Yichen Zhang, Feng Cui, Wu Liu, Wenhao Zhu, Yiming Xiao, Qingcheng Guo and Jiawang Mou
Endurance time is an important factor limiting the progress of flapping-wing aircraft. In this study, this paper developed a prototype of a double-wing flapping-wing micro air…
Abstract
Purpose
Endurance time is an important factor limiting the progress of flapping-wing aircraft. In this study, this paper developed a prototype of a double-wing flapping-wing micro air vehicle (FMAV) that mimics insect-scale flapping wing for flight. Besides, novel methods for optimal selection of motor, wing length and battery to achieve prolonged endurance are proposed. The purpose of this study is increasing the flight time of double-wing FMAV by optimizing the flapping mechanism, wings, power sources, and energy sources.
Design/methodology/approach
The 20.4 g FMAV prototype with wingspan of 21.5 cm used an incomplete gear flapping wing mechanism. The motor parameters related to the lift-to-power ratio of the prototype were first identified and analyzed, then theoretical analysis was conducted to analyze the impact of wing length and flapping frequency on the lift-to-power ratio, followed by practical testing to validate the theoretical findings. After that, analysis and testing examined the impact of battery energy density and efficiency on endurance. Finally, the prototype’s endurance duration was calculated and tested.
Findings
The incomplete gear facilitated 180° symmetric flapping. The motor torque constant showed a positive correlation with the prototype’s lift-to-power ratio. It was also found that the prototype achieved the best lift-to-power ratio when using 100 mm wings.
Originality/value
A gear-driven flapping mechanism was designed, capable of smoothly achieving 180° symmetric flapping. Besides, factors affecting long-duration flight – motor, wings and battery – were identified and a theoretical flight duration analysis method was developed. The experimental result proves that the FMAV could achieve the longest hovering time of 705 s, outperforming other existing research on double-wing FMAV for improving endurance.
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Xiwen Zhang, Zhen Zhang, Wenhao Sun, Jilei Hu, Liangliang Zhang and Weidong Zhu
Under the repeated action of the construction load, opening deformation and disturbed deformation occurred at the precast box culvert joints of the shield tunnel. The objective of…
Abstract
Purpose
Under the repeated action of the construction load, opening deformation and disturbed deformation occurred at the precast box culvert joints of the shield tunnel. The objective of this paper is to investigate the effect of construction vehicle loading on the mechanical deformation characteristics of the internal structure of a large-diameter shield tunnel during the entire construction period.
Design/methodology/approach
The structural response of the prefabricated internal structure under heavy construction vehicle loads at four different construction stages (prefabricated box culvert installation, curved lining cast-in-place, lane slab installation and pavement structure casting) was analyzed through field tests and ABAQUS (finite element analysis software) numerical simulation.
Findings
Heavy construction vehicles can cause significant mechanical impacts on the internal structure, as the construction phase progresses, the integrity of the internal structure with the tunnel section increases. The vertical and horizontal deformation of the internal structure is significantly reduced, and the overall stress level of the internal structure is reduced. The bolts connecting the precast box culvert have the maximum stress at the initial stage of construction, as the construction proceeds the stress distribution among the bolts gradually becomes uniform.
Originality/value
This study can provide a reference for the design model, theoretical analysis and construction technology of the internal structure during the construction of large-diameter tunnel projects.
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Xiaojun Zhan, Wenhao Luo, Hanyu Ding, Yanghao Zhu and Yirong Guo
Prior studies have mainly attributed customer incivility to dispositional characteristics, whereas little attention has been paid to exploring service employees' role in…
Abstract
Purpose
Prior studies have mainly attributed customer incivility to dispositional characteristics, whereas little attention has been paid to exploring service employees' role in triggering or reducing customer incivility. The purpose of the present study is to propose and test a model in which service employees' emotional labor strategies affect customer incivility via influencing customers' self-esteem threat, as well as examine the moderating role of customer's perception of service climate.
Design/methodology/approach
Based on a matched sample consisting of 317 employee-customer dyads in China, multiple regression analysis and indirect effect tests were employed to test our model.
Findings
The study shows that employee surface acting is positively related to customer incivility, whereas deep acting is negatively associated with customer incivility. Moreover, customer self-esteem threat mediates the relationship between both types of emotional labor and customer incivility. Customer perception of service climate moderates the relationship between deep acting and customer self-esteem threat.
Originality/value
The current research broadens the antecedents of customer incivility from the employee perspective and sheds more light on the role of customer self-esteem in the interactions between employees and customers. It also demonstrates a complementary relationship between service climate and individual employees' emotional labor strategies, thereby expanding the existing understanding of the management of employees' emotional labor.
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Daxin Tian, Weiqiang Gong, Wenhao Liu, Xuting Duan, Yukai Zhu, Chao Liu and Xin Li
This paper aims to introduce vehicular network platform, routing and broadcasting methods and vehicular positioning enhancement technology, which are three aspects of the…
Abstract
Purpose
This paper aims to introduce vehicular network platform, routing and broadcasting methods and vehicular positioning enhancement technology, which are three aspects of the applications of intelligent computing in vehicular networks. From this paper, the role of intelligent algorithm in the field of transportation and the vehicular networks can be understood.
Design/methodology/approach
In this paper, the authors introduce three different methods in three layers of vehicle networking, which are data cleaning based on machine learning, routing algorithm based on epidemic model and cooperative localization algorithm based on the connect vehicles.
Findings
In Section 2, a novel classification-based framework is proposed to efficiently assess the data quality and screen out the abnormal vehicles in database. In Section 3, the authors can find when traffic conditions varied from free flow to congestion, the number of message copies increased dramatically and the reachability also improved. The error of vehicle positioning is reduced by 35.39% based on the CV-IMM-EKF in Section 4. Finally, it can be concluded that the intelligent computing in the vehicle network system is effective, and it will improve the development of the car networking system.
Originality/value
This paper reviews the research of intelligent algorithms in three related areas of vehicle networking. In the field of vehicle networking, these research results are conducive to promoting data processing and algorithm optimization, and it may lay the foundation for the new methods.
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Wenhao Zhang, Melvyn Lionel Smith, Lyndon Neal Smith and Abdul Rehman Farooq
This paper aims to introduce an unsupervised modular approach for eye centre localisation in images and videos following a coarse-to-fine, global-to-regional scheme. The design of…
Abstract
Purpose
This paper aims to introduce an unsupervised modular approach for eye centre localisation in images and videos following a coarse-to-fine, global-to-regional scheme. The design of the algorithm aims at excellent accuracy, robustness and real-time performance for use in real-world applications.
Design/methodology/approach
A modular approach has been designed that makes use of isophote and gradient features to estimate eye centre locations. This approach embraces two main modalities that progressively reduce global facial features to local levels for more precise inspections. A novel selective oriented gradient (SOG) filter has been specifically designed to remove strong gradients from eyebrows, eye corners and self-shadows, which sabotage most eye centre localisation methods. The proposed algorithm, tested on the BioID database, has shown superior accuracy.
Findings
The eye centre localisation algorithm has been compared with 11 other methods on the BioID database and six other methods on the GI4E database. The proposed algorithm has outperformed all the other algorithms in comparison in terms of localisation accuracy while exhibiting excellent real-time performance. This method is also inherently robust against head poses, partial eye occlusions and shadows.
Originality/value
The eye centre localisation method uses two mutually complementary modalities as a novel, fast, accurate and robust approach. In addition, other than assisting eye centre localisation, the SOG filter is able to resolve general tasks regarding the detection of curved shapes. From an applied point of view, the proposed method has great potentials in benefiting a wide range of real-world human-computer interaction (HCI) applications.
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Wenhao Zhou, Hailin Li, Liping Zhang, Huimin Tian and Meng Fu
The purpose of this work is to construct a grey entropy comprehensive evaluation model to measure the regional green innovation vitality (GIV) of 31 provinces in China.
Abstract
Purpose
The purpose of this work is to construct a grey entropy comprehensive evaluation model to measure the regional green innovation vitality (GIV) of 31 provinces in China.
Design/methodology/approach
The traditional grey relational proximity and grey relational similarity degree are integrated into the novel comprehensive grey evaluation framework. The evaluation system of regional green innovation vitality is constructed from three dimensions: economic development vitality, innovative transformation power and environmental protection efficacy. The weights of each indicator are obtained by the entropy weight method. The GIV of 31 provinces in China is measured based on provincial panel data from 2016 to 2020. The ward clustering and K-nearest-neighbor (KNN) algorithms are utilized to explore the regional green innovation discrepancies and promotion paths.
Findings
The novel grey evaluation method exhibits stronger ability to capture intrinsic patterns compared with two separate traditional grey relational models. Green innovation vitality shows obvious regional discrepancies. The Matthew effect of China's regional GIV is obvious, showing a basic trend of strong in the eastern but weak in the western areas. The comprehensive innovation vitality of economically developed provinces exhibits steady increasing trend year by year, while the innovation vitality of less developed regions shows an overall steady state of no fluctuation.
Practical implications
The grey entropy comprehensive relational model in this study is applied for the measurement and evaluation of regional GIV, which improves the one-sidedness of traditional grey relational analysis on the proximity or similarity among sequences. In addition, a three-dimensional evaluation system of regional GIV is constructed, which provides the practical guidance for the research of regional development strategic planning as well as promotion paths.
Originality/value
A comprehensive grey entropy relational model based on traditional grey incidence analysis (GIA) in terms of proximity and similarity is proposed. The three-dimensional evaluation system of China's regional GIV is constructed, which provides a new research perspective for regional innovation evaluation and expands the application scope of grey system theory.
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Wenhao Zhou, Hailin Li, Hufeng Li, Liping Zhang and Weibin Lin
Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to…
Abstract
Purpose
Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.
Design/methodology/approach
First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.
Findings
The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.
Originality/value
Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.
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Wenhao Yi, Mingnian Wang, Jianjun Tong, Siguang Zhao, Jiawang Li, Dengbin Gui and Xiao Zhang
The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock…
Abstract
Purpose
The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways.
Design/methodology/approach
Relying on the support vector machine (SVM)-based classification model, the nominal classification of blastholes and nominal zoning and classification terms were used to demonstrate the heterogeneity identification method for the surrounding rock of tunnel face, and the identification calculation was carried out for the five test tunnels. Then, the suggestions for local optimization of the support structures of large-section rock tunnels were put forward.
Findings
The results show that compared with the two classification models based on neural networks, the SVM-based classification model has a higher classification accuracy when the sample size is small, and the average accuracy can reach 87.9%. After the samples are replaced, the SVM-based classification model can still reach the same accuracy, whose generalization ability is stronger.
Originality/value
By applying the identification method described in this paper, the significant heterogeneity characteristics of the surrounding rock in the process of two times of blasting were identified, and the identification results are basically consistent with the actual situation of the tunnel face at the end of blasting, and can provide a basis for local optimization of support parameters.
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Yanqiu Xia, Wenhao Chen, Yi Zhang, Kuo Yang and Hongtao Yang
The purpose of this study is to investigate the effectiveness of a composite lubrication system combining polytetrafluoroethylene (PTFE) film and oil lubrication in steel–steel…
Abstract
Purpose
The purpose of this study is to investigate the effectiveness of a composite lubrication system combining polytetrafluoroethylene (PTFE) film and oil lubrication in steel–steel friction pairs.
Design/methodology/approach
A PTFE layer was sintered on the surface of a steel disk, and a lubricant with additives was applied to the surface of the steel disk. A friction and wear tester was used to evaluate the tribological properties and insulation capacity. Fourier transform infrared spectrometer was used to analyze the changes in the composition of the lubricant, and X-ray photoelectron spectroscopy was used to analyze the chemical composition of the worn surface.
Findings
It was found that incorporating the PTFE film with PSAIL 2280 significantly enhanced both the friction reduction and insulation capabilities at the electrical contact interface during sliding. The system consistently achieved ultra-low friction coefficients (COF < 0.01) under loads of 2–4 N and elucidated the underlying lubrication mechanisms.
Originality/value
This work not only confirm the potential of PTFE films in insulating electrical contact lubrication but also offer a viable approach for maintaining efficient and stable low-friction wear conditions.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0222/
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Wenhao Zhou and Hailin Li
This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough…
Abstract
Purpose
This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.
Design/methodology/approach
Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.
Findings
It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.
Originality/value
Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.