The occurrence of epileptic photosensitivity (Seizures triggered by rapid visual stimuli) has been steadily increasing since the rise of various video platforms. While there are invasive surgical solutions, noninvasiv...
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Accurate removal of brain tumors is always one of the most important challenges for surgeons, as the continuous change of the brain state after opening the skull and releasing the resulting pressure causes the tumor s...
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Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion *** various machine learning models offer promising predictions,one critical but often overlooked challenge ...
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Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion *** various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for *** of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for ***,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL *** approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL *** approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge *** method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional *** also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder *** projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled *** approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.
With the rapid development of modern electronics and computation capability, biomechanical mining is attracting more and more attention. Due to the complexity and inter-person variance among the biomechanical dynamics...
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The state-of-the-art methods for estimating the price of aircraft tickets using machine learning are summarized in this study. Existing methods fall short in providing accurate prediction for flight prices both for sh...
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The aim of this paper is twofold. On the one hand, the applicability of the well-known diagnostic techniques, based on the motor current and stray flux signature analysis, has been experimentally tested both on small ...
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Adopting a malicious IoT device connected to a company's network is a hazard to the enterprise. Firms must be able to distinguish between legitimate devices connected to their network and those that are threats. I...
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Whereas a vast literature exists reporting on mapping of rice paddy fields in Asia based on spaceborne data, especially from radar sen¬sors, comparatively little has been done so far on the European context, wher...
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Deep learning (DL) methods have revolutionized image segmentation by providing tools to automatically identify structures within images, with high levels of accuracy. In particular, Convolutional Neural Networks (CNN)...
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This paper presents the use of a waveguide filter with ridge resonators that exhibits transmission zeros to implement a dielectric permittivity sensor. Thanks to its geometry, the electric field is intense under the r...
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