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Methodologies of Scenario Development for Water Resource Management
Published in Satya Prakash Maurya, Akhilesh Kumar Yadav, Ramesh Singh, Modeling and Simulation of Environmental Systems, 2023
Scenario planning, an essential and helpful process, should be generalized so that any user can apply it to examine the future opportunities or risks for taking the proper measures beforehand. Though the prediction methods could be similar, yet generalized systems should be designed with lesser parameters so that any novice user can apply them easily with the known and non-technical dataset, e.g., time step ahead to forecast any variable of importance. Such an approach of generalization will help to make scenario development applicable everywhere, promoting sustainability all around. Application of GIS in scenario development and planning should be taken forward for prediction besides the presentation and analysis, i.e., the one-dimensional prediction process should be carried forward for two/three dimensional.
Diagnosis
Published in Jay Liebowitz, The Handbook of Applied Expert Systems, 2019
The notion of concept is used in a fairly broad sense. It refers to both concrete (objects, persons, etc.) and abstract things (number 2, set of all integers), elemental concepts (electron) or compounds (atoms), and fictitious (unicorn) or real (detective) entities. In short, a concept is anything about which something is to be said and could, therefore, also be the description of a task, function, action, strategy, reasoning process, etc. When concepts are determined, there is little or no concern for classifications, relations, or details. An expert usually works with approximately 12 concepts. So, if more appear during conceptualization, their number has to be constrained to the above value. Two fairly well-known powerful techniques — generalization and abstraction — are used to reduce the number of concepts. Formally, generalization is the move from considering a concept to considering a set containing that concept, or the move from considering a small to a more comprehensive set of concepts containing the smaller one. And abstraction consists, at least theoretically, of removing irrelevant items, as discussed by Lwoff (Lwoff, 1975) and retaining only what is necessary.
Computational Neuroscience and Compartmental Modeling
Published in Bahman Zohuri, Patrick J. McDaniel, Electrical Brain Stimulation for the Treatment of Neurological Disorders, 2019
Bahman Zohuri, Patrick J. McDaniel
Rote learning is relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalization. Learning that involves generalization leaves, the learner able to perform better in situations not previously encountered. A program that learns past tenses of regular English verbs by rote will not be able to produce the past tense of, for example, “jump” until presented at least once with “jumped.” Whereas a program that is able to generalize from examples can learn the “added” rule and so form the past tense of “jump” in the absence of any previous encounter with this verb. Sophisticated modern techniques enable programs to generalize complex rules from data.
Discussion of article by Zwetsloot and Woodall: A review of some sampling and aggregation strategies for basic statistical process monitoring
Published in Journal of Quality Technology, 2021
In considering generalization of findings, we can apply two principles: domain-based generalization and statistical generalization. Domain-based approaches employ first principles and mechanistic models to describe a phenomenon. Statistical generalization is based on inferring from a sample to a population. In the context of SPM, one should consider “engineering generalization,” that is, extending the results obtained with a specific set of data to other conditions, processes, units, laboratories, etc. In “statistical generalization,” inference is restricted to the population from where the data were collected and over-parametrized data-driven methods are prone to overfitting. Engineering generalizability provides the ability to predict new observations in the future, not necessarily on the basis of data. The key idea of Shewhart was that one could generalize from a rational sample to the process behavior. Designing the rational sample requires engineering knowledge and intuition so that rational samples lead to both statistical and engineering generalizations. ZW mention this point in section 1 without expanding on it.
Uncovering the specificities of CAD tools for industrial design with design theory – style models for generic singularity
Published in International Journal of Design Creativity and Innovation, 2018
Pierre-Antoine Arrighi, Pascal Le Masson, Benoit Weil, Akin Kazakçi
During the design process, the engineer configured a pre-established and mandatory set of DPs to achieve known FRs. The engineer used commands and functions step by step to construct the virtual mock-up, and by doing this generated what is known as a design tree. The design tree contained the functions and parameters, and the relationships among the different geometric entities. For instance, the designer specified certain dimensions of the tank to meet the volume criteria. The design tree also specified certain thickness parameters, for example, the position of ribs, to ensure the stiffness criteria (see Figure 1). A single product was not designed; instead, a parameterized and reconfigurable base of rules were produced that could generate a family of products. The result of the design in engineering CAD was not a single product but rather an algebra of rules. This algebra of rules defines how the components are spatially positioned, in addition to the links between them, the functions, and their parameters. This algebra can be mapped to an infinity of solutions. The engineer guaranteed that the set of created rules was robust to variations in DPs (e.g., different materials or manufacturing methods) or even FRs (e.g., modified volume requirements or a new legal regulation created during the design process). Clearly, the designer could use irrelevant parameters so that the CAD tool allows parametric variations, but these variations are poorly related to important topological variations. Digital engineering appears to be a design process of generalization. The artifact is one representation of a ‘genre,’ that is, it helps to address ‘generic variety.’ The product is robust to changes in its DPs (components, dimensions, etc.) and its FRs (environment, etc.) – one simple illustration of this is the logic of tolerances in engineering design.