Jove
Visualize
Contact Us

Related Concept Videos

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.7K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.7K
Transformers01:26

Transformers

2.3K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
2.3K
Types Of Transformers01:16

Types Of Transformers

1.8K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.8K
Transformers in Distribution System01:27

Transformers in Distribution System

638
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
638
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

693
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
693
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

683
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
683

You might also read

Related Articles

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

Sort by
Same author

[ATM/H2AX and repair of sperm-DNA damage during cryopreservation].

Zhonghua nan ke xue = National journal of andrology·2011
Same author

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model.

Accident; analysis and prevention·2011
Same author

Photothermally enhanced photodynamic therapy delivered by nano-graphene oxide.

ACS nano·2011
Same author

[Characteristics of soil respiration in Phyllostachys edulis forest in Wanmulin Natural Reserve and related affecting factors].

Ying yong sheng tai xue bao = The journal of applied ecology·2011
Same author

Quality changes in sea urchin (Strongylocentrotus nudus) during storage in artificial seawater saturated with oxygen, nitrogen and air.

Journal of the science of food and agriculture·2011
Same author

Global effect of an RNA polymerase β-subunit mutation on gene expression in the radiation-resistant bacterium Deinococcus radiodurans.

Science China. Life sciences·2011
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 Experiment Video

Updated: May 21, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

AI Conflict Observer: Conflict severity and scenario identification for intersection based on Ensemble Transformer.

Guangzhu Luo1, Xuesong Wang1, Jingru Zang1

  • 1College of Transportation, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China.

Accident; Analysis and Prevention
|April 16, 2026
PubMed
Summary

This study introduces an AI Conflict Observer (AICO) model for intersection safety, outperforming traditional methods in identifying and classifying traffic conflicts. AICO offers robust generalization across diverse intersection types for improved traffic management.

Keywords:
AI Conflict ObserverIntersectionTraffic Conflict TechniqueTrajectory dataTransformer

More Related Videos

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

Related Experiment Videos

Last Updated: May 21, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

Area of Science:

  • Traffic Engineering
  • Artificial Intelligence
  • Computer Vision

Background:

  • Traditional intersection conflict analysis methods struggle with data diversity and intersection heterogeneity.
  • Perception and edge computing offer data support but require advanced analytical tools.
  • Limitations exist in current methods for accurately assessing conflict severity and scenarios.

Purpose of the Study:

  • To develop an AI-driven model for advanced intersection conflict analysis.
  • To overcome limitations of threshold-based and rule-based conflict detection methods.
  • To provide a generalized solution for diverse intersection types and data.

Main Methods:

  • Integration of kinematic trajectory features from diverse intersection data.
  • Development of an Ensemble Transformer model (AI Conflict Observer - AICO) using weighted voting.
  • Labeling of 5,339 conflict events for training and validation.

Main Results:

  • AICO achieved high Weighted F1 Scores: 0.846 for conflict severity and 0.902 for conflict scenarios.
  • Case studies showed high classification accuracies (e.g., 89.97% severity, 88.71% scenario at a 4-leg intersection).
  • Consistent spatial and temporal distribution analysis compared to ground truth, with high R² values for temporal distribution.

Conclusions:

  • The AICO model effectively overcomes limitations of traditional methods.
  • It demonstrates strong generalization capabilities for various signalized and unsignalized intersections.
  • AICO enables batch conflict identification and near-real-time operational risk analysis for traffic management.