The current automatic speech recognition (ASR) technology has achieved remarkable success, but it is vulnerable to adversarial attacks. To solve the problem that existing adversarial attack methods are difficult to ca...
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Persistent community detection contributes to a deep understanding of the core components of dynamic networks. The existing persistent community detection methods have the defect of inaccurate granularity setting of n...
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In the context of the combination of Artificial intelligence of things (AIoT) and Food computing, food image segmentation is a current research hotspot. It plays an important role in food selection, intake and nutriti...
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In order to solve the problem of high memory usage and large GPU computation in the case of a single machine. In this paper, MobileNet and Pytorch deep learning framework and Flink big data computing framework are dee...
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This research proposes a refined deep learning framework aimed at boosting the precision and efficacy of detecting surface imperfections in strip steel. This method integrates enhancement and simplification techniques...
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To predict the lithium-ion(Li-ion)battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries pre...
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To predict the lithium-ion(Li-ion)battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries present distinct degradation patterns,and it is challenging to capture negligible capacity fade in early *** the data-driven method showing promising performance,insufficient data is still a big issue since the ageing experiments on the batteries are too slow and *** this study,we proposed twin autoencoders integrated into a two-stage method to predict the early cycles'degradation *** two-stage method can properly predict the degradation from course to *** twin autoencoders serve as a feature extractor and a synthetic data generator,***,a learning procedure based on the long-short term memory(LSTM)network is designed to hybridize the learning process between the real and synthetic *** performance of the proposed method is verified on three datasets,and the experimental results show that the proposed method can achieve accurate predictions compared to its competitors.
QR code is known for rapid and high-capacity information transmission. Being easy to generate and widely used, it's ideal for hiding information. This paper proposes a color QR code-based information hiding scheme...
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The coalescence and missed detection are two key challenges in Multi-Target Tracking(MTT).To balance the tracking accuracy and real-time performance,the existing Random Finite Set(RFS)based filters are generally diffi...
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The coalescence and missed detection are two key challenges in Multi-Target Tracking(MTT).To balance the tracking accuracy and real-time performance,the existing Random Finite Set(RFS)based filters are generally difficult to handle the above problems simultaneously,such as the Track-Oriented marginal Multi-Bernoulli/Poisson(TOMB/P)and Measurement-Oriented marginal Multi-Bernoulli/Poisson(MOMB/P)*** on the Arithmetic Average(AA)fusion rule,this paper proposes a novel fusion framework for the Poisson Multi-Bernoulli(PMB)filter,which integrates both the advantages of the TOMB/P filter in dealing with missed detection and the advantages of the MOMB/P filter in dealing with *** order to fuse the different PMB distributions,the Bernoulli components in different Multi-Bernoulli(MB)distributions are associated with each other by Kullback-Leibler Divergence(KLD)***,an adaptive AA fusion rule is designed on the basis of the exponential fusion weights,which utilizes the TOMB/P and MOMB/P updates to solve these difficulties in ***,by comparing with the TOMB/P and MOMB/P filters,the performance of the proposed filter in terms of accuracy and efficiency is demonstrated in three challenging scenarios.
To solve the problem of shallow cross-modal interaction depth, information loss during feature fusion, and semantic ambiguity leading to low algorithm accuracy in the field of visual question answering, a visual quest...
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In response to the problem that existing methods cannot effectively model inter-row interactions and find it challenging to extract meaningful interaction information, a trajectory prediction method using Multi-head S...
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