Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Stereotype Content Model

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...

You might also read

Related Articles

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

Sort by

Same author

RGCNN-nnUNet: Recurrent group equivariant nnU-Net for robust brain tissue segmentation on stroke NCCT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same author

Multi-sensor and MTConnect dataset of metal cutting anomaly in milling from laboratory and industry settings.

Scientific data·2026

Same author

Ensemble Learning Framework for Anomaly Detection in Autonomous Driving Systems.

Sensors (Basel, Switzerland)·2025

Same author

Impact of On-Road U.S. Vehicle Electrification and Lightweighting on Critical Materials Demand.

Environmental science & technology·2025

Same author

On Evaluating Black-Box Explainable AI Methods for Enhancing Anomaly Detection in Autonomous Driving Systems.

Sensors (Basel, Switzerland)·2024

Same author

In Situ Drift Monitoring and Calibration of Field-Deployed Potentiometric Sensors Using Temperature Supervision.

ACS sensors·2023

Search research articles

Related Experiment Video

Updated: Aug 15, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets.

Mustafa Abdallah¹, Byung-Gun Joung², Wo Jae Lee³

¹Computer and Information Technology, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This summary is machine-generated.

This study introduces deep learning for smart manufacturing anomaly detection. It demonstrates effective predictive failure classification for enhanced predictive maintenance and reduced downtime.

Keywords:

autoencoder defect classification manufacturing dataset mems sensor piezoelectric sensor predictive maintenance rpm smart manufacturing transfer learning vibration sensors

More Related Videos

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Aug 15, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Area of Science:

Manufacturing Engineering
Data Science
Artificial Intelligence

Background:

Smart manufacturing systems aim to minimize downtime and maintenance costs.
Detecting anomalies in sensor data is crucial for identifying potential failures.
Sensor data characteristics can vary with operating conditions, like motor RPM.

Purpose of the Study:

To analyze sensor data from manufacturing testbeds for defect detection.
To evaluate traditional and machine learning forecasting models for sensor data prediction.
To develop a transfer learning approach for defect classification with sparse data.

Main Methods:

Deep learning techniques for anomaly detection and defect level identification.
Analysis of four manufacturing sensor datasets.
Evaluation of traditional and ML-based time series forecasting models.
Transfer learning from high-data-rate sensors to sparse-data sensors for defect classification.

Main Results:

Deep learning effectively detects defect levels in sensor data.
Aggregating multiple predictive RPM values improves training data selection.
Transfer learning enables defect type classification even with sparse sensor data.
The developed methods pave the way for predictive maintenance.

Conclusions:

Predictive failure classification is achievable in smart manufacturing.
The study provides a valuable manufacturing database corpus and code for community use.
The findings support the advancement of smart manufacturing and predictive maintenance strategies.