To profit in today’s market, small manufacturers must be efficient and agile to minimise cost and to meet variable market demand. This paper discusses an envisaged Semantic, Ambient, Generic and Extensible framework (SAGE) focused on the manufacturing environment. SAGE is based on the ambient intelligence philosophy which means it is focused on the human actor with the aim of aiding them to optimise their companies manufacturing processes. In order to achieve this goal SAGE combines several technologies: semantic reasoning, multi-modal communication and context-aware technologies within a Service Oriented Architecture (SOA). SAGE provides the manager with intuitive access to the real-time data describing the status of the shop floor environment and the ability to automate a response. Two manufacturing processes are targeted for systemic innovation: shop floor control and machine maintenance.
An approach focussed on resolving identity of
subjects in a photo using mobile device connectivity
and semantics is presented in this paper. Semantic
Web and mobile device sensors are combined to
provide meaningful photo annotation metadata that
can be used to recall photos from the Web. Useful
metadata can be gleaned from the environment at the
time of capture and inferred from previous metadata
tapped from existing sources.
An approach focussed on resolving identity of
subjects in a photo using mobile device connectivity,
Web services and social network ontologies is
presented in this paper. A framework is described in
which mobile device sensors, Web services and
ontologies are combined to provide meaningful photo
annotation metadata that can be used to recall photos
from the Web. Useful metadata can be gleaned from
the environment at the time of capture and further
information inferred from available Web services.
This paper presents an approach to semi-automate photo annotation. Instead of using content-recognition techniques this approach leverages context information available at the scene of the photo such as time and location in combination with existing photo annotations to provide suggestions to the user. An algorithm exploits a number of technologies including Global Positioning System (GPS), Semantic Web, Web services and Online Social Networks, considering all information and making a best-eort attempt to suggest both people and places depicted in the photo. The user then selects which of the suggestions are correct to annotate the photo. This process accelerates the photo annotation process dramatically which in turn aids photo search for a wide range of query tools that currently trawl the millions of photos on the Web.