Application of a learning model to a forest machinery work and analysis of progress in a machine operation technique
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- Yamaguchi Hirokazu
- Forestry and Forest Products Research Institute
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- Oka Masaru
- Faculty of Agriculture, Kagoshima University
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- Kashima Jun
- Forestry and Forest Products Research Institute
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- Mozuna Masahiro
- Forestry and Forest Products Research Institute
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- Jinkawa Masaki
- Forestry and Forest Products Research Institute
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- Kariya Yoshihiro
- Forestry Mechanization Center
Bibliographic Information
- Other Title
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- 林業機械作業への習熟モデルの適用と技術習得プロセスの分析
- リンギョウ キカイ サギョウ エ ノ シュウジュク モデル ノ テキヨウ ト ギジュツ シュウトク プロセス ノ ブンセキ
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Description
<p>We observed changes in the productivity of log-loading work using a grapple loader and determined how an operator can gain proficiency in operating forest machines by evaluating the subsequent machine work of the operators who were inexperienced in forest machine operations. Consequently, we found that the work time tended to decrease with an increase in the work experience of the operator. This could be because of the improvement in machine operation techniques such as simultaneous operation of actuators or effective uses of machine abilities and the acquisition of work knowledge regarding log treatment or efficient trajectory of the work machine. This consistent decrease in work time and improvement in productivity can be expressed using a log-linear learning model, with a determination coefficient of >0.90 in regression analysis. This showed that an operator’s long-term improvement in forest machine operation can be predicted by observing the early progress of the operator. In addition, we revealed that observation of the work should be performed at least five times to complete an exact model.</p>
Journal
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- Journal of The Japan Forest Engineering Society
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Journal of The Japan Forest Engineering Society 31 (4), 155-, 2016
The Japan Forest Engineering Society
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Details 詳細情報について
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- CRID
- 1390001204464028800
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- NII Article ID
- 40021011744
- 130005284710
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- NII Book ID
- AN10531146
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- ISSN
- 21896658
- 13423134
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- NDL BIB ID
- 027755082
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- Text Lang
- ja
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- Data Source
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- JaLC
- IRDB
- NDL Search
- CiNii Articles
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- Abstract License Flag
- Disallowed