Applications of AI in Intelligent System Development

Dear Colleagues,

To develop a robust system that is constantly interacting with the application environment is a great challenge. One of the reasons is the handling of uncertainties and exceptions. It can be the system's imperfection due to material and assembly process, and it might be the uncertainties of the environmental responses. These uncertainties are often multi-physical/multi-phenomenon in nature, with strong nonlinear interactions. Scientifically speaking, there are great challenges to build a theoretical model to predict the system's response.

Thanks to the rapid development with the AI algorithms and relevant hardware, AI provides a data-driven framework to seamlessly handle the interactions of the multi-physical responses. Applying the data-driven AI model contributes to describing the uncertainty of the multi-physical, multi-mechanism, and multi-phenomenon, which would contribute to building the smart decision-making functionalities for the intelligent system. Hence, the intelligent system can be appropriately developed with the smart decision capability to handle the operation ambiguity.

The success of the field intelligent system not only relies on the AI algorithm, but it is also highly dependent on the field data acquisition system/design and the control system implementation.

To realize the intelligent system, the design of the IoT system should consider the balance of the measurement accuracy and the implementation cost. Moreover, the implementation of the AI algorithm to the existing machine requires an intermediate transcriber, which is essential for system upgradation.

With this Special Issue, we invite authors to submit original research or review articles mainly focused on applying the AI model to describe the uncertainty and/or the multiphysics of the field application and contribute to the design/development/improvement of the intelligent system. Research and development topics for this Special Issue include, but are not limited to:

(1) AI model techniques for multiple physics and multiple phenomena in field application;

(2) AI embedded intelligence system and controller;

(3) IoT system for AI modeling and decision making;

(4) Edge and Fog computing for IoT sensors;

(5) Multisource data sensing and decision making;

(6) Digital twins for an intelligence system.

Dr. Cadmus Yuan
Dr. Hsinshu Peng
Prof. Dr. Chih-Chang Wang
Guest Editors

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• AI model
• machine learning
• IoT system
• decision making
• digital twins