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

Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

338
In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...
338

You might also read

Related Articles

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

Sort by
Same author

Dual-Mode Gated Thermal Switch with Branched Interface for Self-Adaptive Thermoregulation.

ACS applied materials & interfaces·2026
Same author

Programmable Dielectrophoretic Assembly of Carbon Nanotube Arrays for Multidirectional Strain Sensor.

Small methods·2026
Same author

Study on rock energy and microscopic failure under different stress states.

Scientific reports·2026
Same author

From metabolic fingerprints to field solutions: engineering the apple rhizosphere microbiome via host-directed Bacillus recruitment for sustainable apple replant disease control.

Microbiome·2025
Same author

A robust deep learning approach for rock discontinuity identification from large scale 3D point clouds.

Scientific reports·2025
Same author

Experimental study of high-voltage pulse blasting-electrokinetic method for remediation of Cr, Cd and pyrene co-contaminated soil.

Journal of hazardous materials·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 10, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.4K

A Driving Warning System for Explosive Transport Vehicles Based on Object Detection Algorithm.

Jinshan Sun1,2, Ronghuan Zheng3, Xuan Liu3

  • 1State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new warning system for explosive transport vehicles using object detection. The system effectively detects surrounding vehicles and alerts drivers to prevent dangerous situations, enhancing transport safety.

Keywords:
YOLOv4 object detection algorithmcellular automataexplosive transport vehicleintelligent recognition

More Related Videos

Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector
07:57

Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector

Published on: July 25, 2014

19.9K
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

20.3K

Related Experiment Videos

Last Updated: Jun 10, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.4K
Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector
07:57

Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector

Published on: July 25, 2014

19.9K
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

20.3K

Area of Science:

  • Road Safety Engineering
  • Artificial Intelligence in Transportation
  • Hazardous Materials Management

Background:

  • Explosive transport vehicles face significant risks from surrounding traffic, including abnormal approaches and lane changes.
  • Collisions and insufficient avoidance maneuvers can lead to catastrophic explosions and fires.
  • Existing safety measures may not adequately address dynamic traffic interactions.

Purpose of the Study:

  • To develop and validate an intelligent warning system for explosive transport vehicles.
  • To enhance driver decision-making and risk mitigation during transport operations.
  • To improve the overall safety of transporting hazardous materials.

Main Methods:

  • Implementation of an object detection algorithm (YOLOv4) for real-time monitoring of surrounding vehicles.
  • Utilization of consumer-level cameras for comprehensive vehicle body surveillance.
  • Development of a game theory-based cellular automaton model to simulate driver behavior and traffic interactions.

Main Results:

  • The developed warning system successfully identified and distanced surrounding vehicles.
  • Simulations demonstrated the system's ability to issue timely warnings in critical scenarios.
  • The warning system effectively assisted drivers in avoiding risks and ensuring safe vehicle operation.

Conclusions:

  • The object detection-based warning system is effective in mitigating risks associated with explosive transport.
  • The system enhances driver awareness and decision-making capabilities in dynamic traffic environments.
  • This technology offers a significant advancement in the safety protocols for hazardous material transportation.