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Straightness measurement of rail weld joint is of essential importance to railway maintenance. Due to the lack of efficient measurement equipment, there has been limited in-depth research on rail weld joint with a 5-m wavelength range, leaving a significant knowledge gap in this field.

In this study, the authors used the well-established inertial reference method (IR-method), and the state-of-the-art multi-point chord reference method (MCR-method). Two methods have been applied in different types of rail straightness measurement trollies, respectively. These instruments were tested in a high-speed rail section within a certain region of China. The test results were ultimately validated through using traditional straightedge and feeler gauge methods as reference data to evaluate the rail weld joint straightness within the 5-m wavelength range.

The research reveals that IR-method and MCR-method produce reasonably similar measurement results for wavelengths below 1 m. However, MCR-method outperforms IR-method in terms of accuracy for wavelengths exceeding 3 m. Furthermore, it was observed that IR-method, while operating at a slower speed, carries the risk of derailing and is incapable of detecting rail weld joints and low joints within the track.

The research compare two methods’ measurement effects in a longer wavelength range and demonstrate the superiority of MCR-method.

Intelligent and connected vehicle technology is in the ascendant. High-level autonomous driving places more stringent requirements on the accuracy and reliability of environmental perception. Existing research works on multitarget tracking based on multisensor fusion mostly focuses on the vehicle perspective, but limited by the principal defects of the vehicle sensor platform, it is difficult to comprehensively and accurately describe the surrounding environment information.

In this paper, a multitarget tracking method based on roadside multisensor fusion is proposed, including a multisensor fusion method based on measurement noise adaptive Kalman filtering, a global nearest neighbor data association method based on adaptive tracking gate, and a Track life cycle management method based on M/N logic rules.

Compared with fixed-size tracking gates, the adaptive tracking gates proposed in this paper can comprehensively improve the data association performance in the multitarget tracking process. Compared with single sensor measurement, the proposed method improves the position estimation accuracy by 13.5% and the velocity estimation accuracy by 22.2%. Compared with the control method, the proposed method improves the position estimation accuracy by 23.8% and the velocity estimation accuracy by 8.9%.

A multisensor fusion method with adaptive Kalman filtering of measurement noise is proposed to realize the adaptive adjustment of measurement noise. A global nearest neighbor data association method based on adaptive tracking gate is proposed to realize the adaptive adjustment of the tracking gate.

Aircraft assembly is the crucial part of aircraft manufacturing, and to meet the high-precision and high-efficiency requirements, cooperative measurement consisting of multiple measurement instruments and automatic assisted devices is being adopted. To achieve the complete data of all assembly features, measurement devices need to be placed at different positions, and the flexible and efficient transfer relies on Automated Guided Vehicles (AGVs) and robots in the large-size space and close range. This paper aims to improve the automatic station transfer in accuracy and flexibility.

A transferring system with Light Detection and Ranging (LiDAR) and markers is established. The map coupling for navigation is optimized. Markers are distributed according to the accumulated uncertainties. The path planning method applied to the collaborative measurement is proposed for better accuracy. The motion planning method is optimized for better positioning accuracy.

A transferring system is constructed and the system is verified in the laboratory. Experimental results show that the proposed system effectively improves positioning accuracy and efficiency, which improves the station transfer for the cooperative measurement.

A Transferring system for collaborative measurement is proposed. The optimized navigation method extends the application of visual markers. With this system, AGV is capable of the cooperative measurement of large aircraft structural parts.

The purpose of the work is to provide a comprehensive review of the digital image correlation (DIC) technique for those who are interested in performing the DIC technique in the area of manufacturing.

No methodology was used because the paper is a review article.

no fundings.

Herein, the historical development, main strengths and measurement setup of DIC are introduced. Subsequently, the basic principles of the DIC technique are outlined in detail. The analysis of measurement accuracy associated with experimental factors and correlation algorithms is discussed and some useful recommendations for reducing measurement errors are also offered. Then, the utilization of DIC in different manufacturing fields (e.g. cutting, welding, forming and additive manufacturing) is summarized. Finally, the current challenges and prospects of DIC in intelligent manufacturing are discussed.

This paper aims to design a vision-based non-contact real-time accurate heart rate (HR) measurement framework for home nursing assistant.

The study applied Second-Order Blind Signal Identification (SOBI) algorithm to extract remote HR signal and analyzed it with Fast Fourier Transform (FFT). Multiple regions of interest are chosen and analyzed to obtain a more accurate result.

An accurate non-contact hear rate (HR) measurement framework is proposed and proved to be efficient.

The contributions of this HR measurement framework are as follows: accurate measurement of HR, real-time performance, robust under various scenes such as conversation, lightweight computation which is suitable and necessary for home nursing assistance. This framework is designed to be flexibly used in various real-life scenes such as domestic health assistance and affectively intelligent agents and is proved to be robust under such scenes.

This paper proposes a progressive, multi-level framework for diagnosing maintenance performance: rapid performance health checks of key performance for different equipment groups and end-to-end process diagnostics to further locate potential performance issues. A question-based performance evaluation approach is introduced to support the selection and derivation of case-specific indicators based on diagnostic aspects.

The case research method is used to develop the proposed framework. The generic parts of the framework are built on existing maintenance performance measurement theories through a literature review. In the case study, empirical maintenance data of 196 emergency shutdown valves (ESDVs) are collected over a two-year period to support the development and validation of the proposed approach.

To improve processes, companies need a separate performance measurement structure. This paper suggests a hierarchical model in four layers (objective, domain, aspect and performance measurement) to facilitate the selection and derivation of indicators, which could potentially reduce management complexity and help prioritize continuous performance improvement. Examples of new indicators are derived from a case study that includes 196 ESDVs at an offshore oil and gas production plant.

Methodological approaches to deriving various performance indicators have rarely been addressed in the maintenance field. The proposed diagnostic framework provides a structured way to identify and locate process performance issues by creating indicators that can bridge generic evaluation aspects and maintenance data. The framework is highly adaptive as data availability functions are used as inputs to generate indicators instead of passively filtering out non-applicable existing indicators.

While quantification and performance measurement have proliferated widely in academia and the business world, management and organization scholars increasingly agree on the need for a more in-depth focus on the complex dynamics embedded in the construction, use and effects of quantitative measures (pertaining to the thread of research called ethnostatistics). This paper develops a pluralistic method for conducting ethnostatistical research in organizational settings. Whilst presenting practical techniques for conducting research in live settings, it also discusses how historical approaches which focus on source criticism and contextual reconstruction could overcome the limitations of ethnostatistics.

The methodological approach of this paper encompasses an in-depth discussion of the ethnostatistical method, its underlying assumptions and its methodological limitations. Based on this analysis, the authors propose a pluralistic method (model) for conducting ethnostatistical research in organizational settings based on the integration of 1) research practices employed by one of the authors conducting ethnostatistical research in a large multinational organization and 2) best practices from ethnographic and historical research.

This paper suggests how historical approaches can successfully join ethnostatistical enquiries in an attempt to overcome some limitations in existing conventional methods. The developed framework explores four levels of analysis (ethnography, statistics at work, rhetoric of statistics and history of statistics) and suggests practical approaches for each level that can contribute to strengthening the research output and overcoming limitations when using ethnostatistics.

This paper contributes to the ethnostatistical field by discussing the intersection between history and ethnography and the ways for their complementary use in organizational and management research on quantification processes. As such it offers unique insights and hands-on experience from conducting ethnostatistical enquiries in live organizational settings.

To construct a scientific and reasonable indicator system, it is necessary to design a set of standardized indicator primary selection and optimization inspection process. The purpose of this paper is to provide theoretical guidance and reference standards for the indicator system design process, laying a solid foundation for the application of the indicator system, by systematically exploring the expert evaluation method to optimize the index system to enhance its credibility and reliability, to improve its resolution and accuracy and reduce its objectivity and randomness.

The paper is based on system theory and statistics, and it designs the main line of “relevant theoretical analysis – identification of indicators – expert assignment and quality inspection” to achieve the design and optimization of the indicator system. First, the theoretical basis analysis, relevant factor analysis and physical process description are used to clarify the comprehensive evaluation problem and the correlation mechanism. Second, the system structure analysis, hierarchical decomposition and indicator set identification are used to complete the initial establishment of the indicator system. Third, based on expert assignment method, such as Delphi assignments, statistical analysis, t-test and non-parametric test are used to complete the expert assignment quality diagnosis of a single index, the reliability and validity test is used to perform single-index assignment correction and consistency test is used for KENDALL coordination coefficient and F-test multi-indicator expert assignment quality diagnosis.

Compared with the traditional index system construction method, the optimization process used in the study standardizes the process of index establishment, reduces subjectivity and randomness, and enhances objectivity and scientificity.

The innovation point and value of the paper are embodied in three aspects. First, the system design process of the combined indicator system, the multi-dimensional index screening and system optimization are carried out to ensure that the index system is scientific, reasonable and comprehensive. Second, the experts’ background is comprehensively evaluated. The objectivity and reliability of experts’ assignment are analyzed and improved on the basis of traditional methods. Third, aim at the quality of expert assignment, conduct t-test, non-parametric test of single index, and multi-optimal test of coordination and importance of multiple indicators, enhance experts the practicality of assignment and ensures the quality of expert assignment.

The subject of “well-being” has attracted attention from tourism scholars, but differences and misuses in approach have meant that academic contributions and knowledge accumulation to the tourism literature remain relatively little. This paper attempts to clarify the theoretical source of subjective well-being, and critically reflect on the problems existing in the study of well-being when applied to tourism. It is suggested that subjective well-being belongs to the category of “quality of life” and has multiple philosophical foundations and theoretical sources including theories of hedonism, expectation, happiness and various itemised lists of emotions. A hybrid research method is suggested when applying the concept to tourism.

The purpose of this paper is to focus on measuring financial inclusion (FI) level for the developing countries.

By using a two-stage principal component analysis method, we construct a composite FI index to measure the degree of FI. Data are collected through secondary sources including World Bank and IMF reports for the period 2012–2018.

We have built an overall FI index which is considered as a comprehensive measure of FI, a useful tool for policymaking and policy evaluation. Comparison with other studies shows that our FI index corroborates with them.

Building a good FI measurement method is important for developing countries. It helps to assess and compare the level of FI of each country and between countries together, made easily and accurately.

This study emphasizes the important role of FI in the economy. From there, an FI solution is integrated into the construction and calculation of its impact on other factors. This will help policymakers to take effective measures to increase FI levels to achieve sustainable economic growth.