Lina Si, Yan Pan, Xiaoqing Zhang, Jie Wang, Jia Yao, Yanjie Wang, Fengbin Liu and Feng He
This paper aims to clarify the effects of metallic nanoparticles (NPs) additives and room temperature ionic liquids (ILs) on the tribological performance of electric contacts.
Abstract
Purpose
This paper aims to clarify the effects of metallic nanoparticles (NPs) additives and room temperature ionic liquids (ILs) on the tribological performance of electric contacts.
Design/methodology/approach
Tribological properties of copper (Cu) and silver (Ag) NPs as lubricant additives in different lubricants of ILs or polyalphaolefin (PAO) oils under applied electric currents were investigated. After tribological tests, morphologies of worn surfaces were observed; meanwhile, lubrication and anti-wear properties were analyzed.
Findings
The mixture solution of the IL and Cu NPs showed desirable lubrication and anti-wear properties due to the reduction of electrocorrosion and the enhancement of rolling effects of particles in the contact region. The anti-wear performance of Cu NPs is better than that of Ag NPs due to the difference in the particle size. The PAO oil with the Cu NPs additives showed poor lubrication properties due to the low solubility of the particles in the oil. When the direction of applied current was changed, the friction of the lubricant with better conductivity was more stable in the variation trend.
Originality/value
This paper begins with a study of tribological properties of Cu and Ag NPs as lubricant additives in different lubricants of IL or PAO oils under applied electric currents. The authors then propose several methods and possible solutions which could be implemented to improve the tribological performance of electric contacts.
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Yifan Pan, Lei Zhang, Dong Mei, Gangqiang Tang, Yujun Ji, Kangning Tan and Yanjie Wang
This study aims to present a type of metamorphic mechanism-based quadruped crawling robot. The trunk design of the robot has a metamorphic mechanism, which endows it with…
Abstract
Purpose
This study aims to present a type of metamorphic mechanism-based quadruped crawling robot. The trunk design of the robot has a metamorphic mechanism, which endows it with excellent crawling capability and adaptability in challenging environments.
Design/methodology/approach
The robot consists of a metamorphic trunk and four series-connected three-joint legs. First, the walking and steering strategy is planned through the stability and mechanics analysis. Then, the walking and steering performance is examined using virtual prototype technology, as well as the efficacy of the walking and turning strategy.
Findings
The metamorphic quadruped crawling robot has wider application due to its variable trunk configuration and excellent leg motion space. The robot can move in two modes (constant trunk and trunk configuration transformation, respectively, while walking and rotating), which exhibits outstanding stability and adaptability in the examination and verification of prototypes.
Originality/value
The design can enhance the capacity of the quadruped crawling robot to move across a complex environment. The virtual prototype technology verifies that the proposed walking and steering strategy has good maneuverability and stability, which considerably expands the application opportunity in the fields of complicated scene identification and investigation.
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Yanjie Wang, Zhengchao Xie, InChio Lou, Wai Kin Ung and Kai Meng Mok
The purpose of this paper is to examine the applicability and capability of models based on a genetic algorithm and support vector machine (GA-SVM) and a genetic algorithm and…
Abstract
Purpose
The purpose of this paper is to examine the applicability and capability of models based on a genetic algorithm and support vector machine (GA-SVM) and a genetic algorithm and relevance vector machine (GA-RVM) for the prediction of phytoplankton abundances associated with algal blooms in a Macau freshwater reservoir, and compare their performances with an artificial neural network (ANN) model.
Design/methodology/approach
The hybrid models GA-SVM and GA-RVM were developed for the optimal control of parameters for predicting (based on the current month’s variables) and forecasting (based on the previous three months’ variables) phytoplankton dynamics in a Macau freshwater reservoir, MSR, which has experienced cyanobacterial blooms in recent years. There were 15 environmental parameters, including pH, SiO2, alkalinity, bicarbonate (HCO3−), dissolved oxygen (DO), total nitrogen (TN), UV254, turbidity, conductivity, nitrate (NO3−), orthophosphate (PO43−), total phosphorus (TP), suspended solids (SS) and total organic carbon (TOC) selected from the correlation analysis, with eight years (2001-2008) of data for training, and the most recent three years (2009-2011) for testing.
Findings
For both accuracy performance and generalized performance, the ANN, GA-SVM and GA-RVM had similar predictive powers of R2 of 0.73-0.75. However, whereas ANN and GA-RVM models showed very similar forecast performances, GA-SVM models had better forecast performances of R2 (0.862), RMSE (0.266) and MAE (0.0710) with the respective parameters of 0.987, 0.161 and 0.032 optimized using GA.
Originality/value
This is the first application of GA-SVM and GA-RVM models for predicting and forecasting algal bloom in freshwater reservoirs. GA-SVM was shown to be an effective new way for monitoring algal bloom problem in water resources.
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Xuguang Zhang, Honghai Liu and Yanjie Wang
Object tracking has been a challenging problem of robot vision over the decades, which plays a key role in a wide spectrum of visual tracking‐related applications such as…
Abstract
Purpose
Object tracking has been a challenging problem of robot vision over the decades, which plays a key role in a wide spectrum of visual tracking‐related applications such as surveillance, visual servoing, sensing and navigation in robotics, video compression. The purpose of this paper is to present a novel intensity, orientation codes and geometry (IOCG) histogram variant of the mean‐shift algorithm for object tracking.
Design/methodology/approach
Feature cues of intensity, orientation codes and geometric information are fused together to form an IOCG histogram in combination with a conventional mean‐shift‐based tracking algorithm.
Findings
Experimental results demonstrate the effectiveness and efficiency of the proposed method. Not only do fusing orientation codes features allow the proposed algorithm to conduct tracking in a cluttered background, but partial occlusion is also solved in the tracker in that spatial information usually lost in a conventional histogram is compensated by the introduced geometric relations between tracked pixels and the center of a tracker template.
Originality/value
The paper presents a novel vision tracking method for robots.
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Yueqi Wang, Bin Guo and Yanjie Yin
The purpose of this study is to explore organizational factors that act as antecedents of open innovation search. The authors aim to empirically examine whether the extent to…
Abstract
Purpose
The purpose of this study is to explore organizational factors that act as antecedents of open innovation search. The authors aim to empirically examine whether the extent to which the organizational slack is absorbed determines its influence on firms’ openness in innovation search. In addition, the authors also examine the moderating effect of absorptive capacity on the relationship between slack and open innovation search.
Design/methodology/approach
This study adopted secondary data from multiple sources (NBER, Compustat and US census) and then constructed a ten-year balanced panel dataset of 298 manufacturers. The generalized least square method was used to explore the determinants of open innovation search among manufacturing firms.
Findings
The results of this study reveal that the absorption level of organizational slack indeed determines the openness in innovation search. Specifically, absorbed slack negatively affects a firm’s openness in innovation search, whereas unabsorbed slack promotes open innovation search. Additionally, the relationship between absorbed slack and open innovation search will be less negative with the increase of absorptive capacity.
Originality/value
Different from most previous studies that have examined the performance effect of open search among high-tech and large enterprises, this study focuses on the antecedents of open search strategy in both high- and low-tech, large and small firms. The findings reveal that different forms of organizational slack divergently influence a firm’s open search strategy, contributing to the understanding of the relationship between organizational slack and knowledge search behavior in a broader context, as well as the understanding of the moderating effect of absorptive capacity.
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Weihua Liu, Shuang Wei, Yanjie Liang, Di Wang and Jingkun Wang
This study explores the influencing factors on organizational efficiency of the smart logistics ecological chain, and designs the corresponding theoretical framework to guide the…
Abstract
Purpose
This study explores the influencing factors on organizational efficiency of the smart logistics ecological chain, and designs the corresponding theoretical framework to guide the practice of enterprises
Design/methodology/approach
A multi-case study method is adopted in this study. It includes four companies A, B, C and D in China as the case study objects, collects data through enterprise survey and uses the combination of open coding and spindle coding to process the data. By testing the reliability and validity, the theoretical framework is summarized.
Findings
First, organizational efficiency in smart logistics ecological chains is directly related to their service and technology innovation capability. Second, symbiotic relationships, information sharing and customer demand affect the efficiency of smart logistics multi-case ecological chains by influencing their service capacity; their technological innovation capability regulates the mechanism of influence. Third, technological innovation in smart logistics ecological chains positively impacts their service capabilities. Improving technological innovation capability can enhance logistics service capabilities.
Originality/value
According to the characteristics of smart logistics, the theoretical framework about organizational efficiency of smart logistics ecological chain is constructed, which fills the research gap and can provide interesting perspectives for the future research related to the smart logistics ecological chain. At the same time, the findings can also help enterprises to better build the smart logistics ecological chain in practice.
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Guomin Wang, Yuanyuan Wu, Haifu Jiang, Yanjie Zhang, Jiarong Quan and Fuchuan Huang
The purpose of this paper is to use the wavelet neural network and genetic algorithm to study the effects of polyalphaolefin, TMP108 and OCP0016 on the kinematic viscosity…
Abstract
Purpose
The purpose of this paper is to use the wavelet neural network and genetic algorithm to study the effects of polyalphaolefin, TMP108 and OCP0016 on the kinematic viscosity, viscosity index and pour point of lubricating oil.
Design/methodology/approach
Wavelet neural network is used to train the known samples, test the unknown samples and compare the obtained results with those obtained with a traditional empirical formula.
Findings
It is found that the wavelet neural network prediction value is closer to the experimental value than the traditional empirical formula calculation value.
Originality/value
The results show that the wavelet neural network can be used to study the physical and chemical indexes of lubricating oil.
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Yanjie Chen, Weiwei Zhan, Yibin Huang, Zhiqiang Miao and Yaonan Wang
This paper aims to investigate the distributed formation control problem for a multi-quadrotor unmanned aerial vehicle system without linear velocity feedbacks.
Abstract
Purpose
This paper aims to investigate the distributed formation control problem for a multi-quadrotor unmanned aerial vehicle system without linear velocity feedbacks.
Design/methodology/approach
A nonlinear controller is proposed based on the orthogonal group SE(3) to obviate singularities and ambiguities of the traditional parameterized attitude representations. A cascade structure is applied in the distributed controller design. The inner loop is responsible for attitude control, and the outer loop is responsible for translational dynamics. To ensure a linear-velocity-free characteristic, some auxiliary variables are introduced to construct virtual signals in distributed controller design. The stability analysis of the proposed distributed control method by the Lyapunov function is provided as well.
Findings
A group of four quadrotors with constant reference linear velocity and a group of six quadrotors with varying reference linear velocity are adopted to verify the effectiveness of the proposed strategy.
Originality/value
This is a new innovation for multi-robot formation control method to improve assembly automation.
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Zheng Li, Yanjie Chen, Zhixing Zhang, Hang Zhong and Yaonan Wang
This study aims to introduce the fast reactive tree (FRT*) algorithm for enhancing replanning speed and reducing the overall cost of navigation in unknown dynamic environments.
Abstract
Purpose
This study aims to introduce the fast reactive tree (FRT*) algorithm for enhancing replanning speed and reducing the overall cost of navigation in unknown dynamic environments.
Design/methodology/approach
FRT* comprises four key components: inverted tree build, convex hull construction, dead nodes inform activation and lazy-rewiring replanning. First, an initial path is found from the inverted tree where the valid structure is preserved to minimise re-exploration areas during the replanning phase. As the robot encounters environment changes, convex hulls are extracted to sparsely describe impacted areas. Next, the growth direction of the modified tree is biased by the inform activation of dead nodes to avoid unnecessary exploration. In the replanning phase, the tree structure is optimized using the proposed lazy-rewiring replanning to find a high-quality path with low computation burden.
Findings
A series of comprehensive simulation experiments demonstrate that the proposed FRT* algorithm can efficiently replan short-cost feasible paths in unknown dynamic environments. The differential wheeled mobile robot with varying reference linear velocities is used to validate the effectiveness and adaptability of the proposed strategy in real word scenarios. Furthermore, ablation studies are conducted to analyze the significance of the key components of FRT*.
Originality/value
The proposed FRT* algorithm introduces a novel approach to addressing the challenges of navigation in unknown dynamic environments. This capability allows mobile robots to safely and efficiently navigate through unknown and dynamic environments, making the method highly applicable to real-world scenarios.
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For many pattern recognition problems, the relation between the sample vectors and the class labels are known during the data acquisition procedure. However, how to find the…
Abstract
Purpose
For many pattern recognition problems, the relation between the sample vectors and the class labels are known during the data acquisition procedure. However, how to find the useful rules or knowledge hidden in the data is very important and challengeable. Rule extraction methods are very useful in mining the important and heuristic knowledge hidden in the original high-dimensional data. It can help us to construct predictive models with few attributes of the data so as to provide valuable model interpretability and less training times.
Design/methodology/approach
In this paper, a novel rule extraction method with the application of biclustering algorithm is proposed.
Findings
To choose the most significant biclusters from the huge number of detected biclusters, a specially modified information entropy calculation method is also provided. It will be shown that all of the important knowledge is in practice hidden in these biclusters.
Originality/value
The novelty of the new method lies in the detected biclusters can be conveniently translated into if-then rules. It provides an intuitively explainable and comprehensive approach to extract rules from high-dimensional data while keeping high classification accuracy.