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

Discrete Fourier Transform01:15

Discrete Fourier Transform

187
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
187
Instrument Transformers01:23

Instrument Transformers

58
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...
58
Types Of Transformers01:16

Types Of Transformers

923
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...
923
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

122
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...
122
Energy Losses in Transformers01:21

Energy Losses in Transformers

800
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
800
Transformers in Distribution System01:27

Transformers in Distribution System

96
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...
96

You might also read

Related Articles

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

Sort by
Same author

Mixture of TSMixer Experts for Time Series Forecasting.

Biomimetics (Basel, Switzerland)·2026
Same author

Interpretable Multi-Label Classification for Tibiofibula Fracture 2D CT Images with Selective Attention and Data Augmentation.

Diagnostics (Basel, Switzerland)·2024
Same author

Hybrid Precision Floating-Point (HPFP) Selection to Optimize Hardware-Constrained Accelerator for CNN Training.

Sensors (Basel, Switzerland)·2024
Same author

Analog Convolutional Operator Circuit for Low-Power Mixed-Signal CNN Processing Chip.

Sensors (Basel, Switzerland)·2023
Same author

Security Requirements and Challenges of 6G Technologies and Applications.

Sensors (Basel, Switzerland)·2022
Same author

Optimal Architecture of Floating-Point Arithmetic for Neural Network Training Processors.

Sensors (Basel, Switzerland)·2022
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 10, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

894

Reevaluating the Potential of a Vanilla Transformer Encoder for Unsupervised Time Series Anomaly Detection in Sensor

Chan Sik Han1, HyungWon Kim2, Keon Myung Lee1

  • 1Department of Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
Summary
This summary is machine-generated.

This study shows a standard Transformer encoder framework is competitive for unsupervised time series anomaly detection. An asymmetric autoencoder framework using a vanilla Transformer encoder achieves superior or competitive performance on benchmarks.

Keywords:
careful design choicestime series anomaly detectionunsupervised learningvanilla transformer encoders

More Related Videos

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

901
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.5K

Related Experiment Videos

Last Updated: May 10, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

894
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

901
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.5K

Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Time series data from sensors require effective anomaly detection methods.
  • Unsupervised anomaly detection is crucial due to the difficulty of collecting anomalous data.
  • Deep learning, particularly Transformer encoders, shows promise for capturing temporal patterns.

Purpose of the Study:

  • To demonstrate the effectiveness of a vanilla Transformer encoder for time series anomaly detection.
  • To propose an improved framework by incorporating key design choices into a vanilla Transformer encoder.
  • To evaluate the proposed framework against state-of-the-art models.

Main Methods:

  • Developed an asymmetric autoencoder-based framework.
  • Integrated a vanilla Transformer encoder with a linear layer decoder.
  • Evaluated the framework on multiple unsupervised time series anomaly detection benchmarks.

Main Results:

  • The proposed framework achieved performance superior or competitive to existing state-of-the-art models.
  • A vanilla Transformer encoder-based approach proved to be a highly competitive model.
  • Key design choices were identified and incorporated for enhanced performance.

Conclusions:

  • The proposed asymmetric autoencoder framework with a vanilla Transformer encoder is effective for unsupervised time series anomaly detection.
  • Architectural modifications are not always necessary; optimizing design choices can yield strong results.
  • This approach offers a competitive and practical solution for real-world sensor data analysis.