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

Three-Winding Transformers01:19

Three-Winding Transformers

181
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
181
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

373
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
373
Types Of Transformers01:16

Types Of Transformers

941
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...
941
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
93
Instrument Transformers01:23

Instrument Transformers

63
Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
63
Classification of Signals01:30

Classification of Signals

365
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
365

You might also read

Related Articles

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

Sort by
Same author

Co-occurrence network characteristics and key comorbidity node identification based on health examination indicators among rural older adults aged 65 and above.

BMC geriatrics·2026
Same author

Cadmium Enrichment Characteristics in Different <i>Oratosquilla oratoria</i> Tissues During Various Gonadal Development Stages from Shanghai and Its Health Risk Assessment.

Foods (Basel, Switzerland)·2026
Same author

Multi-omics mendelian randomization integrating GWAS and eQTL data revealed potential drug target for irritable bowel syndrome.

Frontiers in genetics·2026
Same author

Job satisfaction and organisational determinants among medical nursing assistants under hospital-based care reforms: a large cross-sectional study in China.

Frontiers in public health·2026
Same author

Online Raman spectroscopy-guided strategy for efficient production of DNA recombinase UvsX in Escherichia coli.

Analytical and bioanalytical chemistry·2026
Same author

The knowledge-behavior gap in nutritional literacy and its associations with chronic disease in older adults: a mediation analysis.

Frontiers in nutrition·2026
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: May 21, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K

MD-Former: Multiscale Dual Branch Transformer for Multivariate Time Series Classification.

Yanling Du1, Shuhao Chu1, Jintao Wang2

  • 1College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
Summary
This summary is machine-generated.

MD-Former, a novel Multiscale Dual-Branch Attention network, effectively captures multiscale temporal and cross-channel relationships in Multivariate Time Series Classification (MTSC). This approach enhances performance in complex MTSC tasks.

Keywords:
UEA datasetsmultiscale architecturemultivariate time series classificationtransformer

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

Related Experiment Videos

Last Updated: May 21, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Multivariate Time Series Classification (MTSC) is complex due to multiscale temporal and cross-channel dependencies.
  • Existing methods often struggle to capture long-term relationships and integrate channel information effectively.
  • Addressing these limitations is crucial for advancing MTSC applications.

Purpose of the Study:

  • To introduce MD-Former, a novel Multiscale Dual-Branch Attention network for MTSC.
  • To effectively model multiscale relationships across both time and channels in Multivariate Time Series (MTS).
  • To improve the capture of long-term temporal dependencies in MTSC.

Main Methods:

  • MD-Former utilizes a Transformer architecture with a Multiscale Dual-Branch Attention mechanism.
  • Channel-Patching (CP) embeds MTS into 2D vectors, preserving channel information.
  • Two branches, Interlaced Attention Branch (IAB) and Channel-Independent Attention Branch (CIAB), process information at distinct time scales.

Main Results:

  • MD-Former successfully captures multiscale relationships across time and channels.
  • The dual-branch design balances information fusion and preservation.
  • Experimental results demonstrate performance comparable to State-Of-The-Art (SOTA) methods in MTSC.

Conclusions:

  • MD-Former offers a powerful new approach for MTSC by addressing limitations in modeling multiscale cross-channel and long-term temporal dependencies.
  • The network architecture effectively integrates information across different scales and channels.
  • This method shows significant promise for real-world MTSC applications.