前端装载机牵引车最大静摩擦力预测回归模型的建立

Q2 Agricultural and Biological Sciences

Journal of Biosystems Engineering Pub Date : 2023-09-01 DOI:10.1007/s42853-023-00194-w

Seung-Jun Kim, Dong-Hyeon Gim, Moon-Kyeong Jang, Seok-Joon Hwang, Jeong-Hun Kim, Yun-Jeong Yang, Ju-Seok Nam

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Development of Regression Model for Predicting the Maximum Static Friction Force of Tractors with a Front-End Loader

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来源期刊

Journal of Biosystems Engineering Agricultural and Biological Sciences-Agricultural and Biological Sciences (miscellaneous)

CiteScore

3.90

自引率

0.00%

发文量

期刊介绍： Journal of Biosystems Engineering offers to publish original research articles, review articles and technical note, quarterly and integrates general engineering with applied agricultural, biological, environmental, food, and livestock systems to solve problems in complex living systems. The journal aims to selectively publish highly notable original articles to advance and disseminate scientific and technical knowledge in the field of biosystems engineering. JBE covers a wide range of topics related to the following categories: i) off-road machinery system engineering (including agricultural farm power and machinery, agricultural safety and health, agricultural and biosystems management, soil and terra-mechanics, and forest machinery), ii) agri-environmental system engineering and energy (including structure and facilities for living systems, indoor environmental control, livestock machinery, horticultural machinery, plant factory, and biomass/renewable energy), iii) agricultural process and food engineering (including postharvest technology, food quality and safety, and food processing and packaging), iv) biological engineering (including biomechanics, biosensors, bioMEMS/NEMS, and tissue engineering), v) information technology and electronics (including information and precision agriculture, instrumentation and controls, and robotics), and vi) emerging technology related to smart farming, micro- and nano-agriculture, artificial intelligence, analysis and applications of big data in the field of biosystems engineering.