Modeling the trust of peers in peer-to-peer networks is pivotal in maintaining the security and functionality of the network. This trust is commonly perceived as a peer's reliability based on past interactions and...
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Wilga 2024 Summer Symposium on Photonics Applications and Web engineering was the 52th edition of the research and technical meetings series. Traditionally, the annual series of technical conferences and topical sessi...
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This study examines the current state of course catalogues (CCs) in the context of student mobility within the Erasmus+ program, addressing persistent challenges in providing efficient, transparent, and uniform access...
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Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacit...
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Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.
Multi-label scene classification (MLSC) in remote sensing (RS) plays a crucial role in recognizing the intricate contents of RS images. However, the MLSC task is challenged by the complexity of label combinations and ...
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The operation of modern railway traffic management systems is based on microprocessor technology, new IT solutions and safe communication using transmission between devices. Important in the new approach to railway tr...
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作者：
Zaman, Muhammad ShahriarVictor Biswas, Richard
Faculty of Science and Technology Department of Computer Science and Engineering Dhaka1229 Bangladesh
Faculty of Engineering Department of Electrical and Electronic Engineering 408/1 Kuratoli Dhaka1229 Bangladesh
Deafness is an unfortunate issue of our society impairing many people. Around 3 million people in Bangladesh suffer from partial or complete deafness. One key challenge for these individuals is communicating with regu...
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Conventional methods of monitoring exercise have mostly focused on individual athletes, neglecting the importance of simultaneously tracking several athletes participating in group workouts. Nowadays, with the advance...
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The article presents the results of a measurement session of power quality parameters at a transformer station supplying a DC railroad traction substation. Measurements were carried out in the 110 kV winding of the 11...
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This research explores machine learning approaches to determine the most significant features related to neonatal mortality in Indonesia. We create prediction tasks with deep learning models including MLP, LSTM, and C...
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