Secure olympics games with technology: Intelligent border surveillance for the 2022 Beijing winter olympics

Ji, Xiangyu; Chen, Kang; Chen, Mengfan; Li, Yuan; Qian, Xuesheng

doi:10.1016/j.sysarc.2022.102634

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…Smart production management, remote sensing and immersive visualization systems, virtual simulation algorithms, and data-driven planning technologies [97][98][99][100] articulate autonomous manufacturing processes. Cyber-physical production and robotized manufacturing systems [101][102][103][104][105] integrate cloud computing technologies, data mining and predictive modeling algorithms [106][107][108], and virtual mapping tools.…”

Section: Discussionmentioning

confidence: 99%

“…[97][98][99][100] IoRT devices and machine intelligence develop on swarm robot and machine learning-based perception algorithms to attain optimal routing path and network performance. [101][102][103][104][105] Citation correlations have covered how smart interconnected objects and technologies, IoRT devices and wireless networks, and sensors and actuators are necessitated in performance evaluation and analysis of autonomous robotic and motion capture systems. Product lifecycle data, remote sensor networks, machine vision algorithms, and data visualization and processing capabilities optimize immersive 3D and smart manufacturing technologies.…”

Section: Methodsmentioning

confidence: 99%

“…Robot visual control systems enhance learning efficiency and decision processes throughout heterogeneous time-varying motion phases. IoRT devices and machine intelligence develop on swarm robot and machine learning-based perception algorithms [101][102][103][104][105] to attain optimal routing path and network performance. Autonomous robotic and motion capture systems harness task scheduling and cloud computing algorithms across wireless sensor networks.…”

Section: Geospatial Simulation and Sensor Fusion Tools In The Iortmentioning

confidence: 99%

“…Correlations with previously published literature comprise analyses on how industrial cloud robotics integrates autonomous production systems, smart manufacturing task management [85][86][87][88], simulation optimization algorithms, and data visualization tools, while disparities encompass our integration of the sources on how autonomous robotized devices develop on virtual simulation tools, real-time monitoring capabilities [89][90][91][92], data fusion techniques, and cloud computing algorithms. The research problems are whether computer vision techniques, geospatial data mining [93][94][95][96], simulation-based digital twins, and real-time monitoring technology [97][98][99][100] optimize remote sensing robots [101][102][103][104][105].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things

Andronie

Lăzăroiu

Iatagan

et al. 2023

IJGI

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The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. The research problems were whether computer vision techniques, geospatial data mining, simulation-based digital twins, and real-time monitoring technology optimize remote sensing robots. Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were leveraged by a Shiny app to obtain the flow diagram comprising evidence-based collected and managed data (the search results and screening procedures). Throughout January and July 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms comprising “Internet of Robotic Things” + “big data management algorithms”, “deep learning-based object detection technologies”, and “geospatial simulation and sensor fusion tools”. As the analyzed research was published between 2017 and 2022, only 379 sources fulfilled the eligibility standards. A total of 105, chiefly empirical, sources have been selected after removing full-text papers that were out of scope, did not have sufficient details, or had limited rigor For screening and quality evaluation so as to attain sound outcomes and correlations, we deployed AMSTAR (Assessing the Methodological Quality of Systematic Reviews), AXIS (Appraisal tool for Cross-Sectional Studies), MMAT (Mixed Methods Appraisal Tool), and ROBIS (to assess bias risk in systematic reviews). Dimensions was leveraged as regards initial bibliometric mapping (data visualization) and VOSviewer was harnessed in terms of layout algorithms.

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