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1 – 4 of 4Liang Wang, Jiaming Wu, Xiaopeng Li, Zhaohui Wu and Lin Zhu
This paper aims to address the longitudinal control problem for person-following robots (PFRs) for the implementation of this technology.
Abstract
Purpose
This paper aims to address the longitudinal control problem for person-following robots (PFRs) for the implementation of this technology.
Design/methodology/approach
Nine representative car-following models are analyzed from PFRs application and the linear model and optimal velocity model/full velocity difference model are qualified and selected in the PFR control.
Findings
A lab PFR with the bar-laser-perception device is developed and tested in the field, and the results indicate that the proposed models perform well in normal person-following scenarios.
Originality/value
This study fills a gap in the research on PRFs longitudinal control and provides a useful and practical reference on PFRs longitudinal control for the related research.
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Keywords
Shashi K. Shahi, Mohamed Dia, Peizhi Yan and Salimur Choudhury
The measurement capabilities of the data envelopment analysis (DEA) models are used to train the artificial neural network (ANN) models for the best performance modeling of the…
Abstract
Purpose
The measurement capabilities of the data envelopment analysis (DEA) models are used to train the artificial neural network (ANN) models for the best performance modeling of the sawmills in Ontario. The bootstrap DEA models measure robust technical efficiency scores and have benchmarking abilities, whereas the ANN models use abstract learning from a limited set of information and provide the predictive power.
Design/methodology/approach
The complementary modeling approaches of the DEA and the ANN provide an adaptive decision support tool for each sawmill.
Findings
The trained ANN models demonstrate promising results in predicting the relative efficiency scores and the optimal combination of the inputs and the outputs for three categories (large, medium and small) of sawmills in Ontario. The average absolute error in predicting the relative efficiency scores varies from 0.01 to 0.04, and the predicted optimal combination of the inputs (roundwood and employees) and the output (lumber) demonstrate that a large percentage of the sawmills shows less than 10% error in the prediction results.
Originality/value
The purpose of this study is to develop an integrated DEA-ANN model that can help in the continuous improvement and performance evaluations of the forest industry working under uncertain business environment.
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