Background: The IoT (Internet of Things) assigns to the capacity of Device-to-Machine (D2M) connections, which is a vital component in the development of the digital economy. IoT integration with a human being enables...
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Background: The main objective of the Internet of Things (IoT) has significantly influenced and altered technology, such as interconnection, interoperability, and sensor devices. To ensure seamless healthcare faciliti...
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Shape from polarization (SfP) method can use the polarization information in reflected light to estimate the surface normal of the target,which can further reconstruct the shape of the *** a simple image capture proce...
Shape from polarization (SfP) method can use the polarization information in reflected light to estimate the surface normal of the target,which can further reconstruct the shape of the *** a simple image capture process,it can use low-cost equipment to meet a high precision imaging requirement,which can be used in remote scenes and other applications.
This study proposes an energy- and area-efficient CMOS neuron and analog max pooling circuit for RRAM-based convolutional neural network (CNN) accelerators. The proposed max pooling circuit implements a 2 × 2 max...
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Machine learning has been massively utilized to construct data-driven solutions for predicting the lifetime of rechargeable batteries in recent years, which project the physical measurements obtained during the early ...
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Machine learning has been massively utilized to construct data-driven solutions for predicting the lifetime of rechargeable batteries in recent years, which project the physical measurements obtained during the early charging/discharging cycles to the remaining useful lifetime. While most existing techniques train the prediction model through minimizing the prediction error only, the errors associated with the physical measurements can also induce negative impact to the prediction accuracy. Although total-least-squares(TLS) regression has been applied to address this issue, it relies on the unrealistic assumption that the distributions of measurement errors on all input variables are equivalent, and cannot appropriately capture the practical characteristics of battery degradation. In order to tackle this challenge, this work intends to model the variations along different input dimensions, thereby improving the accuracy and robustness of battery lifetime prediction. In specific, we propose an innovative EM-TLS framework that enhances the TLS-based prediction to accommodate dimension-variate errors, while simultaneously investigating the distributions of them using expectation-maximization(EM). Experiments have been conducted to validate the proposed method based on the data of commercial Lithium-Ion batteries, where it reduces the prediction error by up to 29.9 % compared with conventional TLS. This demonstrates the immense potential of the proposed method for advancing the R&D of rechargeable batteries.
Video grounding intends to perform temporal localization in multimedia information retrieval. The temporal bounds of the target video span are determined for the given input query. A novel interactive multi-head self-...
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Federated matrix factorization (FedMF) has recently emerged as a privacy-friendly paradigm which runs matrix factorization (MF) in a federated learning (FL) setting and enables users to keep their individual rating da...
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Internet of Vehicles (IoV) integrates with various heterogeneous nodes, such as connected vehicles, roadside units, etc., which establishes a distributed network. Vehicles are managed nodes providing all the services ...
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Implanting a high–dielectric insulating interlayer between the metals and semiconductors of hydrogen sensing diodes can improve the response, yet the underlying mechanism remains unclear. This study examines key fact...
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The growing global demand for sustainable solutions to address energy and environmental challenges has spurred significant interest in catalytic *** has emerged as a sustainable technology for environmental remediatio...
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The growing global demand for sustainable solutions to address energy and environmental challenges has spurred significant interest in catalytic *** has emerged as a sustainable technology for environmental remediation and energy conversion because of its unique characteristics of harvesting mechanical energy into electrochemical *** BiFeO_(3)(BFO)stands out among a range of piezocatalysts for its distinctive integration of piezoelectric,multiferroic,and optical *** review critically examines piezocatalytic mechanisms,including energy band theory,screening charge effects,and displacement current theory,revealing the intricate roles of internal charges,screening charges,and piezoelectric electrons in driving catalytic ***,the evolution of BFO-based piezocatalysis is systematically reviewed,emphasizing its structural characteristics,representative synthesis methods,performance optimization strategies,and diverse applications,such as organic pollutant degradation,H_(2) production,H_(2)O_(2)generation,CO_(2)reduction,and *** particular,the underestimated ferroelectric polarization effect of BFO on CO_(2)reduction is critically analyzed and *** review identifies critical challenges and outlines future research directions to advance high-efficiency BFO-based piezocatalytic ***,this comprehensive analysis underscores the potential of BFO in piezocatalysis,bridging materials engineering with practical applications and offering insights into future advancements.
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