Название: Handbook on Intelligent Healthcare Analytics
Автор: Группа авторов
Издательство: John Wiley & Sons Limited
Жанр: Техническая литература
isbn: 9781119792536
isbn:
It will be useful at this stage to quickly clarify what we refer to as information and how we use this term to describe concepts other than data and evidence. Both terms are sometimes misused in the traditional spoken language; truth and knowledge are often interchangeably used. The hierarchy of data and intellect (and knowledge) is the subject of long-term disputes between epistemologists and IT experts. Since this subject goes well beyond the scope of this chapter, this is our definition of data. Data are objects that have no meaning before they are put in form like symbols, statistics, digits, and indications. The information consists of important processed data. The context in which the data are collected gives it meaning, importance, and intent. Human and electronic information can be collected, shared, and processed. The knowledge is encrypted by code, normally organized in a structure or size, and stored in hard or soft media to accomplish this. Awareness is the condition of information and awareness processing, which requires the chance to act.
New information may be produced as a result of the application of knowledge. The IGES file with a geometrical definition of the surface as a piece of information is an example of this. IGES files will be encoded with numbers and symbols (i.e., data) and will only provide knowledge useful if they understand the meaning (i.e., the fact they are the data of an IGES file). The information which can be collected with a KBE method is regarded as a simple example of the algorithm that reads such an IGES file, reconstructs a specified surface model, intersects it with a floor plane, and, if the crossroad is non-zero, calculates the length of the corresponding curve. It is also sensible to ask why geometry varies from the standard CAD paradigm and enables the creation and manipulation of geometry. Owing to the varying scopes of these systems, the differences are important. Digitized drawing systems, which allow programmers to catch their ideas have been designed to create CAD systems. They build and store the results using the CAD framework’s geometry simulation functions. A set of points, lines, planes, and solids with reference and note are an almost all-inclusive link to the structure. These data provide enough information for the creation of a system that can be used to build a specification by production engineers. In doing so, creators store the specifics of “what,” but they retain the “how” and “why.” In a sense, the CAD approach can be considered a system “posterior,” because before it can be moved to the system it is necessary to know what the principle is like. It can be argued that CAD is geometry or drawing/drawing engineering to distinguish this approach from KBE.
Figure 1.3 KBE.
KBE-supported technology is different. Technology Instead of shifting “what,” engineering experts are trying to move “how” and “why,” encapsulating in the KBE process knowledge and thinking instead of geometry in the CAD framework. Not only does this work by manipulating geometric structures, but programming is needed rather than writing. The “how” and “why” in engineering are in some cases used in textbooks, databases, tip sheets, and several other outlets. Much of the knowledge is held up by engineers, mostly in a manner that is strongly compiled and not specifically suitable for translation to the KBE procedure. This experience should be sufficiently transparent to create a KBE program to be codified into a software application capable of producing all kinds of product specifics, including geometry templates, scores, and data that are not associated with the geometry. Because of its capacity to generate a specification rather than simply text, it is widely referred to as a generative model.
1.5.2 When Can KBE Be Used?
How easy is the use of KBE? It is only necessary to rapidly generate different configurations and variants of a given variable. In certain practical cases, this is not essential, so it may be a wrong expenditure to try to clarify details and program a KBE system. One-off prototypes and designs that need not be optimized or prototype versions for space travel are usually outside the scope of KBE implementation.
It explores the design field through the development of different design variants within the product family and evaluates its performance compared to previously tested versions with the multidisciplinary optimization (MDO) application. KBE will assist in many respects in this case.
It enables stable product parametric models to be generated which make topology changes and the freedom to make adaptation changes usually impossible for those built with a conventional CAD framework. This is important when considering broad variations like those which occur when a yacht manufacturer decides to accept one or more hull settings. It supports the integration into MDO through automation of the generation of necessary disciplinary abstractions of heterogeneous sets of analytical methods (low and high fidelity, in-house and off-shelf). It removes the optimizer from the challenge of managing the spatial integration constraints that generative models should guarantee. This is essential because the user does not need to specify constraints on configuration variables or restrictions to avoid intersection of two elements; or because a certain structural element does not need to remain beyond the same outer mold line; or because, during optimization, two products are expected to have a certain relative position apart.
KBE generative models can be a secret in producing MDO systems that are not multidisciplinary in return for adherence to science and that can handle complex problems reflecting actual industrial circumstances. We discuss the different models of current MDO systems and compare them to advanced KBE implementations in the next section to clarify this claim.
The third set of MDO structure implementation is available to overcome the weaknesses of the two approaches described earlier in this section by introducing generative models into the system. One advantage of this approach is that the exact geometry representations normally used for the use of high faithfulness analysis instruments may serve as a basis for the disciplinary study. It is therefore well adapted to the geometric nuances that are not included in a few general criteria of modern products. This geometry depiction is generated following individual tools of multidisciplinary system analyzers (BB SA) along with others, usually not schematic, product abstractions, and are systematically updated after each optimization loop. These MDO systems can fully resolve multidisciplinary cases without penalizing the degree of faithfulness and can contribute to the early stages of the design phase by addressing substantial changes in the shape and topology. They may also support a more sophisticated modeling method in which complex and accurate geometric models for high fidelity analysis are needed. These functions allow the early use of highly reliable testing approaches to be implemented with novel prototypes that do not have correct or unavailable semi-empirical and predictive technologies. The product modeling scheme which is the key feature of the MDO system is undermined by this approach.
1.5.3 CAD or KBE?
It is a mistake to know whether KBE is greater than CAD or vice versa. One is in the whole sense no bigger than the other, and we argue here that KBE should replace CAD. In certain circumstances, the KBE programming process is more suitable than the interactive application of the CAD platform, given that MDO supports one of the interests of this novel. This chapter is beyond the scope of a general debate about the suitability of one option for the next. The suggestions are as follows:
Where the focus is only on geometry development and manipulation; where considerations such as СКАЧАТЬ