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When an engineer designs and prototypes a system, it is important they analyse:
All of the other chocies
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The inputs to the system
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The outputs of the system
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The interconnections between the components of the system
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The components of the system
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When taking measurements of a time-varying system, it is important to:
Pause the system, and hold it fixed while taking the measurements
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Move at the same, or faster, speed compared to the system
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Stand well back
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Take the measurement samples at an appropriate rate, and with an appropriate amplitude quantisation resolution
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When designing a system, it is important for engineers to:
Use a lot of four-letter acronyms
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Design an attractive logo
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Attend lots of long meetings
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Include models for manufacturing tolerances in components
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It is important for engineers to:
Balance the desire for optimality against the practicalities of real-world issues and operational considerations
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Optimise every element of each design to make sure the absolute limit of performance is achieved
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Understand that “near enough” is most often “good enough”
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Multiply the design tolerances by a factor of two, in order to be on the “safe side”
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Process Flow Diagrams can:
Show the direction of water in an irrigation system
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Both (a) and (b)
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Capture the steps and events of a process, and the links and decision points between those events
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Graphically represent the links between a set of mathematical equations
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Show the direction of electrical current in a circuit
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Engineers develop mathematical and computational models for systems:
Based on intuition
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Based on the measurements, as well as based on fundamental scientific knowledge and principles
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Based solely on measurements
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Based on global economic factors
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Understanding and modelling randomness is a critical aspect of any engineering design, because:
There is always a degree of uncertainty in any system
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If something is not clear in the requirement specification, it may be necessary to randomly make decisions
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Unknown physical phenomena may occur at any time
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The customer will often change their minds
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Engineers approach challenges, problems, and projects, from which perspective:
Simulations and computing perspective
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Emotional perspective
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Purely financial perspective
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Systems perspective
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Engineering system models can include:
All of the other chocies
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Mathematical equation-based models
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Computer simulation parameterised models
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Data-driven learning/neural network models
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Hardware prototypes
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One of the most basic approaches to fitting models to data sets, is to:
Scale up the data
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Use random parameter allocation
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Scale down the data
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Assume an underlying mathematical model and compare it to the measured data
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