Results 81 to 90 of about 2,214 (195)
Non-destructive Detection of Water Content in Porphyra Based on Near-infrared Spectroscopy and Deep Learning [PDF]
In order to explore the feasibility of combining near-infrared (NIR) spectroscopy and deep learning network for quantitative moisture detection, the dried Porphyra was divided into 479 groups, which detected the NIR spectra and moisture content.
Jin QIAN +7 more
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Dizhen xuebao首先利用四分量钻孔应变数据独有的自洽特性，构建震前应变特征数据集；之后基于一维卷积神经网络框架，设计地震震级与方位的预测模型；然后通过混淆矩阵计算准确率、召回率以及F1分数，对模型预测结果进行评价与修正；最后对我国西南地区的永胜、昭通、姑咱及腾冲四个台站的钻孔应变特征分别进行训练与验证，并讨论了不同特征窗长对预测效果的影响。训练完成后的模型效果在测试集上均表现优异，四个台站对震级和方位预测的平均准确率分别可达85%和80%左右，说明四分量钻孔应变数据特征与地震的发生有着很强的相关性 ...
Zining Yu +4 more
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Design of Sequential Wakeup Compute-In-Memory Controller Based on Convolutional Neural Network [PDF]
With the development of artificial intelligence, the demand for intelligent image processing on edge devices has significantly increased. At present, edge devices mainly face issues such as limited energy and low throughput.
SONG Qingzeng, LIU Xiangdong, XU Kangwei, LIU Jiahui, REN Erxiang, LUO Li, WEI Qi, QIAO Fei
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, 2019 目前,事件检测的难点在于一词多义和多事件句的检测.为了解决这些问题,提出了一个新的基于语言模型的带注意力机制的循环卷积神经网络模型(recurrent and convolutional neural network with attention based ..
施喆尔, 陈锦秀
core
Zhendong Ceshi yu Zhenduan为了提高小样本图像条件下列车轮对轴承故障检测水平，提出了一种基于多分辨率孪生神经网络（multi⁃resolution siamese neural network， 简称MrSNN）模型的列车轮对轴承表面缺陷机器视觉检测方法。首先，采用孪生神经网络（siamese neural network， 简称SNN）为基础模型框架，构建了包含不同卷积核尺寸及不同膨胀因子大小的多分辨率卷积融合模块（multi‑resolution convolution fusion block， 简称MrCFB ...
doaj +1 more source
Image data-driven intelligent recognition of permafrost strength and feature visualization based analysis [PDF]
Ensuring the stability of the frozen wall is critical in freezing construction, but traditional onsite detection methods, due to their intermittent nature, fail to provide real-time monitoring, limiting timely responses to potential catastrophic events ...
Hang WEI +3 more
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[Alzheimer's disease classification based on nonlinear high-order features and hypergraph convolutional neural network]. [PDF]
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, 2023 Zeng A +8 more
europepmc +1 more source
Short-Term Wind Power Prediction Method Based on Multimodal Feature Extraction-Convolutional Neural Network-Long-Short Term Memory Network [PDF]
ObjectivesWeather and random factors can alter the statistical characteristics of errors. Therefore, this study considers feature extraction of various climate factors that affect wind power.
GUO Qian, KUANG Honghai
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[Diagnosis of nasopharyngeal carcinoma with convolutional neural network on narrowband imaging]. [PDF]
Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi, 2023 Weng J +9 more
europepmc +1 more source