Model Development in .NET

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Analytical methods require a frame of reference to represent the characteristics of an entity. For most systems, a model is created using an observer s frame of reference, such as the right-handed coordinate system.
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Analytically, model development is similar to system development. SE model developers should fully understand the problem space a model s solution space is intended to satisfy. Based on this understanding, design methodology requires that we rst survey or research the marketplace to see if the model(s) we require has already been developed and is available. If available, we need to determine if it has necessary and suf cient technical detail to support our system or entity application. Conventional design wisdom (Principle 41.1) says that new models should only be developed after you have exhausted all other alternatives to locate an existing model.
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Model Validation
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Models are only as valid as the quality of its behavioral and physical performance characteristics to replicate the real world entity. We refer to the quality of a model in terms of its delity meaning its degree of realism. So, the challenge for SEs is: Even if we develop a model of a system or product, HOW do we gain a level of con dence that the model is valid and accurately and precisely represents the physical instance of an item and its interactions with a simulated real world OPERATING ENVIRONMENT In general, when developing models, we attempt to represent physical reality with simulated or scaled reality. Our goal is to try to achieve convergence of the two within the practicality or resource constraints. So, HOW do we achieve convergence We do this by collecting empirical data from actual physical systems, prototypes, or eld tests. Then, we validate the model by comparing the actual eld data with the simulated behavioral and physical characteristics. Finally, we re ne the model until its results closely match those of the actual system. This leads to the question: HOW do we get eld data to validate a model for a system or product we are developing There are a number ways of obtaining eld data. We can: 1. Collect data using controlled laboratory experiments subjected to OPERATING ENVIRONMENT conditions and scenarios. 2. Install a similar component on a elded system and collect measurement data for OPERATING ENVIRONMENT conditions and scenarios. 3. Instrument a eld platform such as an aircraft with transducers and sensors to collect OPERATING ENVIRONMENT data. Regardless of the method we use, a model is calibrated, adjusted, and re ned until it is validated as an accurate and precise representation of the physical system or device. The model is then placed under formal con guration control. Finally, we may decide to have an independent decision authority or subject matter expert (SME) to certify the model, which brings us to our next topic.
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Model Certi cation
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The creation of a model is one thing; creating a valid model and getting it certi ed is yet another. Remember, SE technical decision making must be founded in objective, fact-based data that accurately represent real world situations and conditions. The same applies with models. So, what is certi cation IEEE 610.12 1990 de nes certi cation as A written guarantee that a system or component complies with its speci ed requirements and is acceptable for operational use. For example, a written authorization that a computer system is secure and is permitted to operate in a de ned environment. For many applications an independent authority validates a model by authenticating that model results identically match those obtained from measurements of an actual system operating under a speci ed set of OPERATING ENVIRONMENT conditions.
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In general, one SE can demonstrate to a colleague, their manager, or a Quality Assurance (QA) representative that the data match. Certi cation comes later when a recognized decision authority within industry or governmental organization reviews the data validation results and of cially issues a Letter of Certi cation declaring the model to be certi ed for use in speci c applications and conditions. Do you need certi ed models This depends on you re the program s needs. Certi cation: 1. Is expensive establish and maintain. 2. Has an intrinsic value to the creator and marketplace. Some models are used one time; others are used repeatedly and re ned over several years. Since engineering decisions must be based on the integrity of data, models are generally validated but not necessarily certi ed.
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