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Machines

Machines

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...

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MiniDeep: A Standalone AI-Edge Platform with a Deep Learning-Based MINI-PC and AI-QSR System.

Yuh-Shyan Chen¹, Kuang-Hung Cheng¹, Chih-Shun Hsu²

¹Department of Computer Science and Information Engineering, National Taipei University, No. 151, University Rd., San Shia District, New Taipei City 237, Taiwan.

Sensors (Basel, Switzerland)

|August 26, 2022

Summary

This summary is machine-generated.

MiniDeep is a novel AI-Edge platform offering a complete deep learning lifecycle environment. It enhances Quick Service Restaurant KIOSK systems with an LSTM-based recommendation engine, outperforming rule-based approaches in accuracy.

Keywords:

MiniDeep cloud computing deep learning edge computing recommendation system

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Area of Science:

Artificial Intelligence
Cloud Computing
Edge Computing

Background:

Developing comprehensive deep learning environments for the entire lifecycle remains a challenge.
Integrating cloud and edge computing for AI applications requires robust platforms.

Purpose of the Study:

To introduce MiniDeep, a novel AI-Edge platform providing a complete deep learning development environment.
To implement and evaluate an AI-QSR KIOSK recommendation system using the MiniDeep platform.

Main Methods:

MiniDeep utilizes a cloud-edge architecture with Amazon Web Services (AWS) and OpenVino.
A mini deep lifecycle (MDLC) system with microservices manages training, packaging, inference, and data provision.
An LSTM-based recommendation system was developed and trained using MiniDeep for AI-QSR KIOSK applications.

Main Results:

The MiniDeep platform successfully facilitated the end-to-end deep learning lifecycle for the AI-QSR application.
The LSTM-based recommendation system demonstrated superior performance compared to a rule-based system.
Key performance metrics including purchase hit accuracy, precision, recall, and F1 score showed significant improvements.

Conclusions:

MiniDeep offers a comprehensive and integrated solution for deep learning development and deployment.
The AI-QSR KIOSK recommendation system effectively leverages deep learning for enhanced user experience and sales.
The proposed platform and recommendation system show significant potential for real-world AI applications.