Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFostering continuous innovation in design with an integrated knowledge management approach
114
0
0.0
(
0
)
Added by
Emmanuel Caillaud
Publication date
2012
fields
Informatics Engineering
and research's language is
English
Authors
J. Xu
Ask ChatGPT about the research
No Arabic abstract
In the global competition, companies are propelled by an immense pressure to innovate. The trend to produce more new knowledge-intensive products or services and the rapid progress of information technologies arouse huge interest on knowledge management for innovation. However the strategy of knowledge management is not widely adopted for innovation in industries due to a lack of an effective approach of their integration. This study aims to help the designers to innovate more efficiently based on an integrated approach of knowledge management. Based on this integrated approach, a prototype of distributed knowledge management system for innovation is developed. An industrial application is presented and its initial results indicate the applicability of the approach and the prototype in practice.
rate research
Read More
Online user innovation communities are becoming a promising source of user innovation knowledge and creative users. With the purpose of identifying valuable innovation knowledge and users, this study constructs an integrated super-network model, i.e., User Innovation Knowledge Super-Network (UIKSN), to integrate fragmented knowledge, knowledge fields, users and posts in an online community knowledge system. Based on the UIKSN, the core innovation knowledge, core innovation knowledge fields, core creative users, and the knowledge structure of individual users were identified specifically. The findings help capture the innovation trends of products, popular innovations and creative users, and makes contributions on mining, and integrating and analyzing innovation knowledge in community based innovation theory.
Embedded systems are playing an increasingly important role in control engineering. Despite their popularity, embedded systems are generally subject to resource constraints and it is therefore difficult to build complex control systems on embedded platforms. Traditionally, the design and implementation of control systems are often separated, which causes the development of embedded control systems to be highly time-consuming and costly. To address these problems, this paper presents a low-cost, reusable, reconfigurable platform that enables integrated design and implementation of embedded control systems. To minimize the cost, free and open source software packages such as Linux and Scilab are used. Scilab is ported to the embedded ARM-Linux system. The drivers for interfacing Scilab with several communication protocols including serial, Ethernet, and Modbus are developed. Experiments are conducted to test the developed embedded platform. The use of Scilab enables implementation of complex control algorithms on embedded platforms. With the developed platform, it is possible to perform all phases of the development cycle of embedded control systems in a unified environment, thus facilitating the reduction of development time and cost.
In this paper IEEE Learning Technology System Architecture (LTSA) for LMS software has been analyzed. It has been observed that LTSA is too abstract to be adapted in a uniform way by LMS developers. A Learners Quanta based high level design that satisfies the IEEE LTSA standard has been proposed for future development of efficient LMS software. A hybrid model of learning fitting into LTSA model has also been proposed while designing.
Imaging methods used in modern neuroscience experiments are quickly producing large amounts of data capable of providing increasing amounts of knowledge about neuroanatomy and function. A great deal of information in these datasets is relatively unexplored and untapped. One of the bottlenecks in knowledge extraction is that often there is no feedback loop between the knowledge produced (e.g., graph, density estimate, or other statistic) and the earlier stages of the pipeline (e.g., acquisition). We thus advocate for the development of sample-to-knowledge discovery pipelines that one can use to optimize acquisition and processing steps with a particular end goal (i.e., piece of knowledge) in mind. We therefore propose that optimization takes place not just within each processing stage but also between adjacent (and non-adjacent) steps of the pipeline. Furthermore, we explore the existing categories of knowledge representation and models to motivate the types of experiments and analysis needed to achieve the ultimate goal. To illustrate this approach, we provide an experimental paradigm to answer questions about large-scale synaptic distributions through a multimodal approach combining X-ray microtomography and electron microscopy.
The goal of this work is to minimize the energy dissipation of embedded controllers without jeopardizing the quality of control (QoC). Taking advantage of the dynamic voltage scaling (DVS) technology, this paper develops a performance-aware power management scheme for embedded controllers with processors that allow multiple voltage levels. The periods of control tasks are adapted online with respect to the current QoC, thus facilitating additional energy reduction over standard DVS. To avoid the waste of CPU resources as a result of the discrete voltage levels, a resource reclaiming mechanism is employed to maximize the CPU utilization and also to improve the QoC. Simulations are conducted to evaluate the performance of the proposed scheme. Compared with the optimal standard DVS scheme, the proposed scheme is shown to be able to save remarkably more energy while maintaining comparable QoC.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
