Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

Liu, Tianyuan; Bao, Jinsong; Wang, Junliang; Wang, Jiacheng

doi:10.1080/0951192x.2021.1901319

Cited by 16 publications

(2 citation statements)

References 151 publications

(141 reference statements)

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“…Deep learning (DL) stands out as the most widely employed AI/ML approach, demonstrating remarkable success across diverse disciplines and delivering substantial performance enhancements when compared with traditional methods [23]. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) stand out as widely employed DL methodologies for RUL prediction, leveraging their abilities in capturing temporal patterns and spatial features in multidimensional time series data.…”

Section: Related Literaturementioning

confidence: 99%

A Two-Stage Attention-Based Hierarchical Transformer for Turbofan Engine Remaining Useful Life Prediction

Fan,

Li,

Chang

2024

Sensors

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The accurate estimation of the remaining useful life (RUL) for aircraft engines is essential for ensuring safety and uninterrupted operations in the aviation industry. Numerous investigations have leveraged the success of the attention-based Transformer architecture in sequence modeling tasks, particularly in its application to RUL prediction. These studies primarily focus on utilizing onboard sensor readings as input predictors. While various Transformer-based approaches have demonstrated improvement in RUL predictions, their exclusive focus on temporal attention within multivariate time series sensor readings, without considering sensor-wise attention, raises concerns about potential inaccuracies in RUL predictions. To address this concern, our paper proposes a novel solution in the form of a two-stage attention-based hierarchical Transformer (STAR) framework. This approach incorporates a two-stage attention mechanism, systematically addressing both temporal and sensor-wise attentions. Furthermore, we enhance the STAR RUL prediction framework by integrating hierarchical encoder–decoder structures to capture valuable information across different time scales. By conducting extensive numerical experiments with the CMAPSS datasets, we demonstrate that our proposed STAR framework significantly outperforms the current state-of-the-art models for RUL prediction.

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Section: Related Literaturementioning

confidence: 99%

A Two-Stage Attention-Based Hierarchical Transformer for Turbofan Engine Remaining Useful Life Prediction

Fan,

Li,

Chang

2024

Sensors

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“…However, they need help with more extensive reconstruction of real-world wall paintings in time dependency and considerable results. Local assembly of object fragments to reconstruct the complete geometry of the architecture is already addressed in various articles [278], [279] by finding the adjacency. On the other hand, Global Assembly focuses on searching the fragment space to find particular pairwise segments by rotation and positional orientation to solve the local assembly problem in a larger space.…”

Section: Other Digital Restoration Techniques a Genetic Algorithmmentioning

confidence: 99%

Digital Restoration of Cultural Heritage With Data-Driven Computing: A Survey

Basu

Paul²,

Ghosh

et al. 2023

IEEE Access

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Digitized methodologies in the recent era contribute to various fields of automation that used to hold different interests and meanings of human life. Buildings with historical significance, cultural values, and beliefs are becoming an interdisciplinary field of interest, engaging more computer scientists nowadays. Such structures need more attention towards reconstructing their values using a flavor of computerized tools instead of brickwork directly. Due to the wear of time, the tiles and engravings of most of the historical monuments are on the verge of ruin, endangering significant historical values. In this survey, we rebuild the values by delving deep into the device and methodologies by providing a comprehensive understanding of emerging fields and some experimental decisions. We discuss heritage restoration from some essential papers on 3D reconstruction, image inpainting, IoT-based methods, genetic algorithms, and image processing. The survey explains Machine Learning, Deep Learning, and Computer Vision-based methods for various restoration tasks in the related field. We divide this into certain parts contributing to different fields that restore cultural heritage. Moreover, we infer that the techniques will be faster, cheaper, and more beneficial to the context of image reconstruction in the near future.

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