Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

387
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
387
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K
Maximum Size of Aggregate01:12

Maximum Size of Aggregate

240
The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
240
Types of Aggregate Grading01:15

Types of Aggregate Grading

832
Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
Well-graded aggregates include a complete range of necessary size fractions that fit together to create a dense matrix with minimal voids, represented by a smooth, continuous gradation curve. This type of grading ensures good...
832
Reducing Line Loss01:18

Reducing Line Loss

196
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
196

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ethical considerations and management strategies for fertility preservation in women of reproductive age with malignant tumors: Chinese practices and perspectives.

Frontiers in endocrinology·2026
Same author

Selection, Aggregation, and Enhancement: Trajectory Consistent Diffusion Model for Image Super-Resolution.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Age-dependent Shifts in Spiral Ganglion Neuron Subtypes Are Associated with Interphase Gap-dependent Modulation of Electrically Evoked Compound Action Potentials in Mice.

Journal of the Association for Research in Otolaryngology : JARO·2026
Same author

Overall survival and long-term safety of olaparib maintenance in patients with platinum-sensitive relapsed ovarian cancer: final analyses of phase III L-MOCA trial.

Journal of ovarian research·2026
Same author

LncRNA WFDC21P mediates the tobacco carcinogen induced malignant transformation of human normal lung epithelial cells by regulating tumor stemness.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association·2026
Same author

Efficacy and Safety of Pamiparib Monotherapy in Recurrent Ovarian Cancer After Prior PARPi Exposure: A Prospective, Open-Label, Single-Arm, Phase II Study.

The journal of obstetrics and gynaecology research·2026

Related Experiment Video

Updated: Sep 16, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.8K

A federated vehicle re-identification benchmark and performance optimization using dual-phase contrastive dynamic

Linhan Huang1, Jianqing Zhu1, Yutao Chen1

  • 1College of Engineering, Huaqiao University, No. 269 Chenghua North Road, Quanzhou, 362021, Fujian, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 9, 2025
PubMed
Summary

Federated vehicle re-identification (FV-REID) enhances privacy by training models across decentralized data. A new dual-phase contrastive dynamic aggregation method improves performance and stability in FV-REID systems.

Keywords:
Aggregation methodBenchmarkFederated learningVehicle re-identification

More Related Videos

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.4K
Profiling Maternal Behavior Responses During Whole-Brain Imaging
07:12

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

1.1K

Related Experiment Videos

Last Updated: Sep 16, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.8K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.4K
Profiling Maternal Behavior Responses During Whole-Brain Imaging
07:12

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

1.1K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Intelligent Transportation Systems

Background:

  • Vehicle re-identification (REID) is crucial for intelligent transportation but raises privacy concerns due to centralized data processing.
  • Existing federated learning methods for REID struggle with data heterogeneity and model variability.

Purpose of the Study:

  • To introduce a benchmark for federated vehicle re-identification (FV-REID) with a multi-domain dataset and evaluation protocols.
  • To propose a novel federated learning aggregation method to improve the performance and stability of FV-REID.

Main Methods:

  • Developed a federated vehicle re-identification (FV-REID) benchmark including a multi-domain dataset and baseline federated-averaging method (FVVR).
  • Proposed a dual-phase contrastive dynamic aggregation (DCDA) method that dynamically adjusts model aggregation weights during federated training.
  • DCDA prioritizes larger weights during early, significant model changes and smaller weights during later, more stable training phases.

Main Results:

  • The baseline FVVR method showed underperformance and high variability compared to traditional REID models, particularly with diverse data distributions.
  • The proposed DCDA method significantly enhanced the performance and stability of federated vehicle re-identification.
  • DCDA effectively addressed data imbalances, enabling clients with smaller datasets to contribute meaningfully to the global model.

Conclusions:

  • The developed FV-REID benchmark provides a standardized platform for evaluating privacy-preserving vehicle re-identification techniques.
  • The DCDA method offers a robust solution for improving federated learning in heterogeneous environments, specifically for vehicle re-identification.
  • This work advances privacy-preserving intelligent transportation systems through more effective and stable federated learning approaches.