Bridging the gap between physics-based modeling and data-driven machine learning promises to reduce the amount of training data required and to improve explainability in predictive maintenance applications. For a smal...
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ISBN:
(纸本)9798350315684
Bridging the gap between physics-based modeling and data-driven machine learning promises to reduce the amount of training data required and to improve explainability in predictive maintenance applications. For a small fleet of industrial forklift trucks, we develop a physically inspired framework for predicting remaining useful life (RUL) for selected components by integrating physically motivated feature extraction, degradation modelling and machine learning. the discussed approach is promising for situations of limited data availability or large data heterogeneity, which often occurs in fleets of customized vehicles optimized for particular tasks.
this work presents DiverSim, a highly customizable simulation tool designed for the generation of diverse synthetic datasets of vulnerable road users to address key challenges in pedestrian detection for Advanced Driv...
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Brain-Computer Interfaces (BCIs) have great potential for improving the lives of people with disabilities. the success of a BCI system is largely driven by the accuracy of the BCI decoder. this accuracy, in turn, may ...
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ISBN:
(纸本)9781665462921
Brain-Computer Interfaces (BCIs) have great potential for improving the lives of people with disabilities. the success of a BCI system is largely driven by the accuracy of the BCI decoder. this accuracy, in turn, may be limited by the amount of labelled training data available for supervised machine learning algorithms. the success of deep learning algorithms in other computer science areas has not reached the field of BCI decoding due to this lack of abundant labelled data. We use a novel deep learning architecture trained in a selfsupervised manner to learn a common vector representation (embedding) of EEG signals that can be used in different BCI tasks. the vector representation is trained using EEG recordings without using any task labels. We validate our embedder using two separate BCI tasks: seizure detection and motor imagery, and assess its usefulness through distance similarity metrics in a clustering approach. the derived embeddings were successful in distinguishing binary classes in both tasks.
Withthe advent of smart transportation, technology plays a key role in improving the safety of people on roads. Pedestrian detection techniques have various applications in driving assistance systems, intelligent veh...
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Maintenance is a critical aspect of modern industrial operations, as it ensures the reliability and longevity of equipment while minimising unplanned downtime. Traditional, schedule-based maintenance approaches are of...
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this research introduces the Photonics-Enhanced Embedded Robotic Intelligence Model (PEERIM), an innovative approach that integrates fiber Bragg grating (FBG) sensors with photonics and deep reinforcement learning (DR...
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this research explores the therapeutic potential of music, specifically Indian classical ragas, for individuals suffering from diabetes, hypertension, and thyroid disorders. the proposed system uses a LGBM classifier ...
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Amid the transformative advancements of Generative Adversarial Networks (GANs) in machine learning, a pertinent challenge arises: discerning real instances from synthetic ones. this research introduces a novel neural ...
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this note surveys developments in particle physics due to advances made in the fields of statistics, machine learning, and artificial intelligence. Withthe aid of examples and recent work, this article attempts to gi...
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ISBN:
(纸本)9783031585012;9783031585029
this note surveys developments in particle physics due to advances made in the fields of statistics, machine learning, and artificial intelligence. Withthe aid of examples and recent work, this article attempts to give a flavor of the effect of these advances on particle physics, including brief mention of cloud computing, classic machine learning techniques, statistics applications, new ML/AI techniques, reinforcement learning, and other advances. Suggestions are made regarding the future.
ML that creates a global framework by gathering knowledge from a number of different dispersed edge clients. FL allows on-device training, keeps client information in private, and updates the frameworks. FL approaches...
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