Aiming at the low accuracy of mass spectrum data recognition due to the subjectivity of artificial feature extraction and the limitation of a single sample and unbalanced mass spectrum data in current research, a clas...
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With the progress of technology and the development of industrialisation, power electronic equipment is developing towards high performance and miniaturisation, and its heat dissipation needs are gradually increasing....
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Due to the complexity of temporal and spatial characteristics during microwave heating, the unpredictability of the magnetic field makes it difficult to accurately predict the heating effect by traditional modeling me...
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To apply the advantages of deep learning in 2D image recognition to the fault diagnosis of three-phase inverters, this paper proposes a Parallel Convolutional Neural Network (CNN) model based on short-time Fourier tra...
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Salient object detection mimics the human visual system, segmenting targets using RGB and thermal infrared images suitable for low-light and occlusion scenarios. This technology faces challenges such as mismatched ima...
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Lithium-ion battery lifespan prediction is a core function of battery health management systems, directly impacting user satisfaction, safety assurance, and providing guidance for battery replacement. However, two maj...
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Incorporating domain-specific visual information into text poses one of the critical challenges for domain-specific multi-modal neural machine translation (DMNMT). While most existing DMNMT methods often borrow multi-...
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Pancreatic diseases, including mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma(PDAC), present with similar imaging features, leading to diagnostic complexities. Deep Learning (DL) methods...
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Pancreatic diseases, including mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma(PDAC), present with similar imaging features, leading to diagnostic complexities. Deep Learning (DL) methodshave been shown to perform well on diagnostic tasks. Existing DL pancreatic lesion diagnosis studies basedon Magnetic Resonance Imaging (MRI) utilize the prior information to guide models to focus on the lesionregion. However, over-reliance on prior information may ignore the background information that is helpful fordiagnosis. This study verifies the diagnostic significance of the background information using a clinical ***, the Prior Difference Guidance Network (PDGNet) is proposed, merging decoupled lesion andbackground information via the Prior Normalization Fusion (PNF) strategy and the Feature Difference Guidance(FDG) module, to direct the model to concentrate on beneficial regions for diagnosis. Extensive experiments inthe clinical dataset demonstrate that the proposed method achieves promising diagnosis performance: PDGNetsbased on conventional networks record an ACC (Accuracy) and AUC (Area Under the Curve) of 87.50% and89.98%, marking improvements of 8.19% and 7.64% over the prior-free benchmark. Compared to lesion-focusedbenchmarks, the uplift is 6.14% and 6.02%. PDGNets based on advanced networks reach an ACC and AUC of89.77% and 92.80%. The study underscores the potential of harnessing background information in medical imagediagnosis, suggesting a more holistic view for future research.
Underwater wireless sensor networks can monitor ocean information, which provides a new approach to marine environmental monitoring, disaster warning and resource exploration. However, the development of underwater wi...
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Accurate positioning in shallow water regions is crucial for various underwater applications. TDOA algorithms estimate the signal source location by measuring the time differences of signal arrival at multiple receive...
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