Innovative Technologies and Applications in Engineering Sensing Through Deep and Machine Learning
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".
Deadline for manuscript submissions: 20 January 2026 | Viewed by 3583
Special Issue Editor
Special Issue Information
Dear Colleagues,
The pervasive integration of deep learning (DL) and machine learning (ML) in engineering sensing technologies marks a significant paradigm shift, providing unparalleled advancements in sensor fabrication, deployment, and calibration. Machine learning algorithms now play a crucial role in optimizing sensor configurations for various applications, ensuring precision and efficiency from the onset of data collection. Deep learning contributes extensively to the development of intelligent calibration techniques, adapting to diverse environmental conditions and counteracting sensor degradation to maintain long-term reliability and accuracy. These advanced computational methods extend their influence to data refinement, offering solutions for enhancing data accuracy, interpolating missing values, and facilitating intelligent data classification and recognition. The predictive modelling capabilities of these techniques introduce possibilities for proactive maintenance and anomaly detection within engineering infrastructures, guaranteeing safety and enhancing the lifespan of vital equipment. Furthermore, these technologies are central to innovating new sensing modalities and methodologies, addressing the growing complexity and evolving demands of contemporary engineering challenges.
The aim of this Special Issue is to highlight and promote recent advancements in algorithm-assisted sensing and its applications within the engineering field, covering aspects from sensor pre-processing, data collection, and analysis to practical engineering applications utilizing machine learning and deep learning. We are interested in a variety of topics, including but not limited to:
- Enhancing sensing accuracy with ML/DL;
- ML/DL-supported sensor fabrication, deployment, and calibration;
- Advanced data classification and prediction through ML/DL;
- Applying ML/DL-assisted sensing in diverse engineering scenarios;
- ML/DL algorithm optimization in engineering sensing;
- Targeted optimization strategies for applying ML/DL in engineering scenarios.
Dr. Haoran Fu
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- deep learning
- engineering applications
- data analysis
- sensor optimization
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