Search results
1 – 6 of 6Yunxiang Li, Yunfei Ai, Jinzhou Zou, Liangyu Liu, Chengjian Liu, Siheng Fu, Dehua Zou and Wang Wei
By analyzing the shortcomings of existing insulator robots, a novel ultra high voltage (UHV) insulator climbing robot, which could transfer between adjacent insulator strings, is…
Abstract
Purpose
By analyzing the shortcomings of existing insulator robots, a novel ultra high voltage (UHV) insulator climbing robot, which could transfer between adjacent insulator strings, is proposed for operation on 800KV multiple-string insulators. An extended inchworm-like configuration was chosen and a stable gripping claw suitable for the insulator string was designed to enable the robot to multiple-string insulators. Then a set of nonheuristic structural parameters that can influence energy consumption was chosen to formulate a nonlinear optimization problem based on the configuration, which improved the energy efficiency of the robot during progressing along a string of insulator.
Design/methodology/approach
The purpose of this paper is to design an insulator climbing robot for operation on UHV multiple-string insulators, which could transfer between adjacent insulator strings and progressed along a string of insulator with high energy efficiency.
Findings
A physical prototype was constructed that can operate at the speed of six pieces per minute (approximately 1.44 meters per minute) on a single string and complete transference between adjacent strings in 45 s. The energy consumption of joints during progressed along a string of insulator had been reduced by 38.8% with the optimized parameters, demonstrating the consistency between the experimental and simulation results.
Originality/value
An insulator climbing robot for operation on UHV multiple-string insulators has been developed with energy consumption optimization design. The robot can transfer between adjacent insulator strings and progressed along a string of insulator with high energy efficiency. The CLIBOT could be expanded to detect or clean the insulators with similar specification.
Details
Keywords
Yuze Wu, Jianbin Liao, Liangyu Liu, Yu Yan, Yunfei Ai, Yunxiang Li and Wang Wei
This paper aims to address the challenges of the capacitor tower maintenance robot during bolt tightening in high-voltage substations, including difficulties in bolt positioning…
Abstract
Purpose
This paper aims to address the challenges of the capacitor tower maintenance robot during bolt tightening in high-voltage substations, including difficulties in bolt positioning due to tilted angles and anti-bird cover occlusion and issues with fast and accurate docking of bolts while the base is moving.
Design/methodology/approach
This paper proposes a visual servoing method for the capacitor tower maintenance robot, including bolt pose estimation and visual servoing control. Bolt pose estimation includes four components: constructing a keypoint detection network to identify the approximate position, precise positioning, rapid prediction and calculation of bolt pose. In visual servoing, an improved position-based visual servoing (PBVS) is proposed, which eliminate steady-state error and enhance response speed during dynamic tracking by incorporating integral and differential components.
Findings
The bolt detection method exhibits high robustness against varying lighting conditions, partial occlusions, shooting distances and angles. The maximum positioning error at a distance of 250 mm is 2.8 mm. The convergence speed of the improved PBVS is 10% higher than that of the traditional PBVS when the base and target remain relatively stationary. When the base moves at a constant speed, the improved method eliminates steady-state error in dynamic tracking. When the base moves rapidly and intermittently, the maximum error of the improved method in the tracking process is 30% smaller than that of traditional PBVS.
Originality/value
This method enables real-time detection and positioning of bolts in an unstructured environment with tilt angles, variable lighting conditions and occlusion by anti-bird covers. An improved PBVS is proposed to enhance its capability in tracking dynamic targets.
Details
Keywords
Ahsan Ali, Xianfang Xue, Nan Wang, Xicheng Yin and Hussain Tariq
The aim of this study is to investigate how team-level leader-member exchange (LMX) and the instrumental use of artificial intelligence (AI) by team members influence team…
Abstract
Purpose
The aim of this study is to investigate how team-level leader-member exchange (LMX) and the instrumental use of artificial intelligence (AI) by team members influence team psychological empowerment and information systems development (ISD) team performance.
Design/methodology/approach
A survey approach was employed to collect time-lagged, multi-source data for testing the proposed model of this study (N = 514 responses from 88 teams). PROCESS macro was used to analyze the data to generate empirical results.
Findings
The results suggest that instrumental AI use indirectly influences ISD team performance by enhancing team psychological empowerment. Additionally, it moderates the effects of team-level LMX on team psychological empowerment and ISD team performance. Furthermore, the results demonstrate that the interaction effect of LMX and instrumental AI use on ISD team performance is mediated by team psychological empowerment.
Originality/value
While research on ISD consistently demonstrates that teams, data, and technology collectively contribute to the success of these projects. What is less known, however, is how the exchange relationship between ISD teams and their leader, as well as technological factors, contribute to ISD projects. This study draws on LMX theory to propose how team-level LMX and the instrumental use of AI by team members influence team psychological empowerment and ISD team performance. The study puts forth a mediated moderation model to develop a set of hypotheses. It offers valuable contributions to AI and LMX, along with implications for ISD team management.
Details
Keywords
Giustina Secundo, Gioconda Mele, Giuseppina Passiante and Angela Ligorio
In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of…
Abstract
Purpose
In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of innovative project. This paper aims to prospect the promising opportunities coming from the application of Machine Learning (ML) algorithms to project risk management for organizational innovation, where a large amount of data supports the decision-making process within the companies and the organizations.
Design/methodology/approach
Moving from a structured literature review (SLR), a final sample of 42 papers has been analyzed through a descriptive, content and bibliographic analysis. Moreover, metrics for measuring the impact of the citation index approach and the CPY (Citations per year) have been defined. The descriptive and cluster analysis has been realized with VOSviewer, a tool for constructing and visualizing bibliometric networks and clusters.
Findings
Prospective future developments and forthcoming challenges of ML applications for managing risks in projects have been identified in the following research context: software development projects; construction industry projects; climate and environmental issues and Health and Safety projects. Insights about the impact of ML for improving organizational innovation through the project risks management are defined.
Research limitations/implications
The study have some limitations regarding the choice of keywords and as well the database chosen for selecting the final sample. Another limitation regards the number of the analyzed papers.
Originality/value
The analysis demonstrated how much the use of ML techniques for project risk management is still new and has many unexplored areas, given the increasing trend in annual scientific publications. This evidence represents an opportunities for supporting the organizational innovation in companies engaged into complex projects whose risk management become strategic.
Details
Keywords
Niv Yonat, Shabtai Isaac and Igal M. Shohet
The purpose of this research is to provide a theoretical and practical theory and application that provides understanding and means to manage complex infrastructures.
Abstract
Purpose
The purpose of this research is to provide a theoretical and practical theory and application that provides understanding and means to manage complex infrastructures.
Design/methodology/approach
In this research, complexity, nonlinear, noncontinuous effects and aleatoric and data unknowns are bypassed by directly addressing systems' responses. Graph theory, statistics and digital signal processing (DSP) tools are applied within a theoretical framework of the theory of faults (ToF). Motivational complex infrastructure systems (CISs) are difficult to model. Data are often missing or erroneous, changes are not well documented and processes are not well understood. On top of it, under complexity, stalwart analytical tools have limited predictive power. The aleatoric risk, such as rain and risk cascading from interconnected infrastructures, is unpredictable. Mitigation, response and recovery efforts are adversely affected.
Findings
The theory and application are presented and demonstrated by a step-by-step development of an application to a municipal drainage system. A database of faults is analyzed to produce system statistics, spatio-temporal morphology, behavior and traits. The gained understanding is compared to the physical system's design and to its modus operandi. Implications for design and maintenance are inferred; DSP tools to manage the system in real time are developed.
Research limitations/implications
Sociological systems are interest driven. Some events are intentionally created and directed to the benefit and detriment of the opposing parties in a project. Those events may be explained and possibly predicted by understanding power plays, not power functions. For those events, sociological game theories provide better explanatory value than mathematical gain theories.
Practical implications
The theory provides a thematic network for modeling and resolving aleatoric uncertainty in engineering and sociological systems. The framework may be elaborated to fields such as energy, healthcare and critical infrastructure.
Social implications
ToF provides a framework for the modeling and prediction of faults generated by inherent aleatoric uncertainties in social and technological systems. Therefore, the framework and theory lay the basis for automated monitoring and control of aleatoric uncertainties such as mechanical failures and human errors and the development of mitigation systems.
Originality/value
The contribution of this research is in the provision of an explicatory theory and a management paradigm for complex systems. This theory is applicable to a wide variety of fields from facilities and construction project management to maintenance and from academic studies to commercial use.
Details
Keywords
Evans Sokro, Theresa Obuobisa-Darko and Bernard Okpattah
This study examines learner satisfaction and success as mechanisms through which online learning quality translates into learners’ continuous intentions of use by extending DeLone…
Abstract
Purpose
This study examines learner satisfaction and success as mechanisms through which online learning quality translates into learners’ continuous intentions of use by extending DeLone and McLean’s information system success model. It also examines the moderating effect of perceived supervisory support and learners’ self-regulation on online learning quality in Higher Education Institutions.
Design/methodology/approach
Survey data were obtained from 540 students in both private and public higher institutions of learning in Ghana. The Partial Least Squares – Structural Equations Modelling (PLS-SEM) technique was used to test the hypothesised relationships.
Findings
The results revealed that system quality emerged as the single most important variable in the DeLone and McLean model, that influences learner success and satisfaction. Further, learner satisfaction has a significant positive effect on learner attitudes, whilst self-regulation was found to moderate the relationship between online learning quality and learner success as well as learner satisfaction.
Originality/value
The study appears to be among the first to explore the inter-relationship among online learning environment quality and learner attitudes and moderating factors perceived supervisory support and self-regulation. The study highlights insightful practical implications for students, faculty and administrators of higher institutions.
Details