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

Bus Impedance Matrix01:24

Bus Impedance Matrix

521
Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
521
Energy to Drive Translocation01:37

Energy to Drive Translocation

2.8K
Mitochondrial protein import is powered by two distinct energy sources: ATP hydrolysis and electrochemical potential across the inner membrane. Newly synthesized precursors are bound by cytosolic chaperones of the Hsp70 family, which guide them to the import receptors on the mitochondrial surface. Utilizing the energy of ATP hydrolysis, Hsp70 chaperones transfer these precursors to the TOM receptors on the mitochondrial outer membrane.
Generally, polypeptides are unfolded by two distinct...
2.8K
M-Cdk Drives Transition Into Mitosis02:15

M-Cdk Drives Transition Into Mitosis

6.5K
Checkpoints throughout the cell cycle serve as safeguards and gatekeepers, allowing the cell cycle to progress in favorable conditions and slow or halt it in problematic ones. This regulation is known as the cell cycle control system.
Cyclin-dependent kinases, or Cdks, work in concert with cyclins to control cell cycle transitions. M-Cdk, a complex of Cdk1 bound to M cyclin, is a well-known example of this coordinated control that drives the transition from the G2 to the M phase.
M cyclin...
6.5K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
What is Behavior?00:54

What is Behavior?

10.3K
Behaviors are actions that an organism engages in—they can be related to finding food, reproducing, defending against threats, and many other possible actions. Behaviors include activities related to the environment around the animal—such as migration—as well as social interactions within a species or population. Many behaviors involve motor output—that is, muscle movements—while others involve less visible actions, such as learning.
10.3K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.7K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.7K

You might also read

Related Articles

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

Sort by
Same author

A Data-Driven Spatiotemporal Risk Assessment Framework for Transformer Overload in Distributed Renewable Energy System.

Sensors (Basel, Switzerland)·2026
Same author

PestCLIP: an incremental pest recognition framework based on a vision-language model.

Pest management science·2026
Same author

Causality-inspired crop pest recognition based on Decoupled Feature Learning.

Pest management science·2024
Same author

Prior knowledge auxiliary for few-shot pest detection in the wild.

Frontiers in plant science·2023
Same author

WSRD-Net: A Convolutional Neural Network-Based Arbitrary-Oriented Wheat Stripe Rust Detection Method.

Frontiers in plant science·2022
Same author

Algorithm for wireless sensor networks in ginseng field in precision agriculture.

PloS one·2022

Related Experiment Video

Updated: Jan 27, 2026

Driving Under the Influence: How Music Listening Affects Driving Behaviors
07:25

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Published on: March 27, 2019

13.1K

A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data.

Jun Zhang1,2, ZhongCheng Wu3,4, Fang Li5

  • 1High Magnetic Field Laboratory, and Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China. zhang_jun@hmfl.ac.cn.

Sensors (Basel, Switzerland)
|March 21, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning framework to identify unique driver behaviors using Controller Area Network-BUS (CAN-BUS) data. The method effectively captures complex temporal patterns for accurate driver identification.

Keywords:
CNNGRULSTMattention mechanismdeep learningdriving behavior identification

More Related Videos

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K

Related Experiment Videos

Last Updated: Jan 27, 2026

Driving Under the Influence: How Music Listening Affects Driving Behaviors
07:25

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Published on: March 27, 2019

13.1K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Automotive Engineering

Background:

  • Driver identification is crucial for applications like auto-theft systems.
  • Controller Area Network-BUS (CAN-BUS) data offers a rich source for analyzing driving behaviors.
  • Traditional methods struggle to capture the complex temporal dynamics inherent in driving data.

Purpose of the Study:

  • To develop an advanced deep learning framework for accurate and automated driver behavior identification.
  • To overcome limitations of traditional methods in modeling temporal features from CAN-BUS data.
  • To create a robust system for recognizing individual driving patterns.

Main Methods:

  • An end-to-end deep learning framework fusing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
  • Integration of an attention mechanism to focus on salient temporal features within the time series CAN-BUS data.
  • Automatic feature learning and correlation analysis across multi-sensor data for comprehensive behavior representation.

Main Results:

  • The proposed framework successfully models complex temporal features from CAN-BUS sensor data.
  • It automatically extracts relevant driving behavior characteristics without requiring manual feature engineering.
  • Demonstrated superior performance in real-world driving behavior identification tasks compared to existing state-of-the-art methods.

Conclusions:

  • The developed deep learning approach provides an effective solution for driver behavior identification using CAN-BUS data.
  • The fusion of CNNs, RNNs, and attention mechanisms enhances the ability to model intricate temporal patterns.
  • This framework offers a promising advancement for intelligent transportation systems and vehicle security.