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Article
Publication date: 18 May 2021

Zhirui Wang, Yezhuo Li, Bo Su, Lei Jiang, Ziming Zhao and Yan-An Yao

The purpose of this paper is to introduce a tetrahedral mobile robot with only revolute joints (TMRR). By using rotation actuators, the mechanism of the robot gains favorable…

245

Abstract

Purpose

The purpose of this paper is to introduce a tetrahedral mobile robot with only revolute joints (TMRR). By using rotation actuators, the mechanism of the robot gains favorable working space and eliminates the engineering difficulties caused by the multilevel extension compared with liner actuators. Furthermore, the rolling locomotion is improved to reduce displacement error based on dynamics analysis.

Design/methodology/approach

The main body of deforming mechanism with a tetrahedral exterior shape is composed of four vertexes and six RRR chains. The mobile robot can achieve the rolling locomotion and reach any position on the ground by orderly driving the rotation actuators. The global kinematics of the mobile modes are analyzed. Dynamics analysis of the robot falling process is carried out during the rolling locomotion, and the rolling locomotion is improved by reducing the collision impulse along with the moving direction.

Findings

Based on global kinematics analysis of TMRR, the robot can realize the continuous mobility based on rolling gait planning. The main cause of robot displacement error and the corresponding improvement locomotion are gained through dynamic analysis. The results of the theoretical analysis are verified by experiments on a physical prototype.

Originality/value

The work introduced in this paper is a novel exploration of applying the mechanism with only revolute joints to the field of tetrahedral rolling robots. It is also an attempt to use the improved rolling locomotion making this kind of mobile robot more practical. Meanwhile, the reasonable engineering structure of the robot provides feasibility for load carrying.

Details

Industrial Robot: the international journal of robotics research and application, vol. 48 no. 4
Type: Research Article
ISSN: 0143-991X

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Article
Publication date: 20 March 2024

Ziming Zhou, Fengnian Zhao and David Hung

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine…

98

Abstract

Purpose

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

Design/methodology/approach

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

Findings

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

Originality/value

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 34 no. 8
Type: Research Article
ISSN: 0961-5539

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Article
Publication date: 8 May 2023

Ahmed Mohammed Sayed Mostafa, Suhaer Yunus, Wee Chan Au and Ziming Cai

Not much is known about the conditions under which the negative relationship between co-worker undermining and employee outcomes may wax or wane. This study seeks to address this…

905

Abstract

Purpose

Not much is known about the conditions under which the negative relationship between co-worker undermining and employee outcomes may wax or wane. This study seeks to address this issue by analysing the role of leadership in mitigating the negative impact of co-worker undermining on employee outcomes. Drawing on expectancy violation theory (EVT), the study proposes that servant leadership will alleviate the association between co-worker undermining, emotional exhaustion and consequently organisational commitment.

Design/methodology/approach

Two-wave time-lagged data were collected from a sample of 345 nurses working under 33 supervisors in a large public hospital in Malaysia. To account for the nested nature of the data, generalised multilevel structural equation modeling (GSEM) in STATA was used to test the hypotheses.

Findings

After controlling for transformational leadership, co-worker undermining was indirectly related to organisational commitment via emotional exhaustion, and this indirect relationship was weaker when servant leadership was high.

Practical implications

Organisations need to invest in interventions that help reduce co-worker undermining and put emphasis on promoting servant leadership.

Originality/value

The study extends the literature by introducing EVT as a new theoretical lens to analyse the consequences of co-worker undermining on employee outcomes. The study also addresses calls for research on the role of leadership in ameliorating the negative consequences of co-worker undermining.

Details

Journal of Managerial Psychology, vol. 38 no. 3
Type: Research Article
ISSN: 0268-3946

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Article
Publication date: 14 March 2016

Juan Wu, Ziming Kou and Gongjun Cui

The purpose of this paper is to prepare carbon fiber-reinforced polyimide matrix composites and to investigate the single role of carbon fiber in polyimide composites on…

360

Abstract

Purpose

The purpose of this paper is to prepare carbon fiber-reinforced polyimide matrix composites and to investigate the single role of carbon fiber in polyimide composites on tribological performance under distilled water condition.

Design/methodology/approach

Three carbon fiber-reinforced polyimide matrix composites were fabricated by using a hot press molding technique. The tribological behaviors of carbon fiber-reinforced polyimide matrix composites sliding against steel ball were evaluated with a ball-on-disk tribotester under distilled water condition. Meanwhile, the effect of different length of carbon fiber on the wear resistance of polyimide matrix composites was investigated during the sliding process.

Findings

The friction coefficients and specific wear rates of polyimide composites containing 100 μm carbon fibers were lower than those of other specimens. The wear mechanism of carbon fiber-reinforced composites was delamination under distilled water condition. The interfacial combination between the carbon fiber and matrix became worse with the increase of length of carbon fiber.

Originality/value

This paper reported the effect of the different length of carbon fiber on polyimide matrix composites to prepare mechanical parts in mining industrial fields.

Details

Industrial Lubrication and Tribology, vol. 68 no. 2
Type: Research Article
ISSN: 0036-8792

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Article
Publication date: 24 June 2024

Yanxinwen Li, Ziming Xie, Buqing Cao and Hua Lou

With the introduction of graph structure learning into service classification, more accurate graph structures can significantly improve the precision of service classification…

58

Abstract

Purpose

With the introduction of graph structure learning into service classification, more accurate graph structures can significantly improve the precision of service classification. However, existing graph structure learning methods tend to rely on a single information source when attempting to eliminate noise in the original graph structure and lack consideration for the graph generation mechanism. To address this problem, this paper aims to propose a graph structure estimation neural network-based service classification (GSESC) model.

Design/methodology/approach

First, this method uses the local smoothing properties of graph convolutional networks (GCN) and combines them with the stochastic block model to serve as the graph generation mechanism. Next, it constructs a series of observation sets reflecting the intrinsic structure of the service from different perspectives to minimize biases introduced by a single information source. Subsequently, it integrates the observation model with the structural model to calculate the posterior distribution of the graph structure. Finally, it jointly optimizes GCN and the graph estimation process to obtain the optimal graph.

Findings

The authors conducted a series of experiments on the API data set and compared it with six baseline methods. The experimental results demonstrate the effectiveness of the GSESC model in service classification.

Originality/value

This paper argues that the data set used for service classification exhibits a strong community structure. In response to this, the paper innovatively applies a graph-based learning model that considers the underlying generation mechanism of the graph to the field of service classification and achieves good results.

Details

International Journal of Web Information Systems, vol. 20 no. 4
Type: Research Article
ISSN: 1744-0084

Keywords

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Article
Publication date: 5 June 2024

Liuyong Wang, Qi Wu, Ziming Song, Yue Li, Xuewen Li, Bing Tu and Yulong Li

This study aims to investigate the wetting behavior of AgCuTi and AgCu filler metals on selective laser melting (SLMed) Ti/TiB2, and to analyze the microstructure and fracture…

77

Abstract

Purpose

This study aims to investigate the wetting behavior of AgCuTi and AgCu filler metals on selective laser melting (SLMed) Ti/TiB2, and to analyze the microstructure and fracture characteristics of SLMed Ti/TiB2/AgCuTi or AgCu alloy/SLMed Ti/TiB2 brazed joints. The wetting behavior of AgCuTi and AgCu filler metals on the selective laser melted (SLMed) Ti/TiB2 has been studied. The analysis of microstructures and fracture characteristics in vacuum-brazed SLMed Ti/TiB2 substrate, using AgCuTi and AgCu filler metals, has been conducted to elucidate the influence of brazing temperature and alloy composition on the shear strength of the brazed joints.

Design/methodology/approach

Brazing SLMed-Ti/TiB2 in a vacuum using AgCuTi and AgCu filler metals, this study aims to explore the optimal parameters for brazed joints at various brazing temperatures (800°C−950°C).

Findings

The findings suggest that elevated brazing temperatures lead to a more extensive diffusion region in the joint as a result of the partial melting of the filler metal. The joint composition changes from distinct Ti2Cu layer/TiCu layer/filler metal to a-Ti (ss) + ß-Ti (ss)/TiCu. As the brazing temperature increases, the fracture mode shifts from brittle cleavage to ductile fracture, mainly attributed to a decrease in the CuTi within the brazed joint. This change in fracture behavior indicates an improvement in the ductility and toughness of the joint.

Originality/value

The originality of this study lies in the comprehensive analysis of the microstructure and shear strength of vacuum brazing SLMed Ti/TiB2 using AgCuTi and AgCu filler metals.

Details

Soldering & Surface Mount Technology, vol. 36 no. 4
Type: Research Article
ISSN: 0954-0911

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Article
Publication date: 16 February 2022

Ziming Zeng, Shouqiang Sun, Jingjing Sun, Jie Yin and Yueyan Shen

Dunhuang murals are rich in cultural and artistic value. The purpose of this paper is to construct a novel mobile visual search (MVS) framework for Dunhuang murals, enabling users…

292

Abstract

Purpose

Dunhuang murals are rich in cultural and artistic value. The purpose of this paper is to construct a novel mobile visual search (MVS) framework for Dunhuang murals, enabling users to efficiently search for similar, relevant and diversified images.

Design/methodology/approach

The convolutional neural network (CNN) model is fine-tuned in the data set of Dunhuang murals. Image features are extracted through the fine-tuned CNN model, and the similarities between different candidate images and the query image are calculated by the dot product. Then, the candidate images are sorted by similarity, and semantic labels are extracted from the most similar image. Ontology semantic distance (OSD) is proposed to match relevant images using semantic labels. Furthermore, the improved DivScore is introduced to diversify search results.

Findings

The results illustrate that the fine-tuned ResNet152 is the best choice to search for similar images at the visual feature level, and OSD is the effective method to search for the relevant images at the semantic level. After re-ranking based on DivScore, the diversification of search results is improved.

Originality/value

This study collects and builds the Dunhuang mural data set and proposes an effective MVS framework for Dunhuang murals to protect and inherit Dunhuang cultural heritage. Similar, relevant and diversified Dunhuang murals are searched to meet different demands.

Details

The Electronic Library , vol. 40 no. 3
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 20 July 2022

Tingting Li, Ziming Zeng, Jingjing Sun and Shouqiang Sun

The deployment of vaccines is the primary task in curbing the COVID-19 pandemic. The purpose of this paper is to understand the public’s opinions on vaccines and then design…

584

Abstract

Purpose

The deployment of vaccines is the primary task in curbing the COVID-19 pandemic. The purpose of this paper is to understand the public’s opinions on vaccines and then design effective interventions to promote vaccination coverage.

Design/methodology/approach

This paper proposes a research framework based on the spatiotemporal perspective to analyse the public opinion evolution towards COVID-19 vaccine in China. The framework first obtains data through crawler tools. Then, with the help of data mining technologies, such as emotion computing and topic extraction, the evolution characteristics of discussion volume, emotions and topics are explored from spatiotemporal perspectives.

Findings

In the temporal perspective, the public emotion declines in the later stage, but overall emotion performance is positive and stabilizing. This decline in emotion is mainly associated with ambiguous information about the COVID-19 vaccine. The research progress of vaccines and the schedule of vaccination have driven the evolution of public discussion topics. In the spatial perspective, the public emotion tends to be positive in 31 regions, whereas local emotion increases and decreases in different stages. The dissemination of distinctive information and the local epidemic prevention and control status may be potential drivers of topic evolution in local regions.

Originality/value

The analysis results of media information can assist decision-makers to accurately grasp the subjective thoughts and emotional expressions of the public in terms of spatiotemporal perspective and provide decision support for macro-control response strategies and risk communication.

Details

The Electronic Library , vol. 40 no. 4
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 7 February 2025

Qingqing Li, Ziming Zeng, Shouqiang Sun and Tingting Li

Aspect category-based sentiment analysis (ACSA) has been widely used in consumer preference mining and marketing strategy formulation. However, existing studies ignore the…

13

Abstract

Purpose

Aspect category-based sentiment analysis (ACSA) has been widely used in consumer preference mining and marketing strategy formulation. However, existing studies ignore the variability in features and the intrinsic correlation among diverse aspect categories in ACSA tasks. To address these problems, this paper aims to propose a novel integrated framework.

Design/methodology/approach

The integrated framework consists of three modules: text feature extraction and fusion, adaptive feature selection and category-aware decision fusion. First, text features from global and local views are extracted and fused to comprehensively capture the potential information in the different dimensions of the review text. Then, an adaptive feature selection strategy is devised for each aspect category to determine the optimal feature set. Finally, considering the intrinsic associations between aspect categories, a category-aware decision fusion strategy is constructed to enhance the performance of ACSA tasks.

Findings

Comparative experimental results demonstrate that the integrated framework can effectively detect aspect categories and their corresponding sentiment polarities from review texts, achieving a macroaveraged F1 score (Fmacro) of 72.38% and a weighted F1 score (F1) of 79.39%, with absolute gains of 2.93% to 27.36% and 4.35% to 20.36%, respectively, compared to the baselines.

Originality/value

This framework can simultaneously detect aspect categories and corresponding sentiment polarities from review texts, thereby assisting e-commerce enterprises in gaining insights into consumer preferences, prioritizing product improvements, and adjusting marketing strategies.

Details

The Electronic Library, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 18 November 2024

Shangjie Feng, Buqing Cao, Ziming Xie, Zhongxiang Fu, Zhenlian Peng and Guosheng Kang

With the continuous increase in Web services, efficient identification of Web services that meet developers’ needs and understanding their relationships remains a challenge…

31

Abstract

Purpose

With the continuous increase in Web services, efficient identification of Web services that meet developers’ needs and understanding their relationships remains a challenge. Previous research has improved recommendation effectiveness by using correlations between Web services through graph neural networks (GNNs), while it has not fully leveraged service descriptions, limiting the depth and diversity of learning. To this end, a Web services recommendation method called LLMSARec, based on Large Language Model and semantic alignment, is proposed. This study aims to extract potential semantic information from services and learn deeper relationships between services.

Design/methodology/approach

This method consists of two core modules: profile generation and maximizing mutual information. The profile generation module uses LLM to analyze the descriptions of services, infer and construct service profiles. Concurrently, it uses LLM as text encoders to encode inferred service profiles for enhanced service representation learning. The maximizing mutual information model aims to align the semantic features of the services text inferred by LLM with structural semantic features of the services captured by GNNs, thus achieving a more comprehensive representation of services. The aligned representation serves as an input for the model to identify services with superior matching accuracy, thereby enhancing the service recommendation capability.

Findings

Experimental comparisons and analyses were conducted on the Programmable Web platform data set, and the results demonstrated that the effectiveness of Web service recommendations can be significantly improved by using LLMSARec.

Originality/value

In this study, the authors propose a Web service recommendation approach based on Large Language Model and semantic alignment. By extracting latent semantic information from services and effectively aligning semantic features with structural features, new representations can be generated to significantly enhance recommendation accuracy.

Details

International Journal of Web Information Systems, vol. 21 no. 1
Type: Research Article
ISSN: 1744-0084

Keywords

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