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Open Access
Article
Publication date: 24 May 2024

Long Li, Binyang Chen and Jiangli Yu

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point…

Abstract

Purpose

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Design/methodology/approach

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

Findings

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

Originality/value

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

Details

Industrial Robot: the international journal of robotics research and application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 23 January 2020

Zhuolin She, Quan Li, Manuel London, Baiyin Yang and Bin Yang

The purpose of this paper is to examine the relationships between CEO narcissism and strategic decision-making (SDM) processes (decision comprehensiveness and decision speed), and…

1808

Abstract

Purpose

The purpose of this paper is to examine the relationships between CEO narcissism and strategic decision-making (SDM) processes (decision comprehensiveness and decision speed), and to explore the mediating role of top management team (TMT) members’ participation in decision making and the moderating role of TMT power distance.

Design/methodology/approach

Data were collected from a multisource, time-lagged survey of 103 CEOs and their corresponding TMT members in China. Structural equation modeling was used to test the hypothesized relationships.

Findings

The results indicated that CEO narcissism was negatively related to decision comprehensiveness and positively related to decision speed. These relationships were mediated by TMT members’ participation in decision making, especially when TMT power distance was high.

Practical implications

The results show the potential negative effects of CEOs’ narcissistic personality and suggest ways to attenuate it by increasing TMT participation and decreasing TMT power distance.

Originality/value

This study is an initial attempt to empirically examine how and under what conditions CEOs’ narcissism is a barrier to more comprehensive and more deliberate (slower) SDM.

Details

Journal of Managerial Psychology, vol. 35 no. 1
Type: Research Article
ISSN: 0268-3946

Keywords

Content available
Article
Publication date: 1 April 2005

178

Abstract

Details

Disaster Prevention and Management: An International Journal, vol. 14 no. 2
Type: Research Article
ISSN: 0965-3562

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