In this study, the damage evolution of slight overcharging cycles is revealed, and a novel early warning method is proposed based on the prediction of abnormal capacity fading caused by a slight overcharge fault. With...
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In the acoustic scene classification task, the method of using mel-spectrogram to express the acoustic scene information is widely applied. However, mel-spectrogram has defects and it ignores important information abo...
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ISBN:
(纸本)9781450376570
In the acoustic scene classification task, the method of using mel-spectrogram to express the acoustic scene information is widely applied. However, mel-spectrogram has defects and it ignores important information about some acoustic scenes. The paper improves the mel-spectrogram and its generation algorithm. Including: I. For the sensitivity of the acoustic scene to high-frequency acoustic signals, the paper changes the filter design method of Mel Frequency Cepstrum Coefficient (MFCC). This method preserves more high frequency information by applying the equal-height triangular filter banks and increasing the number of the filters. II. Based on the previous step, an enhancement algorithm is proposed for the problem of the lack of high-frequency weak signals in the characteristic spectrum. The algorithm performs nonlinear mapping on the mel-spectrogram, which makes the transformed high-frequency weak signal feature information more obvious. The algorithm is verified by DCASE 2018 acoustic scene classification dataset and LITIS ROUEN dataset. The experimental results demonstrate the effectiveness of the proposed algorithm.
With the growing maturity of the advanced edge-cloud collaboration and integrated sensing-communication-computing systems, edge intelligence has been envisioned as one of the enabling technologies for ubiquitous and l...
With the growing maturity of the advanced edge-cloud collaboration and integrated sensing-communication-computing systems, edge intelligence has been envisioned as one of the enabling technologies for ubiquitous and latency-sensitive machine learning based services in future wireless
In STT-MRAM, data is stored in a magnetic tunnel structure (MTJ) containing multiple ferromagnetic layers. Each ferromagnetic layer generates a stray field, which affects the critical switching current, switching time...
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Conversational Question Generation (CQG) is a new concern in Question Generation (QG) study. Recently Seq2Seq neural network model has been widely used in the QG area. CQG model is also based on the Seq2Seq neural net...
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The development of new biomarkers or therapeutic targets for cancer immunotherapies requires deep understanding of *** date,the complete landscape and systematic characterization of long noncoding RNAs(lncRNAs)in T ce...
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The development of new biomarkers or therapeutic targets for cancer immunotherapies requires deep understanding of *** date,the complete landscape and systematic characterization of long noncoding RNAs(lncRNAs)in T cells in cancer immunity are ***,by systematically analyzing full-length single-cell RNA sequencing(scRNA-seq)data of more than 20,000 libraries of T cells across three cancer types,we provided the first comprehensive catalog and the functional repertoires of lncRNAs in human T ***,we developed a custom pipeline for de novo transcriptome assembly and obtained a novel lncRNA catalog containing 9433 *** increased the number of current human lncRNA catalog by 16%and nearly doubled the number of lncRNAs expressed in T *** found that a portion of expressed genes in single T cells were lncRNAs which had been overlooked by the majority of previous *** on metacell maps constructed by the MetaCell algorithm that partitions scRNA-seq datasets into disjointed and homogenous groups of cells(metacells),154 signature lncRNA genes were *** were associated with effector,exhausted,and regulatory T cell ***,84 of them were functionally annotated based on the co-expression networks,indicating that lncRNAs might broadly participate in the regulation of T cell *** findings provide a new point of view and resource for investigating the mechanisms of T cell regulation in cancer immunity as well as for novel cancer-immune biomarker development and cancer immunotherapies.
Accurate segmentation of pulmonary blood vessels from CT images is of great significance for lung disease detection and segmentation of other lung structures. Manual segmentation is difficult to accurately segment vas...
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Some of the latest released Code Large Language Models (Code LLMs) have been trained on repository-level code data, enabling them to perceive repository structures and utilize cross-file code information. This capabil...
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Recently, breast cancer histopathology image classification using convolutional neural networks has achieved more and more attentions with the great progress. To capture more discriminant deep features for the classif...
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ISBN:
(纸本)9781450385183
Recently, breast cancer histopathology image classification using convolutional neural networks has achieved more and more attentions with the great progress. To capture more discriminant deep features for the classification, this paper proposes a novel triplet-attention residual network, i.e., TAResNet, to distinguish the breast cancer histopathology image. TAResNet employs the representative ResNet18 model to extract deep features of histopathology images, followed by a triplet-attention module to further boost the discriminability of deep features through expanding feature diversity and enhancing inter-dimensional dependency. Extensive experiments carried out on the public BreakHis dataset well evaluate the effectiveness the given TAResNet model. More specifically, TAResNet achieves its optimal classification accuracy of 98.34% and 98.77% at the image level and patient level, respectively.
With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge computing (MEC) addresses this need by relocating computing resources to the netw...
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