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Article
Publication date: 2 January 2025

Yahui Zhang, Aimin Li, Haopeng Li, Fei Chen and Ruiying Shen

A tightly coupled global navigation satellite system (GNSS)-Vision-IMU-wheel odometer (GVIWO) system is proposed, which can realize robust positioning in extreme environments. The…

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Abstract

Purpose

A tightly coupled global navigation satellite system (GNSS)-Vision-IMU-wheel odometer (GVIWO) system is proposed, which can realize robust positioning in extreme environments. The purpose of this study is to achieve adaptive initialization in complex environments, sensor anomaly detection and processing, and adaptive robust localization in extreme environments.

Design/methodology/approach

Adaptive initialization includes traditional dynamic and static initialization and extreme condition initialization. To deal with the unstable visual features in the state of excited motion, a method of wheel odometer assisted initialization is designed. According to the abnormal condition of the sensor, the anomaly detection and attenuation mechanism are designed to realize the accurate positioning of the sensor under abnormal condition.

Findings

Tight coupling optimization of GNSS signals, RGB+Depth Map cameras, inertial measurement units and wheel odometers ensures accurate positioning in both indoor and outdoor environments. Through open data sets and field validation experiments, the proposed tightly coupled system has strong adaptability, especially in extreme environments.

Originality/value

A new framework is proposed by integrating GNSS, visual, inertial measurement unit (IMU) and wheel odometer sensors to form an efficient positioning solution. An adaptive initialization method is proposed to enhance the robustness and real-time performance of the positioning system in complex and dynamic environments. A mechanism for detecting and attenuating sensor anomalies is designed, enabling quasideterministic positioning under sensor anomalies.

Details

Sensor Review, vol. 45 no. 2
Type: Research Article
ISSN: 0260-2288

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Article
Publication date: 20 February 2025

Ying Zhao, Tao Zhang, Jie Xu, Jie Yang and Wen-Ze Wu

This study aims to design a novel seasonal discrete grey model for forecasting monthly natural gas consumption by incorporating damping accumulation and time-polynomial term.

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Abstract

Purpose

This study aims to design a novel seasonal discrete grey model for forecasting monthly natural gas consumption by incorporating damping accumulation and time-polynomial term.

Design/methodology/approach

Considering the principle of new information priority and nonlinear patterns in the original series of monthly natural gas consumption, we establish a novel discrete seasonal grey model by adding the damping accumulation and time-polynomial term into the existing model. In addition, the order of damping accumulation and the coefficient of time-power term can be determined by the moth flame optimization (MFO) algorithm.

Findings

The empirical cases show that the proposed model has a better prediction performance when compared with other benchmark models, including six seasonal grey models, one statistical model and one artificial intelligent model. Based on forecasts, the proposed model can be considered a promising tool for monthly natural gas consumption (NGC) in US.

Originality/value

By combining the damping accumulation and the time-polynomial term, a new discrete seasonal grey model for improving the prediction performance of the existing grey model is proposed. The properties of the proposed model are given, and the newly-designed model is initially applied to predict monthly NGC in US.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 29 November 2024

Ditte Barnoth, Scott Brown, Renan Saraiva, Marlena Wagner and Hayley Joanne Cullen

Post-event information (PEI) may distort eyewitness memory and lead to erroneous eyewitness testimonies. This paper aims to explore whether factors such as volitional engagement…

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Abstract

Purpose

Post-event information (PEI) may distort eyewitness memory and lead to erroneous eyewitness testimonies. This paper aims to explore whether factors such as volitional engagement with PEI (e.g. choice to engage with a co-witness) and memory distrust influence misinformation acceptance and the perceived credibility of a co-witness.

Design/methodology/approach

Participants (n = 223) completed the Memory Distrust Scale and then watched a short mock crime video. Thereafter, two-thirds of the participants were asked whether they would prefer or not to listen to a co-witness’ account of the witnessed event (choice condition), and one-third of the participants did not have the choice (control condition). Every participant listened to the co-witness account (which contained items of misinformation); thus, those who preferred to listen to the testimony were in the choice-yes (i.e., volition) condition and those who preferred not to listen were in the choice-no (i.e., non-volition) condition. Finally, participants completed a cued recall task assessing their memory of the video and acceptance of misinformation. They also provided ratings to establish the perceived credibility of the co-witness.

Findings

The results indicated that neither volition nor memory distrust influenced misinformation acceptance. However, those who preferred to listen to the testimony (i.e., the choice-yes condition) perceived the co-witness as more credible than those in the choice-no or control conditions.

Practical implications

The findings suggest that witnesses are susceptible to misinformation regardless of their willingness to engage with or avoid PEI. Further implications and future research directions are discussed.

Originality/value

To the best of the authors’ knowledge, this study is the first to investigate the role of volition and memory distrust as a trait in eyewitnesses tendency to engage with or avoid post-event information. The research explores whether these mechanisms impact upon memory conformity and perceived co-witness credibility.

Details

Journal of Criminal Psychology, vol. 15 no. 2
Type: Research Article
ISSN: 2009-3829

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Article
Publication date: 25 October 2024

Rahul Kumar Choubey, Mayur Patil and Prashant K. Jain

Induction heating as an energy source is a novel, recent method in extrusion-based metal additive manufacturing. The purpose of this paper is to develop an optimized coil for…

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Abstract

Purpose

Induction heating as an energy source is a novel, recent method in extrusion-based metal additive manufacturing. The purpose of this paper is to develop an optimized coil for extrusion-based metal wire additive manufacturing. The optimized coil is so designed that uniform temperature distribution can be achieved in the extruder, achieving uniform material deposition in a semi-solid state, which is required for additive manufacturing.

Design/methodology/approach

Coil shape optimization is achieved by using arrangement of coil turns as a control variable in the form optimization process, and the objective function is to minimize the gradient in the distribution of the magnetic field to achieve uniform heating in the extruder for maintaining consistent solid and liquid fraction during material deposition. A combination of numerical solutions and geometrical optimization has been used for this study.

Findings

Experimental and simulation results reveal that the optimized induction coil produced a more uniform axial temperature distribution in the extruder, which is suitable for maintaining a uniform solid-to-liquid fraction ratio during material deposition.

Originality/value

The author has investigated the use of optimized-shaped induction coils in extrusion-based additive manufacturing. The optimized coil can achieve a more uniform temperature distribution in the extruder in comparison to the standard helical coil used in the existing process, which means optimized coil achieves a more uniform solid-to-liquid ratio during printing in comparison to existing standard coil shapes used for heating extruders and fulfils the requirement of additive manufacturing.

Details

Rapid Prototyping Journal, vol. 31 no. 2
Type: Research Article
ISSN: 1355-2546

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Article
Publication date: 20 April 2023

Jie Yang, Xinkai Zhang and Yujing Pei

From a knowledge-management perspective, this paper aims to analyze the digital transformation of the business models of traditional Chinese sporting goods companies in the…

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Abstract

Purpose

From a knowledge-management perspective, this paper aims to analyze the digital transformation of the business models of traditional Chinese sporting goods companies in the context of the pandemic crisis and to explore the role of their digital transformation in coping with the crisis.

Design/methodology/approach

Using theoretical sampling, typical sporting goods companies are selected for case studies. We provide an in-depth analysis of how these companies achieve high performance levels through the digital transformation of their business models in the post-COVID-19 era and discuss the key role of knowledge management in this achievement.

Findings

Focusing on the challenges faced by Chinese sporting goods enterprises during the pandemic crisis from the knowledge-management perspective, we find that through the digital transformation of their business models, enterprises can improve their knowledge-management capabilities, enhance their flexibility to respond to sudden crises and maintain a higher level of corporate performance.

Research limitations/implications

This paper has significant implications for sporting goods companies wishing to achieve high corporate performance through the digital transformation of their business models in the post-COVID-19 era. Future research should address the dynamic mechanism of the digital transformation of business models to improve enterprise knowledge-management capabilities and the impact mechanism of knowledge-management capabilities on interenterprise organizational resilience.

Originality/value

This paper proposes specific strategies in the process of the digital transformation of business models that are essential for improving enterprises’ internal and external knowledge-management capabilities.

Details

Journal of Knowledge Management, vol. 29 no. 3
Type: Research Article
ISSN: 1367-3270

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Article
Publication date: 21 February 2025

Yong Yang, Yue Li, Xinyuan Zhao, Rob Law and Hongjin Song

Based on the advice response theory perspective, this study aims to investigate the effects of human managers and artificial intelligence (AI) systems on customer-contact…

4

Abstract

Purpose

Based on the advice response theory perspective, this study aims to investigate the effects of human managers and artificial intelligence (AI) systems on customer-contact employees’ aversion to AI systems in the hospitality industry. It examined the mediating role of advice content characteristics (efficacy, feasibility and implementation limitations) and advice delivery (facework and comprehensibility) on customer-contact employees’ aversion to AI systems.

Design/methodology/approach

Two scenario-based experiments were conducted (Nexperiment 1 = 499 and Nexperiment 2 = 300). Experiment 1 compared the effects of different advisor types (human managers vs AI systems) on employees’ aversion to AI systems. Experiment 2 investigated the mediating role of advice content characteristics (efficacy, feasibility and implementation limitations) and advice delivery (facework and comprehensibility).

Findings

The results showed employees tended to prioritize advice from human managers over output from AI systems. Moreover, advice content characteristics (efficacy, feasibility and implementation limitations) and advice delivery (facework and comprehensibility) played mediating roles in the relationship between advisor type characteristics and employees’ aversion to AI systems.

Practical implications

These findings contribute to the understanding of AI system aversion and provide theoretical insights into management practices involving customer-contact employees who interact with AI technology in the hospitality industry.

Originality/value

The primary contribution of this study is that it enriches the literature on employee aversion to AI systems by exploring the dual mediators (advice content characteristics and advice delivery) through which advisor type characteristics affect AI system aversion.

Details

International Journal of Contemporary Hospitality Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-6119

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Article
Publication date: 10 November 2023

Hasliza Abdul Halim, Noor Hazlina Ahmad and Ali Waqas

This study aims to explore the key factors that hinder technopreneur’s success.

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Abstract

Purpose

This study aims to explore the key factors that hinder technopreneur’s success.

Design/methodology/approach

The finalization of the most appropriate method to conduct any study is based on the nature of the research questions (Shaw, 1999; Morse and Richards, 2002). As this study is exploratory, a qualitative approach was used to collect the data. Morse and Richards (2002) have emphasized that the qualitative technique to collect is useful for in-depth assessment of the participant’s experiences, their understanding regarding the matter and their interpretation of their experiences.

Findings

Technopreneurs face four significant problems that have an impact on their business agility and competitiveness. These four factors are as follows: the entrepreneur’s skills and preparedness; their organization’s insufficient capabilities and talent to deal with the challenges; a lack of support mechanisms from relevant institutions; and, finally, the rapidly changing business environment in terms of technology and competition.

Originality/value

This study explores the hindrance factors through qualitative techniques faced by young technopreneurs in the context of Malaysia. This study will provide deep insight regarding the key issues facing new startups and will be helpful for policymakers.

Details

Journal of Science and Technology Policy Management, vol. 16 no. 2
Type: Research Article
ISSN: 2053-4620

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Article
Publication date: 26 February 2025

Lianfeng Shen, Jinhua Sun, Lei Miao, Haiping Gu, Shuzhen Qiao, Lei Wang and Wei Wei

The application of galvanized steel is widespread across industries due to its protective zinc coating that protects against atmospheric corrosion. However, previous studies have…

0

Abstract

Purpose

The application of galvanized steel is widespread across industries due to its protective zinc coating that protects against atmospheric corrosion. However, previous studies have primarily focused on long-term corrosion rates rather than the full-scale corrosion behavior of the zinc. This paper aims to study the full-scale corrosion evolution of galvanic steel under simulated marine atmospheric environment using real-time EIS measurement.

Design/methodology/approach

Electrochemical impedance spectroscopy (EIS) provides an advanced method in monitoring such behavior. Therefore, the EIS method has been used to conduct a comprehensive investigation on the corrosion behavior of galvanic steel in a full-time manner.

Findings

The results indicate that the corrosion process of galvanic steel can be divided into three stages: an initial stage with an increased corrosion rate, a subsequent stage with a reduced corrosion rate, and finally a third stage with the lowest and constant corrosion rate. The evolution of corrosion resistance is closely related to changes in composition and structure of the patina layer. In the initial stage, galvanized steel undergoes the formation of soluble ZnCl2 and needle-like Zn5(OH)8Cl2·H2O, which promotes the generation and maintenance of an electrolyte layer, consequently leading to an increase in corrosion rate. With prolonged corrosion time, there is a continuous accumulation of Zn5(OH)8Cl2·H2O within the patina layer, which reduces the content of soluble components and promotes the development of a denser inner layer, thus enhancing corrosion resistance.

Originality/value

This work holds significance in the monitoring of corrosion, understanding the evolution of corrosion and predicting the lifespan of galvanized steel.

Details

Anti-Corrosion Methods and Materials, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0003-5599

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Article
Publication date: 15 January 2024

Faris Elghaish, Sandra Matarneh, Essam Abdellatef, Farzad Rahimian, M. Reza Hosseini and Ahmed Farouk Kineber

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly…

187

Abstract

Purpose

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

Design/methodology/approach

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

Findings

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

Practical implications

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

Originality/value

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

Details

Smart and Sustainable Built Environment, vol. 14 no. 2
Type: Research Article
ISSN: 2046-6099

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Article
Publication date: 14 December 2023

Yajun Chen, Zehuan Sui and Juan Du

This paper aims to focus on the research progress of intelligent self-healing anti-corrosion coatings in the aviation field in the past few years. The paper provides certain…

249

Abstract

Purpose

This paper aims to focus on the research progress of intelligent self-healing anti-corrosion coatings in the aviation field in the past few years. The paper provides certain literature review supports and development direction suggestions for future research on intelligent self-healing coatings in aviation.

Design/methodology/approach

This mini-review uses a systematic literature review process to provide a comprehensive and up-to-date review of intelligent self-healing anti-corrosion coatings that have been researched and applied in the field of aviation in recent years. In total, 64 articles published in journals in this field in the last few years were analysed in this paper.

Findings

The authors conclude that the incorporation of multiple external stimulus-response mechanisms makes the coatings smarter in addition to their original self-healing corrosion protection function. In the future, further research is still needed in the research and development of new coating materials, the synergistic release of multiple self-healing mechanisms, coating preparation technology and corrosion monitoring technology.

Originality/value

To the best of the authors’ knowledge, this is one of the few systematic literature reviews on intelligent self-healing anti-corrosion coatings in aviation. The authors provide a comprehensive overview of the topical issues of such coatings and present their views and opinions by discussing the opportunities and challenges that self-healing coatings will face in future development.

Details

Anti-Corrosion Methods and Materials, vol. 72 no. 2
Type: Research Article
ISSN: 0003-5599

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