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Search Results (3)

Search Parameters:
Keywords = collided position identification

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27 pages, 2755 KiB  
Article
An IMU-Based Machine Learning System for Container Collision Position Identification
by Xin Zhang, Zihan Song, Do-Myung Park and Byung-Kwon Park
J. Mar. Sci. Eng. 2025, 13(6), 1144; https://doi.org/10.3390/jmse13061144 - 9 Jun 2025
Viewed by 382
Abstract
The accurate identification of collision positions on containers is critical in logistics and trade for enhancing cargo safety and determining accident liability. Traditional visual inspection methods are labor-intensive, time-consuming, and costly. This study leverages data from an Inertial Measurement Unit sensor and evaluates [...] Read more.
The accurate identification of collision positions on containers is critical in logistics and trade for enhancing cargo safety and determining accident liability. Traditional visual inspection methods are labor-intensive, time-consuming, and costly. This study leverages data from an Inertial Measurement Unit sensor and evaluates combinations of machine learning models and feature selection methods to identify the optimal approach for collision position detection. Five machine learning models (decision tree, k-nearest neighbors, support vector machine, random forest, and extreme gradient boosting) and five feature selection methods (Pearson’s correlation coefficient, mutual information, sequential forward selection, sequential backward selection, and extra trees) were assessed using three performance metrics: accuracy, execution time, and CPU utilization. Statistical analysis with the Friedman test confirmed significant differences in model and feature selection performance. The combination of k-nearest neighbors and extra trees achieved the highest accuracy of 97.1%, demonstrating that inexpensive IMU acceleration data can provide a cost-effective, efficient, and reliable solution for collision detection. This has strong practical implications for improving accident accountability and reducing inspection costs in the logistics industry. Full article
(This article belongs to the Section Ocean Engineering)
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8 pages, 385 KiB  
Article
Looking for New Strategies to Probe Low-Mass Axion-like Particles in Ultraperipheral Heavy-Ion Collisions at the LHC
by Pedro Nogarolli, Victor P. Gonçalves and Murilo S. Rangel
Universe 2025, 11(3), 80; https://doi.org/10.3390/universe11030080 - 1 Mar 2025
Viewed by 594
Abstract
The possibility to search for long-lived axion-like particles (ALPs) decaying into photons is investigated in ultraperipheral PbPb collisions at the Large Hadron Collider (LHC). We propose a search strategy for low-mass ALPs using the LHCb and ALICE experiments. The ALP identification is performed [...] Read more.
The possibility to search for long-lived axion-like particles (ALPs) decaying into photons is investigated in ultraperipheral PbPb collisions at the Large Hadron Collider (LHC). We propose a search strategy for low-mass ALPs using the LHCb and ALICE experiments. The ALP identification is performed by requiring the decay vertex be reconstructed outside the region where a primary vertex is expected, which strongly suppress the contribution associated with the decay of light mesons. We also use the fact that a fraction of the photons convert into electron–positron pairs, allowing the reconstruction of the particle decay position. We present the predictions for the pseudorapidity and transverse momentum distributions of the ALPs and photons. Moreover, predictions for the fiducial cross-sections, derived considering the characteristics of the ALICE and LHCb detectors, are presented for different values of the ALP mass and the ALP—photon coupling. Full article
(This article belongs to the Special Issue Universe: Feature Papers 2024—High Energy Nuclear and Particle Physics)
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18 pages, 834 KiB  
Article
Managing the Number of Tag Bits Transmitted in a Bit-Tracking RFID Collision Resolution Protocol
by Hugo Landaluce, Asier Perallos and Ignacio Angulo
Sensors 2014, 14(1), 1010-1027; https://doi.org/10.3390/s140101010 - 8 Jan 2014
Cited by 11 | Viewed by 6039
Abstract
Radio Frequency Identification (RFID) technology faces the problem of message collisions. The coexistence of tags sharing the communication channel degrades bandwidth, and increases the number of bits transmitted. The window methodology, which controls the number of bits transmitted by the tags, is applied [...] Read more.
Radio Frequency Identification (RFID) technology faces the problem of message collisions. The coexistence of tags sharing the communication channel degrades bandwidth, and increases the number of bits transmitted. The window methodology, which controls the number of bits transmitted by the tags, is applied to the collision tree (CT) protocol to solve the tag collision problem. The combination of this methodology with the bit-tracking technology, used in CT, improves the performance of the window and produces a new protocol which decreases the number of bits transmitted. The aim of this paper is to show how the CT bit-tracking protocol is influenced by the proposed window, and how the performance of the novel protocol improves under different conditions of the scenario. Therefore, we have performed a fair comparison of the CT protocol, which uses bit-tracking to identify the first collided bit, and the new proposed protocol with the window methodology. Simulations results show that the proposed window positively decreases the total number of bits that are transmitted by the tags, and outperforms the CT protocol latency in slow tag data rate scenarios. Full article
(This article belongs to the Section Sensor Networks)
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