Fusion of Spiral Convolution-LSTM for Intrusion Detection Modeling

Aiming at the problems of low accuracy and slow convergence speed of current intrusion detection models,SpiralConvolution is combined with Long Short-Term Memory Network to construct a new intrusion detection model.The dataset is first preprocessed using solo thermal encoding and normalization functions.Then the spiral convolution-Long Short-Term Memory Network model is constructed,which consists of spiral convolution,a two-layer long short-term memory network,and a classifier.It is shown through experiments that the model is characterized by high accuracy,small model computation,and fast convergence speed relative to previous deep learning models.The model uses a new neural network to achieve fast and accurate network traffic intrusion detection.The model in this paper achieves 0.9706 and 0.8432 accuracy rates on the NSL-KDD dataset and the UNSWNB-15 dataset under five classifications and ten classes,respectively.

Audiovisual speech recognition based on a deep convolutional neural network

Audiovisual speech recognition is an emerging research topic.Lipreading is the recognition of what someone is saying using visual information,primarily lip movements.In this study,we created a custom dataset for Indian English linguistics and categorized it into three main categories:(1)audio recognition,(2)visual feature extraction,and(3)combined audio and visual recognition.Audio features were extracted using the mel-frequency cepstral coefficient,and classification was performed using a one-dimension convolutional neural network.Visual feature extraction uses Dlib and then classifies visual speech using a long short-term memory type of recurrent neural networks.Finally,integration was performed using a deep convolutional network.The audio speech of Indian English was successfully recognized with accuracies of 93.67%and 91.53%,respectively,using testing data from 200 epochs.The training accuracy for visual speech recognition using the Indian English dataset was 77.48%and the test accuracy was 76.19%using 60 epochs.After integration,the accuracies of audiovisual speech recognition using the Indian English dataset for training and testing were 94.67%and 91.75%,respectively.

Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network

In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.

Ensemble of High Performance Structured Binary Convolutional LDPC Codes with Moderate Rates

An algebraic construction methodology is proposed to design binary time-invariant convolutional low-density parity-check(LDPC)codes.Assisted by a proposed partial search algorithm,the polynomialform parity-check matrix of the time-invariant convolutional LDPC code is derived by combining some special codewords of an(n,2,n−1)code.The achieved convolutional LDPC codes possess the characteristics of comparatively large girth and given syndrome former memory.The objective of our design is to enable the time-invariant convolutional LDPC codes the advantages of excellent error performance and fast encoding.In particular,the error performance of the proposed convolutional LDPC code with small constraint length is superior to most existing convolutional LDPC codes.

基于1DCNN-GRU的启闭机液压系统故障诊断

由于启闭机液压系统内部结构复杂,故障信号不易采集,使用AMESim软件搭建启闭机液压系统仿真模型,构建6种典型故障数据集。基于这些数据集,提出一维卷积神经网络(1 Dimensional Convolutional Neural Network,1DCNN)与门控循环单元(Gated Recurrent Unit,GRU)相结合的故障诊断方法,利用1DCNN提取信号数据的空间特征和GRU提取信号数据的时间特征,实现对信号数据空间及时间特征的融合,并对融合特征进行分类识别。

基于LSTM与1DCNN的导弹轨迹预测方法

针对弹道导弹等超远程攻击目标的轨迹难以预测的问题,提出一种基于长短期记忆(long short-term memory,LSTM)网络与一维卷积神经网络(1-dimensional convolutional neural network,1DCNN)的目标轨迹预测方法。首先,建立三自由度导弹运动模型,依据再入类型设计3种目标轨迹数据,构建机动数据库,解决轨迹数据的来源问题。其次,采用重复分割与滑动窗口的方法对轨迹数据进行预处理。然后,基于LSTM与1DCNN设计了一种目标类型分类网络,对目标进行初步分类。最后,基于1DCNN设计轨迹预测网络,对目标轨迹进行预测。仿真结果表明,提出的轨迹预测网络能够完成轨迹预测任务,预测误差在合理范围内。

Fourier级数的(C,1)求和法

用(C,1)求和法来研究Fourier级数的可和性,即用三角多项式任意的一致逼近一周期为2π的局部可积函数.

基于Sentinel-1A影像和一维CNN的中国南方生长季早期作物种类识别

作物的早期识别对粮食安全至关重要。在以往的研究中,中国南方作物早期识别面临的主要挑战包括:1)云层覆盖时间长、地块尺寸小且作物类型丰富;2)缺少高时空分辨率合成孔径雷达(synthetic aperture radar,SAR)数据。欧洲航天局Sentinel-1A(S1A)卫星提供的SAR图像具有12 d的重访周期,空间分辨率达10 m,为中国南方作物早期识别提供了新的机遇。为在作物早期识别中充分利用S1A影像的时间特征,本研究提出一维卷积神经网络(one-dimensional convolutional neural network,1D CNN)的增量训练方法:首先利用生长季内全时间序列数据来训练1D CNN的超参数,称为分类器;然后从生长季内第一次S1A影像获取开始,在每个数据获取时间点输入该点之前(包括该点)生长季内所有数据训练分类器在该点的其他参数。以中国湛江地区2017年生长季为研究实例,分别基于VV、VH和VH+VV,评估不同极化数据在该地区的作物分类效果。为验证该方法的有效性,本研究同时应用经典的随机森林(random forest,RF)模型对研究区进行试验。结果表明:1)基于VH+VV、VH和VV极化数据的分类精度依次降低,其中,基于VH+VV后向散射系数时间序列1D CNN和RF测试结果的Kappa系数最大值分别为0.924和0.916,说明S1A时间序列数据在该地区作物分类任务中有效;2)在研究区域内2017年生长季早期,基于1D CNN和RF的5种作物的F-measure均达到0.85及以上,说明本文所构建的1D CNN在该地区主要作物早期分类任务中有效。研究结果证明,针对中国南方作物早期分类,本研究提出的1D CNN训练方案可行。研究结果可为深度学习在作物早期分类任务中的应用提供参考。

轻量级(2+1)D卷积结构的动态手势识别研究

目前,基于卷积神经网络的动态手势识别方法取得了巨大的进展,但神经网络模型具有很大的参数量,计算成本和内存占用较大,很难应用在设备资源有限的场合.以减少计算量和参数量为出发点,提出了一种轻量级(2+1)D卷积结构.该结构在(2+1)D卷积结构的基础上,将其中的3D卷积替换为3D深度可分离卷积,在输出向量维度不变的前提下,进一步减少了(2+1)D卷积结构的计算量和参数量.为了弥补时空特征在表征动态手势上的不足,融合注意力机制模块,专注于对运动特征的提取,结合轻量级(2+1)D卷积结构提取的时空特征,可以更好地表征手势动作.实验结果表明,注意力机制模块的插入,在不增加太多额外计算和空间成本的前提下,进一步提高了模型的识别精度.基于以上结构构建的模型,在20BN-jester、EgoGesture和IsoGD数据集上分别取得了96.62%、91.83%和60.1%的识别精度,模型参数量和浮点计算量分别为5.05M和12.81GFLOPs,相比于其他手势识别模型,计算成本和内存占用大大减少,实时手势识别速度达到每秒70帧.

IDSSCNN-XgBoost:Improved Dual-Stream Shallow Convolutional Neural Network Based on Extreme Gradient Boosting Algorithm for Micro Expression Recognition

Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial expressions.Numerous deep neural networks(DNNs)with convolutional structures have been proposed.However,unlike DNNs,shallow convolutional neural networks often outperform deeper models in mitigating overfitting,particularly with small datasets.Still,many of these methods rely on a single feature for recognition,resulting in an insufficient ability to extract highly effective features.To address this limitation,in this paper,an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm(IDSSCNN-XgBoost)is introduced for ME Recognition.The proposed method utilizes a dual-stream architecture where motion vectors(temporal features)are extracted using Optical Flow TV-L1 and amplify subtle changes(spatial features)via EulerianVideoMagnification(EVM).These features are processed by IDSSCNN,with an attention mechanism applied to refine the extracted effective features.The outputs are then fused,concatenated,and classified using the XgBoost algorithm.This comprehensive approach significantly improves recognition accuracy by leveraging the strengths of both temporal and spatial information,supported by the robust classification power of XgBoost.The proposed method is evaluated on three publicly available ME databases named Chinese Academy of Sciences Micro-expression Database(CASMEII),Spontaneous Micro-Expression Database(SMICHS),and Spontaneous Actions and Micro-Movements(SAMM).Experimental results indicate that the proposed model can achieve outstanding results compared to recent models.The accuracy results are 79.01%,69.22%,and 68.99%on CASMEII,SMIC-HS,and SAMM,and the F1-score are 75.47%,68.91%,and 63.84%,respectively.The proposed method has the advantage of operational efficiency and less computational time.

基于1-D CNN的二阶段OFDM系统定时同步方法

针对存在多径干扰的正交频分复用系统的定时同步准确性低的问题,提出基于一维卷积神经网络(1-D CNN)的二阶段OFDM系统定时同步方法。在第一阶段,利用经典互相关方法实现路径特征初始抽取,捕获可分辨路径上的定时辅助同步点;基于定时辅助同步点构建1-D CNN网络学习第二阶段中的定时偏移;最后,结合两阶段处理,获得系统最终的定时同步偏移估计。相比于基于压缩感知的定时同步方法和基于极限学习机的定时同步方法,所研究的二阶段OFDM系统定时同步方法提高了定时同步准确性,并有效地降低计算复杂度与处理延迟。

基于1d-MSCNN+GRU的工业入侵检测方法研究

针对传统机器学习方法对特征依赖大,以及传统卷积神经网络只通过提取重要的局部特征来完成识别分类,收敛速度慢的问题,提出了一维多尺度卷积神经网络和门控循环单元相结合的入侵检测方法。该方法使用一维多尺度卷积神经网络加强对特征的捕捉能力,加快收敛速度,采用门控循环单元把握空间特征,减少通道数量扩张,降低数据维度。使用KDD CUP 99数据集和密西西比州大学的天然气管道的数据集进行仿真实验,结果表明与经典的机器学习分类器相比,该方法具有较高的入侵检测性能和较好的泛化能力。

Operator Methods and SU(1,1) Symmetry in the Theory of Jacobi and of Ultraspherical Polynomials

Starting from general Jacobi polynomials we derive for the Ul-traspherical polynomials as their special case a set of related polynomials which can be extended to an orthogonal set of functions with interesting properties. It leads to an alternative definition of the Ultraspherical polynomials by a fixed integral operator in application to powers of the variable u in an analogous way as it is possible for Hermite polynomials. From this follows a generating function which is apparently known only for the Legendre and Chebyshev polynomials as their special case. Furthermore, we show that the Ultraspherical polynomials form a realization of the SU(1,1) Lie algebra with lowering and raising operators which we explicitly determine. By reordering of multiplication and differentiation operators we derive new operator identities for the whole set of Jacobi polynomials which may be applied to arbitrary functions and provide then function identities. In this way we derive a new “convolution identity” for Jacobi polynomials and compare it with a known convolution identity of different structure for Gegenbauer polynomials. In short form we establish the connection of Jacobi polynomials and their related orthonormalized functions to the eigensolution of the Schrödinger equation to Pöschl-Teller potentials.

Risk Assessment and Prediction of Construction Project Based on 1D-CNN-Attention-BP

In order to solve the problem of low accuracy of construction project duration prediction, this paper proposes a CNN attention BP combination model project risk prediction model based on attention mechanism, one-dimensional convolutional neural network (1d-cnn) and BP neural network. Firstly, the literature analysis method is used to select the risk evaluation index value of construction project, and the attention mechanism is used to determine the weight of risk factors on construction period prediction;then, BP neural network is used to predict the project duration, and accuracy, cross entropy loss function and F1 score are selected to comprehensively evaluate the performance of 1d-cnn-attention-bp combined model. The experimental results show that the duration risk prediction accuracy of the risk prediction model proposed in this paper is more than 90%, which can meet the risk prediction of construction projects with high accuracy.

基于深度残差网络的(n,1,m)卷积码盲识别

针对传统(n,1,m)卷积码识别方法容错性能较差或所需数据量较大的问题,提出了一种基于深度残差网络(Residual Network, ResNet)的方法。对图像识别领域常用的二维ResNet模型进行结构调整,使其适用于一维卷积编码序列的处理;仿真生成大量卷积码比特序列,以不同的误比特率在序列中随机加入误比特,并按固定长度从序列截取片段作为ResNet的训练样本,分别完成编码类型和起点识别模型的训练;将待识别卷积码序列输入网络,即可输出识别结果。仿真结果表明,相比传统方法,该方法以略高的计算复杂度为代价,获得了更好的容错性和较低的识别数据量需求。

Bearings Intelligent Fault Diagnosis by 1-D Adder Neural Networks

Integrated with sensors,processors,and radio frequency(RF)communication modules,intelligent bearing could achieve the autonomous perception and autonomous decision-making,guarantying the safety and reliability during their use.However,because of the resource limitations of the end device,processors in the intelligent bearing are unable to carry the computational load of deep learning models like convolutional neural network(CNN),which involves a great amount of multiplicative operations.To minimize the computation cost of the conventional CNN,based on the idea of AdderNet,a 1-D adder neural network with a wide first-layer kernel(WAddNN)suitable for bearing fault diagnosis is proposed in this paper.The proposed method uses the l1-norm distance between filters and input features as the output response,thus making the whole network almost free of multiplicative operations.The whole model takes the original signal as the input,uses a wide kernel in the first adder layer to extract features and suppress the high frequency noise,and then uses two layers of small kernels for nonlinear mapping.Through experimental comparison with CNN models of the same structure,WAddNN is able to achieve a similar accuracy as CNN models with significantly reduced computational cost.The proposed model provides a new fault diagnosis method for intelligent bearings with limited resources.

AR-MED共振特征增强的风电齿轮箱故障诊断

针对风电齿轮箱故障时脉冲成分往往淹没在其他频率分量中,早期故障特征难以有效提取的问题,提出一种自回归最小熵解卷积(AR-MED)共振特征增强的风电齿轮箱故障诊断方法,并结合一维卷积神经网络(1DCNN),实现齿轮箱高精度故障诊断。首先,使用共振稀疏分解算法(RSSD)将振动信号分解成含有噪声和谐波成分的高共振分量和含有故障冲击成分的低共振分量;其次,对低共振分量使用自回归最小熵解卷积运算,增强低共振分量中微弱的周期性冲击成分;最后,构建自回归最小熵解卷积共振特征增强的1DCNN模型,将分解得到的谐波分量和周期性冲击分量进行特征融合以及有针对的训练和分类。实验结果表明,与现有故障诊断模型相比,所提方法在提取风电齿轮箱的故障特征信息以及提高故障诊断精度方面具有有效性和优越性。

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

自监督深度学习的心脏磁共振图像配准算法

目的通过使用合成图像的方法解决在配准过程中缺少金标准的问题,并应用深度学习算法进行心脏T_(1)定量图配准。方法首先利用T_(1)加权图像的先验信息合成无运动的参考图像;其次使用DeepIPMCNet卷积神经网络来学习和配准层内运动。另一个网络DeepTPMDNet用于检测和消除穿层运动。使用在自由呼吸条件下采集的STONE序列T_(1)映射数据集进行训练、验证和测试,以验证本文方法的有效性。通过T_(1)标准差和SD map标准差来评估性能。结果在配准后,左心室和室间隔的Dice系数、T_(1)标准差和SD map标准差均得到了改善(通过DeepIPMCNet,左心室的Dice系数从0.88提高到0.90,室间隔的T_(1)标准差从121.91 ms降低到86.99 ms,SD map标准差从46.49 ms降低到36.53 ms;通过DeepTPMCNet,左心室的Dice系数从0.74提高到0.93,室间隔的T_(1)标准差从192.02 ms降低到114.37 ms,SD map标准差从93.41 ms降低到50.53 ms),差异均有统计学意义(P<0.001)。结论本研究提出的深度学习方法可有效缓解心脏和呼吸运动对心脏T_(1)定量图的影响。

基于卷积神经网络的GNSSGR海面风速反演方法研究

传统基于地球物理模型函数(geophysical model function,GMF)的全球导航卫星系统反射测量(global navigation satellite system reflectometry,GNSS-R)海面风速反演存在特征提取准确度低、模型复杂度高等问题。针对上述问题,提出了一种基于卷积神经网络的GNSS-R海面风速反演方法。通过构建卷积模块自动提取时延-多普勒映射图像(delay-Doppler map,DDM)中的观测特征,特征融合模块将提取的特征与辅助特征关联,全连接模块将上述特征向量逐级映射到海面风速。以“捕风一号”卫星观测数据为例验证了上述方法的有效性,较传统GMF方法,风速反演精度在均方根误差(root mean square error,RMSE)和平均偏差(mean bias error,MBE)上分别降低了0.51 m/s和0.19 m/s,反演效果分别提升了21%和16%。试验结果表明:该方法能够有针对性地自动提取DDM特征,有效提高特征提取的精度,同时显著降低模型的复杂度。本研究为同类卫星各种地表参数反演提供了新思路。