Feature validation in a feature-based design system

The Loughborough University of Technology Feature-Based Design System (LUTFBDS) allows detail design to be carried out in a computer aided design (CAD) environment by the addition of form features to stock material or part-machined components. An iconic user interface assists in the description parts in terms of a set of primitive features such as holes, pockets and slots or higher level compound features such as patterns of holes and counterbored holes. This feature representation is generated in parallel with the geometric data structure of the underlying boundary representation solid modeller. The feature representation is useful for a range of downstream manufacturing activities, but our research focusses on the integration of CAD with process planning. LUT-FBDS functions allows the designer or manufacturing engineer to progressively construct the final geometry of a part, and facilities are provided for the designer to modify parameters which relate to feature dimensions, location, orientation and relationships with other features. These changes may result in changes to the feature representation and hence there is a need for feature validation to ensure the integrity of the model

Elastic-Net Regularization in Learning Theory

Within the framework of statistical learning theory we analyze in detail the so-called elastic-net regularization scheme proposed by Zou and Hastie for the selection of groups of correlated variables. To investigate on the statistical properties of this scheme and in particular on its consistency properties, we set up a suitable mathematical framework. Our setting is random-design regression where we allow the response variable to be vector-valued and we consider prediction functions which are linear combination of elements ({\em features}) in an infinite-dimensional dictionary. Under the assumption that the regression function admits a sparse representation on the dictionary, we prove that there exists a particular ``{\em elastic-net representation}'' of the regression function such that, if the number of data increases, the elastic-net estimator is consistent not only for prediction but also for variable/feature selection. Our results include finite-sample bounds and an adaptive scheme to select the regularization parameter. Moreover, using convex analysis tools, we derive an iterative thresholding algorithm for computing the elastic-net solution which is different from the optimization procedure originally proposed by Zou and HastieComment: 32 pages, 3 figure

Structure-Based Evolutionary Design Applied to Wire Antennas

A new design technique for antennas, namely the Structure-based Evolutionary Design (SED), is introduced and described in detail. SED is a new global random search method derived by the “genetic programming”, a strategy proposed by Koza. The proposed technique will be compared with the genetic algorithms (GA), a widely used design technique, showing the numerous advantages of our approach with respect to standard ones. SED assumes no “a priori” structure, but it builds up the structure of the individuals as the procedure evolves. Therefore SED is able to determine both the structure shape and dimensions as an outcome of the procedure (infinite-dimensional solution space), acting on subparts of the whole structure, and allowing to explore effectively the far more vast solution space. We thoroughly discuss both the general features of SED and its application to wire antenna design. The antenna internal representation, which is a key to the successful implementation of SED, and the construction of fitness functions from the antenna specifications will be described in detail. The proposed approach has been assessed with many different cases, using as design requirements both Gain and VSWR in a frequency band as wide as possible, and with the smallest size. The results obtained with SED are finally compared with other popular algorithms like Particle Swarm Optimization (PSO) and Differential Evolution (DE), showing that both the computational cost and the complexity are of the same order of magnitude, but the performances obtained by SED are significantly higher

Review of research in feature-based design

Research in feature-based design is reviewed. Feature-based design is regarded as a key factor towards CAD/CAPP integration from a process planning point of view. From a design point of view, feature-based design offers possibilities for supporting the design process better than current CAD systems do. The evolution of feature definitions is briefly discussed. Features and their role in the design process and as representatives of design-objects and design-object knowledge are discussed. The main research issues related to feature-based design are outlined. These are: feature representation, features and tolerances, feature validation, multiple viewpoints towards features, features and standardization, and features and languages. An overview of some academic feature-based design systems is provided. Future research issues in feature-based design are outlined. The conclusion is that feature-based design is still in its infancy, and that more research is needed for a better support of the design process and better integration with manufacturing, although major advances have already been made

Ambiguity is a double-edged sword: similarity references in communication

Designers often explain new concepts and new ideas by reference to existing designs. This is parsimonious, as it only requires a pointer to the referent and a description of the modifications. Such descriptions can be extremely powerful, expressing the entire context of a design or a process in a few words. However similarity assertions are inherently ambiguous, because they depend not only on the chosen description but also on the intention behind the similarity comparison. In this paper we attempt to analyse the effect that the ambiguity of similarity references has on communication and idea generation in design. The reinterpretation of a similarity assertion can be extremely creative, where ambiguity allows for new interpretations of a problem. At the same time, it can make accurate communication extremely difficult because every assertion can be interpreted differently unless the context is fully shared

Supporting Knitwear Design Using Case-Based Reasoning

Organised by: Cranfield UniversityKnitwear design is a creative activity that is hard to automate using the computer. The production of the associated knitting pattern, however, is repetitive, time-consuming and error-prone, calling for automation. Our objectives are two-fold: to facilitate the design and to ease the burden of calculations and checks in pattern production. We conduct a feasibility study for applying case-based reasoning in knitwear design: we describe appropriate methods and show how they can be implemented.Mori Seiki – The Machine Tool Compan

Towards a theory of software engineering

A theory of software engineering (SE) is presented and its application to explaining and analysing SE situations is illustrated. The theory is based on a characterization of SE representations and the fundamental activities that are applied to them. Motivations for developing a theory and means of establishing its validity are also discussed

EEG source imaging assists decoding in a face recognition task

EEG based brain state decoding has numerous applications. State of the art decoding is based on processing of the multivariate sensor space signal, however evidence is mounting that EEG source reconstruction can assist decoding. EEG source imaging leads to high-dimensional representations and rather strong a priori information must be invoked. Recent work by Edelman et al. (2016) has demonstrated that introduction of a spatially focal source space representation can improve decoding of motor imagery. In this work we explore the generality of Edelman et al. hypothesis by considering decoding of face recognition. This task concerns the differentiation of brain responses to images of faces and scrambled faces and poses a rather difficult decoding problem at the single trial level. We implement the pipeline using spatially focused features and show that this approach is challenged and source imaging does not lead to an improved decoding. We design a distributed pipeline in which the classifier has access to brain wide features which in turn does lead to a 15% reduction in the error rate using source space features. Hence, our work presents supporting evidence for the hypothesis that source imaging improves decoding