online septembre 2016 (pas prévu dans Issue Dec. 2016, donc surement en 2017)International audienceThis conceptual paper investigates the common concern among managers that the physical separation of workers within a global virtual team may hinder the transfer of knowledge amongst the team members that is required to carry out their work efficiently, especially in the context of knowledge-intensive enterprises. Workers and work teams in knowledge-intensive enterprises are often involved in creative tasks that are carried out jointly and involve team members with diversified competencies exchanging knowledge related to their projects and assignments to create innovative outcomes. We investigate some popular creativity-enhancing techniques in the perspective of their use as catalysts for knowledge transfer in this context. We assess whether the use of these techniques may alleviate the limitations imposed on global virtual team members by their use of telecommunications and collaborative work tools that might otherwise adversely affect the effectiveness of the knowledge transfer. These techniques are designed to be used individually, by groups or within a virtual community. The physical and temporal separation of the global virtual team members does not hinder the knowledge-intensive dimension of these enterprises when aided by creativity-stimulating techniques. Therefore, we suggest that global virtual teams making use of creativity-enhancing techniques may be more efficient in transferring complex knowledge
Proper maintenance and troubleshooting of complex mechanical equipment is a difficult task. A large amount of information, such as sensor data and previous repair actions, is available but infrequently used for interpretation by technical staff which is continually losing expertise due to turnover. Well structured knowledge-based systems can provide effective techniques for assisting in this task.
Aircraft gas turbine engine maintenance is a complex task requiring not only specialized technical skill but effective integration of many sources of information. Traditionally, military maintenance technicians make extensive use of common sense knowledge, equipment manuals, pilot reports, instrument readings, engine settings and physical observations. Reasoning based upon patterns in sensor data, case histories and past maintenance is infrequently carried out. Difficulties in maintenance arise from the need to quickly restore the engines to an operational state, the frequent reassignment of technicians and the awkward access to, and interpretation of, data. There is a need to overcome these factors.This paper describes a knowledge-based diagnostic system for military gas turbine aero-engines. The objectives were to develop a system which encodes the heuristics of technicians and to provide an expandable framework for automating the technical manuals and incorporating explanations, data interpretation, as well as case history and model-based reasoning. The eventual goal is to apply the system to the maintenance of complex mechanical equipment and have it reason, in an on-line mode, with data obtained from a data acquisition system.A description of the application area and the features of the system, in its current stage of development, are discussed. This paper will be of practical benefit to those developing knowledge-based maintenance systems for complex mechanical equipment.
/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. Documentation, 44, 2, pp. 91-118, 1988-06 Expert systems and the use of information in building design and construction Davidson, C. H.; Davidson, P. L.; Ruberg, K. Journal of Institute for Research in Construction, ~atidnal Research Council of CanadaThe building industry, through its structure and its mandate, faces endemic infonnation problems; expert systems are expected to impact positively. Expert systems arc suited to situations of uncertainty; knowledge and reasoning are separated, allowing easier updating. Knowledge acquisition from human experts is difficult and problems of information reliability arise, suggesting the scope for cooperation between knowledge engineers and documentalists familiar with the domain. In building. prevailing conditions seem to indicate the appropriateness of expert systems, particularly during the design phase; however, written documentation and general research results are rarely consulted. TbThi a highlights the need for an information 'refining' stage between production and use. It is easier to set up expert systems for specialised subdomains; however, on-going research is attempting to develop a comprehensive approach to project-specific information that would be operational from initial design through to completed construction. Criteria for a comprehensive design information system can be listed. Organisation. The building industry, in management jargon, is a 'multiindustry', and each building project is undertaken by a 'temporary multiorganization' [I]. The industry as a whole consists of a large number of enterprises, both professional and consultant practices, and manufacturing and construction companies. Each of them exists over a long period of time, but must form a team with others for short periods to participate in particular building projects. Long-term survival depends on a proper sequence of shortterm activities. As a result, each firm has its own long-term modus operandi and its own ways of ensuring its presence on the market place; it develops and maintains some form of in-house information system, if only to record its acquired experience. Also, each short-term project team must develop effective coordination, by contract and by inducement, so that the firms called upon to work together (and who may never have worked together before), produce the required building within the imposed constraints; within the short time span, a project-specific information system must be...
The paper describes a generalized knowledge-based tool for diagnosis which is currently being applied to jet engine maintenance. A domain dependent diagnostic tree is created for a particular jet engine by filling in an empty hypothesis frame for each diagnostic node in the tree. The knowledge in the tree is reasoned about using a generalized and explicit reasoning strategy. This strategy can be guided by rules specific to the activation of a particular diagnostic hypothesis in the tree.The user interacts with the system through a window interface which features definitions, glossary information, schematics and explanations of session reasoning, which are all linked to the knowledge contained in the system. A demonstration prototype which runs under the ART (LISP-based) environment on Symbolics 3620 and Sun 3/60 workstations was completed in December 1988. The preliminary prototype diagnoses a subset of the acceleration faults on the General Electric J85-CAN-15 jet engine and is being field tested and evaluated by potential users.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.