Sixian Chan, Jian Tao, Xiaolong Zhou, Binghui Wu, Hongqiang Wang and Shengyong Chen
Visual tracking technology enables industrial robots interacting with human beings intelligently. However, due to the complexity of the tracking problem, the accuracy of visual…
Abstract
Purpose
Visual tracking technology enables industrial robots interacting with human beings intelligently. However, due to the complexity of the tracking problem, the accuracy of visual target tracking still has great space for improvement. This paper aims to propose an accurate visual target tracking method based on standard hedging and feature fusion.
Design/methodology/approach
For this study, the authors first learn the discriminative information between targets and similar objects in the histogram of oriented gradients by feature optimization method, and then use standard hedging algorithms to dynamically balance the weights between different feature optimization components. Moreover, they penalize the filter coefficients by incorporating spatial regularization coefficient and extend the Kernelized Correlation Filter for robust tracking. Finally, a model update mechanism to improve the effectiveness of the tracking is proposed.
Findings
Extensive experimental results demonstrate the superior performance of the proposed method comparing to the state-of-the-art tracking methods.
Originality/value
Improvements to existing visual target tracking algorithms are achieved through feature fusion and standard hedging algorithms to further improve the tracking accuracy of robots on targets in reality.
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Xiaolong Zhou, Pinghao Wang, Sixian Chan, Kai Fang and Jianwen Fang
Visual object tracking plays a significant role in intelligent robot systems. This study aims to focus on unlocking the tracking performance potential of the deep network and…
Abstract
Purpose
Visual object tracking plays a significant role in intelligent robot systems. This study aims to focus on unlocking the tracking performance potential of the deep network and presenting a dynamic template update strategy for the Siamese trackers.
Design/methodology/approach
This paper presents a novel and efficient Siamese architecture for visual object tracking which introduces densely connected convolutional layers and a dynamic template update strategy into Siamese tracker.
Findings
The most advanced performance can be achieved by introducing densely connected convolutional neural networks that have not yet been applied to the tracking task into SiamRPN. By using the proposed architecture, the experimental results demonstrate that the performance of the proposed tracker is 5.8% (area under curve), 5.4% expected average overlap (EAO) and 3.5% (EAO) higher than the baseline on the OTB100, VOT2016 and VOT2018 data sets and achieves an excellent EAO score of 0.292 on the VOT2019 data set.
Originality/value
This study explores a deeper backbone network with each convolutional network layer densely connected. In response to tracking errors caused by templates that are not updated, this study proposes a dynamic template update strategy.
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Guangde Zhou, Menghao Zhan, Dan Huang, Xiaolong Lyu and Kanghao Yan
By seamlessly integrating physical laws, physics-informed neural networks (PINNs) have flexibly solved a wide variety of partial differential equations (PDEs). However, encoding…
Abstract
Purpose
By seamlessly integrating physical laws, physics-informed neural networks (PINNs) have flexibly solved a wide variety of partial differential equations (PDEs). However, encoding PDEs and constraints as soft penalties in the loss function can cause gradient imbalances, leading to training and accuracy issues. This study aims to introduce the augmented Lagrangian method (ALM) and transfer learning to address these challenges and enhance the effectiveness of PINNs for hydrodynamic lubrication analysis.
Design/methodology/approach
The loss function was reformatted by ALM, adaptively adjusting the loss weights during training. Transfer learning was used to accelerate the convergence of PINNs under similar conditions. Additionally, the iterative process for load balancing was reframed as an inverse problem by extending film thickness as a trainable variable.
Findings
ALM-PINNs significantly reduced the maximum absolute boundary error by almost 80%. Transfer learning accelerated PINNs for solving the Reynolds equation, reducing training epochs by an order of magnitude. The iterative process for load balancing was effectively eliminated by extending the thickness as a trainable parameter, achieving a maximum percentage error of 2.31%. These outcomes demonstrated strong agreement with FDM results, analytical solutions and experimental data.
Originality/value
This study proposes a PINN-based approach for hydrodynamic lubrication analysis that significantly improves boundary accuracy and the training process. Additionally, it effectively replaces the load balancing procedure. This methodology demonstrates considerable potential for broader applications across various boundary value problems and iterative processes.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0277/
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Yang Zhou, Long Wang, Yongbin Lai and Xiaolong Wang
The coupling process between the loading mechanism and the tank car mouth is a crucial step in the tank car loading process. The purpose of this paper is to design a method to…
Abstract
Purpose
The coupling process between the loading mechanism and the tank car mouth is a crucial step in the tank car loading process. The purpose of this paper is to design a method to accurately measure the pose of the tanker car.
Design/methodology/approach
The collected image is first subjected to a gray enhancement operation, and the black parts of the image are extracted using Otsu’s threshold segmentation and morphological processing. The edge pixels are then filtered to remove outliers and noise, and the remaining effective points are used to fit the contour information of the tank car mouth. Using the successfully extracted contour information, the pose information of the tank car mouth in the camera coordinate system is obtained by establishing a binocular projection elliptical cone model, and the pixel position of the real circle center is obtained through the projection section. Finally, the binocular triangulation method is used to determine the position information of the tank car mouth in space.
Findings
Experimental results have shown that this method for measuring the position and orientation of the tank car mouth is highly accurate and can meet the requirements for industrial loading accuracy.
Originality/value
A method for extracting the contours of various types of complex tanker mouth is proposed. This method can accurately extract the contour of the tanker mouth when the contour is occluded or disturbed. Based on the binocular elliptic conical model and perspective projection theory, an innovative method for measuring the pose of the tanker mouth is proposed, and according to the space characteristics of the tanker mouth itself, the ambiguity of understanding is removed. This provides a new idea for the automatic loading of ash tank cars.
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Xiaolong Yuan, Feng Wang, Mianlin Deng and Wendian Shi
Based on conservation of resources (COR) theory, this study aims to examine the impact of daily illegitimate tasks on employees' daily silence and daily voice behavior, as well as…
Abstract
Purpose
Based on conservation of resources (COR) theory, this study aims to examine the impact of daily illegitimate tasks on employees' daily silence and daily voice behavior, as well as the mediating role of daily ego depletion and the moderating role of trait mindfulness.
Design/methodology/approach
Through daily diary approach, 81 employees were followed for 10 consecutive workdays. Multilevel analysis was employed to examine the hypothesized relationships.
Findings
The results showed that daily illegitimate tasks are positively related to daily silence behavior and negatively related to daily voice behavior; daily ego depletion plays a mediating role in these relationships. Trait mindfulness moderates the effect of daily illegitimate tasks on daily ego depletion and the indirect effect of daily illegitimate tasks on daily silence and daily voice.
Practical implications
Managers should be mindful of minimizing the assignment of illegitimate tasks. Additionally, it is recommended that the organization provide training courses for employees to help them reduce ego depletion. Finally, organizations should focus on fostering high levels of mindfulness among their employees.
Originality/value
This study contributes to the existing literature by investigating the immediate impact of illegitimate tasks on employee voice and silence at within-person level. By doing so, it enhances comprehension of the consequences associated with illegitimate tasks. Meanwhile, this study offers additional insights into the underlying mechanisms and boundary conditions of the effect of illegitimate tasks from a resource perspective.
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Ruipeng Tong, Hui Zhao, Na Zhang, Hongwei Li, Xiaolong Wang and Hongqing Yang
The purpose of this study is to establish a modified accident causation model for highway construction accidents (ACM-HC) and describe the establishment process of the ACM-HC.
Abstract
Purpose
The purpose of this study is to establish a modified accident causation model for highway construction accidents (ACM-HC) and describe the establishment process of the ACM-HC.
Design/methodology/approach
Based on the 2–4 Model, a framework of the ACM-HC was constructed, and the accident causal factors (CF) were extracted from four aspects of human, material, environment and management. In addition, association rule mining (ARM) was introduced to analyze accident investigation reports to obtain the interrelationships between the factors. Based on the framework, factors and ARM results, the ACM-HC was established. Finally, the ACM-HC was verified with a tunnel collapse accident.
Findings
Both the external and internal causes of contractor cause accidents. The flaws of safety management of other stakeholders are external causes. In terms of the internal causes, there are four stages: direct causes, indirect causes, radical causes and root causes. More specifically, the direct causes refer to the unsafe acts and the unsafe conditions; ineffective safety supervision and poor individual factors of frontline workers constitute the indirect causes; the radical causes lie in the flaws of construction procedures and technical schemes; the root causes are related to the poor individual factors of decision makers and managers.
Originality/value
The ACM-HC expresses the causes, sequence and mechanism of highway construction accidents in a visual way. In addition, this study describes a process of using a qualitative–quantitative hybrid approach to establish a modified ACM, which provides a different perspective for the establishment of an ACM.
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Xiaolong Tian and Tom Christensen
Compared with the worldwide reform trend of transcending new public management (NPM) during the past two decades, China's service-oriented government (SOG) reforms are a…
Abstract
Purpose
Compared with the worldwide reform trend of transcending new public management (NPM) during the past two decades, China's service-oriented government (SOG) reforms are a relatively different reform approach. After building an SOG was politically identified in 2004, China launched three rounds of SOG reforms in 2008, 2013 and 2018. The purpose of this article is to examine what is meant by China's SOG approach and analyze the reasons behind its emergence. In particular, it explores how this approach might be interpreted in NPM, and particularly post-NPM terms.
Design/methodology/approach
The main theoretical basis of the paper is three theoretical perspectives from organizational theory – the instrumental, cultural and myth perspectives, but more specifically, the concepts complexity and hybridity. The empirical examples are selected from the SOG reforms of 2008, 2013 and 2018. The data used are a combination of public documents and scholarly secondary literature.
Findings
This paper discusses the SOG approach in China as a response to the negative effects of NPM-related reforms and informed by the western post-NPM reforms. It contends that China's SOG is a complex and hybrid approach in which NPM and post-NPM elements coexist and their balance is different from the west.
Originality/value
Few authors have considered China's SOG approach in NPM and post-NPM terms. This paper contributes not only to a wider understanding of the ongoing SOG reform process in China, but also to the understanding of the relevance of public administration theories in a comparative perspective.
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Ankang Ji, Xiaolong Xue, Limao Zhang, Xiaowei Luo and Qingpeng Man
Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack…
Abstract
Purpose
Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.
Design/methodology/approach
This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.
Findings
In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.
Originality/value
The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.
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Xiaolong Lyu, Dan Huang, Liwei Wu and Ding Chen
Parameter estimation in complex engineering structures typically necessitates repeated calculations using simulation models, leading to significant computational costs. This paper…
Abstract
Purpose
Parameter estimation in complex engineering structures typically necessitates repeated calculations using simulation models, leading to significant computational costs. This paper aims to introduce an adaptive multi-output Gaussian process (MOGP) surrogate model for parameter estimation in time-consuming models.
Design/methodology/approach
The MOGP surrogate model is established to replace the computationally expensive finite element method (FEM) analysis during the estimation process. We propose a novel adaptive sampling method for MOGP inspired by the traditional expected improvement (EI) method, aiming to reduce the number of required sample points for building the surrogate model. Two mathematical examples and an application in the back analysis of a concrete arch dam are tested to demonstrate the effectiveness of the proposed method.
Findings
The numerical results show that the proposed method requires a relatively small number of sample points to achieve accurate estimates. The proposed adaptive sampling method combined with the MOGP surrogate model shows an obvious advantage in parameter estimation problems involving expensive-to-evaluate models, particularly those with high-dimensional output.
Originality/value
A novel adaptive sampling method for establishing the MOGP surrogate model is proposed to accelerate the procedure of solving large-scale parameter estimation problems. This modified adaptive sampling method, based on the traditional EI method, is better suited for multi-output problems, making it highly valuable for numerous practical engineering applications.
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Huanguang Qiu, Ganxiao Leng, Xiaolong Feng and Sansi Yang
This paper aims to examine impacts of the poverty alleviation relocation (PAR) program on diet quality of low-income households in China. We explore the impact mechanism of…
Abstract
Purpose
This paper aims to examine impacts of the poverty alleviation relocation (PAR) program on diet quality of low-income households in China. We explore the impact mechanism of relocation on diet quality and the heterogeneous effects of different relocation modes.
Design/methodology/approach
A fixed effects model is constructed using panel data of 1126 low-income households collected over three years in eight provinces of China. The PAR program provides a natural experiment which dramatically changes the living conditions surrounding farmers. We are able to identify the causal effects of relocation on diet quality free from selection bias.
Findings
The empirical results show that the PAR program improves diet quality of low-income households and that better market access and increasing incomes induced by relocation play an important role in this improvement. Improved market access significantly reduces the over-consumption of staple foods, whereas higher income significantly reduces the intake divergences of non-staple foods. The impacts of different relocation modes on diet quality are highly heterogeneous.
Practical implications
Our findings indicate that the PAR program benefits diet quality of low-income households through greater market access and increases in total household income. Market improvements and food subsidies are conducive to improving the diet quality of the low income.
Originality/value
Despite widespread evidences of healthy diets being associated with household environments and income, selection bias remains. This paper utilizes an exogenous program to explore the causal impacts of market access and family income on diet quality and to separate their different effects.