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

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

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...

You might also read

Related Articles

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

Sort by

Same author

Assessing causal relationships between oral microbiota and asthma: evidence from two-sample mendelian randomization analysis.

The Journal of asthma : official journal of the Association for the Care of Asthma·2026

Same author

Zwitterionic Engineering of Vinylene-Linked Covalent Organic Frameworks for Superior Protonic Electrolytes.

Angewandte Chemie (International ed. in English)·2026

Same author

Photoelectron-driven modulation of carbon metabolism enhances hydrogen production in a ZnIn<sub>2</sub>S<sub>4</sub>-Shewanella oneidensis biohybrid system.

Bioresource technology·2026

Same author

The impact of college students' physical exercise on negative emotion: The chain mediating role of self-efficacy and smartphone addiction.

PloS one·2026

Same author

Impact of physical exercise on sleep quality in college students: A Chain mediating role of self-efficacy and emotional control.

PloS one·2026

Same author

Transcriptome Analysis Suggests Dietary Tributyrin Enhances Feeding Intensity via Modulating Steroid Biosynthesis in Mandarin Fish (<i>Siniperca chuatsi</i>).

Genes·2025

Search research articles

Related Experiment Video

Updated: Oct 11, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Integrated Circuit Board Object Detection and Image Augmentation Fusion Model Based on YOLO.

Szu-Yin Lin¹, Hao-Yu Li²

¹Department of Computer Science and Information Engineering, National Ilan University, Yilan City, Taiwan.

Frontiers in Neurorobotics

|December 3, 2021

Summary

This study introduces an advanced AI model for real-time Integrated Circuit Board (ICB) recognition in smart manufacturing. The proposed system achieves high accuracy, improving production efficiency and reducing waste.

Keywords:

Internet of Things YOLO deep learning object recognition smart manufacturing

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Related Experiment Videos

Last Updated: Oct 11, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

Manufacturing Technology
Artificial Intelligence
Computer Vision

Background:

Industry 4.0 integrates Cyber-Physical Systems (CPS), AI, and IoT for smart factories.
Traditional Automated Optical Inspection (AOI) for quality management is time-consuming and discards ~30% of products.
Flexible Manufacturing Systems (FMS) were a precursor to current smart factory concepts.

Purpose of the Study:

To analyze Region-based Convolutional Neural Network (R-CNN) and YOLO models for Integrated Circuit Board (ICB) recognition.
To propose a real-time image recognition model and architecture for ICB manufacturing.
To enhance the efficiency and accuracy of quality inspection in smart manufacturing.

Main Methods:

Collected and utilized diverse ICB datasets for model training.
Developed a preliminary image recognition model for ICB classification and prediction.
Implemented image augmentation fusion and optimization techniques.

Main Results:

Achieved an average accuracy of 96.53% using data augmentation.
Demonstrated real-time ICB directionality detection and recognition in under 1 second with 98% accuracy.
Successfully classified and predicted different ICB types based on feature points.

Conclusions:

The proposed AI model meets the real-time demands of smart manufacturing.
Instant object image recognition significantly saves labor, boosts equipment effectiveness, and increases production capacity and yield.
The developed model offers a substantial improvement to the overall manufacturing process.