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.
Details
Keywords
Yanqing Li, Daming Li, Shean Bie, Zhichao Wang, Hongqiang Zhang, Xingchen Tang and Zhu Zhen
A new coupled model is developed to simulate the interaction between fluid droplet collisions on discrete particles (DPs) by using mathematic function.
Abstract
Purpose
A new coupled model is developed to simulate the interaction between fluid droplet collisions on discrete particles (DPs) by using mathematic function.
Design/methodology/approach
In this model, the smoothed particle hydrodynamics (SPH) is used based on the kernel function and the time step which takes into consideration to the fluid domain in accordance with the discrete element method (DEM) with resistance function. The interaction between fluid and DPs consists of three parts, which are repulsive force, viscous shear force and attractive force caused by the capillary action. The numerical simulation of droplet collision on DPs presents the whole process of droplet motion. Otherwise, an experimental data were conducted to record the realistic process for verification.
Findings
The comparison result indicated that the numerical simulation is capable of capturing the entire process for droplet collision on DPs.
Research limitations/implications
However, based on the difference of experimental environment, type of the DP and setups, the maximum spreading dimeters of could not fit the experimental data exactly.
Originality/value
In sum, the coupled SPH-DEM method simulation shows that the coupled model of SPH-DEM developed an entire effectiveness process for fluid–solid interaction problem.
Details
Keywords
Hongqiang Ma, Yue Xie, Xingpeng Song, Yu Liu, Xinmei Luo and Shengxun Wang
The purpose of this paper is to recover the waste heat of flue gas heat exchanger (FGHE) as efficiently as possible and avoid the acid dew corrosion of that.
Abstract
Purpose
The purpose of this paper is to recover the waste heat of flue gas heat exchanger (FGHE) as efficiently as possible and avoid the acid dew corrosion of that.
Design/methodology/approach
A novel flue gas waste heat recovery system was proposed in the paper. The dynamic mathematical models of key equipment in that were established based on theory and experiment method. The proportion integration differentiation-differentiation (PID-P) cascade control method based on particle swarm optimization algorithm was used to control the outlet temperature of FGHE. The dynamic characteristics of the flue gas heat exchange system were simulated by the particle swarm optimization algorithm with different fitness functions.
Findings
The PID-P temperature controller parameters can be quickly and effectively obtained by the particle swarm optimization algorithm based on the fitness function of integral time absolute error (ITAE). The overshoot, rise time and adjusting time of the novel system are 2, 83 and 105s, respectively. Compared with the traditional two-step tuning (T-ST) method, the novel system is better in dynamic and steady-state performance. The overshoot and the adjustment time of the system are reduced by 44% and 328s, respectively. ITAE is a performance evaluation index for control system with good engineering practicability and selectivity.
Originality/value
The dynamic mathematical model of key equipment in the new flue gas waste heat recovery system is established and the system's control strategies and methods are explored.
Details
Keywords
Hongqiang Sang, Fang Huang, Wei Lu, Rui Han and Fen Liu
The patient-side manipulator (PSM) achieves high torque capability by combining harmonic servo system with high reduction ratio and low torque motor. However, high reduction ratio…
Abstract
Purpose
The patient-side manipulator (PSM) achieves high torque capability by combining harmonic servo system with high reduction ratio and low torque motor. However, high reduction ratio can increase inertia and decrease compliance of the manipulator. To enhance the backdrivability of the minimally invasive surgical robot, this paper aims to propose a resistance torque compensation algorithm.
Design/methodology/approach
A resistance torque compensation algorithm based on dynamics and Luenberger observer is proposed. The dynamics are established, considering joint flexibility and an improved Stribeck friction model. The dynamic parameters are experimentally identified by using the least squares method. With the advantages of clear structure, simple implementation and fast solution speed, the Luenberger observer is selected to estimate the unmeasured dynamic information of PSM and realize the resistance torque compensation.
Findings
For low-speed surgical robots, the centrifugal force term in the dynamic model can be simplified to reduce computational complexity. Joint flexibility and an improved Stribeck friction model can be considered to improve the accuracy of the dynamic model. Experiment results show that parameter identification and estimated results of the Luenberger observer are accurate. The backdrivability of the PSM is enhanced in ease and smoothness.
Originality/value
This algorithm provides potential application prospects for surgical robots to maintain high torque while remaining compliant. Meanwhile, the enhanced backdrivability of the manipulator helps to improve the safety of the preoperative manual adjustment.
Details
Keywords
Jun Peng, Jiaming Bian, Shuhai Jia, Xilong Kang, Hongqiang Yu and Yaowen Yang
This study aims to address the issue of high-precision measurement of AC electric field. An electro-optical sensor with high sensitivity is proposed for this purpose.
Abstract
Purpose
This study aims to address the issue of high-precision measurement of AC electric field. An electro-optical sensor with high sensitivity is proposed for this purpose.
Design/methodology/approach
The proposed sensor combines electromagnetic induction and fiber Bragg grating (FBG) sensing techniques. It is composed of a sensing probe, a piece or stack of piezoelectric ceramics (PZT) and an FBG. A signal processing circuit is designed to rectify and amplify the induced voltage. The processed signal is applied to the PZT and the deformation of PZT is detected by FBG. Theoretical calculation and simulation are conducted to verify the working principle of the probe. The sensor prototype is fabricated and its performance is tested.
Findings
The results of this study show that the sensor has good linearity and repeatability. The sensor sensitivity is 0.061 pm/Vm−1 in the range from 250 to 17,500 V/m, enabling a measurement resolution of electric field strength of 16.3 V/m. The PZT stack is used to enhance the sensor sensitivity and the resolution can be improved up to 3.15 V/m.
Originality/value
A flexure hinge lever mechanism is used to amplify the deformation of PZT for further enhancement of sensitivity. The results show that the proposed sensor has high sensitivity and can be used for the accurate measurement of an electric field. The proposed sensor could have potential use for electric field measurement in the power industry.
Details
Keywords
Wajhat Ali, Don Amila Sajeevan Samarasinghe, Zhenan Feng, Suzanne Wilkinson and James Olabode Bamidele Rotimi
This study identifies key challenges to adopting smart real estate (SRE) technologies and offers insights and recommendations to enhance decision-making for stakeholders…
Abstract
Purpose
This study identifies key challenges to adopting smart real estate (SRE) technologies and offers insights and recommendations to enhance decision-making for stakeholders, including buyers and property investors.
Design/methodology/approach
To achieve the aim of the study, a rigorous research approach was employed, conducting an in-depth analysis of 41 academic papers utilising PRISMA guidelines and checklists. The chosen methodology also applies a PEST (Political, Economic, Social and Technological) framework to identify factors influencing technology adoption in the real estate sector.
Findings
The study uncovers critical challenges to adopting smart real estate technologies, such as regulatory ambiguity, high implementation costs, and societal resistance. PEST analysis reveals that unclear standards and guidelines, coupled with the high financial burden of technology implementation, are significant obstacles. Socially, resistance to change and difficulties in integrating new technologies are prevalent. The study also underscores the potential of artificial intelligence (AI) for predictive analytics and blockchain for secure transactions and records, though their adoption is currently hindered by inadequate infrastructure and regulatory challenges. These findings underscore the need for strategic interventions to address these challenges and facilitate the effective integration of advanced technologies in the real estate sector, thereby enhancing industry innovation and competitiveness.
Practical implications
The study offers insights for real estate stakeholders to embrace technology effectively, with a conceptual framework contributing to industry advancements.
Originality/value
The study’s key contribution is offering real estate stakeholders execution tactics and recommendations to navigate challenges and utilise technology, thereby driving industry innovation and enhancing competitiveness.
Details
Keywords
Tirth Patel, Brian H.W. Guo and Yang Zou
This article aims to explore valuable insights into the construction progress monitoring (CPM) research domain, which include main research topics, knowledge gaps and future…
Abstract
Purpose
This article aims to explore valuable insights into the construction progress monitoring (CPM) research domain, which include main research topics, knowledge gaps and future research themes. For a long time, CPM has been significantly researched with increasing enthusiasm. Although a few review studies have been carried out, there is non-existence of a quantitative review study that can deliver a holistic picture of CPM.
Design/methodology/approach
The science mapping-based scientometric analysis was systematically processed with 1,835 CPM-related journal articles retrieved from Scopus. The co-authorship analysis and direct citation analysis were carried out to identify the most influential researchers, countries and publishers of the knowledge domain. The co-occurrence analysis of keyword was assessed to reveal the most dominating research topics and research trend with the visual representation of the considered research domain.
Findings
This study reveals seven clusters of main research topics from the keyword co-occurrence analysis. The evolution of research confirms that CPM-related research studies were mainly focused on fundamental and traditional CPM research topics before 2007. The period between 2007 and 2020 has seen a shift of research efforts towards digitalization and automation. The result suggests Building Information Modelling (BIM) as the most common, growing and influential research topic in the CPM research domain. It has been used in combination with different data acquisition technologies (e.g. photogrammetry, videogrammetry, laser scanning, Internet of Things (IoT) sensors) and data analytics approaches (e.g. machine learning and computer vision).
Practical implications
This study provides the horizon of potential research in the research domain of CPM to researchers, policymakers and practitioners by availing of main research themes, current knowledge gaps and future research directions.
Originality/value
This paper represents the first scientometric study depicting the state-of-the-art of the research by assessing the current knowledge domain of CPM.
Details
Keywords
Siyu Su, Youchao Sun, Chong Peng and Yuanyuan Guo
The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.
Abstract
Purpose
The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.
Design/methodology/approach
This paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors.
Findings
The global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models.
Originality/value
Unlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management.