Detecting Iris Liveness with Batch Normalized Convolutional Neural Network

Aim to countermeasure the presentation attack for iris recognition system,an iris liveness detection scheme based on batch normalized convolutional neural network(BNCNN)is proposed to improve the reliability of the iris authentication system.The BNCNN architecture with eighteen layers is constructed to detect the genuine iris and fake iris,including convolutional layer,batch-normalized(BN)layer,Relu layer,pooling layer and full connected layer.The iris image is first preprocessed by iris segmentation and is normalized to 256×256 pixels,and then the iris features are extracted by BNCNN.With these features,the genuine iris and fake iris are determined by the decision-making layer.Batch normalization technique is used in BNCNN to avoid the problem of over fitting and gradient disappearing during training.Extensive experiments are conducted on three classical databases:the CASIA Iris Lamp database,the CASIA Iris Syn database and Ndcontact database.The results show that the proposed method can effectively extract micro texture features of the iris,and achieve higher detection accuracy compared with some typical iris liveness detection methods.

融合Inception V1-CBAM-CNN的轴承剩余寿命预测模型

针对现有的滚动轴承剩余寿命(RUL)预测方法精度低、轴承健康指标(HI)构建困难等问题,提出了一种基于卷积神经网络(CNN)并融合Inception V1模块和卷积注意力机制模块(CBAM)的滚动轴承RUL预测模型。首先,在CNN中添加了CBAM机制,并进行了加权处理,在通道和空间维度对重要特征进行了强化,对次要特征进行了抑制,通过添加改进的InceptionV1模块,提高了CNN通道间信息交互水平,全面提取了退化特征;然后,进行了网络优化,采用全局最大池化(GMP)方法对模型进行了简化,采用Dropout和批量归一化(BN)方法,避免了过拟合,提高了精度,且克服了训练时出现的梯度消失问题;最后,对数据进行了处理,将降噪后的信号重组为三维张量,将其作为HI,构建了退化标签,引入了评价指标,采用PHM2012轴承数据集进行了实验验证,在3种工况下将其与深度神经网络(DNN)、CNN方法、结合注意力机制的残差网络方法(ResNet)进行了对比。研究结果表明:该方法在变负载条件下的平均RMSE为0.033,较其他方法的RMSE值分别降低了86%、78%和69%,在预测精度和泛化能力方面具有明显优势。

SPNCC与一维双通道CNN-LSTM相结合的变压器局部放电故障音频检测

变压器运行时产生的声音信号包含了丰富的状态信息,可作为变压器故障诊断的重要依据。为提高变压器故障音频诊断效果,首先将变压器运行时现场采集的声音信号分为工作环境噪声信号、正常工作音频信号以及局部放电故障音频信号;然后通过卷积神经网络分类、小波包分解以及巴特沃斯带通滤波的方法去除原始音频信号中的非稳态环境噪声和短时稳态环境噪声信号;并建立了基于简化版幂律归一化倒谱系数特征的一维双通道卷积神经网络−长短时记忆网络的变压器局部放电故障识别模型。通过现场采集某500 kV变电站不同自然环境下的变压器运行声音信号与故障仿真实验,验证了提出的局部放电故障识别模型的可行性,相较于传统的音频故障诊断方法,提出的模型具有更快的收敛速度,更好的故障识别准确率与故障分类准确率。

Investigation on the Chinese Text Sentiment Analysis Based on Convolutional Neural Networks in Deep Learning

Nowadays,the amount of wed data is increasing at a rapid speed,which presents a serious challenge to the web monitoring.Text sentiment analysis,an important research topic in the area of natural language processing,is a crucial task in the web monitoring area.The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data.Deep learning is a hot research topic of the artificial intelligence in the recent years.By now,several research groups have studied the sentiment analysis of English texts using deep learning methods.In contrary,relatively few works have so far considered the Chinese text sentiment analysis toward this direction.In this paper,a method for analyzing the Chinese text sentiment is proposed based on the convolutional neural network(CNN)in deep learning in order to improve the analysis accuracy.The feature values of the CNN after the training process are nonuniformly distributed.In order to overcome this problem,a method for normalizing the feature values is proposed.Moreover,the dimensions of the text features are optimized through simulations.Finally,a method for updating the learning rate in the training process of the CNN is presented in order to achieve better performances.Experiment results on the typical datasets indicate that the accuracy of the proposed method can be improved compared with that of the traditional supervised machine learning methods,e.g.,the support vector machine method.

COVID-19 Detection via a 6-Layer Deep Convolutional Neural Network

Many people around the world have lost their lives due to COVID-19.The symptoms of most COVID-19 patients are fever,tiredness and dry cough,and the disease can easily spread to those around them.If the infected people can be detected early,this will help local authorities control the speed of the virus,and the infected can also be treated in time.We proposed a six-layer convolutional neural network combined with max pooling,batch normalization and Adam algorithm to improve the detection effect of COVID-19 patients.In the 10-fold cross-validation methods,our method is superior to several state-of-the-art methods.In addition,we use Grad-CAM technology to realize heat map visualization to observe the process of model training and detection.

An Optimized Convolutional Neural Network with Combination Blocks for Chinese Sign Language Identification

(Aim)Chinese sign language is an essential tool for hearing-impaired to live,learn and communicate in deaf communities.Moreover,Chinese sign language plays a significant role in speech therapy and rehabilitation.Chinese sign language identification can provide convenience for those hearing impaired people and eliminate the communication barrier between the deaf community and the rest of society.Similar to the research of many biomedical image processing(such as automatic chest radiograph processing,diagnosis of chest radiological images,etc.),with the rapid development of artificial intelligence,especially deep learning technologies and algorithms,sign language image recognition ushered in the spring.This study aims to propose a novel sign language image recognition method based on an optimized convolutional neural network.(Method)Three different combinations of blocks:Conv-BN-ReLU-Pooling,Conv-BN-ReLU,Conv-BN-ReLU-BN were employed,including some advanced technologies such as batch normalization,dropout,and Leaky ReLU.We proposed an optimized convolutional neural network to identify 1320 sign language images,which was called as CNN-CB method.Totally ten runs were implemented with the hold-out randomly set for each run.(Results)The results indicate that our CNN-CB method gained an overall accuracy of 94.88±0.99%.(Conclusion)Our CNN-CB method is superior to thirteen state-of-the-art methods:eight traditional machine learning approaches and five modern convolutional neural network approaches.

Enhanced Attention-Based Encoder-Decoder Framework for Text Recognition

Recognizing irregular text in natural images is a challenging task in computer vision.The existing approaches still face difficulties in recognizing irre-gular text because of its diverse shapes.In this paper,we propose a simple yet powerful irregular text recognition framework based on an encoder-decoder archi-tecture.The proposed framework is divided into four main modules.Firstly,in the image transformation module,a Thin Plate Spline(TPS)transformation is employed to transform the irregular text image into a readable text image.Sec-ondly,we propose a novel Spatial Attention Module(SAM)to compel the model to concentrate on text regions and obtain enriched feature maps.Thirdly,a deep bi-directional long short-term memory(Bi-LSTM)network is used to make a con-textual feature map out of a visual feature map generated from a Convolutional Neural Network(CNN).Finally,we propose a Dual Step Attention Mechanism(DSAM)integrated with the Connectionist Temporal Classification(CTC)-Attention decoder to re-weights visual features and focus on the intra-sequence relationships to generate a more accurate character sequence.The effectiveness of our proposed framework is verified through extensive experiments on various benchmarks datasets,such as SVT,ICDAR,CUTE80,and IIIT5k.The perfor-mance of the proposed text recognition framework is analyzed with the accuracy metric.Demonstrate that our proposed method outperforms the existing approaches on both regular and irregular text.Additionally,the robustness of our approach is evaluated using the grocery datasets,such as GroZi-120,Web-Market,SKU-110K,and Freiburg Groceries datasets that contain complex text images.Still,our framework produces superior performance on grocery datasets.

An improved micro-expression recognition algorithm of 3D convolutional neural network

The micro-expression lasts for a very short time and the intensity is very subtle.Aiming at the problem of its low recognition rate,this paper proposes a new micro-expression recognition algorithm based on a three-dimensional convolutional neural network(3D-CNN),which can extract two-di-mensional features in spatial domain and one-dimensional features in time domain,simultaneously.The network structure design is based on the deep learning framework Keras,and the discarding method and batch normalization(BN)algorithm are effectively combined with three-dimensional vis-ual geometry group block(3D-VGG-Block)to reduce the risk of overfitting while improving training speed.Aiming at the problem of the lack of samples in the data set,two methods of image flipping and small amplitude flipping are used for data amplification.Finally,the recognition rate on the data set is as high as 69.11%.Compared with the current international average micro-expression recog-nition rate of about 67%,the proposed algorithm has obvious advantages in recognition rate.

基于特征相似性和特征规范化的注意力模块

近年来,注意力机制在图像分类、目标检测和语义分割等领域取得了巨大成功,但现有的注意力机制大多只能在通道或空间维度上实现特征融合,这极大限制了其在通道和空间维度上变化的灵活性,导致无法充分利用特征信息。为此,文中提出一种基于特征相似性和特征规范化的、可同时利用特征图各维度信息的卷积神经网络注意力模块FSNAM。该模块由特征相似性模块(FSM)和特征规范化模块(FNM)两部分组成,FSM利用输入特征图的通道特征信息和局部空间特征信息生成一个二维的特征相似性权重图;FNM利用输入特征图的全局空间特征信息生成一个三维的特征规范化权重图;两个模块生成的权重图融合在一起,生成一个三维的注意力权重图,以此实现通道特征信息和空间特征信息的融合。为证明FSNAM的可行性和有效性,进行了消融实验,结果表明:在图像分类任务方面,FSNAM模块对分类网络在CIFAR数据集上的性能提升明显优于其他主流注意力模块;在目标检测任务方面,使用FSNAM模块的目标检测网络对VOC数据集中的小目标和中等大小目标的检测准确率分别提高了3.9和1.2个百分点;在语义分割任务方面,使用FSNAM模块可以提高HRNet模型的性能,在SBD数据集上模型的平均像素准确率提高了0.58个百分点。

改进YOLOv5的汽车齿轮配件表面缺陷检测

针对汽车齿轮配件表面缺陷检测存在效率低和精度差的问题,提出一种基于YOLOv5改进的缺陷检测模型YOLO-CNF。在骨干网络中增加CBAM注意力模块,使模型更加关注齿轮配件的缺陷区域,提高对微小缺陷的识别能力;设计了F2C模块用于融合浅层特征,在一定程度上缓解了微小缺陷位置信息丢失的问题;利用NWD(normalized Wasserstein distance)对回归损失进行优化,减少对小目标位置偏差的敏感性,从而进一步提高目标位置的准确率和精度。实验结果表明,改进模型的平均精度均值达到了86.7%,相较于原始模型提高了3.2个百分点,检测速度为43帧/s,基本满足了对汽车齿轮配件表面缺陷检测的需求。

基于CNN-LSTM的工业出水水质预测模型

工业废水含有多种污染物,提前预测工业废水水质从而快速对其进行相应处理具有重要意义。为此,研究提出了一种新的卷积神经网络(convolutional neural network,CNN)和长短期记忆网络(long short-term memory,LSTM)融合的工业废水水质污染物指标预测模型(CNN-LSTM)。为了更好地捕捉工业废水数据的时序性和动态性,模型设置了多个滑动窗口。使用CNN算法将时间序列数据进行高维特征提取,利用LSTM模型学习时间序列数据的时序特征,建立CNN-LSTM工业废水预测模型,并对废水水质中的化学需氧量(COD_(Cr))、氨氮、总磷(TP)3项指标进行预测分析。结果表明,与CNN和LSTM两个基准模型相比,CNN-LSTM预测模型的平均绝对值误差率(MAE)和均方误差率(MSE)均较小,预测效果较优。该模型能较好地实现对工业废水出水水质的准确预测,可为工业废水水质的在线监测和精准控制提供有效的、可行的技术支持和决策依据。

基于NCC的快速匹配算法

景象匹配在地形辅助导航中具有十分重要的地位,归一化互相关(NCC)技术是一种相对抗噪声能力强、匹配准确的匹配算法,因此,在导航制导中广泛应用。但由于其运算量比较大,对于要求实时匹配的地形辅助导航系统来说,其运算速度需要进一步提高。在NCC方法的基础上,把卷积用于景象匹配,大大简化并提高了NCC的匹配速度。仿真实验证明了此方法的快速性和有效性。

动校正域CNN法压制自由表面多次波

【目的】自由表面多次波的存在影响了地震资料的解释精度,有效压制多次波是地震处理的重要环节。多次波是具有与有效信号相似特征的相干噪声,传统的卷积神经网络(convolution neural network, CNN)方法难以从全波场数据中分辨出多次波信号。此外,不同工区的自由表面多次波差异较大,导致CNN方法在迁移时面临更严重的问题。【方法】为此,引入物理先验,提出一种基于动校正(normal moveout correction,NMO)域的CNN方法。利用NMO域上一次波与多次波呈现出的曲率特征差异训练CNN,以实现对多次波的有效识别和压制。通过仿真和真实数据测试了方法的性能。【结果和结论】实验结果表明:动校正域上训练的CNN能够有效识别并压制多次波信号,同时保护一次波反射信号。与传统Radon算法相比,提出的方法减少了人为调整参数并降低了计算复杂度,且有效信号泄漏更少;与直接端到端基于CNN的自由表面多次波压制方法相比,新方法面向新数据的适应能力更好。研究成果可为提高地震资料解释精度和降低计算成本方面提供新思路。

基于CNN和Group Normalization的校园垃圾图像分类

为解决大学校园的垃圾回收分类问题,提出了一种基于卷积神经网络和归一化技术的垃圾图像分类方法,不需要对输入的图像进行复杂的处理,网络模型即可根据算法提取图像特征,通过加入群组归一化(Group Normalization)和网络模型各层之间的协作,克服传统分类算法的缺点,实现对垃圾图像的分类。实验表明,该识别方法具有较高准确率,可以较好识别不可回收及可回收垃圾。

联合傅里叶卷积与通道注意力的光场角度重建

光场相机能够同时捕获光线的强度和方向信息,但由于成像传感器尺寸的限制,无法同时获得高空间和角度分辨率的光场图像。提出了一种联合傅里叶卷积和通道注意力的光场角度重建方法,通过使用稀疏光场图像4个边角位置的参考视图,可以间接地重建出密集光场图像。考虑到光场数据的内在4D结构,采用通道级密集快速傅里叶残差卷积块,在空域和频域对光场图像的空间和角度相关性进行建模,然后采用基于全局响应归一化的通道注意块,以实现通道间的自适应融合。此外,还提出了一种改进的视点加权间接合成方法,通过为每个参考视图分配一个置信图,为参考视图之间建立联系以合成更真实的新视图。实验结果表明,相比于现有先进的光场角度重建算法IRVAE,所提方法的重建光场图像质量在自然光场数据集30Scenes,Occlusion和Reflective上的平均PSNR分别提高了0.08,0.13和0.13 dB。所提方法在保证光场角度一致性的前提下取得了清晰的重建结果。

基于SimAM注意力机制的轴承故障迁移诊断模型

针对轴承故障在跨工况迁移诊断时,其域不变特征难以提取,易出现模型过拟合这一问题,提出了一种基于无参数注意力模块(SimAM)的轴承故障迁移诊断方法。首先,以一维卷积神经网络作为基本框架,利用自适应批量归一化(AdaBN)对各输出层进行了归一化处理,经两层卷积层和两层池化层后,对输出特征进行了随机节点失活操作;然后,利用改进后的参数化修正线性单元(PReLU)激活函数自适应提取负值输入权值系数,分别以交叉熵损失函数监督训练有标签的源域数据,以均方对数误差(MSLE)作为损失函数训练无标签的目标数据;最后,利用自制实验台数据和凯斯西储轴承公开数据对模型进行了验证,分别以不同的单一工况作为源域,其余工况作为目标域进行了迁移诊断任务研究。研究结果表明:基于SimAM的轴承故障迁移诊断方具有较好的域不变特征提取的性能,且所提特征具有较好的聚类效果;自制实验台中的平均迁移精度在89.1%以上,最高均值可达97.85%,CWRU数据集中的平均迁移精度达98.68%。该成果可为后续轴承故障由实验向工业现场的迁移诊断奠定基础。

基于卷积神经网络的轻量级水稻叶片病害识别模型

水稻病害一直是影响水稻产量的重要因素之一,为了快速、准确地检测水稻病害,本研究提出了一种基于卷积神经网络的轻量级水稻叶片病害识别模型。首先,从参数量的角度对注意力机制进行改进,得到轻量级注意力机制模块,对水稻叶片病害特征图中的潜在注意力信息进行深度挖掘;其次,使用深度可分离卷积代替部分标准卷积,进一步降低模型的参数量;最后,为了提高模型的泛化能力,让模型学习过程更快、更稳定,采用了自带内部归一化属性的扩展型指数线性单元函数(SELU)与外部组归一化模块相结合的方法。通过在公共数据集中进行验证,本研究构建模型的平均精度最高(0.990 0),模型在参数量和平均单次迭代时间方面也有一定优势,与其他模型相比,具有相对较好的性能。

基于深度学习的结直肠腺体自动分割算法研究

为实现腺体自动化分割,减轻病理学医生的工作量,帮助医生做出更加准确的临床决策,提出一种基于注意力机制和可变形卷积的适合腺体分割的深度神经网络模型(Adaptive-Gland-Segmentation-Net,AGS-Net)。该模型使用分组卷积和注意力机制使模型具有更强的表征能力,增加可变形卷积层以适应不同分化程度的腺体形状。在GlaS数据集上,加入染色标准化预处理的AGS-Net在检测结果、分割性能和形状相似性等三方面与竞争方法相比,具有很大的优势。

预应力RPC-NC叠合梁挠度试验及计算方法

为提高桥梁结构抵抗变形的能力,提出了预应力活性粉末混凝土(RPC)-普通混凝土(NC)叠合梁结构形式,并设计了10根预应力RPC-NC叠合模型梁和1根NC对照梁。试验研究了预应力度、RPC高度、NC等级等因素影响下叠合梁跨中挠度发展特性及计算方法。结果表明:叠合梁跨中挠度发展可分为开裂前弹性阶段、带裂缝工作阶段、普通钢筋屈服后迅速增长阶段3个阶段。预应力度越高,开裂前弹性段越长,开裂后屈服和强化阶段的刚度下降越快。根据试验值对铁路桥规和混凝土规范中的公式进行修正,修正后的计算结果与试验值吻合良好。

基于卷积神经网络的光信噪比监测方法

光信噪比(OSNR)与光纤通信的传输性能息息相关,因此,OSNR监测是光性能监测技术中至关重要的一环,同时,传输链路中的色散会导致光信号脉冲展宽,使OSNR监测准确性下降。针对这一问题,设计了一种卷积神经网络模型,以异步延迟采样图(ADTP)作为网络输入特征,引入实例批量标准化模块,继承了神经网络不同深度下特征发散分布的优点,提高了神经网络对色散变化的适应性。实验结果表明,在10 Gb/s NRZ-OOK信号无色散干扰监测场景下,该模型的平均绝对误差(MAE)为0.2 dB,在色散变化的场景下,MAE最高降低了0.61 dB。