An automated method mapping parametric features between computer aided design software

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonEnterprise efficiency is limited by data exchange. A product designer might specify the geometry of a product with a Computer Aided Design program, an engineer might re-use that geometry data to calculate physical properties of the product using a Finite Element Analysis program. These different domains place different requirements on the product representation. Representations of product data required for different tasks is dependent on the vendor software associated with those tasks, sharing data between different vendor programs is limited by incompatibility of the vendor formats used. In the case of Computer Aided Design where the virtual form of an object is modelled, no standard data format captures complete model data. Common data standards transfer model surface geometry without capturing the topological elements from which these geometries are constructed. There are prescriptive data representations to allow these features to be specified in a neutral format, but little incentive for vendors to adopt these schemes. Recent efforts instead focus on identifying similar feature elements between different vendor CAD programs, however this approach relies on onerous manual identification requiring frequent revision. This research develops methods to automate the task of mapping relationships between different data format representations. Two independent matching techniques identify similar CAD feature functions between heterogeneous programs. Text similarity and object geometry matching techniques are combined to match the data formats associated with CAD programs. An efficient search for matching function parameters is performed using a genetic algorithm that incorporates semantic data matching and geometry data matching. A greedy semantic matching algorithm is developed that compares with the Doc2vec short text matching technique over the API dataset tested. A SVD geometric surface registration technique is developed that requires fewer calculations than an equivalent Iterative Closest Point method

Using technology to overcome the language barrier: the cognitive assessment for aphasia app

Purpose: We developed and explored the feasibility and user acceptance of the Cognitive Assessment for Aphasia App: a non-immersive virtual reality cognitive assessment for stroke survivors, designed to be inclusive of individuals with aphasia. Methods: Participants were assessed on a battery of pen-and-paper cognitive tests and the Cognitive Assessment for Aphasia App. Feasibility was explored by quantifying missing data for test completion, determining user acceptance for the app by measuring participants’ preferred testing method, enjoyment and perceived task difficulty and time-taken to complete the test. Results: Sixty-four stroke participants (35 with aphasia, 29 without aphasia) and 32 controls were recruited. Only one participant with aphasia was unable to complete all the Cognitive Assessment for Aphasia App tasks, whereas 13 participants were unable to complete all pen-and-paper tasks. Only 14% of participants preferred the pen-and-paper tests, and preference did not significantly differ between groups. Ninety-five per cent of participants were neutral or enjoyed the app and 4% perceived it to be very difficult. Higher age was negatively associated with user acceptance measures. Conclusion: The study shows preliminary evidence for the Cognitive Assessment for Aphasia App to be a feasible cognitive assessment for stroke survivors with and without aphasia. The app is currently being validated in stroke.Implications for rehabilitationThe Cognitive Assessment for Aphasia App is a feasible tool for assessing post-stroke cognition in acute, inpatient rehabilitation and community settings.In research trials examining cognition, individuals with aphasia are often excluded. The Cognitive Assessment for Aphasia App permits the inclusion of these individuals, enhancing generalizability.The Cognitive Assessment for Aphasia App provides an alternative method to assess cognition that is quicker and preferred over standard neuropsychological tests