A fault diagnosis method based on pseudo-label-1D DenseNet-KNN with fewer labeled samples is proposed to classify open-set of compound faults for photovoltaic arrays. First, the I-V characteristic curves of var
摘 要 恶意代码的API 调用序列可以反映恶意行为,深度学习模型可以应用在基于API 调用序列检测恶意代码中㊂本文基于一维卷积神经网络和稠密网络结构设计了1D⁃CNN⁃Densenet 网络模型,将恶意代码动态API 调用序列处理成文本特征作为输入,横向一维卷积计算,纵向构建稠密结构网络,将前面所有层输出的相加作为下一层的输入...
We introduce DenseKANets - a DenseNet-like model with KAN convolutions instead of regular ones. Main classDenseKANetcould be foundmodels/reskanet.py. Our implementation supports blocks with KAN, Fast KAN, KALN, KAGN, and KACN convolutional layers. ...
1.一种结合DenseNet和resBi-LSTM的中文句子级唇语识别方法,其特征在于,包括以下步骤: 步骤一,视觉特征提取: 拼音预测模型的输入是唇部图片序列,假设该输入序列为T×H×W(时间×高度×宽度),先使用时空卷积提取时空特征,捕获唇部区域短时的运动特征,该部分的使用64个5×7×7(时间/高度/宽度)大小的三维卷积核,卷...
the cantilever damage problem simulated by ABAQUS is selected as a case study to discuss the excellent performance of the proposed method.The results show that the ensemble 1D DenseNet damage identification method outperforms any submodel in terms of accuracy.Furthermore,the submodel is visualized ...
1D DenseNet is built using standard 1D CNN and DenseNet basic blocks, and the acceleration data obtained from multiple sampling points is brought into the 1D DenseNet training to generate submodels after offset sampling. When using submodels for damage identification, the voting ...
To solve this problem, in this paper, a new method of EEG emotion recognition based on 1D-DenseNet is proposed. Firstly, we extract the band energy and sample entropy of EEG signal to form a 1D vector instead of the original sequence signal to reduce noise interference. Secondly, we ...
1D DenseNet is built using standard 1D CNN and DenseNet basic blocks, and the acceleration data obtained from multiple sampling points is brought into the 1D DenseNet training to generate submodels after offset sampling. When using submodels for damage identification, the voting method ideas in ...
本文基于一维卷积神经网络和稠密网络结构设计了1D-CNN-Densenet网络模型,将恶意代码动态API调用序列处理成文本特征作为输入,横向一维卷积计算,纵向构建稠密结构网络,将前面所有层输出的相加作为下一层的输入,更深层次学习恶意代码的文本特征。实验表明1D-CNN-Densenet的恶意代码检测准确率达到了96.60%,在恶意代码检测方面有...
This project is dedicated to the implementation and research of Kolmogorov-Arnold convolutional networks. The repository includes implementations of 1D, 2D, and 3D convolutions with different kernels, ResNet-like and DenseNet-like models, training code b