Identification of Nonlinear Dynamic Systems Using Diagonal Recurrent Neural Networks

In order to apply a new dynamic neural network- Diagonal Recurrent Neural NetWork (DRNN) to the system identificationof nonlinear dynamic Systems and construct more accurate system models, the structure and learning method (DBP algorithm) of theDRNN are Present6d. Nonlinear system characteriStics can be identified by presenting a set of input / output patterns tO the DRNN andadjusting its weights with the DBP algorithm. Experimental results show that the DRNN has good performances in the identification ofnonlinear dynamic systems in comparison with BP networks.

Artificial Neural Network Method Based on Expert Knowledge and Its Application to Quantitative Identification of Potential Seismic Sources

In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.

Modelling missile motion system using neural networks

The models of noallnear systems are idendried by recmeive pedctive ermrs(RPE) methed based on thelayered neural networks. To improve the identification precision, gain callcient and arentUm factor are itheucedinto the algorithm for the data are dids by noses and vny suddnly. this lerthm is applied to the twcmedeiling of rolling and pitchng angles of ndssiles. Simulation results shoW tha the proposed algurithm is sultable forthe modelling of nodrinear systems.

Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks

Grape diseases are main factors causing serious grapes reduction.So it is urgent to develop an automatic identification method for grape leaf diseases.Deep learning techniques have recently achieved impressive successes in various computer vision problems,which inspires us to apply them to grape diseases identification task.In this paper,a united convolutional neural networks(CNNs)architecture based on an integrated method is proposed.The proposed CNNs architecture,i.e.,UnitedModel is designed to distinguish leaves with common grape diseases i.e.,black rot,esca and isariopsis leaf spot from healthy leaves.The combination of multiple CNNs enables the proposed UnitedModel to extract complementary discriminative features.Thus the representative ability of United-Model has been enhanced.The UnitedModel has been evaluated on the hold-out PlantVillage dataset and has been compared with several state-of-the-art CNN models.The experimental results have shown that UnitedModel achieves the best performance on various evaluation metrics.The UnitedModel achieves an average validation accuracy of 99.17%and a test accuracy of 98.57%,which can serve as a decision support tool to help farmers identify grape diseases.

Crack identification in functionally graded material framed structures using stationary wavelet transform and neural network

In this paper,an integrated procedure is proposed to identify cracks in a portal framed structure made of functionally graded material(FGM)using stationary wavelet transform(SWT)and neural network(NN).Material properties of the structure vary along the thickness of beam elements by the power law of volumn distribution.Cracks are assumed to be open and are modeled by double massless springs with stiffness calculated from their depth.The dynamic stiffness method(DSM)is developed to calculate the mode shapes of a cracked frame structure based on shape functions obtained as a general solution of vibration in multiple cracked FGM Timoshenko beams.The SWT of mode shapes is examined for localization of potential cracks in the frame structure and utilized as the input data of NN for crack depth identification.The integrated procedure proposed is shown to be very effective for accurately assessing crack locations and depths in FGM structures,even with noisy measured mode shapes and a limited amount of measured data.

A new neural-network-based method for structural damage identification in single-layer reticulated shells

Single-layer reticulated shells(SLRSs)find widespread application in the roofs of crucial public structures,such as gymnasiums and exhibition center.In this paper,a new neural-network-based method for structural damage identification in SLRSs is proposed.First,a damage vector index,NDL,that is related only to the damage localization,is proposed for SLRSs,and a damage data set is constructed from NDL data.On the basis of visualization of the NDL damage data set,the structural damaged region locations are identified using convolutional neural networks(CNNs).By cross-dividing the damaged region locations and using parallel CNNs for each regional location,the damaged region locations can be quickly and efficiently identified and the undamaged region locations can be eliminated.Second,a damage vector index,DS,that is related to the damage location and damage degree,is proposed for SLRSs.Based on the damaged region identified previously,a fully connected neural network(FCNN)is constructed to identify the location and damage degree of members.The effectiveness and reliability of the proposed method are verified by considering a numerical case of a spherical SLRS.The calculation results showed that the proposed method can quickly eliminate candidate locations of potential damaged region locations and precisely determine the location and damage degree of members.

Mooring Damage Identification of Floating Wind Turbine Using a Non-Probabilistic Approach Under Different Environmental Conditions

This paper discusses the damage identification in the mooring line system of a floating wind turbine(FWT)exposed to various environmental loads.The proposed method incorporates a non-probabilistic method into artificial neural networks(ANNs).The non-probabilistic method is used to overcome the problem of uncertainties.For this purpose,the interval analysis method is used to calculate the lower and upper bounds of ANNs input data.This data contains some of the natural frequencies utilized to train two different ANNs and predict the output data which is the interval bounds of mooring line stiffness.Additionally,in order to reduce computational time and more importantly,identify damage in various conditions,the proposed method is trained using constant loads(CL)case(deterministic loads,including constant wind speed and airy wave model)and is tested using random loads(RL)case(including Kaimal wind model and JONSWAP wave theory).The superiority of this method is assessed by applying the deterministic method for damage identification.The results demonstrate that the proposed non-probabilistic method identifies the location and severity of damage more accurately compared to a deterministic one.This superiority is getting more remarkable as the difference in uncertainty levels between training and testing data is increasing.

数据驱动的半无限介质裂纹识别模型研究

缺陷识别是结构健康监测的重要研究内容,对评估工程结构的安全性具有重要的指导意义,然而,准确确定结构缺陷的尺寸十分困难.论文提出了一种创新的数据驱动算法,将比例边界有限元法(scaled boundary finite element methods,SBFEM)与自编码器(autoencoder,AE)、因果膨胀卷积神经网络(causal dilated convolutional neural network,CDCNN)相结合用于半无限介质中的裂纹识别.在该模型中,SBFEM用于模拟波在含不同裂纹状缺陷半无限介质中的传播过程,对于不同的裂纹状缺陷,仅需改变裂纹尖端的比例中心和裂纹开口处节点的位置,避免了复杂的重网格过程,可高效地生成足够的训练数据.模拟波在半无限介质中传播时,建立了基于瑞利阻尼的吸收边界模型,避免了对结构全域模型进行计算.搭建了CDCNN,确保了时序数据的有序性,并获得更大的感受野而不增加神经网络的复杂性,可捕捉更多的历史信息,AE具有较强的非线性特征提取能力,可将高维的原始输入特征向量空间映射到低维潜在特征向量空间,以获得低维潜在特征用于网络模型训练,有效提升了网络模型的学习效率.数值算例表明:提出的模型能够高效且准确地识别半无限介质中裂纹的量化信息,且AE-CDCNN模型的识别效率较单CDCNN模型提高了约2.7倍.

基于ST-CNN的脉冲型地震动与脉冲周期融合识别方法

如何快速准确地识别脉冲型地震动是困扰学术界和工程界的关键难题,定量识别方法虽然能够克服人工识别的经验性限制,但是传统定量识别方法存在识别结果不一致、适用范围不广泛、难以同时识别脉冲周期或识别的脉冲周期部分情况下差异明显等问题。为此建立了一种问题针对性融合学习规则并结合卷积神经网络(CNN),开发出了一种新的脉冲型地震动与脉冲周期同步识别方法。该学习规则通过对基于不同识别原理的多个传统典型识别方法进行融合学习并采用全球范围的30000条任意方向地震动数据进行训练和验证,摒弃了以往繁琐的人工标记过程并得到了3个问题针对性识别模型,分别命名为Strict识别模型、General识别模型以及TP识别模型。除此之外,为解决地震动时序输入信息不足从而导致模型泛化能力较弱的问题,对CNN的输入结构进行了优化增强,提出了ST-CNN模型。其引入了S变换层以将地震动时序变换至时频,从而增加了频域分布信息并进一步提高了识别精度。结果表明:Strict识别模型能严格区分脉冲型与非脉冲型地震动,识别结果得到已有方法的一致认可;General识别模型的识别能力更强,适用范围更加广泛;TP识别模型识别的脉冲周期更加准确,并可与前述识别模型并用以同步输出识别结果。提出的问题针对性融合学习规则还可推广至其他工程领域与其他机器学习模型,建立的识别方法可为脉冲型地震动研究提供科学指导。

Welch功率谱与卷积神经网络结合的滚动轴承故障诊断

针对滚动轴承故障诊断在小训练样本下和强噪声下无法取得高精度识别的问题,提出一种基于Welch功率谱结合卷积神经网络进行诊断的方法。该方法以原始时域振动信号作为输入,用Welch功率谱转换数据形态同时对高强度噪声进行抑制,再用得到的功率谱训练卷积神经网络,最后将训练好的模型用于轴承的故障诊断。与WDCNN[1]等方法进行对比,实验发现在混合负载下,该方法平均识别率正确达到99%,其它方法达到这个精度至少需要20倍以上的训练样本量,明显优于WDCNN等方法。抗噪实验结果表明噪声对信号的干扰越强烈,该方法的抗噪表现越好,其抗噪性能要显著优于WDCNN等方法。

基于YOLOv5神经网络模型的变电所压板开关状态的识别方法

煤矿变电所是大型煤矿供电系统的重要组成部分,变电所压板开关状态的精确识别是监测煤矿供电状态的重要环节。然而,随着变电所电气控制柜上压板开关数量的大幅增加,传统人工巡检存在的巡检速度慢、巡检精度低的问题愈发显著。针对上述问题,提出了一种基于YOLOv5神经网络模型的变电所压板开关状态识别方法。使用Pytorch深度学习框架进行了模型训练;设计了针对压板开关图像的预处理方法;采用得到的最佳模型对预处理后的压板开关图像进行检测并评估检测结果。实验结果表明该方法可以实现压板开关状态的智能识别,且具有速度快、精度高的特点。

六自由度机器人重力误差识别与补偿方法

为了提高六自由度关节机器人末端执行器的位置精度,文章基于自主设计研发的六自由度关节机器人进行重力误差识别和补偿方法研究。首先,基于D-H法建立机器人各关节转角与末端位置坐标的关系模型,提出采用Levenberg-Marquardt进行主要关节实际扭转刚度的计算方法;其次,基于有限元分析软件,进行不同位姿下机器人在重力场作用下的静力学分析,建立机器人位姿参数为输入、末端变形为输出的BP神经元网络模型,以实现机器人不同位姿下连杆弯曲变形导致的末端位置误差的在线预测;最后,基于自主设计的专用测头进行机器人末端位置误差补偿实验。实验数据表明,补偿后的定位精度比补偿前在X和Z方向分别提高了93.6%和92.2%。

二氧化氮浓度时空预测:一种区间二型直觉模糊神经网络方法

空气中二氧化氮浓度的高低对环境保护和公共健康具有重要影响。目前二氧化氮浓度预测方法在表征时空关联性方面存在不足。鉴于此,提出了新的使用区间二型直觉模糊神经网络时空预测二氧化氮浓度的方法。首先,阐述了该区间二型直觉模糊神经网络框架,引入可变系数加权其隶属部分和非隶属部分的输出,并采用随机向量泛函链接神经网络作为规则后件;然后,为确定网络结构和参数,采用分层聚类算法得到模糊规则库,并通过最小二乘法优化网络后件的输出权值;最后,使用2018年1月至3月采集的北京市二氧化氮浓度真实数据进行数值验证。实验结果表明,与现有方法相比,该方法在短期和长期时空预测方面均取得了较高的预测精度和效率。

远程LIBS结合GA-arPLS的爆炸物识别研究

为了保障公共安全和预防恐怖袭击事件的发生,提出基于遗传算法(GA)优化非对称重加权惩罚最小二乘(arPLS)的远程LIBS基线校正预处理方法,结合ANN分类模型实现6m距离下的四种爆炸物(TNT、RDX、HMX和CL-20)快速、准确识别。GA-arPLS算法基于arPLS引入适应度函数评估拟合基线,寻找候选参数空间中的最优解来实现拟合LIBS基线。由于LIBS光谱信号通常包括连续辐射、原子与分子发射线等噪声信息,其覆盖了LIBS光谱较宽的光波段;直接通过LIBS光谱对相似元素的有机物定性分析时,难以捕捉相似爆炸物的特征光谱之间微小差异实现分类,故远距离环境下通过GA-arPLS预处理来提高特征谱线辨识能力很有必要,因此提升光谱分析的准确度很有必要。研究将GA-arPLS校正前后的LIBS数据集分别作为支持向量机(SVM)和最邻近分类(KNN)的输入,SVM的分类准确率提升了8.4%,而KNN分类模型的准确率提升8.7%。分类准确率表明,该GA-arPLS基线校正预处理方法可有效降低远程LIBS光谱的连续背景,而结合人工神经网络(ANN)构建的分类模型对相似爆炸物的识别准确率从89.2%提升至100%,分类识别效果达到最优。研究表明,该基线校正预处理方法不仅有效减小远距离LIBS的连续背景辐射和噪声干扰,而且提升了远程LIBS分类模型的鲁棒性和预测能力。研究成果有望提升远程LIBS在爆炸物检测方面的准确性和效率,以更好地应对潜在的爆炸物威胁。

岩石变形局部化智能识别的DSCM-CNN方法

岩石变形局部化的识别对于岩石破坏机理、岩土工程灾害预测预警有着重要的意义。本文将数字散斑相关方法(digital speckle correlation methods,DSCM)与卷积神经网络(convolutional neural networks,CNN)相结合,提出了一种用于岩石变形局部化智能识别的DSCM-CNN模型。通过DSCM获取岩石试件在单轴压缩实验过程中的最大剪应变场云图,根据变形局部化带位置进行标注,完成数据集的构建;利用训练数据集对DSCM-CNN智能识别模型进行训练。通过红砂岩单轴压缩实验对该方法进行验证,结果表明:DSCM-CNN模型可以实现岩石变形局部化带位置的自动识别,子集准确率、精确度、召回率等指标分别达到94.19%,97.21%和96.41%,证明了岩石变形局部化智能识别的DSCM-CNN模型的可行性,为岩石变形局部化智能监测提供了一种新的思路。

基于卷积神经网络的通信网络安全告警方法

常规的通信网络安全告警结构多设定为独立形式,安全告警的范围难以进行扩展,导致告警响应时间不断延长,为此提出对基于卷积神经网络的通信网络安全告警方法的设计与验证分析,根据当前测试需求及标准的变化,先进行基础安全告警机制的制定,采用多目标的方式,扩展通信网络实际的安全告警覆盖范围,同时设计多目标交叉网络安全告警结构,以此为基础,构建卷积神经网络通信网络告警模型,采用自适应锁定的方式来实现安全告警处理。最终的测试结果表明:对比于传统时空特征融合通信网络安全告警测试组、传统邻域搜索粒子群通信网络安全告警测试组,此次所设计的卷积神经网络通信网络安全告警测试组最终得出的告警响应时间被较好地控制在0.25s以下,说明在卷积神经网络技术的辅助下,当前所设计的告警效果更佳,针对性更强,具有实际的应用价值。

基于GWO-BiLSTM的岩性识别方法研究与应用

为解决常规岩性识别方法精度不高、耗时较长且受人为影响较大等问题,构建了基于GWO-BiLSTM的岩性识别方法。结合录井资料、岩心资料以及测井资料,采用常规方法对研究区进行岩性识别,效果较差,进而利用GWO-BiLSTM模型在研究区展开岩性识别工作。根据皮尔逊函数对各测井曲线与岩性进行分析,优选出相关系数绝对值大于0.3的测井曲线值作为输入特征,采用灰狼优化算法对BiLSTM超参数组合随机生成与更新,从而更加快速地获取最优解,进一步提高模型的效率以及准确率。实验表明,基于GWO-BiLSTM模型的岩性识别准确率达96%,与BiLSTM模型、RF模型、BP神经网络和SVM模型相比具有较高的准确率,验证了该模型在识别复杂岩性时的可靠性,并为复杂岩性识别提供了方法参考。

基于注意力卷积神经网络的服装款式图廓特征识别方法

针对现有服装款式图廓特征识别方法存在特征识别技术复杂和识别精度不高的问题,提出基于注意力卷积神经网络的服装款式图廓特征识别方法。首先,采用数据增强方法对服装款式图廓类型标签进行分类;其次,通过损失函数计算并确定图廓特征分布梯度;然后,通过卷积神经网络构建特征识别模型;最后,引入注意力机制模块识别服装款式图廓特征。验证结果表明:与基于改进Resnet34和基于改进边缘检测算法的服装款式识别方法比,本文方法始终具有较高的复杂图廓识别精准度,对连衣裙款式样衣的图廓识别精准度可达99.1%,外套、裤子、短袖的款式均能达到90%以上。本文方法的识别效果精准有效,可推广于现实中服装款式图廓特征的识别。

基于曲率模态的结构损伤识别

随着国内桥梁数量已趋于饱和,更多的桥梁结构进入运维阶段,对老旧桥梁的损伤识别、对新建桥梁的健康监测已经成为亟待解决的社会问题。基于桥梁结构的动力特征响应信息,利用人工神经网络的数据分析方法,提出桥梁结构损伤识别的三步识别法:首先利用固有频率判别桥梁结构是否发生了损伤;若发生损伤,则利用曲率模态分析损伤位置;在损伤位置确定后,利用人工神经网络进一步对数据的隐含信息进行提取,识别损伤程度。研究证明人工神经网络在桥梁结构损伤识别方面有较强的适用性,可以为今后的发展提供一定参考。

CNN-ERI地质识别模型的研究与应用

基建工程对地质勘探精度的要求不断提高,研究人工智能技术在地质勘探数据分析处理中的应用具有重要意义。基于卷积神经网络技术的数据降维处理功能,提出一种改进的CNN-ERI地质识别模型,分析了该模型在地质识别方面的优越性,探讨了数据量和环境因素对模型识别精度的影响,并结合某隧洞工程,探析了模型的具体应用问题。