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

Assumption management in model-based systems engineering: an aircraft design perspective.

Early design of complex systems is characterised by significant uncertainty due to lack of knowledge, which can impede the design process. In order to proceed with the latter, assumptions are typically introduced to fill knowledge gaps. However, the uncertainty inherent in the assumptions constitutes a risk to be mitigated. In fact, assumptions can negatively impact the system if they turn out to be invalid, such as causing system failure, violation of requirements, or budget and schedule overruns. Within this context, the aim of this research was to develop a computational approach to support assumption management in model-based systems engineering, with an explicit consideration of the uncertainty in assumptions. To achieve the research aim, the objectives were to: (1) devise methods to enable assumption management in a model-based design environment; and (2) devise methods to manage risk of change due to invalid assumptions, with an explicit consideration of both assumptions and margins. The scope was limited to the early stages of aircraft design. To evaluate this research, a demonstration was performed based on two use cases to assess whether the methods work as intended. The developed methods were demonstrated to industry experts in order to obtain feedback on expected usefulness in practice, thus assessing the impact of this research. The experts concluded that the proposed methods are innovative, useful and relevant to industry, where these methods can lead to: (i) fewer undesired iterations, due to earlier identification and management of risks associated with assumptions; and (ii) a better margin balance, due to timely and interactive margin revision. Future work includes further industrial evaluation, extending the research scope and studying the scalability and associated costs of the proposed methods.PhD in Aerospac

Analysis and Synthesis of Magnetically Negative (MNG) Material using Softcomputing Techniques

Unique properties of Metamaterial are widely used in Electromagnetic Engineering, and the metamaterial has gained significant attention to be a major research area. Some of its recent research areas are carpet cloaking and metasurface design. The unique properties of these materials include simultaneous negative electromagnetic property, i.e., both permeability and permittivity are negative, because of which a negative refractive index is generated.Thus there are three primary classes of metamaterials. When only the permittivity is negative, the material is called ENG (Electrical Negative). Similarly material with only negative permeability is known as MNG (Magnetic Negative). Further when both are negative the material is regarded as DNG (Double Negative). Out of these three, the analysis and synthesis of MNG is very complicated and difficult. Therefore, the focus in this work is only on MNG, and the word "metamaterial" refers to MNG unless otherwise mentioned specifically. These type of materials don’t occur in nature and hence manufactured by making array of small unit cells of specific structure(s) made up of conductors. Although the concept of the existence of negative refractive index was proposed in the 1960s by Veselago, it took around 40 years to be verified practically when smith et al. did the experiment in 2001. They used an array of unit cell structures as Split-Ring-Resonators (SRR) and thin wires to verify the concept. Thereafter researchers are working to develop different forms of metamaterial unit cells and for which metamaterial is still an open area of research. However, while designing a metamaterial unit cell, absence of an empirical formula makes the model analysis and synthesis difficult. Although with the help of EM simulation tools this is possible, it usually is too difficult, time consuming and costly. Due to this researchers are motivated to look for alternative methods. In this work, some techniques to develop CAD models are presented based on soft computing techniques for metamaterial analysis and synthesis. Use of different soft computing techniques in the field of microwave engineering is documented in the literature. However, unconventional unit cell structures are difficult to analysis because of unavailability of predefined mathematical formulas and equivalent analysis. This can be done by the complex Modified Nicolson-Ross-Weir (NRW) method with the support of EM simulation tools which are expensive. Frequency dependency of metamaterial characteristics for any kind of unit cell structure follows a similar pattern which is obtained from Lorentz model. The basic idea in this work, which develops CAD Models for metamaterial unit cell of unconventional structures is based on the assumption that each type of unit cell can be mapped to an equivalent SRR structure, for which empirical formula is available. This is done by implementing the concept of Space Mapping technique or surrogate based modeling. Most important contribution of the work is the development of Space Mapped CAD model for analysis of an Ω atom. The developed model is validated with a Deformed-Ω atom, which is developed by integrating the concept of Space Mapping (SM) and Artificial Neural Network. Thereafter, the work progresses with proposing CAD models for synthesis of SRR. The objective is to find the design parameters of SRR for a desired material characteristic and frequency. With the availability of only a complex non-linear analysis formula, the synthesis becomes a reverse engineering problem, which is difficult to process. Three different models are proposed to solve the problem. The first approach is use of Inverse Artificial Neural Network concept, which uses a trained neural network (IANN) to perform output-to-input mapping. The developed CAD model using this approach includes integration of three concepts: IANN, Prior Knowledge Input-Difference (PKI-D) and SM. Although the model is capable of synthesizing a metamaterial unit cell, still it has some disadvantages. To overcome the disadvantages (such as lower convergence rate, lower accuracy and complex programming), use of Evolutionary Algorithms (Genetic Algorithm and Differential Evolution) is proposed. While developing CAD model based on EA, the methodology is first tested by synthesizing Rectangular Microstrip Antenna (RMPA) and then using the same concept, an SRR is synthesized. A comparison shows DE based model to be more efficient than IANN and GA based models in terms of convergence speed, accuracy and robustness

Recent Advances in Multidisciplinary Analysis and Optimization, part 1

This three-part document contains a collection of technical papers presented at the Second NASA/Air Force Symposium on Recent Advances in Multidisciplinary Analysis and Optimization, held September 28-30, 1988 in Hampton, Virginia. The topics covered include: helicopter design, aeroelastic tailoring, control of aeroelastic structures, dynamics and control of flexible structures, structural design, design of large engineering systems, application of artificial intelligence, shape optimization, software development and implementation, and sensitivity analysis

Towards inclusive design through constraint modelling and computer aided ergonomics

Inclusive Design is a concept that aims to design mainstream products, workplaces, services and facilities that can accommodate or `include' a maximum percentage of the user population disregarding their age and/or disabilities. The main idea behind Inclusive Design is to design products or workplaces that can be used by all including older, disabled and able-bodied people rather than having two streams of products. There are many social and economic benefits in achieving inclusivity in design such as improving the life of the elderly and disabled people and reaping the profits from the market that extend because of the increased number of consumers. Origins of Inclusive Design go back several decades and are due mainly to the demographic, legislative, and social as well as economic changes that occurred during this period. This research was conducted to study methods of implementation of Inclusive Design. The research has shown that although there are many advantages of designing for the whole population, designers are reluctant to do this mainly because of the enormity of the task which can take up a huge amount of time and man-power. One solution to this can be found in design tools, which provide the designers with a means to achieve inclusivity relatively quickly and with less effort. Therefore this research has developed a new methodology and a computer tool to assist designers to implement Inclusive Design with ease. The methodology discussed in this thesis incorporates the physical characteristics of the users of products and workplaces in the design process in order to search for better configurations for designs. It is shown here that by considering the physical aspects of the individual users such as their anthropometry, joint constraints, capabilities etc in a design optimisation process, the percentage user accommodation of a product can be maximised. In order to achieve this, ergonomics analysis methods and mathematical methods were used to interpret user characteristics in terms of design variables and then constraint modelling was used to model the whole design problem and search for better solutions within the constraints of the design. To implement this method a software tool called SHIELDS was created. This tool utilises the capabilities of four other pieces of software to accomplish the design synthesis. These are HADRIAN and SAMMIE for ergonomics evaluation and MATHEMATICA for mathematical functions fitting and SWORDS constraint modeller to find best solutions. Two case studies were performed to test the functionality of the software and the validity of the methodology developed

Constructing 3D faces from natural language interface

This thesis presents a system by which 3D images of human faces can be constructed using a natural language interface. The driving force behind the project was the need to create a system whereby a machine could produce artistic images from verbal or composed descriptions. This research is the first to look at constructing and modifying facial image artwork using a natural language interface. Specialised modules have been developed to control geometry of 3D polygonal head models in a commercial modeller from natural language descriptions. These modules were produced from research on human physiognomy, 3D modelling techniques and tools, facial modelling and natural language processing. [Continues.