Abstract: Effective knowledge support relies on providing
operation-relevant knowledge to workers promptly and accurately. A
knowledge flow represents an individual-s or a group-s
knowledge-needs and referencing behavior of codified knowledge
during operation performance. The flow has been utilized to facilitate
organizational knowledge support by illustrating workers-
knowledge-needs systematically and precisely. However,
conventional knowledge-flow models cannot work well in cooperative
teams, which team members usually have diverse knowledge-needs in
terms of roles. The reason is that those models only provide one single
view to all participants and do not reflect individual knowledge-needs
in flows. Hence, we propose a role-based knowledge-flow view model
in this work. The model builds knowledge-flow views (or virtual
knowledge flows) by creating appropriate virtual knowledge nodes
and generalizing knowledge concepts to required concept levels. The
customized views could represent individual role-s knowledge-needs
in teamwork context. The novel model indicates knowledge-needs in
condensed representation from a roles perspective and enhances the
efficiency of cooperative knowledge support in organizations.
Abstract: Using vision based solution in intelligent vehicle application often needs large memory to handle video stream and image process which increase complexity of hardware and software. In this paper, we present a FPGA implement of a vision based lane departure warning system. By taking frame of videos, the line gradient of line is estimated and the lane marks are found. By analysis the position of lane mark, departure of vehicle will be detected in time. This idea has been implemented in Xilinx Spartan6 FPGA. The lane departure warning system used 39% logic resources and no memory of the device. The average availability is 92.5%. The frame rate is more than 30 frames per second (fps).
Abstract: High quality requirements analysis is one of the most
crucial activities to ensure the success of a software project, so that
requirements verification for software system becomes more and more
important in Requirements Engineering (RE) and it is one of the most
helpful strategies for improving the quality of software system.
Related works show that requirement elicitation and analysis can be
facilitated by ontological approaches and semantic web technologies.
In this paper, we proposed a hybrid method which aims to verify
requirements with structural and formal semantics to detect
interactions. The proposed method is twofold: one is for modeling
requirements with the semantic web language OWL, to construct a
semantic context; the other is a set of interaction detection rules which
are derived from scenario-based analysis and represented with
semantic web rule language (SWRL). SWRL based rules are working
with rule engines like Jess to reason in semantic context for
requirements thus to detect interactions. The benefits of the proposed
method lie in three aspects: the method (i) provides systematic steps
for modeling requirements with an ontological approach, (ii) offers
synergy of requirements elicitation and domain engineering for
knowledge sharing, and (3)the proposed rules can systematically assist
in requirements interaction detection.
Abstract: Vision-based intelligent vehicle applications often require large amounts of memory to handle video streaming and image processing, which in turn increases complexity of hardware and software. This paper presents an FPGA implement of a vision-based blind spot warning system. Using video frames, the information of the blind spot area turns into one-dimensional information. Analysis of the estimated entropy of image allows the detection of an object in time. This idea has been implemented in the XtremeDSP video starter kit. The blind spot warning system uses only 13% of its logic resources and 95k bits block memory, and its frame rate is over 30 frames per sec (fps).