Designing the interface between research, learning and teaching.

Abstract: This paper’s central argument is that teaching and research need to be reshaped so that they connect in a productive way. This will require actions at a whole range of levels, from the individual teacher to the national system and include the international communities of design scholars. To do this, we need to start at the level of the individual teacher and course team. This paper cites some examples of strategies that focus on what students do as learners and how teachers teach and design courses to enhance research-led teaching. The paper commences with an examination of the departmental context of (art and) design education. This is followed by an exploration of what is understood by research-led teaching and a further discussion of the dimensions of research-led teaching. It questions whether these dimensions are evident, and if so to what degree in design departments, programmes and courses. The discussion examines the features of research-led departments and asks if a department is not research-led in its approach to teaching, why it should consider changing strategies

Prototype of intelligent data management system for computer animation (iMCA)

In recent years, one of the most noticeable“” issues of current animation production is the challenge from the exponential growth of animation data known as an increasingly data-intensive process. There are obvious gaps between the animation production needs and research development, which call for novel design and new technology to tackle the emerging challenge of handling huge amounts of data. “iMCA” is designed to develop intelligent data management solution with the capability to handle massive and hyper type animation asset and analyze/summarize information for reuse of data to facilitate human creativity providing innovative interaction to allow the manipulation of massive animation data

Towards Industrial Implementation of Emerging Semantic Technologies

Every new design, project, or procedure within a company generates a considerable amount of new information and important knowledge. Furthermore, a tremendous amount of legacy knowledge already exists in companies in electronic and non-electronic formats, and techniques are needed for representing, structuring and reusing this knowledge. Many researchers have spent considerable time and effort developing semantic knowledge management systems, which in theory are presumed to address these problems. Despite significant research investments, little has been done to implement these systems within an industrial setting. In this paper we identify five main requirements to the development of an industry-ready application of semantic knowledge management systems and discuss how each of these can be addressed. These requirements include the ease of new knowledge management software adoption, the incorporation of legacy information, the ease of use of the user interface, the security of the stored information, and the robustness of the software to support multiple file types and allow for the sharing of information across platforms. Collaboration with Raytheon, a defense and aerospace systems company, allowed our team to develop and demonstrate a successful adoption of semantic abilities by a commercial company. Salient features of this work include a new tool, the e-Design MemoExtractor Software Tool, designed to mine and capture company information, a Raytheon-specific extension to the e-Design Framework, and a novel semantic environment in the form of a customized semantic wikiSMW+. The advantages of this approach are discussed in the context of the industrial case study with Raytheon

The reuse of machining knowledge to improve designer awareness through the configuration of knowledge libraries in PLM

The nature of competition induces the need to constantly improve and perform better. For global aerospace manufacturers, this is as timely an epithet as ever as market forces urge for more growth, better financial return and market position. The macroeconomic aspect is compounded by the growth of product complexity and the need for higher product quality, hence the drive to reduce waste places emphasis upon production costs and the need to improve product performance. This paper focuses upon a rapid development and deployment method that enables the capture and representation of machining knowledge so that it may be shared and reused by design engineers to accelerate the design-make process. The study and mapping of information and knowledge relationships are described and put forward as a lightweight ontology. From this, a set of knowledge document templates were created to facilitate the capture, structuring and sharing of machining knowledge within a collaborative multidisciplinary aerospace engineering environment. An experimental pilot system has been developed to test and demonstrate that knowledge document templates can accelerate the sharing of machining knowledge within an industrial product lifecycle management environment. The results are discussed to provide a case for further development and application within the product domain

A Conceptual Representation of Documents and Queries for Information Retrieval Systems by Using Light Ontologies

International audienceThis article presents a vector space model approach to representing documents and queries, based on concepts instead of terms and using WordNet as a light ontology. Such representation reduces information overlap with respect to classic semantic expansion techniques. Experiments carried out on the MuchMore benchmark and on the TREC-7 and TREC-8 Ad-hoc collections demonstrate the effectiveness of the proposed approach

Towards Accessible, Usable Knowledge Frameworks in Engineering

A substantial amount of research has been done in the field of engineering knowledge management, where countless ontologies have been developed for various applications within the engineering community. However, despite the success shown in these research efforts, the techniques have not been adopted by industry. This research aims to uncover the reasons for the slow adoption of engineering knowledge frameworks, namely ontologies, in industry. There are two projects covered in this thesis. The first project is the development of a cross-domain ontology for the Biomesh Project, which spans the fields of mechanical engineering, biology, and anthropology. The biology community is known for its embrace of ontologies and has made their use quite popular with the creation of the Gene Ontology. This ontology spawned the establishment of the Open Biological and Biomedical Ontologies (OBO) Foundry, a consortium which approves and curates ontologies in the biology field. No such consortium exists in the field of engineering. This project demonstrates the usefulness of curated reference ontologies. Ontological knowledge bases in four different domains were imported and integrated together to connect previously disparate information. A case study with data from the Biomesh Project demonstrates cross-domain queries and inferences that were not possible before the creation of this ontology. In the second part of this thesis we investigate the usability of current ontology tools. Protégé, the most popular ontology editing tool, is compared to OntoWiki, a semantic wiki. This comparison is done using proven techniques from the field of Human-computer interaction to uncover usability problems and point out areas where each system excels. A field of 16 subjects completed a set of tasks in each system and gave feedback based on their experience. It is shown that while OntoWiki offers users a satisfying interface, it lacks in some areas that can be easily improved. Protégé provides users with adequate functionality, but it is not intended for a novice user

Semantic networks for engineering design: State of the art and future directions

This is the author accepted manuscript. The final version is available from the American Society of Mechanical Engineers via the DOI in this recordIn the past two decades, there has been increasing use of semantic networks in engineering design for supporting various activities, such as knowledge extraction, prior art search, idea generation and evaluation. Leveraging large-scale pre-trained graph knowledge databases to support engineering design-related natural language processing (NLP) tasks has attracted a growing interest in the engineering design research community. Therefore, this paper aims to provide a survey of the state-of-the-art semantic networks for engineering design and propositions of future research to build and utilize large-scale semantic networks as knowledge bases to support engineering design research and practice. The survey shows that WordNet, ConceptNet and other semantic networks, which contain common-sense knowledge or are trained on non-engineering data sources, are primarily used by engineering design researchers to develop methods and tools. Meanwhile, there are emerging efforts in constructing engineering and technical-contextualized semantic network databases, such as B-Link and TechNet, through retrieving data from technical data sources and employing unsupervised machine learning approaches. On this basis, we recommend six strategic future research directions to advance the development and uses of large-scale semantic networks for artificial intelligence applications in engineering design