Design descriptions to support reasoning about tolerances

This thesis is concerned with the use of Artificial Intelligence techniques to support human designers. The thesis argues that support for human designers can be improved by adopting an Al-based rather than a geometry-based approach to engineering design. Design Support Systems (DSSs) are proposed as an effective means of delivering this improved support. Representing and reasoning about tolerance statements in design is introduced as a valid area to test these claims. Tolerance statements describe the allowable variations in the geometry of a designed artefact. Two distinct, but related problems involving the use of toler¬ ance statements in design are tackled, namely: tolerance combination (including the way tolerance distributions combine), and tolerance allocation. The problem of tolerance combination (and distribution) involves determining the necessary consequences of the application of known tolerance statements to one or more designed artefact features. Tolerance allocation concerns the assignment of tol¬ erance statements during the design process. Solutions to this second problem are essential before manufactured instances of designed artefacts can be tested for compliance with design descriptions. The use of an experimental DSS, the Edinburgh Designer System (EDS), to solve design problems is illustrated. The implementation of techniques to im¬ prove the support of tolerance combination and tolerance allocation is described and where possible has been tested using EDS. The way that design is situated within the product creation process is investigated and the derivation of parts list information from an EDS design description is demonstrated. The thesis con¬ cludes that the Al-based approach can improve support for human designers, but that further research will be required to demonstrate the effective delivery of this support through DSSs

Evaluation of computer aided software as a space analysis tool for outfit unit design and planning

http://deepblue.lib.umich.edu/bitstream/2027.42/1123/2/89009.0001.001.pd

Applications of AI in design research at Carnegie Mellon University's EDRC

Technical Repor

The 1993 Goddard Conference on Space Applications of Artificial Intelligence

This publication comprises the papers presented at the 1993 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, MD on May 10-13, 1993. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Knowledge-based design support and inductive learning

Designing and learning are closely related activities in that design as an ill-structure problem involves identifying the problem of the design as well as finding its solutions. A knowledge-based design support system should support learning by capturing and reusing design knowledge. This thesis addresses two fundamental problems in computational support to design activities: the development of an intelligent design support system architecture and the integration of inductive learning techniques in this architecture.This research is motivated by the belief that (1) the early stage of the design process can be modelled as an incremental learning process in which the structure of a design problem or the product data model of an artefact is developed using inductive learning techniques, and (2) the capability of a knowledge-based design support system can be enhanced by accumulating and storing reusable design product and process information.In order to incorporate inductive learning techniques into a knowledge-based design model and an integrated knowledge-based design support system architecture, the computational techniques for developing a knowledge-based design support system architecture and the role of inductive learning in Al-based design are investigated. This investigation gives a background to the development of an incremental learning model for design suitable for a class of design tasks whose structures are not well known initially.This incremental learning model for design is used as a basis to develop a knowledge-based design support system architecture that can be used as a kernel for knowledge-based design applications. This architecture integrates a number of computational techniques to support the representation and reasoning of design knowledge. In particular, it integrates a blackboard control system with an assumption-based truth maintenance system in an object-oriented environment to support the exploration of multiple design solutions by supporting the exploration and management of design contexts.As an integral part of this knowledge-based design support architecture, a design concept learning system utilising a number of unsupervised inductive learning techniques is developed. This design concept learning system combines concept formation techniques with design heuristics as background knowledge to build a design concept tree from raw data or past design examples. The design concept tree is used as a conceptual structure for the exploration of new designs.The effectiveness of this knowledge-based design support architecture and the design concept learning system is demonstrated through a realistic design domain, the design of small-molecule drugs one of the key tasks of which is to identify a pharmacophore description (the structure of a design problem) from known molecule examples.In this thesis, knowledge-based design and inductive learning techniques are first reviewed. Based on this review, an incremental learning model and an integrated architecture for intelligent design support are presented. The implementation of this architecture and a design concept learning system is then described. The application of the architecture and the design concept learning system in the domain of small-molecule drug design is then discussed. The evaluation of the architecture and the design concept learning system within and beyond this particular domain, and future research directions are finally discussed

Proceedings of the 9th Arab Society for Computer Aided Architectural Design (ASCAAD) international conference 2021 (ASCAAD 2021): architecture in the age of disruptive technologies: transformation and challenges.

The ASCAAD 2021 conference theme is Architecture in the age of disruptive technologies: transformation and challenges. The theme addresses the gradual shift in computational design from prototypical morphogenetic-centered associations in the architectural discourse. This imminent shift of focus is increasingly stirring a debate in the architectural community and is provoking a much needed critical questioning of the role of computation in architecture as a sole embodiment and enactment of technical dimensions, into one that rather deliberately pursues and embraces the humanities as an ultimate aspiration

Undergraduate Course Catalog of the University of San Diego 2022-2023

569 pages. Includes information about academics, expenses, campus and the college, the 2022-2023 academic calendar, and school policies.https://digital.sandiego.edu/coursecatalogs-undergrad/1030/thumbnail.jp

Undergraduate Course Catalog of the University of San Diego 2021-2022

845 pages. Includes information about academics, expenses, campus and the college, the 2021-2022 academic calendar, and school policies.https://digital.sandiego.edu/coursecatalogs-undergrad/1029/thumbnail.jp

Modeling and responding to pandemic influenza : importance of population distributional attributes and non-pharmaceutical interventions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2009.Cataloged from PDF version of thesis.Includes bibliographical references.After reviewing prevalent approaches to the modeling pandemic influenza transmission, we present a simple distributional model that captures the most significant population attributes that alter the dynamics of the outbreak. We describe how diversities in activity, susceptibility and infectivity can drive or dampen the spread of infection. We expand the model to show infection spread between several linked heterogeneous communities; this multi-community model is based on analytical calculations and Monte Carlo simulations. Focusing on mitigation strategies for a global pandemic influenza, we use our mathematical models to evaluate the implementation and timing of non-pharmaceutical intervention strategies such as travel restrictions, social distancing and improved hygiene. In addition, as we witnessed with the SARS outbreak in 2003, human behavior is likely to change during the course of a pandemic. We propose several different novel approaches to incorporating reactive social distancing and hygiene improvement and its impact on the epidemic curve. Our results indicate that while a flu pandemic could be devastating; there are non-pharmaceutical coping methods that when implemented quickly and correctly can significantly mitigate the severity of a global outbreak. We conclude with a discussion of the implications of the modeling work in the context of university planning for a pandemic.by Karima Robert Nigmatulina.Ph.D