Human-centric artificial intelligence architecture for industry 5.0 applications

International Journal of Production Research, Год журнала: 2022, Номер 61(20), С. 6847 - 6872

Опубликована: Ноя. 7, 2022

Human-centricity is the core value behind evolution of manufacturing towards Industry 5.0. Nevertheless, there a lack architecture that considers safety, trustworthiness, and human-centricity at its core. Therefore, we propose an integrates Artificial Intelligence (Active Learning, Forecasting, Explainable Intelligence), simulated reality, decision-making, users' feedback, focussing on synergies between humans machines. Furthermore, align proposed with Big Data Value Association Reference Architecture Model. Finally, validate it three use cases from real-world case studies.

Язык: Английский

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Machine learning bridges omics sciences and plant breeding

Trends in Plant Science, Год журнала: 2022, Номер 28(2), С. 199 - 210

Опубликована: Сен. 21, 2022

Язык: Английский

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Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124678 - 124678

Опубликована: Июль 5, 2024

Condition monitoring plays a vital role in ensuring the reliability and optimal performance of various engineering systems. Traditional methods for condition rely on physics-based models statistical analysis techniques. However, these approaches often face challenges dealing with complex systems limited availability accurate physical models. In recent years, physics-informed machine learning (PIML) has emerged as promising approach monitoring, combining strengths modelling data-driven learning. This study presents comprehensive overview PIML techniques context monitoring. The central concept driving is incorporation known laws constraints into algorithms, enabling them to learn from available data while remaining consistent principles. Through fusing domain knowledge learning, offer enhanced accuracy interpretability comparison purely approaches. this survey, detailed examinations are performed regard methodology by which principles integrated within frameworks, well their suitability specific tasks Incorporation ML model may be realized variety methods, each having its unique advantages drawbacks. distinct limitations integration physics detailed, considering factors such computational efficiency, interpretability, generalizability different fault detection. Several case studies works literature utilizing emerging presented demonstrate efficacy applications. From reviewed, versatility potential demonstrated. Novel an innovative solution addressing complexities associated challenges. survey helps form foundation future work field. As technology continues advance, expected play crucial enhancing maintenance strategies, system reliability, overall operational efficiency

Язык: Английский

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Deep learning in fringe projection: A review

Neurocomputing, Год журнала: 2024, Номер 581, С. 127493 - 127493

Опубликована: Март 7, 2024

Язык: Английский

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A Comprehensive Survey of Deep Learning Approaches in Image Processing

Sensors, Год журнала: 2025, Номер 25(2), С. 531 - 531

Опубликована: Янв. 17, 2025

The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach traditional methodologies. This survey offers an in-depth exploration DL approaches that have redefined processing, tracing their evolution from early innovations to latest state-of-the-art developments. It also analyzes progression architectural designs and paradigms significantly enhanced ability process interpret complex visual data. Key such as techniques improving model efficiency, generalization, robustness, are examined, showcasing DL's address increasingly sophisticated image-processing tasks across diverse domains. Metrics used for rigorous evaluation discussed, underscoring importance performance assessment in varied application contexts. impact is highlighted through its tackle challenges generate actionable insights. Finally, this identifies potential future directions, including emerging technologies like quantum computing neuromorphic architectures efficiency federated privacy-preserving training. Additionally, it highlights combining with edge explainable artificial intelligence (AI) scalability interpretability challenges. These advancements positioned further extend applications DL, driving innovation processing.

Язык: Английский

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Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods

Knowledge and Information Systems, Год журнала: 2025, Номер unknown

Опубликована: Фев. 22, 2025

Язык: Английский

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Degrees of algorithmic equivalence between the brain and its DNN models

Trends in Cognitive Sciences, Год журнала: 2022, Номер 26(12), С. 1090 - 1102

Опубликована: Окт. 7, 2022

Язык: Английский

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Multi-View Action Recognition using Contrastive Learning

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Год журнала: 2023, Номер unknown, С. 3370 - 3380

Опубликована: Янв. 1, 2023

In this work, we present a method for RGB-based action recognition using multi-view videos. We supervised contrastive learning framework to learn feature embedding robust changes in viewpoint, by effectively leveraging data. use an improved loss and augment the positives with those coming from synchronized viewpoints. also propose new approach classifier probabilities guide selection of hard negatives loss, more discriminative representation. Negative samples confusing classes based on posterior are weighted higher. show that our leads better domain generalization compared standard training synthetic Extensive experiments real (NTU-60, NTU-120, NUMA) (RoCoG) data demonstrate effectiveness approach.

Язык: Английский

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Harnessing synthetic data for enhanced detection of Pine Wilt Disease: An image classification approach

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 218, С. 108690 - 108690

Опубликована: Фев. 2, 2024

Язык: Английский

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Incorporating physics into data-driven computer vision

Nature Machine Intelligence, Год журнала: 2023, Номер 5(6), С. 572 - 580

Опубликована: Июнь 1, 2023

Язык: Английский

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