Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network

Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.

Prediction of Load-Displacement Curve of Flexible Pipe Carcass Under Radial Compression Based on Residual Neural Network

The carcass layer of flexible pipe comprises a large-angle spiral structure with a complex interlocked stainless steel cross-section profile, which is mainly used to resist radial load. With the complex structure of the carcass layer, an equivalent simplified model is used to study the mechanical properties of the carcass layer. However, the current equivalent carcass model only considers the elastic deformation, and this simplification leads to huge errors in the calculation results. In this study, radial compression experiments were carried out to make the carcasses to undergo plastic deformation. Subsequently, a residual neural network based on the experimental data was established to predict the load-displacement curves of carcasses with different inner diameter in plastic states under radial compression.The established neural network model’s high precision was verified by experimental data, and the influence of the number of input variables on the accuracy of the neural network was discussed. The conclusion shows that the residual neural network model established based on the experimental data of the small-diameter carcass layer can predict the load-displacement curve of the large-diameter carcass layer in the plastic stage. With the decrease of input data, the prediction accuracy of residual network model in plasticity stage will decrease.

A COVID-19 Detection Model Based on Convolutional Neural Network and Residual Learning

Amodel that can obtain rapid and accurate detection of coronavirus disease 2019(COVID-19)plays a significant role in treating and preventing the spread of disease transmission.However,designing such amodel that can balance the detection accuracy andweight parameters ofmemorywell to deploy a mobile device is challenging.Taking this point into account,this paper fuses the convolutional neural network and residual learning operations to build a multi-class classification model,which improves COVID-19 pneumonia detection performance and keeps a trade-off between the weight parameters and accuracy.The convolutional neural network can extract the COVID-19 feature information by repeated convolutional operations.The residual learning operations alleviate the gradient problems caused by stacking convolutional layers and enhance the ability of feature extraction.The ability further enables the proposed model to acquire effective feature information at a lowcost,which canmake ourmodel keep smallweight parameters.Extensive validation and comparison with other models of COVID-19 pneumonia detection on the well-known COVIDx dataset show that(1)the sensitivity of COVID-19 pneumonia detection is improved from 88.2%(non-COVID-19)and 77.5%(COVID-19)to 95.3%(non-COVID-19)and 96.5%(COVID-19),respectively.The positive predictive value is also respectively increased from72.8%(non-COVID-19)and 89.0%(COVID-19)to 88.8%(non-COVID-19)and 95.1%(COVID-19).(2)Compared with the weight parameters of the COVIDNet-small network,the value of the proposed model is 13 M,which is slightly higher than that(11.37 M)of the COVIDNet-small network.But,the corresponding accuracy is improved from 85.2%to 93.0%.The above results illustrate the proposed model can gain an efficient balance between accuracy and weight parameters.

Rockburst Intensity Grade Prediction Model Based on Batch Gradient Descent and Multi-Scale Residual Deep Neural Network

Rockburst is a phenomenon in which free surfaces are formed during excavation,which subsequently causes the sudden release of energy in the construction of mines and tunnels.Light rockburst only peels off rock slices without ejection,while severe rockburst causes casualties and property loss.The frequency and degree of rockburst damage increases with the excavation depth.Moreover,rockburst is the leading engineering geological hazard in the excavation process,and thus the prediction of its intensity grade is of great significance to the development of geotechnical engineering.Therefore,the prediction of rockburst intensity grade is one problem that needs to be solved urgently.By comprehensively considering the occurrence mechanism of rockburst,this paper selects the stress index(σθ/σc),brittleness index(σ_(c)/σ_(t)),and rock elastic energy index(Wet)as the rockburst evaluation indexes through the Spearman coefficient method.This overcomes the low accuracy problem of a single evaluation index prediction method.Following this,the BGD-MSR-DNN rockburst intensity grade prediction model based on batch gradient descent and a multi-scale residual deep neural network is proposed.The batch gradient descent(BGD)module is used to replace the gradient descent algorithm,which effectively improves the efficiency of the network and reduces the model training time.Moreover,the multi-scale residual(MSR)module solves the problem of network degradation when there are too many hidden layers of the deep neural network(DNN),thus improving the model prediction accuracy.The experimental results reveal the BGDMSR-DNN model accuracy to reach 97.1%,outperforming other comparable models.Finally,actual projects such as Qinling Tunnel and Daxiangling Tunnel,reached an accuracy of 100%.The model can be applied in mines and tunnel engineering to realize the accurate and rapid prediction of rockburst intensity grade.

融合Residual Network-50残差块与卷积注意力模块的地震断层自动识别

传统的断层识别是由地质解释人员以人工标记的方式进行检测,不仅耗时长、效率低,且识别结果存在一定的人为误差。为解决以上问题,提高断层识别的精度,提出了一种基于深度学习的断层识别方法,利用注意力机制聚焦目标特征的能力,在U-Net网络的解码层引入了卷积注意力模块(Convolutional Block Attention Module,CBAM),在编码层引入了ResNet-50残差块,建立基于卷积神经网络(Convolutional Neural Networks,CNN)的断层识别方法(Res-CBAM-UNet)。将合成地震数据与相应的断层标签进行数据增强操作,新生成的训练数据集作为输入对网络模型进行训练,以提高模型的泛化能力。随后将该模型与CBAM-UNet、ResNet34-UNet和ResNet50-UNet网络进行对比分析,利用实际工区地震数据进行测试。结果表明,设计的Res-CBAM-UNet网络对断层具有较好的识别效果,且识别出的断层连续性好,计算效率高。

基于ResNet-LSTM的航空发动机性能异常检测方法

为了实现数据驱动的航空发动机性能异常的智能检测,提出了一种基于残差网络(ResNet)-长短期记忆网络(LSTM)的发动机性能异常检测方法。采用发动机性能数据图像化方法,在数据降维的同时,完备保留数据的关联特征和时序特征;以残差单元构建发动机性能异常检测模型,在加深网络结构的同时,消除深层网络梯度消失问题,提高发动机性能图像空间关联特征的提取能力。同时,引入LSTM,提出基于ResNet-LSTM的发动机性能异常检测模型,通过ResNet与LSTM的融合,强化异常检测模型对时序特征的提取,提升发动机性能异常检测的准确率;通过发动机运行数据进行验证。结果表明:在训练集上,该方法的异常检测准确率为94.95%,比基于ResNet18、ResNet34、ResNet50异常检测模型的分别提高10.87%、8.00%、3.23%;在测试集上,该方法的异常检测准确率为92.15%,比基于ResNet18、ResNet34、ResNet50异常检测模型的分别提高11.81%、9.45%、3.78%。

Neural Network Ensemble Residual Kriging Application for Spatial Variability of Soil Properties

High quality, agricultural nutrient distribution maps are necessary for precision management, but depend on initial soil sample analyses and interpolation techniques. To examine the methodologies for and explore the capability of interpolating soil properties based on neural network ensemble residual kriging, a silage field at Hayes, Northern Ireland, UK, was selected for this study with all samples being split into independent training and validation data sets. The training data set, comprised of five soil properties: soil pH, soil available P, soil available K, soil available Mg and soil available S,was modeled for spatial variability using 1) neural network ensemble residual kriging, 2) neural network ensemble and 3) kriging with their accuracies being estimated by means of the validation data sets. Ordinary kriging of the residuals provided accurate local estimates, while final estimates were produced as a sum of the artificial neural network (ANN)ensemble estimates and the ordinary kriging estimates of the residuals. Compared to kriging and neural network ensemble,the neural network ensemble residual kriging achieved better or similar accuracy for predicting and estimating contour maps. Thus, the results demonstrated that ANN ensemble residual kriging was an efficient alternative to the conventional geo-statistical models that were usually used for interpolation of a data set in the soil science area.

基于CNN-BiGRU-ResNet的网络入侵检测研究

网络入侵检测是网络安全中的一项重要工作,其主要是通过网络、系统等信息对入侵行为进行判断,它可以及时地发现网络中的攻击行为,传统的网络入侵检测方法存在准确率低并且误报率高的问题,针对上述问题,提出了一种融合双向门控循环单元(BiGRU)、卷积神经网络(CNN)以及残差网络(ResNet)的网络入侵检测方法,该方法通过双向门控循环单元对时间序列特征以及卷积神经网络和残差网络对局部空间特征的提取,利用softmax分类器获得最终的分类结果。实验表明,与基于GRU和ResNet等方法相比,该方法的网络入侵检测效果比较好,其准确率较高,误报率更低。

Route Temporal⁃Spatial Information Based Residual Neural Networks for Bus Arrival Time Prediction

Bus arrival time prediction contributes to the quality improvement of public transport services.Passengers can arrange departure time effectively if they know the accurate bus arrival time in advance.We proposed a machine⁃learning approach,RTSI⁃ResNet,to forecast the bus arrival time at target stations.The residual neural network framework was employed to model the bus route temporal⁃spatial information.It was found that the bus travel time on a segment between two stations not only had correlation with the preceding buses,but also had common change trends with nearby downstream/upstream segments.Two features about bus travel time and headway were extracted from bus route including target section in both forward and reverse directions to constitute the route temporal⁃spatial information,which reflects the road traffic conditions comprehensively.Experiments on the bus trajectory data of route No.10 in Shenzhen public transport system demonstrated that the proposed RTSI⁃ResNet outperformed other well⁃known methods(e.g.,RNN/LSTM,SVM).Specifically,the advantage was more significant when the distance between bus and the target station was farther.

Ensemble Recurrent Neural Network-Based Residual Useful Life Prognostics of Aircraft Engines

Residual useful life(RUL)prediction is a key issue for improving efficiency of aircraft engines and reducing their maintenance cost.Owing to various failure mechanism and operating environment,the application of classical models in RUL prediction of aircraft engines is fairly difficult.In this study,a novel RUL prognostics method based on using ensemble recurrent neural network to process massive sensor data is proposed.First of all,sensor data obtained from the aircraft engines are preprocessed to eliminate singular values,reduce random fluctuation and preserve degradation trend of the raw sensor data.Secondly,three kinds of recurrent neural networks(RNN),including ordinary RNN,long shortterm memory(LSTM),and gated recurrent unit(GRU),are individually constructed.Thirdly,ensemble learning mechanism is designed to merge the above RNNs for producing a more accurate RUL prediction.The effectiveness of the proposed method is validated using two characteristically different turbofan engine datasets.Experimental results show a competitive performance of the proposed method in comparison with typical methods reported in literatures.

Object Grasping Detection Based on Residual Convolutional Neural Network

Robotic grasps play an important role in the service and industrial fields,and the robotic arm can grasp the object properly depends on the accuracy of the grasping detection result.In order to predict grasping detection positions for known or unknown objects by a modular robotic system,a convolutional neural network(CNN)with the residual block is proposed,which can be used to generate accurate grasping detection for input images of the scene.The proposed model architecture was trained on the standard Cornell grasp dataset and evaluated on the test dataset.Moreover,it was evaluated on different types of household objects and cluttered multi-objects.On the Cornell grasp dataset,the accuracy of the model on image-wise splitting detection and object-wise splitting detection achieved 95.5%and 93.6%,respectively.Further,the real detection time per image was 109 ms.The experimental results show that the model can quickly detect the grasping positions of a single object or multiple objects in image pixels in real time,and it keeps good stability and robustness.

基于改进Res-UNet的昼夜地基云图分割网络

针对昼夜地基云图在分割中细节信息丢失、分割精度低等问题,提出一种基于改进Res-UNet(Residual network-UNetwork)的昼夜地基云图分割网络CloudRes-UNet(Cloud ResNet-UNetwork),整体采用编码器-解码器的网络结构。首先,编码器使用ResNet50提取特征,增强特征提取能力;其次,设计多级特征提取(Multi-Stage)模块,该模块结合分组卷积、膨胀卷积和通道打乱这3种技巧,获取高强度语义信息;再次,加入高效通道注意力(ECA‑Net)模块,在通道维度上聚焦重要信息,加强对地基云图中云区域的关注,提高分割精度;最后,解码器使用双线性插值对特征进行上采样,提高分割图像的清晰度并减少目标和位置信息丢失。实验结果表明,与当前基于深度学习表现较好的地基云图分割网络(Cloud-UNet)相比,CloudRes-UNet在昼夜地基云图分割数据集上的分割准确率提升了1.5个百分点,平均交并比(MIoU)上升了1.4个百分点,更准确地获取了云量信息,对天气预报、气候研究和光伏发电等方面具有积极意义。

拉曼光谱结合WGANGP-ResNet算法鉴别病原菌种类

快速准确识别病原菌在防止传染病的传播、帮助对抗抗菌素耐药性和改善病人预后方面起着关键作用。拉曼光谱结合机器学习算法能够简单快捷地对病原菌进行无标记检测。然而,病原菌种类和表型繁多,并且深度学习需要依赖大量样本训练,而收集大批量病原菌拉曼光谱劳神费力,且易受荧光等因素影响。针对上述问题,提出一种基于WGAN-GP数据增强方法和ResNet结合的病原菌拉曼光谱检测模型。采用五种常见眼科病原菌的拉曼光谱。将采集到的原始数据归一化作为ResNet和传统卷积神经网络(1D-CNN)的输入,将经过SG滤波、airPLS基线校正、PCA降维等预处理后的数据作为K近邻(KNN)的输入,对比分析发现ResNet模型效果最优,其分类精度可达96%;搭建Wasserstein生成式对抗网络加梯度惩罚模型(WGAN-GP),生成大量与真实数据相似的高分辨率光谱数据。同时与偏移法、深度卷积生成式对抗神经网络(DCGAN)2种数据增强方法进行比对,证明WGAN-GP的可靠性;为验证生成数据可以丰富数据多样性,进而提高分类精度,将扩充后的数据集重新放入ResNet进行训练,最终WGAN-GP结合ResNet的分类准确率提高到99.3%。结果表明:基于ResNet的分类模型无需复杂数据预处理,在开发效率和分类精度上均有提高;改进的WGAN-GP模型适用于拉曼光谱数据增强,解决了传统数据增强方法生成光谱的有效性与类别准确性不匹配的问题,相比于GAN提高了训练过程的速度和稳定性;利用表面增强拉曼光谱技术(SERS)结合WGANGP-ResNet模型对病原菌拉曼光谱分类,减少了对大量训练数据的需求,有利于快速学习和分析低信噪比的拉曼光谱,并将光谱采集时间缩减到1/10。在临床快速、免培养鉴别病原菌方面具有重要研究意义与应用价值。

利用压电传感器基于GAF-ResNet的管道焊缝缺陷分类

针对管道焊缝缺陷分类难度大的问题,提出了利用压电传感器数据,结合格拉姆角场(Gramian Angular Field,GAF)和残差神经网络(ResNet)的焊缝缺陷分类方法。先采用GAF原理将一维时间序列数据转化为二维图像,将转化后的二维图像数据集输入,训练最优二维残差神经网络模型用于焊缝缺陷分类。实验中管道焊缝预制了10个缺陷(5种类型),使用导波和超声技术分别对焊缝中1-5号缺陷进行检测,分析Precision(精确率)、Recall(召回率)、F1-score(F1评分)三个指标,证实了基于GAF-ResNet方法的可行性,同时6-10号缺陷验证了该方法的可靠性和普适性。

seqAFF-ResNet:面向新冠肺炎的诊断模型

新冠肺炎的计算机辅助诊断是一种实现智能化影像诊断、临床诊断及临床分型的方法,在新冠肺炎的辅助诊断过程中,图像的病灶区域与组织边界对比不明显,导致模型不能较好地关注病灶区域,对有效特征的提取不够充分。针对上述问题,提出一个新冠肺炎辅助诊断模型seqAFF-ResNet(sequential attentional feature fusion-residual neural network)。设计串行注意力特征融合(sequential attentional feature fusion,seqAFF)模块,该模块串联条带注意力特征融合(strip attentional feature fusion,SAFF)模块和全局局部注意力特征融合(global local attentional feature fusion,GLAFF)模块,获取图像的纹理信息以及全局和局部信息,弥补卷积神经网络对于细节特征提取能力的不足,使得模型可以更好地关注于病灶区域;构造深浅层特征融合(deep and shallow feature fusion,DSFF)模块,使用深层特征的语义信息来影响浅层信息,同时将浅层的空间信息传入深层特征中,使深浅层特征进行有效融合,捕获丰富的上下文信息,实现跨层注意力特征增强,使网络能够更好地定位病变区域。与残差神经网络(residual neural network,ResNet)相比,seqAFF-ResNet准确率提升了3.42%,精确率提升了3.53%,F1分数提升了2.77%,AUC值提升了0.9%,实验结果表明,所提模型可以提高新冠肺炎的识别准确率,且与同类模型相比具有更好的性能。所提方法为新冠肺炎的辅助诊断提供了有效的识别方法,对新冠肺炎的计算机辅助诊断具有重要意义。

Multi-Classification of Polyps in Colonoscopy Images Based on an Improved Deep Convolutional Neural Network

Achieving accurate classification of colorectal polyps during colonoscopy can avoid unnecessary endoscopic biopsy or resection.This study aimed to develop a deep learning model that can automatically classify colorectal polyps histologically on white-light and narrow-band imaging(NBI)colonoscopy images based on World Health Organization(WHO)and Workgroup serrAted polypS and Polyposis(WASP)classification criteria for colorectal polyps.White-light and NBI colonoscopy images of colorectal polyps exhibiting pathological results were firstly collected and classified into four categories:conventional adenoma,hyperplastic polyp,sessile serrated adenoma/polyp(SSAP)and normal,among which conventional adenoma could be further divided into three sub-categories of tubular adenoma,villous adenoma and villioustublar adenoma,subsequently the images were re-classified into six categories.In this paper,we proposed a novel convolutional neural network termed Polyp-DedNet for the four-and six-category classification tasks of colorectal polyps.Based on the existing classification network ResNet50,Polyp-DedNet adopted dilated convolution to retain more high-dimensional spatial information and an Efficient Channel Attention(ECA)module to improve the classification performance further.To eliminate gridding artifacts caused by dilated convolutions,traditional convolutional layers were used instead of the max pooling layer,and two convolutional layers with progressively decreasing dilation were added at the end of the network.Due to the inevitable imbalance of medical image data,a regularization method DropBlock and a Class-Balanced(CB)Loss were performed to prevent network overfitting.Furthermore,the 5-fold cross-validation was adopted to estimate the performance of Polyp-DedNet for the multi-classification task of colorectal polyps.Mean accuracies of the proposed Polyp-DedNet for the four-and six-category classifications of colorectal polyps were 89.91%±0.92%and 85.13%±1.10%,respectively.The metrics of precision,recall and F1-score were also improved by 1%∼2%compared to the baseline ResNet50.The proposed Polyp-DedNet presented state-of-the-art performance for colorectal polyp classifying on white-light and NBI colonoscopy images,highlighting its considerable potential as an AI-assistant system for accurate colorectal polyp diagnosis in colonoscopy.

基于ResNet-Peca的信号分选方法

针对传统的信号分选方法在信噪比过低时,调制信号识别效果不佳的问题,提出一种融合通道注意力机制(ECA)和位置注意力机制(PAM)的残差网络(ResNet-Peca),该网络可同时获得通道和位置维度特征权重,提升网络的特征学习能力。研究结果表明,融合注意力机制的残差网络(ResNet-Peca)整体效果,相较ResNet-ECA方法识别准确率提升了约1.5%,较ResNet-PAM方法识别准确率提升了约1.6%,较CNN方法识别准确率提升了约3.8%。

基于改进ResNet50模型的咖啡生豆质量和缺陷检测方法

咖啡生豆的质量决定着商品咖啡豆的价格,目前对咖啡生豆的筛选主要由人工完成,费时费力。提出一种基于改进ResNet50模型来识别咖啡生豆的方法,首先收集8000张咖啡生豆图像建立数据集,并对其进行数据增强,基于ResNet50模型加入CBAM注意力机制,引入迁移学习机制,并使用深度可分离卷积来代替ResNet50残差单元中的传统卷积,构建适用于咖啡生豆分类识别的ResNet50-CBAM-DW模型。为评估模型改进的有效性,与ResNet50、AlexNet、VGG16、MobileNetV2等模型进行比较,改进后模型准确率达到91.1%,相较于原ResNet50模型准确率提升3.0%,参数量降低39.0%。

基于改进WGAN-GP和ResNet的车联网入侵检测方法

为保护车联网系统免受网络攻击的威胁,同时提高车联网入侵检测的准确率,针对车辆网络数据流量大且攻击类型不平衡的特点,提出了一种新的车联网入侵检测方法(AQVAE-RGSNet)。该方法通过一种对抗量化变分自编码器以对车辆网络数据进行不平衡处理,该编码器通过结合矢量量化变分自编码器与带梯度惩罚的生成对抗网络进行构建,以缓解数据集中异常攻击类型样本数量极度不平衡的问题,并使用ResNet网络与改进的分段残差神经网络对输入的样本数据进行联合学习并预测其攻击类型。实验结果表明:AQVAE-RGSNet在车联网数据集CICIDS2017和CAN-intrusion-dataset上的F1得分分别达到了0.9986和0.9997;在保证最佳训练效果的前提下,能够更有效地识别车辆网络之中的攻击威胁。

融入CBAM的Res-UNet高分辨率遥感影像语义分割模型

针对现有语义分割方法处理复杂遥感影像细节特征识别能力差、信息丢失等问题,提出一种融合注意力机制的遥感影像语义分割网络模型。模型主干网络采用编码器-解码器架构的U-Net模型,为了缓解梯度和网络退化问题,将残差结构嵌入到主干网络中;同时融入通道、空间注意力模块,兼顾影像的细节特征和模型鲁棒性。在ISPRS Potsdam数据集上进行分析验证,实验结果表明,在去除“噪声”、地物边缘“平滑”、细窄地物“连续”、细小目标分割等方面,融入CBAM模块的ResUNet语义分割精度要优于传统网络模型。