Study of intelligent load analysis system using genetic algorithm
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Year
: 2014DOI: 10.1049/iet-its.2012.0142
Keyword(s): automated highways,genetic algorithms,road vehicles,drive vehicle information,driving sessions,genetic algorithm,illegal axle manipulation problems,indoor experiment,intelligent embedded load analysis systems,load vehicle information,managing system,overloaded vehicles,road behaviour,societal infrastructure,underground box-type indoor model
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Study of intelligent load analysis system using genetic algorithm
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| contributor author | Jo, Byung-Wan | |
| contributor author | Yoon, Kwang-Won | |
| contributor author | Lee, Yi-Shu | |
| contributor author | Choi, Ji-Sun | |
| date accessioned | 2020-03-13T00:16:35Z | |
| date available | 2020-03-13T00:16:35Z | |
| date issued | 2014 | |
| identifier issn | 1751-956X | |
| identifier other | 6870204.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1140760?locale-attribute=en | |
| format | general | |
| language | English | |
| publisher | IET | |
| title | Study of intelligent load analysis system using genetic algorithm | |
| type | Journal Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8323151 | |
| subject keywords | automated highways | |
| subject keywords | genetic algorithms | |
| subject keywords | road vehicles | |
| subject keywords | drive vehicle information | |
| subject keywords | driving sessions | |
| subject keywords | genetic algorithm | |
| subject keywords | illegal axle manipulation problems | |
| subject keywords | indoor experiment | |
| subject keywords | intelligent embedded load analysis systems | |
| subject keywords | load vehicle information | |
| subject keywords | managing system | |
| subject keywords | overloaded vehicles | |
| subject keywords | road behaviour | |
| subject keywords | societal infrastructure | |
| subject keywords | underground box-type indoor model | |
| identifier doi | 10.1049/iet-its.2012.0142 | |
| journal title | Intelligent Transport Systems, IET | |
| journal volume | 8 | |
| journal issue | 5 | |
| filesize | 647695 | |
| citations | 0 |