With the continuous expansion of the data center, its energy consumption is also increasing. Aiming at the problem that the high redundancy of modern data center network causes low energy-consumption utilization, this...
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Water scarcity is a significant global challenge, worsened by global warming and population growth, straining water resources. Agriculture, a major water user, often employs inefficient irrigation methods, leading to ...
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Electroencephalography (EEG) is a crucial tool for monitoring electrical brain activity and diagnosing neurological conditions. Manual analysis of EEG signals is time-consuming and prone to variability, necessitating ...
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Underwater object detection plays a significant role in marine exploration activities such as ecological monitoring, conservation of undersea ecosystems, and underwater robotics. In contrast to detection in the atmosp...
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Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric *** study examines ten machine learning architectures,Including Deep Belief N...
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Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric *** study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 *** indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s *** computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction *** GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational *** contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery *** of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep *** findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained *** work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.
Efficient transportation systems are crucial for the ever-growing smart cities. With the increasing urbanization and growth in vehicular traffic, congestion has become a significant challenge. This research paper addr...
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Satellite communication is considered to be one of the most important communication methods in the future. Satellite communication can provide essential communication links and services in areas without network covera...
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Hyperspectral imaging (HSI) datasets contain hundreds of contiguous narrow spectral bands, which can create challenges in data analysis due to the curse of dimensionality. However, much of this data is redundant, nece...
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In the modern business landscape, the widespread adoption of data-driven decision systems underscores the importance of integrating ethical principles such as fairness, accountability, transparency, and explainability...
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Missing data pose significant obstacles in data analysis. Many imputation methods, operating under the assumption that similar instances exhibit similar feature values, often overlook the essential role of the feature...
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