpredictable.<br />Thevulnerabilityof a system is proportional to its distance from critical complexity.<br />A well-managed system is kept at a safe distance from critical complexity.<br />Sudden changes in complexity point to traumas(endogenous or exogenous) and may be seen asearly warnings and precursors of crises.<br />Upper complexity bound (critical complexity) <br />Ccritical – Ccurrent<br />Current system complexity<br />Robustness is function of Ccritical – Ccurrent. This measure is known as topological robustness and quantifies the system’s ability to preserve its functionality.<br />This is the key concept behind our innovative complexity-based rating scheme.<br />Lower complexity bound<br />
  27. 27. What Happens at Critical Complexity<br />Critical complexity (upper complexity bound)<br />y<br />Entropydominates – systemis “chaotic”<br />y<br />y<br />x<br />x<br />y<br />x<br />Current complexity<br />Structure dominates – system is deterministic<br />0<br />The x-y scatter plots shown in this slide represent typical relationships between pairs of variables in a system which functions in the proximity of the lower and upper bounds of complexity. <br />x<br />Lower complexity bound<br />
  28. 28. Complexity vs Time<br />Step i<br />Step i+1<br />Step i+2<br />In systems which evolve in time, complexity changes, as well as the corresponding lower and upper bounds. The same may be said of the Complexity Map, its structure, density, hubs, etc.<br />In such cases data is analyzed using a moving-window approach.<br />t1 t2<br />Time<br />Time t1<br />Complexity = 28.5<br />Time t2<br />Complexity = 44.8<br />
  29. 29. Characteristics of a Highly Complex Business or System<br /> Difficult to manage<br />Highly exposed<br />Vulnerable<br />Unhealthy<br />Fragile<br />Unsustainable<br />
  30. 30. 18<br />Complexity x Uncertainty = Fragility<br />When uncertainty meets high complexity, the result is fragility. Simple systems can cope better with uncertainty than highly complex systems. <br />Highly complex systems are more exposed to the effects of uncertainty because of the countless ways in which they process information. They can fail in many ways, often due to apparently innocent causes.<br />Uncertainty in the environment, cannot be avoided. We must learn to live with it. Hence the need to manage complexity.<br />Since fragility is the prelude to risk, risk management can be accomplished via complexity management.<br />
  31. 31. 19<br />Nature Increases Complexity (Functionality): There is a Price to Pay!<br />Fragility<br />Functionality<br />Time<br />
  32. 32. 20<br />Complexity x Uncertainty = Fragility<br />CdesignX(Umanufacturing + Uenvironment) = Fproduct<br /><ul><li>A highly sophisticated design will result in a fragile product if:
  33. 33. The manufacturing process is of poor quality
  34. 34. The environment is very “turbulent”
  35. 35. Hence, a more robust product requires:
  36. 36. A high-quality manufacturing process, or
  37. 37. A less severe environment in which to function, or
  38. 38. A less “ambitious” initial design</li></li></ul><li>21<br />Complexity-Based Design<br />A less complex solution is generally:<br />Less expensive to design and engineer<br />Less expensive to manufacture <br />Less expensive to service (replace broken components, etc.)<br />Cheaper<br />Easier to operate<br />Less fragile. This means:<br />Less warranty costs<br />Less recalls<br />Less law-suits<br />
  39. 39. 22<br />Complexity-Based Computer-Aided Design: Pedestrian Bridge<br />Design parameters:<br />Height<br />Dimension fraction<br />Rib spacing<br />Thickness factor<br />Cut Depth<br />Cut width<br />Radius<br />Spacing factor<br />Flange distance<br />
  40. 40. 23<br />Complexity-Based CAD: Pedestrian Bridge Geometric parameters <br />Quarter model view:<br />Rib Spacing is the amount of holes between ribs <br />T<br />The dimension fraction is D/T<br />D<br />x<br />The spacing factor is S/T <br />S<br />Height<br />t is the flange distance <br />t<br />Thickness factor = x/Height<br />If the thickness factor is increased<br />Cut depth, width and radius determine the shape of the ribs<br />
  41. 41. 24<br />Complexity-Based CAD – The Concept<br />Starting from the initial nominal model, a sequence of randomly generated solutions is created. This is done using Monte Carlo techniques and a multi-run environment.<br />For every solution, a CAD system is used to automatically generate an FE mesh.<br />For every mesh a static and an eignevalue analysis is run in order to determine stresses, deflections and natural frequencies.<br />The process is repeated a few hundred times and is fully automatic (one loop).<br />The results are processed and feasible solutions are determined by specifying desired levels of:<br />Stresses<br />Deflections <br />Natural frequencies<br />Various solutions are found to satisfy constraints and performance objectives.<br />
  42. 42. 25<br />Complexity-Based CAD – The Concept<br />Which one is best? What is “best”?<br />
  43. 43. 26<br />Complexity-Based CAD – The Concept<br />Solution 1<br />Solution 2<br />
  44. 44. 27<br />Example of Complexity-Based Design: Turbine Disk Design<br />Solution 1<br />Solution 2<br />Solution 2 has much lower complexity (15.8 vs. 22.6) <br />and slightly higher robustness than Solution 1.<br />
  45. 45. 28<br />Example of Complexity-Based Design: The James Webb Space Telescope<br />Option 1<br />Option 2<br />Option 3<br />Option 4<br />Best option: lowest complexity with same performance<br />James Webb Space Telescope payload adapter.<br />Courtesy EADS CASA Espacio.<br />
  46. 46. 29<br />Crash Test Data Processing<br />Analysis of crash-test<br />data shows that over the <br />past decade, complexity<br />has been increasing.<br />1980 2005<br />1980 2005<br />
  47. 47. 30<br />Measuring Robustness in Mechanical Systems<br />Robust design and related techniques have been object of discussion for over a decade. However, the robustness of designs conceived using such methods has never actually been measured and no global measure of robustness has<br />ever been proposed. <br />Recently developed complexity-based robustness measures allow engineers to quantify the global robustness of any<br />dynamical system.<br />
  48. 48. 31<br />More on Robustness: The Connectivity Histogram<br />Loss of this hub<br />damages greatly<br />the Process Map<br />Robust<br />Fragile<br />Additional information on robustness may be obtained examining the shape of the Connectivity Histogram. Spiky histograms (known as Zipfian) denote fragile topologies, while flatter ones point to more robust systems.<br />
  49. 49. 32<br />Holistic Plant Monitoring<br />Courtesy, PBMR Ltd.<br />
  50. 50. 33<br />Power Turbine Monitoring<br />Critical complexity<br />Alert complexity<br />Complexity<br />Minimum complexity<br />Time<br />
  51. 51. Process Plant: Vulnerability Analysis<br />To evaluate the complexity of a process or a system it is first necessary to obtain the equivalent process map(s). These are computed automatically by OntoSpace™. In order to extract the maps, OntoSpace™ requires data from the process sampled with a certain frequency at a set of significant locations or sensor points. <br />
  52. 52. 35<br />Air Traffic Monitoring<br />Safety<br />
  53. 53. Complexity Map<br />Traffic complexity<br />Radar<br />Traffic scenario<br />Traffic safety<br />The complexity of the traffic (3D dynamic cloud of points) maps onto the corresponding complexity map.<br />
  54. 54. ATC: Clasifying Airports Using Complexity<br />Airport 1<br />Airport 3<br />Airport 2<br />
  55. 55. 38<br />In-Flight Structural Health Monitoring<br />Engine 1 Complexity Map<br />Airframe Complexity Map<br />Engine 3 has a vibration problem. This manifests<br />itself in a complexity value which is 30% greater <br />than that of the other 3 engines.<br />Engine 2 Complexity Map<br />Engine 4 Complexity Map<br />Engine 3 Complexity Map<br />
  56. 56. Desalinization Plant Monitoring<br />All relationships between<br />Prod (FL) and other parameters<br />Process Entropy<br />Process Complexity<br />STAGE 1 (red nodes)<br />HUB: key parameter<br />STAGE 1 (blue nodes)<br />Process Robustness<br />Week 19<br />Week 20<br />The abovemaps, whichcorrespondtotwodifferentweeks, illustrate some basicfeatures and information which can be<br />obtainedbyanalyzingProcessMaps.<br />39<br />
  57. 57. Desalinization Plant Monitoring<br />The evolutionsofentropy and complexity show howevenduring a single month the processishighlynon-stationary. Onenoticeshow on givenday the very low levelofCLcoincideswith a minimum valueofbothcomplexity and entropy<br />The time-historyshowshow at a certainpoint Brine (CN) increases. <br />The correspondingportionof the time-historyishighlightedby the redcircle (seealsoentropyevolution). <br />The reddottedarrowshowshowentropyprogressivelygrows. Thiskindofbehaviourpointsto some kindof “accumulationofenergy” afterwhich the system maysuddenlyswitchto a differentmode ofbehavior – seebluedottedarrowpointing down.<br />
  58. 58. Bioreactor Analysis<br />
  59. 59. Holistic Chemical Plant Analysis<br />Raw Materials<br />Production<br />Sales<br />
  60. 60. Monitoring Patients During Thoracic Surgery<br />Operation<br />Intensive Care<br />Large oscillations of complexity point<br />to globally unstable patient.<br />Patient becomes critically unstable.<br />Courtesy Erasmus Medical Center<br />
  61. 61. 44<br />Measuring The Credibility of a Computer Model<br />Test<br />Simulation<br />How well does the numerical model emulate the real thing?<br />
  62. 62. 45<br />Model Credibility Index<br />MCIc =1 - |(Ctest - Cmodel)|/ Ctest<br />Weak Condition: Ctest = Cmodel<br /><ul><li>Ctest > Cmodel - Model (generally) misses physics
  63. 63. Ctest < Cmodel - Model (generally) generates noise</li></ul>Complexity measures the amount of structured information<br />
  64. 64. 46<br />Measuring Model Credibility – Process Map Topology<br />Crash Test<br />Crash Simulation<br />Strong Condition: Map test = Map model<br /><ul><li>MCIt = 1- (Ttest - Tmodel)/ Ttest
  65. 65. Based on this index, the credibility of this industrial crash model is 0.8</li></li></ul><li>47<br />From Data to Knowledge<br />Model<br />Data<br />Knowledge Map<br />Simulation<br />A dynamic and inter-related set of rules constitutes a body of knowledge which can evolve in time as new data is gained. Such maps allow users to understand how sophisticated systems really work, how disciplines interact, which potential failue modes exist and provide measures of vulnerability (robustness).<br />
  66. 66. 48<br />Complexity-Based CAE – A Systems Approach<br />Aerodynamics<br />Process Maps which gives Users an integrated and holistic view of:<br /><ul><li> Interaction between disciplines
  67. 67. Degrees of coupling
  68. 68. Critical variables
  69. 69. Global robustness measures
  70. 70. Failure modes
  71. 71. Complexity</li></ul>Power & Transmission<br />Safety<br />NVH & handling<br />Crashworthiness<br />
  72. 72. 49<br />Is Optimality Convenient?<br />In highly turbulent environments, seeking optimality is unjustified. In fact, optimal designs are inherently fragile. Robust solutions should be sought instead. This can be accomplished not by maximising (arbitrary) objective functions but by accepting compromises in terms of performance and seeking simpler solutions to problems.<br />
  73. 73. Pre-Alarms and Crisis-Anticipation<br />SERVICES: CRISIS ANTICIPATION<br />Complexity may be measured and monitored versus time. Sudden fluctuations of complexity point to instabilities, traumas or imminent crises. The magnitude of the trauma may be quantified by the difference in complexity before and after the event. Based on similar information it is possible to identify thresholds of complexity beyond which one may expect a crisis and therefore take measures in order to mitigate its effects.<br />Collapsing system. Loss of complexity is equivalent to loss of functionality. When complexity reaches a zero value, the system has no longer any structure and ceases all activity.<br />System with step-wise increases complexity. This case corresponds to the US housing market. The time span is 5 years. The 2007 sub-prime bubble is indicated by red arrow.<br />System with mildly increasing complexity (middle orange curve is complexity, the other curves correspond to lower and upper complexity bounds.<br />Highly unstable system – the case is relative to a hospital patient in an ICU. Each complexity fluctuation corresponds to crisis. Time span is 8 hours.<br />
  74. 74. Crisis Anticipation in IT Systems<br />SERVICES: CRISIS ANTICIPATION<br />Courtesy, Banca Popolare di Sondrio<br />IT systems in banks are dynamical systems composed of HW, SW, human interaction (client access via web). The dynamics of large IT systems brings them on occasions to states of high vulnerability which may be anticipated via real-time complexity monitoring.<br />
  75. 75. Evolution of Design Paradigms<br />Analysis-based Simulation-based<br />20-th Century<br />21-st Century<br />System Fragility<br />Complexity Management <br />Complexity-based Design<br />(MCS)<br />Uncertainty Management <br />(MCS)<br />Robust Design (MCS)<br />Stochastics (MCS)<br />MDO<br />Optimisation<br />Sensitivity analyses<br />System Complexity<br />Parametric studies<br />Trial and error<br />MCS = Monte Carlo Simulation<br />
  76. 76. Backup Information<br />
  77. 77. About Ontonix<br />Established in 2005 byexperts in the aerospace, nuclear and civilengineeringindustries. Over 60 man-yearsexperience in advancedrisk management and Monte Carlo Simulation.<br />Ontonix is the first company tohavedevelopedrationalmeansofmeasuringcomplexityand relatingittobusiness performance and sustainability.<br />Initiallyestablished in the USA, Ontonix isnowheadquartered in Como, Italy and hasoffices in the USA, Poland, Braziland South Africa.<br />In 2005 Ontonix launched OntoSpace™, the first SW system forpracticalcomplexity management. <br />In 2007 the first on-line rating service hasbeeninaugurated.<br />Ontonix currentlyservescustomers in suchfieldsas banking, <br />air-traffic management, medicine, defense and engineering.<br />
  78. 78. Publications<br />Computational Stochastic Mechanics in a Meta-Computing Perspective. 1997<br />Theory of Eigenvalue Orbits.1998<br />Principles of Simulation-Based Computer-Aided Engineering. 1999<br />Application Strategies of Robust Design and Complexity Management in Engineering. 2009. Co-author Dr. H. Sippel<br />Beyond Optimization in Computer-Aided Engineering. 2002<br />Practical Complexity Management. 2009<br />A New Theory of Risk and Rating. 2010<br />
  79. 79. Saconsulting<br />Madrid, Spain<br />Our Global Presence<br />Ontonix UK<br />Glasgow, UK<br />CPS<br />Frankfurt, D<br />Ontonix Sp z o.o.<br />Warsaw, Poland<br />Business Dimensions<br />Geneva, CH<br />Ontonix LLC<br />Novi, USA<br />BLUE Eng.<br />Bursa, Turkey<br />Soyotec<br />Beijing, PRC<br />VAS<br />Hinteregg, CH<br />OntoMed LLC<br />Ann Arbor, USA<br />Ontonix S.r.l.<br />Como, Italy<br />FlexSci<br />Beijing, PRC<br />Ontonix RSA<br />Pretoria, RSA<br />Ontonix Brasil<br />Sao Paulo, Brazil<br />
  80. 80. References<br />