基于知识迁移的深度学习无线电信号聚类方法

现有的无线电信号调制识别方法在先验数据不足时通常很难对无类标信号进行有效识别。针对这个问题,本文提出了一种基于知识迁移的深度学习无线电信号聚类方法(DTC)。该方法基于样本对比,分析样本间的相似性,并利用卷积神经网络(CNN)提取无线电信号的特征,同时设计了一种预训练框架,通过迁移同领域数据集的知识,有效提升了CNN特征提取能力,实现了引导聚类方向、提升聚类性能的目标。实验结果表明,该方法在多个公开数据集上的聚类性能都显著优于现有的聚类方法。与现有方法相比,DTC在RML 2016.10A和RML 2016.04C数据集上的聚类精度分别提升了30.34%和28.04%。     

基于深度学习的人体行为识别研究

为解决传统人体行为识别算法存在的运动前景检测不准确、特征提取模糊以及训练识别耗时长等问题,本文提出了基于深度学习的人体行为识别研究方法。利用骨架提取方法对运动前景进行检测及特征提取;针对人体行为动作的时序性,提出了连续帧组合方法;在模型训练环节,对比了不同的网络模型参数,选择了最优的激活函数、优化算法以及dropout系数。最后,结合网络模型,分类识别测试样本集中的各种行为,并将识别的结果和当前流行的算法进行比较,通过对比实验,最终实验结果证明了本文所提方法优于其他方法,平均识别率相比其他方法有较大的提高。     

流场特征识别的无量纲时程深度学习方法

流场的特征直接影响结构的流致振动状态,对结构绕流流场的特征分析具有重要的研究意义。在中高雷诺数与不同流速的情况下,尾流中隐含的流场特征不同,传统数学物理方法很难对其特征进行提取与识别。该文提出了采用无量纲的物理量时程进行流场特征识别的深度学习方法,消除了不同来流速度的影响,仅通过时程的时变特征进行特征识别,扩大了特征识别方法的应用范围。采用两种不同深度学习模型对三种棱柱的尾流进行了特征提取与识别,通过比较可以发现：归一化的时程中仍包含不同形状物体所引起流场的关键特征,可用于流场的特征提取;使用归一化时程进行流场特征识别可降低模型训练难度,又提高了特征提取的精度,是一种流场特征提取的新方法。     

基于深度学习的人体姿态识别系统设计与实现

当一个运动的物体作用于人眼时,眼睛通过分析人体的关键特征点随时间的变化趋势,在动态标记特征点的基础上对运动状态进行识别,从而做出相应的判断。在对眼睛物理机理和目标识别过程进行研究的过程中,有学者结合神经网络运算方法提出了OpenPose姿态识别算法,该算法是实现人体姿态识别的主流算法。该文结合OpenPose算法研究了人体姿态识别系统的实现过程。在实现算法的过程中,利用RNN网络的预测值对辨识结果进行修正,从而进一步提高姿态识别的精度。     

基于多层深度特征融合的行人再识别研究

本文采用多层深度特征融合方法,首先,利用经典卷积神经网络对行人图像进行处理;然后,对卷积神经网络每一层得到的特征进行PCA降维,保留其主要成分,并将各层降维后的特征进行融合;最后,基于欧氏距离判断待查询行人与图像库中各行人的相似性,得到再识别结果.实验结果表明,与已有的行人再识别方法相比,本文方法准确率更高.     

基于深度学习的验证码图像识别

验证码是一种区分用户是计算机还是人的公共全自动程序.为了尽可能大批量地获取某网站的信息,就需要让机器可以全自动地识别该网站的验证码.为了破解验证码,对深度学习的验证码图像识别方法进行了研究.提出使用图像标注的方法来生成验证码图像中的字母序列.实验采用深度学习框架Caffe,将卷积神经网络与循环神经网络相结合进行训练.将卷积神经网络的输出用于训练循环神经网络,来不断地预测出序列中下一个最有可能出现的字母.训练的目标是将输出的词尽量和预期的词一致.测试结果表明,该模型能够对该网站的验证码图像做到97%的识别准确率.该方法比只采用卷积神经网络进行识别效果好.     

深度卷积神经网络在混沌振动识别中的应用研究

针对混沌振动信号识别中,混沌特征指数计算量大、运算耗时长,难以满足实时性的要求,提出一种基于深度卷积神经网络的智能混沌识别方法.首先通过相空间重构技术,得到不同振动信号的吸引子图;在此基础上,优化设计了经典网络模型AlexNet的结构参数并进行训练;最后将改进后的模型用于混沌信号的智能识别.仿真和实测信号的结果表明,该...     

水下瞬态信号的检测与识别

对于水下瞬态信号这种突发性很强、持续时间又非常短、且出现后一般不再重复的信号,如何才能进行全自动的快速有效的检测与识别,是近期国内外研究的热点。针对水下目标快速变速所产生的瞬态信号的自动检测与识别问题,传统信号处理方法难以适用,文中引入了图像处理的有关理念和技术,提出了"取重心处理"的方法,并提出了一种便于工程实现的快速算法。对海试数据的分析结果表明,该方法稳定可靠,有望应用于工程实践中。     

基于改进一维卷积神经网络的滚动轴承故障识别

滚动轴承的故障识别对于防止旋转机械系统故障恶化并保证其安全运行具有重要意义。针对现有智能诊断模型参数多、识别效率低的问题,提出一种基于改进一维卷积神经网络的滚动轴承故障识别(FRICNN–1D)方法。通过引入1×1卷积核增强一维卷积神经网络模型的非线性表达能力;并用全局平局池化层代替传统卷积神经(CNN)网络中的全连接层,以降低模型参数和计算量,且防止过拟合现象。试验结果表明,该方法可以准确识别滚动轴承不同故障状态,具有一定的工程实际应用潜力。     

Deep Learning Based Underground Sewer Defect Classification Using a Modified RegNet

The sewer system plays an important role in protecting rainfall and treating urban wastewater. Due to the harsh internal environment and complex structure of the sewer, it is difficult to monitor the sewer system. Researchers are developing different methods, such as the Internet of Things and Artificial Intelligence, to monitor and detect the faults in the sewer system. Deep learning is a promising artificial intelligence technology that can effectively identify and classify different sewer system defects. However, the existing deep learning based solution does not provide high accuracy prediction and the defect class considered for classification is very small, which can affect the robustness of the model in the constraint environment. As a result, this paper proposes a sewer condition monitoring framework based on deep learning, which can effectively detect and evaluate defects in sewer pipelines with high accuracy. We also introduce a large dataset of sewer defects with 20 different defect classes found in the sewer pipeline. This study modified the original RegNet model by modifying the squeeze excitation (SE) block and adding the dropout layer and Leaky Rectified Linear Units (LeakyReLU) activation function in the Block structure of RegNet model. This study explored different deep learning methods such as RegNet, ResNet50, very deep convolutional networks (VGG), and GoogleNet to train on the sewer defect dataset. The experimental results indicate that the proposed system framework based on the modified-RegNet (RegNet+) model achieves the highest accuracy of 99.5 compared with the commonly used deep learning models. The proposed model provides a robust deep learning model that can effectively classify 20 different sewer defects and be utilized in real-world sewer condition monitoring applications.     

Picture-Induced EEG Signal Classification Based on CVC Emotion Recognition System

Emotion recognition systems are helpful in human–machine interactions and Intelligence Medical applications. Electroencephalogram (EEG) is closely related to the central nervous system activity of the brain. Compared with other signals, EEG is more closely associated with the emotional activity. It is essential to study emotion recognition based on EEG information. In the research of emotion recognition based on EEG, it is a common problem that the results of individual emotion classification vary greatly under the same scheme of emotion recognition, which affects the engineering application of emotion recognition. In order to improve the overall emotion recognition rate of the emotion classification system, we propose the CSP_VAR_CNN (CVC) emotion recognition system, which is based on the convolutional neural network (CNN) algorithm to classify emotions of EEG signals. Firstly, the emotion recognition system using common spatial patterns (CSP) to reduce the EEG data, then the standardized variance (VAR) is selected as the parameter to form the emotion feature vectors. Lastly, a 5-layer CNN model is built to classify the EEG signal. The classification results show that this emotion recognition system can better the overall emotion recognition rate: the variance has been reduced to 0.0067, which is a decrease of 64% compared to that of the CSP_VAR_SVM (CVS) system. On the other hand, the average accuracy reaches 69.84%, which is 0.79% higher than that of the CVS system. It shows that the overall emotion recognition rate of the proposed emotion recognition system is more stable, and its emotion recognition rate is higher.     

基于深度学习的图像分类算法框架研究

目的 提高图像分类精度是实现自动化生产的基础,提出一种更加准确的图像分类方法,使自动化包装和生产更加高效.方法 基于ResNeSt特征图组的思想,通过引入通道域和空间域注意力机制,并将自适应卷积核思想和Gem池化引入空间域注意力模块,从而使网络在空间域注意力机制中能够对不同图片使用不同的感受野使其关注更重要的部分,提出一种具有通道域和空间域注意力机制,且具有很好移植性的图像分类网络模型结构.结果 文中方法提高了图像分类准确度,在ImageNet数据集上,top-1准确度为81.39％.结论 文中提出的ResNeSkt算法框架优于目前的主流图像分类方法,同时网络整体结构具有很好的移植性,可以作为图像检测、语义分割等其他图像研究领域的主干网络.     

基于深度学习的烟包识别与分类

目的 提取烟包图像数据训练深度学习目标检测模型,提升烟包流水线拣包效率和准确性。方法 基于深度学习建立一种烟包识别分类模型,对原始YOLOv3模型进行改进,在原网络中加入设计的多空间金字塔池化结构(M–SPP),将64×64尺度的特征图下采样与32×32尺度的特征图进行拼接,并去除16×16尺度的预测特征层,提高模型的检测准确率和速度,并采用K–means++算法对先验框参数进行优化。结果 实验表明该目标检测模型平均准确率达到99.68%,检测速度达到70.82帧/s。结论 基于深度学习建立的图像识别分类模型准确率高且检测速度快,有效满足烟包流水线自动化实时检测。     

基于小波包时频图特征和卷积神经网络的水声信号分类

水下声信号分类是水声学研究的一个重要方向.一个有效的特征提取和分类决策方法对水声信号分类技术至关重要.文章将鱼声、商船辐射噪声和风关噪声三类实测的水声信号在小波包分解的基础上提取时频图特征,并搭建了一个七层结构的卷积神经网络作为分类器.研究结果表明:三种水声信号的小波包时频图特征结合卷积神经网络在不同测试集可达到(98...     

Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning

Violence recognition is crucial because of its applications in activities related to security and law enforcement. Existing semi-automated systems have issues such as tedious manual surveillances, which causes human errors and makes these systems less effective. Several approaches have been proposed using trajectory-based, non-object-centric, and deep-learning-based methods. Previous studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other methods. However, the their performance must be improved. This study explores the state-of-the-art deep learning architecture of convolutional neural networks (CNNs) and inception V4 to detect and recognize violence using video data. In the proposed framework, the keyframe extraction technique eliminates duplicate consecutive frames. This keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate frames. For feature selection and classification tasks, the applied sequential CNN uses one kernel size, whereas the inception v4 CNN uses multiple kernels for different layers of the architecture. For empirical analysis, four widely used standard datasets are used with diverse activities. The results confirm that the proposed approach attains 98% accuracy, reduces the computational cost, and outperforms the existing techniques of violence detection and recognition.     

基于深度学习的情感化设计

目的使用深度学习实现情感化设计,满足用户个性化的情感需求,加速传统设计过程,提升用户体验。方法研究深度学习中可用于情感化设计的算法,使用卷积神经网络(CNN)实现名画复制品的个性化自动生成;抓取互联网数据,使用LSTM模型挖掘用户真实需求,进行当前流行游戏的周边产品设计;以张裕葡萄酒庄旅游纪念品设计为例,使用深度学习基于用户个人信息和行为数据推荐个性化的葡萄酒包装。结论基于CNN的名画复制品的个性化生成丰富了图像的可修改空间,满足了用户个性化的情感诉求;基于LSTM的用户需求分析高效和准确地反映了用户的真实需求,加速了传统用户调研过程;基于深度学习的旅游纪念品个性化设计进一步提升了用户体验。将深度学习应用于情感化设计有利于挖掘用户内心的真实需求,节省人力物力,满足用户情感诉求和提升用户体验,进一步为设计学与计算机科学的交叉提供了有效方法。     

基于深度神经网络的货架商品识别方法

目的为快速统计货架商品信息,提出一种基于深度神经网络的货架商品自动识别方法。方法摄像头采集的货架商品图像经过深度神经网络算法处理,得到了图像中商品的SKU和位置。针对货架商品识别这种密集检测场景,文中方法改进了通用深度神经网络目标检测算法,将算法分为检测和分类2个阶段且重新设计了部分网络结构。最后,将文中方法和传统货架商品识别方法以及通用深度神经网络目标检测方法进行了比较。结果实验证明该方法的检测阶段的模型平均正确率达到96.5%,分类阶段的分类准确率达到99.9%。整图测试的查准率为97.56%,查全率为99.26%。结论相较于以往使用传统的目标检测模型进行货架商品识别以及使用SIFT等人工算子提取特征并分类识别商品具体SKU,文中方法的商品检出率和分类准确率都有了大幅度的提升,具有很好的应用潜力。     

An Optimal Deep Learning Based Computer-Aided Diagnosis System for Diabetic Retinopathy

Diabetic Retinopathy (DR) is a significant blinding disease that poses serious threat to human vision rapidly. Classification and severity grading of DR are difficult processes to accomplish. Traditionally, it depends on ophthalmoscopically-visible symptoms of growing severity, which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity. This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization (OPSO) algorithm-based Convolutional Neural Network (CNN) Model EOPSO-CNN in order to perform DR detection and grading. The proposed EOPSO-CNN model involves three main processes such as preprocessing, feature extraction, and classification. The proposed model initially involves preprocessing stage which removes the presence of noise in the input image. Then, the watershed algorithm is applied to segment the preprocessed images. Followed by, feature extraction takes place by leveraging EOPSO-CNN model. Finally, the extracted feature vectors are provided to a Decision Tree (DT) classifier to classify the DR images. The study experiments were carried out using Messidor DR Dataset and the results showed an extraordinary performance by the proposed method over compared methods in a considerable way. The simulation outcome offered the maximum classification with accuracy, sensitivity, and specificity values being 98.47%, 96.43%, and 99.02% respectively.     

Research on Privacy Disclosure Detection Method in Social Networks Based on Multi-Dimensional Deep Learning

In order to effectively detect the privacy that may be leaked through socialnetworks and avoid unnecessary harm to users, this paper takes microblog as the researchobject to study the detection of privacy disclosure in social networks. First, we performfast privacy leak detection on the currently published text based on the fastText model. Inthe case that the text to be published contains certain private information, we fullyconsider the aggregation effect of the private information leaked by different channels,and establish a convolution neural network model based on multi-dimensional features(MF-CNN) to detect privacy disclosure comprehensively and accurately. Theexperimental results show that the proposed method has a higher accuracy of privacydisclosure detection and can meet the real-time requirements of detection.