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Showing 1 to 5 of 5 for “"Load Classification"”.
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A Study of Machine Learning based on Physical Load Classification
… verified to be usable for physical load recognition and classification work. As a result, the processed data used for machine learning is divided into three main categories: no-load, light-load and heavy-load. The traditional LSTM was able to achieve 71.1% accuracy for the …
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Armband EMG-based Lifting Detection and Load Classification Algorithms using Static and Dynamic Lifting Trials
… occupational risk factors, such as lifting loads. However, such algorithms are often developed and validated using the dataset collected from the same lab-based experimental set-up, which limits their external validity. The recent development of wearable armbands with surface …
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A load identification and diagnostic framework for aggregate power monitoring
… of data from the operation of electromechanical loads, such as measurements of power, torque, vibration, and acoustic signals. These measurements can act as unique identifiers for the early identification of degrading system performance, providing a rich feature space for fault detection and …
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Machine learning classification techniques for non-intrusive load monitoring
Non-intrusive load monitoring is the concept of determining the operational loads using single-point sensing. The features contained within the electrical load’s signal are used to identify a unique signature which is used by a machine learning classifier to automate the load identification …
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Cognitive load examination via pen interactions
Cognitive load refers to the mental effort when a cognitive task is conducted. As an important mental construct affecting task performance and subjective experience, cognitive load is considered a key factor in human-computer interaction system design and user operation efficiency improvement. …