Neural network approach to hoist deceleration control
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F14%3A00224844" target="_blank" >RIV/68407700:21220/14:00224844 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6889831" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6889831</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN.2014.6889831" target="_blank" >10.1109/IJCNN.2014.6889831</a>
Alternative languages
Result language
angličtina
Original language name
Neural network approach to hoist deceleration control
Original language description
This paper introduces a neural network approach to hoist deceleration control of industrial hoist mechanisms, with particular focus to crane applications. The necessity for investigation in this field arises from the increasing demands in terms of safetywithin in the industry. Should the industrial hoist feature too high deceleration this can lead to overstressing of the hoist mechanism and structure, further, damaging of the load due to large dynamical forces. Furthermore, too low deceleration can lead to incompliance with industrial standards and thus being a safety issue, due to potential loss of load in the worst case. Till this day various solutions and devices have been proposed to achieve controlled deceleration of the industrial hoist braking.However, there still lies a necessity for deeper study into this problem, to achieve quicker response towards the desired behavior of the hoist deceleration as well as improved adherence with the desired behavior. Thus, this paper analys
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BC - Theory and management systems
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Neural Networks (IJCNN), 2014 International Joint Conference on - Scopus ISBN
ISBN
978-1-4799-1484-5
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
1864-1869
Publisher name
IEEE
Place of publication
Piscataway
Event location
Beijing
Event date
Jul 6, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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