Recognizing human activities in Industry 4.0 scenarios through an analysis-modeling- recognition algorithm and context labels

Bordel, Borja

doi:10.3233/ica-210667

Cited by 16 publications

(7 citation statements)

References 46 publications

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“…Besides, the ASS-Sub-Agent can enhance the generalization and transferability among data of different classes. Industrial scenarios are more complex, so the transferability of agent is more important [79].…”

Section: Transfer Learning Based On the Hierarchical Frameworkmentioning

confidence: 99%

3D reconstruction based on hierarchical reinforcement learning with transferability

Fan

et al. 2023

ICA

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3D reconstruction is extremely important in CAD (computer-aided design)/CAE (computer-aided Engineering)/CAM (computer-aided manufacturing). For interpretability, reinforcement learning (RL) is used to reconstruct 3D shapes from images by a series of editing actions. However, typical applications of RL for 3D reconstruction face problems. The search space will increase exponentially with the action space due to the curse of dimensionality, which leads to low performance, especially for complex action spaces in 3D reconstruction. Additionally, most works involve training a specific agent for each shape class without learning related experiences from others. Therefore, we present a hierarchical RL approach with transferability to reconstruct 3D shapes (HRLT3D). First, actions are grouped into macro actions that can be chosen by the top-agent. Second, the task is accordingly decomposed into hierarchically simplified sub-tasks solved by sub-agents. Different from classical hierarchical RL (HRL), we propose a sub-agent based on augmented state space (ASS-Sub-Agent) to replace a set of sub-agents, which can speed up the training process due to shared learning and having fewer parameters. Furthermore, the ASS-Sub-Agent is more easily transferred to data of other classes due to the augmented diverse states and the simplified tasks. The experimental results on typical public dataset show that the proposed HRLT3D performs overwhelmingly better than recent baselines. More impressingly, the experiments also demonstrate the extreme transferability of our approach among data of different classes.

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Section: Transfer Learning Based On the Hierarchical Frameworkmentioning

confidence: 99%

3D reconstruction based on hierarchical reinforcement learning with transferability

Fan

et al. 2023

ICA

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“…Conventional bridge crack detection predominantly depends on manual inspection, a method characterized by inefficiency and imprecision (Bordel et al., 2022). Additionally, it is susceptible to the expertise of inspection personnel and the adaptability of inspection equipment, resulting in subjective assessments that pose challenges for precise bridge safety evaluations (Guo & Wang, 2022).…”

Section: Introductionmentioning

confidence: 99%

Fine‐grained crack segmentation for high‐resolution images via a multiscale cascaded network

Chu,

Chun

2023

Computer aided Civil Eng

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High‐resolution (HR) crack images offer more detailed information for assessing structural conditions compared to low‐resolution (LR) images. This wealth of detail proves indispensable in bolstering the safety of unmanned aerial vehicle (UAV)‐based inspection procedures and elevating the precision of small crack segmentation. Nonetheless, achieving a balance between segmentation accuracy and GPU memory consumption poses a substantial challenge for deep learning models when processing HR crack images. To overcome this challenge, a novel “HR crack segmentation framework” (HRCSF) is proposed, specifically designed to meticulously segment crack images with resolutions exceeding 4K. First, a multiscale crack feature extraction network (MsCFEN) was proposed with the embedment of the strip pooling operation to enhance the representation of the transverse and longitudinal crack pixels from the complex backgrounds. Subsequently, two cascaded operations were tailored to MsCFEN, enabling a comprehensive refinement process that incorporates both global and local aspects. Furthermore, to fully leverage the potential of each proposed component in the refinement process, the complete architecture was trained using a loss function with embedded boundary optimization. Conclusively, a UAV‐based case study was conducted on a real bridge in Changsha, demonstrating HRCSF's practicability in segmenting HR crack images. The implementation of HRCSF allows the UAV to perform crack inspection effectively from a distance of 3 m away from the girder, resulting in a significant 50% reduction in inspection time compared to LR segmentation methods while maintaining high detection accuracy.

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“…Industry 4.0 [1] is typically defined as the next industrial revolution, characterized by the seamless integration of computational and physical processes. Paradigms such as Cyber-Physical Systems [2] or Artificial Intelligence [3] are usually referred to enabling technologies in future Industry 4.0 scenarios. However, current challenges related to Industry 4.0 technologies are closer to the hardware level than to knowledge or data management.…”

Section: Introductionmentioning

confidence: 99%

Predictor-corrector models for lightweight massive machine-type communications in Industry 4.0

Bordel

Alcarria

Chung

et al. 2023

ICA

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Future Industry 4.0 scenarios are characterized by seamless integration between computational and physical processes. To achieve this objective, dense platforms made of small sensing nodes and other resource constraint devices are ubiquitously deployed. All these devices have a limited number of computational resources, just enough to perform the simple operation they are in charge of. The remaining operations are delegated to powerful gateways that manage sensing nodes, but resources are never unlimited, and as more and more devices are deployed on Industry 4.0 platforms, gateways present more problems to handle massive machine-type communications. Although the problems are diverse, those related to security are especially critical. To enable sensing nodes to establish secure communications, several semiconductor companies are currently promoting a new generation of devices based on Physical Unclonable Functions, whose usage grows every year in many real industrial scenarios. Those hardware devices do not consume any computational resource but force the gateway to keep large key-value catalogues for each individual node. In this context, memory usage is not scalable and processing delays increase exponentially with each new node on the platform. In this paper, we address this challenge through predictor-corrector models, representing the key-value catalogues. Models are mathematically complex, but we argue that they consume less computational resources than current approaches. The lightweight models are based on complex functions managed as Laurent series, cubic spline interpolations, and Boolean functions also developed as series. Unknown parameters in these models are predicted, and eventually corrected to calculate the output value for each given key. The initial parameters are based on the Kane Yee formula. An experimental analysis and a performance evaluation are provided in the experimental section, showing that the proposed approach causes a significant reduction in the resource consumption.

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Recognizing human activities in Industry 4.0 scenarios through an analysis-modeling- recognition algorithm and context labels

Cited by 16 publications

References 46 publications

3D reconstruction based on hierarchical reinforcement learning with transferability

3D reconstruction based on hierarchical reinforcement learning with transferability

Fine‐grained crack segmentation for high‐resolution images via a multiscale cascaded network

Predictor-corrector models for lightweight massive machine-type communications in Industry 4.0

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