193,638 research outputs found
Experimental Design: Design Experimentation
This paper was selected for publication in MIT’s Design Issues. The research takes an original approach by positioning experimentation as a comprehensive design methodology, rather than using the traditional industrial design approach of employing experimentation as a problem-solving tool within a standard design model. It is an evolution of design thinking on non-linear design methods first developed by Hall and presented to the ‘International Association of Societies of Design Research Conference’, Seoul, South Korea (2009), and in a paper entitled ‘Innovation design engineering: Non-linear progressive education for diverse intakes’ presented at the ‘International Conference on Engineering and Product Design Education’, University of Brighton, UK, which offered a non-linear pedagogy (Hall and Childs 2009) that uniquely supports a diverse interdisciplinary intake.
Experimental design is well known in the science domain but very little evidence has been recorded of experimentation in industrial design and its position in relation to work in other science and research domains. Connections are made with theories on research methods, an analysis of case studies and comparisons of literature on experimentation from science disciplines, especially that of Kuhn (1962), Galison (1987), Pasteur’s quadrant for scientific research in Stokes (1997) and Borgdorff (2007). Hall makes significant claims in exploring and articulating a model of design experimentation that highlights the differences between scientific and design experimentation. This work was original in describing an experimental design model for the increasing activity in early phases of design development by recording and enhancing knowledge in this important area for future design research and practice. The methods researched in the paper were later used in experimental design workshops in Daegu, South Korea (2011) and Busan, South Korea (2012)
Survey on Additive Manufacturing, Cloud 3D Printing and Services
Cloud Manufacturing (CM) is the concept of using manufacturing resources in a
service oriented way over the Internet. Recent developments in Additive
Manufacturing (AM) are making it possible to utilise resources ad-hoc as
replacement for traditional manufacturing resources in case of spontaneous
problems in the established manufacturing processes. In order to be of use in
these scenarios the AM resources must adhere to a strict principle of
transparency and service composition in adherence to the Cloud Computing (CC)
paradigm. With this review we provide an overview over CM, AM and relevant
domains as well as present the historical development of scientific research in
these fields, starting from 2002. Part of this work is also a meta-review on
the domain to further detail its development and structure
Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild
In this paper, we seek to better understand Android obfuscation and depict a
holistic view of the usage of obfuscation through a large-scale investigation
in the wild. In particular, we focus on four popular obfuscation approaches:
identifier renaming, string encryption, Java reflection, and packing. To obtain
the meaningful statistical results, we designed efficient and lightweight
detection models for each obfuscation technique and applied them to our massive
APK datasets (collected from Google Play, multiple third-party markets, and
malware databases). We have learned several interesting facts from the result.
For example, malware authors use string encryption more frequently, and more
apps on third-party markets than Google Play are packed. We are also interested
in the explanation of each finding. Therefore we carry out in-depth code
analysis on some Android apps after sampling. We believe our study will help
developers select the most suitable obfuscation approach, and in the meantime
help researchers improve code analysis systems in the right direction
From Design to Production Control Through the Integration of Engineering Data Management and Workflow Management Systems
At a time when many companies are under pressure to reduce "times-to-market"
the management of product information from the early stages of design through
assembly to manufacture and production has become increasingly important.
Similarly in the construction of high energy physics devices the collection of
(often evolving) engineering data is central to the subsequent physics
analysis. Traditionally in industry design engineers have employed Engineering
Data Management Systems (also called Product Data Management Systems) to
coordinate and control access to documented versions of product designs.
However, these systems provide control only at the collaborative design level
and are seldom used beyond design. Workflow management systems, on the other
hand, are employed in industry to coordinate and support the more complex and
repeatable work processes of the production environment. Commercial workflow
products cannot support the highly dynamic activities found both in the design
stages of product development and in rapidly evolving workflow definitions. The
integration of Product Data Management with Workflow Management can provide
support for product development from initial CAD/CAM collaborative design
through to the support and optimisation of production workflow activities. This
paper investigates this integration and proposes a philosophy for the support
of product data throughout the full development and production lifecycle and
demonstrates its usefulness in the construction of CMS detectors.Comment: 18 pages, 13 figure
Empirical analysis of rough set categorical clustering techniques based on rough purity and value set
Clustering a set of objects into homogeneous groups is a fundamental operation
in data mining. Recently, attention has been put on categorical data clustering,
where data objects are made up of non-numerical attributes. The implementation of
several existing categorical clustering techniques is challenging as some are unable
to handle uncertainty and others have stability issues. In the process of dealing
with categorical data and handling uncertainty, the rough set theory has become
well-established mechanism in a wide variety of applications including databases.
The recent techniques such as Information-Theoretic Dependency Roughness (ITDR),
Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA)
outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness
(TR), Min-Min Roughness (MMR), and standard-deviation roughness (SDR). This
work explores the limitations and issues of ITDR, MDA and MSA techniques on
data sets where these techniques fails to select or faces difficulty in selecting their
best clustering attribute. Accordingly, two alternative techniques named Rough Purity
Approach (RPA) and Maximum Value Attribute (MVA) are proposed. The novelty
of both proposed approaches is that, the RPA presents a new uncertainty definition
based on purity of rough relational data base whereas, the MVA unlike other rough
set theory techniques uses the domain knowledge such as value set combined with
number of clusters (NoC). To show the significance, mathematical and theoretical
basis for proposed approaches, several propositions are illustrated. Moreover, the
recent rough categorical techniques like MDA, MSA, ITDR and classical clustering
technique like simple K-mean are used for comparison and the results are presented
in tabular and graphical forms. For experiments, data sets from previously utilized
research cases, a real supply base management (SBM) data set and UCI repository
are utilized. The results reveal significant improvement by proposed techniques for
categorical clustering in terms of purity (21%), entropy (9%), accuracy (16%), rough
accuracy (11%), iterations (99%) and time (93%).
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