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.

An Experimental Artificial Neural Network Model:Investigating and Predicting Effects of Quenching Process on Residual Stresses of AISI 1035 Steel Alloy

The present study establishes a new estimation model using an artificial neural network(ANN) to predict the mechanical properties of the AISI 1035 alloy.The experiments were designed based on the L16 orthogonal array of the Taguchi method.A proposed numerical model for predicting the correlation of mechanical properties was supplemented with experimental data.The quenching process was conducted using a cooling medium called “nanofluids”.Nanoparticles were dissolved in a liquid phase at various concentrations(0.5%,1%,2.5%,and 5% vf) to prepare the nanofluids.Experimental investigations were done to assess the impact of temperature,base fluid,volume fraction,and soaking time on the mechanical properties.The outcomes showed that all conditions led to a noticeable improvement in the alloy's hardness which reached 100%,the grain size was refined about 80%,and unwanted residual stresses were removed from 50 to 150 MPa.Adding 5% of CuO nanoparticles to oil led to the best grain size refinement,while adding 2.5% of Al_(2)O_(3) nanoparticles to engine oil resulted in the greatest compressive residual stress.The experimental variables were used as the input data for the established numerical ANN model,and the mechanical properties were the output.Upwards of 99% of the training network's correlations seemed to be positive.The estimated result,nevertheless,matched the experimental dataset exactly.Thus,the ANN model is an effective tool for reflecting the effects of quenching conditions on the mechanical properties of AISI 1035.

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.

Prediction about residual stress and microhardness of material subjected to multiple overlap laser shock processing using artificial neural network

In this work,the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material,and the experimental parameters in multiple overlap laser shock processing(LSP)treatment were selected based on orthogonal experimental design.The experimental data of residual stress and microhardness were measured in the same depth.The residual stress and microhardness laws were investigated and analyzed.Artificial neural network(ANN)with four layers(4-N-(N-1)-2)was applied to predict the residual stress and microhardness of FGH95 subjected to multiple overlap LSP.The experimental data were divided as training-testing sets in pairs.Laser energy,overlap rate,shocked times and depth were set as inputs,while residual stress and microhardness were set as outputs.The prediction performances with different network configuration of developed ANN models were compared and analyzed.The developed ANN model with network configuration of 4-7-6-2 showed the best predict performance.The predicted values showed a good agreement with the experimental values.In addition,the correlation coefficients among all the parameters and the effect of LSP parameters on materials response were studied.It can be concluded that ANN is a useful method to predict residual stress and microhardness of material subjected to LSP when with limited experimental data.

Mural Anomaly Region Detection Algorithm Based on Hyperspectral Multiscale Residual Attention Network

Mural paintings hold significant historical information and possess substantial artistic and cultural value.However,murals are inevitably damaged by natural environmental factors such as wind and sunlight,as well as by human activities.For this reason,the study of damaged areas is crucial for mural restoration.These damaged regions differ significantly from undamaged areas and can be considered abnormal targets.Traditional manual visual processing lacks strong characterization capabilities and is prone to omissions and false detections.Hyperspectral imaging can reflect the material properties more effectively than visual characterization methods.Thus,this study employs hyperspectral imaging to obtain mural information and proposes a mural anomaly detection algorithm based on a hyperspectral multi-scale residual attention network(HM-MRANet).The innovations of this paper include:(1)Constructing mural painting hyperspectral datasets.(2)Proposing a multi-scale residual spectral-spatial feature extraction module based on a 3D CNN(Convolutional Neural Networks)network to better capture multiscale information and improve performance on small-sample hyperspectral datasets.(3)Proposing the Enhanced Residual Attention Module(ERAM)to address the feature redundancy problem,enhance the network’s feature discrimination ability,and further improve abnormal area detection accuracy.The experimental results show that the AUC(Area Under Curve),Specificity,and Accuracy of this paper’s algorithm reach 85.42%,88.84%,and 87.65%,respectively,on this dataset.These results represent improvements of 3.07%,1.11%and 2.68%compared to the SSRN algorithm,demonstrating the effectiveness of this method for mural anomaly detection.

Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism

Printed Circuit Board(PCB)surface tiny defect detection is a difficult task in the integrated circuit industry,especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks.To improve the performance of PCB surface tiny defects detection,a PCB tiny defects detection model based on an improved attention residual network(YOLOX-AttResNet)is proposed.First,the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet(Squeeze and Excitation Network)attention network;then the improved K-means-SENet network is fused with the directly mapped edges of the traditional ResNet network to form an augmented residual network(AttResNet);and finally,the AttResNet module is substituted for the traditional ResNet structure in the backbone feature extraction network of mainstream excellent detection models,thus improving the ability to extract small features from the backbone of the target detection network.The results of ablation experiments on a PCB surface defect dataset show that AttResNet is a reliable and efficient module.In Torify the performance of AttResNet for detecting small defects in large-size complex circuit images,a series of comparison experiments are further performed.The results show that the AttResNet module combines well with the five best existing target detection frameworks(YOLOv3,YOLOX,Faster R-CNN,TDD-Net,Cascade R-CNN),and all the combined new models have improved detection accuracy compared to the original model,which suggests that the AttResNet module proposed in this paper can help the detection model to extract target features.Among them,the YOLOX-AttResNet model proposed in this paper performs the best,with the highest accuracy of 98.45% and the detection speed of 36 FPS(Frames Per Second),which meets the accuracy and real-time requirements for the detection of tiny defects on PCB surfaces.This study can provide some new ideas for other real-time online detection tasks of tiny targets with high-resolution images.

Automatic Detection of COVID-19 Using Chest X-Ray Images and Modified ResNet18-Based Convolution Neural Networks

The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages.In this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal cases.The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures.Transfer Learning technique has been implemented in this work.Transfer learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images.The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection.Since all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the results.The proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 models.Based on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.

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.

An Adapted Convolutional Neural Network for Brain Tumor Detection

In medical imaging, particularly for analyzing brain tumor MRIs, the expertise of skilled neurosurgeons or radiologists is often essential. However, many developing countries face a significant shortage of these specialists, which impedes the accurate identification and analysis of tumors. This shortage exacerbates the challenge of delivering precise and timely diagnoses and delays the production of comprehensive MRI reports. Such delays can critically affect treatment outcomes, especially for conditions requiring immediate intervention, potentially leading to higher mortality rates. In this study, we introduced an adapted convolutional neural network designed to automate brain tumor diagnosis. Our model features fewer layers, each optimized with carefully selected hyperparameters. As a result, it significantly reduced both execution time and memory usage compared to other models. Specifically, its execution time was 10 times shorter than that of the referenced models, and its memory consumption was 3 times lower than that of ResNet. In terms of accuracy, our model outperformed all other architectures presented in the study, except for ResNet, which showed similar performance with an accuracy of around 90%.

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.

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.

A study on small magnitude seismic phase identification using 1D deep residual neural network

Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio.With improved seismometers and better global coverage,a sharp increase in the volume of recorded seismic data has been achieved.This makes handling seismic data rather daunting by using traditional approaches and therefore fuels the need for more robust and reliable methods.In this study,we develop 1D deep Residual Neural Network(ResNet),for tackling the problem of seismic signal detection and phase identification.This method is trained and tested on the dataset recorded by the Southern California Seismic Network.Results demonstrate that the proposed method can achieve robust performance for the detection of seismic signals and the identification of seismic phases.Compared to previously proposed deep learning methods,the introduced framework achieves around 4%improvement in earthquake detection and a slightly better performance in seismic phase identification on the dataset recorded by Southern California Earthquake Data Center.The model generalizability is also tested further on the STanford EArthquake Dataset.In addition,the experimental result on the same subset of the STanford EArthquake Dataset,when masked by different noise levels,demonstrates the model’s robustness in identifying the seismic phases of small magnitude.

基于信号图像化和CNN-ResNet的配电网单相接地故障选线方法

配电网发生单相接地故障时,零序电流呈现较强的非线性与非平稳性,故障选线较为困难,针对此问题,提出一种基于信号图像化和卷积神经网络-残差网络的配电网单相接地故障选线方法。首先,利用排列熵优化变分模态分解算法的参数,将零序电流信号分解成一系列固有模态函数;其次,引入新的数据预处理方式,将固有模态函数转成二维图像,获得零序电流信号的时频特征图;最后,利用一维卷积神经网络提取零序电流信号的相关性和特征,利用残差网络提取时频特征图的特征,将两个网络融合,构建混合卷积神经网络结构,实现故障选线。仿真与实验结果表明,该方法能够在高阻接地、采样时间不同步、强噪声等情况下准确地选择出故障线路,可满足配电网对故障选线准确性和可靠性的需求。

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%。

基于scSE非局部双流ResNet网络的行为识别

针对双流网络对包含冗余信息的视频帧存在识别率低的问题,在双流网络的基础上引入scSE(Spatial and Channel Squeeze&Excitation Block)和非局部操作,构建SC_NLResNet行为识别框架。该框架将视频划分为等分不重叠的时序段并在每段上稀疏采样,提取RGB帧以及光流图作为scSE模块的输入;将经过scSE处理的特征输入非局部双流ResNet网络中,融合各分段得到最终的预测结果。在UCF101以及Hmdb51数据集上实验准确率分别达到96.9%和76.2%,结果表明,非局部操作与scSE模块结合可以增强特征时空上以及通道间的信息提高准确率,验证了SC_NLResNet网络的有效性。

ResNet-UAN-AUD:基于深度学习的水声上行非正交多址通信系统活动用户检测方法

水下声学网络(Underwater Acoustic Networks,UAN)是探测未知水域的重要技术手段。非正交多址(Non-Orthogonal Multiple Access,NOMA)是一种新颖的移动通信技术,支持时域、频域、空域/编域的非正交分配,可有效地提高网络容量和用户接入数,为性能和电量受限的UAN提供创新解决方案。活动用户检测(Active User Detection,AUD)是NOMA通信系统的基础支撑,对于NOMA系统消除信号干扰和提高接收性能至关重要。ResNet是基于残差模块跳跃连接的神经网络,解决了深度学习的梯度消失和网络退化问题。提出了一种基于深度学习的水声上行NOMA通信系统AUD检测方案。首先,构建水声上行NOMA通信系统基本模型;其次,实施NOMA活动用户检测问题的数学表征;接着,开发基于ResNet网络的水声NOMA系统活动节点检测方法(ResNet-UAN-AUD);最后,执行仿真实验。结果表明,ResNet-UAN-AUD的检测性能接近基于长短期记忆网络的活动用户检测(LSTM-UAN-AUD)方案,而复杂度略高于基于卷积神经网络的活动用户检测(CNN-UAN-AUD)技术,实现了次优目标,适合水声上行NOMA系统使用。

基于ResNet的害虫图像质量评估方法

提出一种基于ResNet的害虫图像质量评估方法,从而对林业害虫图像进行预评估。该方法首先提取害虫图像特征,并通过Wasserstein距离计算不同图像特征间的相似分布距离作为质量伪标签进行训练。通过预评估区分出不同质量的林业害虫图像,对其进行筛选、识别、分类,从而达到提高识别准确率的效果。实验结果表明,经过该方法筛选后的林业害虫数据集在ResNet18和ResNet50网络上识别准确率分别提升2.97%,2.57%。