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Article
Publication date: 14 August 2018

Yang Chuangui, Liu Xingbao, Yue Xiaobin, Mi Liang, Wang Junwen, Xia Yangqiu, Yu Hailian and Chen Heng

This paper aims to solve the nonlinear problem in the uncertainty evaluation of the measurement of the positioning repeatability (RP) of industrial robots and provide guidance to…

236

Abstract

Purpose

This paper aims to solve the nonlinear problem in the uncertainty evaluation of the measurement of the positioning repeatability (RP) of industrial robots and provide guidance to restrict the uncertainty of measurement of RP (uRP).

Design/methodology/approach

Firstly, some uncertain sources existing in the measurement procedure of RP are identified. Secondly, the probability distribution function (PDF) of every source is established on the basis of its measurements. Some spatial combined normal distributions are adopted. Then, a method, based on Monte Carlo method (MCM) and established measurement model, is developed for the estimation of uRP. Thirdly, some tests are developed for the identification and validation of the selected PDFs of uncertain sources. Afterwards, the proposed method is applied for the evaluation and validation of the uRP. Finally, influence analyses of some key factors are proposed for the quantification of their relative contributions to uRP.

Findings

Results show that the proposed method can reasonably and objectively estimate the uRP of the selected industrial robot, and changes of the industrial robots’ position and the laser trackers measurement are correlated. Additionally, the uRP of the selected industrial robot can be restricted by using the results of its key factors on uRP.

Originality/value

This paper proposes the spatial combined normal distribution to model the uncertainty of the repeatability of the laser tracker and industrial robot. Meanwhile, the proposed method and influence analyses can be used in estimating and restricting the uRP and thus useful in determining whether the RP of a tested industrial robot meets its requirements.

Details

Industrial Robot: An International Journal, vol. 45 no. 4
Type: Research Article
ISSN: 0143-991X

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Article
Publication date: 8 June 2012

Sifeng Liu, Keqin Sheng and Jeffrey Forrest

The purpose of this paper is to show which models, uncertain or certain, simple or complicated, are more suitable when they are faced with incomplete information and inaccurate…

508

Abstract

Purpose

The purpose of this paper is to show which models, uncertain or certain, simple or complicated, are more suitable when they are faced with incomplete information and inaccurate data.

Design/methodology/approach

The characteristics of fuzzy mathematics, grey system theory, rough set theory and the basic characteristics of incomplete information and inaccurate data in uncertain systems are analysed.

Findings

The similarities and differences among fuzzy mathematics, grey system theory, rough set theory and probability statistics are compared. The principle of simplicity of scientific theories, methods, and models are discussed.

Practical implications

It is suggested that the tendency to concentrate on a complicated model isn't always necessary when faced with the condition of incomplete information and inaccurate data.

Originality/value

The paper shows that a more satisfied result can be obtained with an uncertain model than with a meticulous model on a certain situation.

Details

Kybernetes, vol. 41 no. 5/6
Type: Research Article
ISSN: 0368-492X

Keywords

Available. Content available
Book part
Publication date: 29 January 2024

Abstract

Details

Digital Technology and Changing Roles in Managerial and Financial Accounting: Theoretical Knowledge and Practical Application
Type: Book
ISBN: 978-1-80455-973-4

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Article
Publication date: 27 July 2023

Mianzhi Yang, Qing Hui, Qingru Yang, Mengwei Fan and Xin Li

China has recently introduced a new audit law that aims to increase the scope of audit supervision and raise the standards for preventing risks in auditing national public…

241

Abstract

Purpose

China has recently introduced a new audit law that aims to increase the scope of audit supervision and raise the standards for preventing risks in auditing national public projects. This paper presents a systematic research study on the causes of audit risks in national public projects and discusses the process by which these causes contribute to the emergence of such risks. Furthermore, the paper investigates the core risk sources in various types of national construction project audit. This paper aims to provide theoretical support for auditors of national construction projects in risk avoidance when conducting audits.

Design/methodology/approach

In this study, the authors carefully selected five national public audit projects from China and performed a comprehensive analysis of 85 relevant audit documentation. The textual analysis was conducted using Nvivo12 software, and the grounded theory approach was adopted for generalization purposes.

Findings

Based on the research results, the findings suggest that there are five key causes contributing to the audit risk of national construction projects: professional competence, risk awareness, management capacity, level of attention and deliberate fraud. The most critical factor identified is management capability, with 59.93% of the data supporting this view. This conclusion was based on an analysis of state-owned enterprises, administrative organs and public institutions. Building upon this, a framework titled “the mechanism of audit risk factors with management capability as the core” was constructed.

Originality/value

This paper employs qualitative analysis methods to examine national construction projects in China, contributing new literature to the theoretical study of audit risk management. The article also provides practical recommendations for auditors on how to mitigate audit risks and improve the quality of audit services in national project governance.

Details

Engineering, Construction and Architectural Management, vol. 32 no. 1
Type: Research Article
ISSN: 0969-9988

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