Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes
2nd Edition
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The aim of this Special Issue is to explore the multifaceted aspects of data-driven intelligent modeling and optimization algorithms for industrial processes. The main goals are to harness the power of data to improve control, decision making, and parameter optimization, and to drive industrial systems to unprecedented levels of efficiency, reliability, and adaptability. Research areas in this Special Issue include digital twin technology, multimodal data recognition, sensor data ingestion and real-time processing, multi-objective path-planning, conditional generative adversarial network, gene...
The aim of this Special Issue is to explore the multifaceted aspects of data-driven intelligent modeling and optimization algorithms for industrial processes. The main goals are to harness the power of data to improve control, decision making, and parameter optimization, and to drive industrial systems to unprecedented levels of efficiency, reliability, and adaptability. Research areas in this Special Issue include digital twin technology, multimodal data recognition, sensor data ingestion and real-time processing, multi-objective path-planning, conditional generative adversarial network, generating job recommendations, comprehensive risk assessment, large language models, self-supervised key-point learning, trustworthy article ranking, engine optimization model, and bioinspired generative design. These powerful and intelligent algorithms use data for control, decision making, and parameter optimization, driving industrial systems to unprecedented levels of efficiency, reliability, and adaptability. By sharing their practice and insights in the development and application of these new technologies, the authors of the articles in this reprint have demonstrated the value of data-driven intelligent modeling and optimization algorithms for industrial processes, providing readers with valuable ideological inspiration in the field.
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