Reconstructing the trajectory of the object's motion on the basis of measuring the components of its velocity

Ligęza, Paweł

doi:10.1016/j.measurement.2023.113546

Cited by 3 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Starting from the initial state, we monitor and track the fastening of the safety harness screw and represent the process in a time series [27,28]. By collecting data at each discrete time point, a series of safety rope fastening actions for scaffold workers working at heights is established.…”

Section: Spatial Motion Trajectory Model Of Safety Rope Buckle Fasten...mentioning

confidence: 99%

Inertial Measurement Unit- and Time Series Approach-Based Motion Trajectory Reconstruction of the Safety Rope Fastening Behaviour

Han,

Wang

2024

Buildings

View full text Add to dashboard Cite

Monitoring workers’ safety compliance is critical to construction accident prevention. However, most of the previous research focuses on checking the entry of workers wearing safety belts and the monitoring of the fastening action of safety rope screw buckles has not yet been considered. In this paper, a reconstruction method for the safety rope fastening behaviour of workers is proposed based on inertial measurement units (IMU) and a time series approach to monitor safety belt use. The proposed method was applied and evaluated through on-site construction experiments. The experimental results show that the acceleration, angular velocity, and magnetic induction intensity data obtained by the inertial measurement unit exhibit clear behavioural characteristics during safety rope fastening. The trajectory of the safety rope can be reconstructed and monitored through inertial measurement units and a time series approach. The results of this study will contribute to the reconstruction and monitoring of safety rope attachment trajectories for scaffolding workers working at heights in order to prevent falls at construction sites.

show abstract

Section: Spatial Motion Trajectory Model Of Safety Rope Buckle Fasten...mentioning

confidence: 99%

Inertial Measurement Unit- and Time Series Approach-Based Motion Trajectory Reconstruction of the Safety Rope Fastening Behaviour

Han,

Wang

2024

Buildings

View full text Add to dashboard Cite

show abstract

“…The search for new sources and effective processing methods for energy harvesting is a constant challenge for teams of scientists and engineers. The directions for exploration are of course open, but it is worth considering, for example, the use of post-industrial and post-mining infrastructure, waste storages, air and water turbulence, or biological potential [62][63][64][65][66][67][68][69][70].…”

mentioning

confidence: 99%

On Search for Unconventional Energy Sources for Harvesting

Ligęza

2024

Energies

Self Cite

View full text Add to dashboard Cite

Energy transformation requires replacing power plants based on fossil raw materials with renewable energy. Energy harvesting plays an important, although not fully appreciated, role here. Distributed, local power supply systems for small receivers, based on various sources which previously dissipated energy, may contribute to changing the current energy paradigm. This article presents an overview of energy harvesting technologies and various energy sources used in this process. Particular attention was paid to sources of a less conventional nature. The aim of this article is to encourage and direct scientists with the potential to explore this topic to look for new, previously unexploited energy sources and innovative and effective methods of obtaining useful energy in the harvesting process.

show abstract

A novel method for measuring center-axis velocity of unmanned aerial vehicles through synthetic motion blur images

Zhan,

Zhou,

Zhang

et al. 2024

Auton. Intell. Syst.

View full text Add to dashboard Cite

Accurate velocity measurement of unmanned aerial vehicles (UAVs) is essential in various applications. Traditional vision-based methods rely heavily on visual features, which are often inadequate in low-light or feature-sparse environments. This study presents a novel approach to measure the axial velocity of UAVs using motion blur images captured by a UAV-mounted monocular camera. We introduce a motion blur model that synthesizes imaging from neighboring frames to enhance motion blur visibility. The synthesized blur frames are transformed into spectrograms using the Fast Fourier Transform (FFT) technique. We then apply a binarization process and the Radon transform to extract light-dark stripe spacing, which represents the motion blur length. This length is used to establish a model correlating motion blur with axial velocity, allowing precise velocity calculation. Field tests in a hydropower station penstock demonstrated an average velocity error of 0.048 m/s compared to ultra-wideband (UWB) measurements. The root-mean-square error was 0.025, with an average computational time of 42.3 ms and CPU load of 17%. These results confirm the stability and accuracy of our velocity estimation algorithm in challenging environments.

show abstract

Reconstructing the trajectory of the object's motion on the basis of measuring the components of its velocity

Cited by 3 publications

References 33 publications

Inertial Measurement Unit- and Time Series Approach-Based Motion Trajectory Reconstruction of the Safety Rope Fastening Behaviour

Inertial Measurement Unit- and Time Series Approach-Based Motion Trajectory Reconstruction of the Safety Rope Fastening Behaviour

On Search for Unconventional Energy Sources for Harvesting

A novel method for measuring center-axis velocity of unmanned aerial vehicles through synthetic motion blur images

Contact Info

Product

Resources

About