基于小样本学习的SAR图像识别

深度学习已成为图像识别领域的一个研究热点。与传统图像识别方法不同,深度学习从大量数据中自动学习特征,并且具有强大的自学习能力和高效的特征表达能力。但在小样本条件下,传统的深度学习方法如卷积神经网络难以学习到有效的特征,造成图像识别的准确率较低。因此,提出一种新的小样本条件下的图像识别算法用于解决SAR图像的分类识别。该算法以卷积神经网络为基础,结合自编码器,形成深度卷积自编码网络结构。首先对图像进行预处理,使用2D Gabor滤波增强图像,在此基础上对模型进行训练,最后构建图像分类模型。该算法设计的网络结构能自动学习并提取小样本图像中的有效特征,进而提高识别准确率。在MSTAR数据集的10类目标分类中,选择训练集数据中10%的样本作为新的训练数据,其余数据为验证数据,并且,测试数据在卷积神经网络中的识别准确率为76.38%,而在提出的卷积自编码结构中的识别准确率达到了88.09%。实验结果表明,提出的算法在小样本图像识别中比卷积神经网络模型更加有效。     

基于条件生成对抗网络的侧信道攻击技术研究

近年来,深度学习技术广泛应用于侧信道攻击(side channel attack,SCA)领域.针对在基于深度学习的侧信道攻击中训练集数量不足的问题,提出了一种用于侧信道攻击的功耗轨迹扩充技术,使用条件生成对抗网络(conditional generate against network,CGAN)实现对原始功耗轨迹的...     

晶圆表面缺陷模式识别的二维主成分分析卷积自编码器

由于半导体制造过程的高度复杂性和动态性,各种过程故障通常导致晶圆表面出现各种缺陷模式.为了有效地识别晶圆表面缺陷模式从而及时地诊断和控制故障源,提出一种深度神经网络模型--二维主成分分析卷积自编码器(two-dimensional principal component analysis-based convolutional autoencoder, PCACAE).首先,提出一种基于改进的二维主成分分析算法(conditional2DPCA,C2DPCA)的图像卷积核,形成PCACAE的第1个卷积层;其次,对卷积输出进行池化操作并卷积编码重构,构建一个卷积编码器,并提取其编码部分作为PCACAE的第2层卷积层的初始化权值,从而形成一个深度网络模型,实现晶圆图像的特征学习;最后, PCACAE网络进行训练微调得到最终网络模型.将PCACAE应用于WM-811K晶圆图像数据库并与其他算法进行对比测试,实验结果表明, PCACAE在晶圆表面缺陷识别上的性能优于其他经典的卷积神经网络模型(如GoogLeNet,DensNet等),从而验证了该方法的有效性与工业可应用性.     

Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders (E-HAE)

Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence. We collectively refer to the convergence of different technology sectors as the internet of blended environment. The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder. An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02% detection accuracy. Furthermore, performance of the proposed approach was compared with various single model (autoencoder)-based network intrusion detection approaches: autoencoder, variational autoencoder, convolutional variational autoencoder, and long short-term memory variational autoencoder. The proposed model outperformed all compared models, demonstrating F1-score improvements of 4.99%, 2.25%, 1.92%, and 3.69%, respectively.     

基于DCNN-GRU模型的XSS攻击检测方法

为了提高跨站脚本攻击的检测效率,利用一维DCNN快速处理时序问题的能力和GRU模型处理上下文具有长期依赖关系问题的能力,提出基于DCNN-GRU模型的XSS攻击检测方法.对原始数据做规范化处理,将数据转化为可以对深度学习网络模型进行输入的特征向量.通过卷积层和池化层处理特征向量,GRU层作为门控机制来保留代码间的依赖关...     

Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish malignant skin lesions from benign lesions in dermoscopic images. This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoencoder (OSSAE) based feature extractor with backpropagation neural network (BPNN), named the OSSAE-BPNN technique. The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region. In addition, the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis. Moreover, the parameter tuning of the SSAE model is carried out by the use of sea gull optimization (SGO) algorithm. To showcase the enhanced outcomes of the OSSAE-BPNN model, a comprehensive experimental analysis is performed on the benchmark dataset. The experimental findings demonstrated that the OSSAE-BPNN approach outperformed other current strategies in terms of several assessment metrics.     

基于密集连接卷积神经网络的远程监督关系抽取

密集连接卷积神经网络(DenseNet)是一种新型深度卷积神经网络架构,通过建立不同层间的连接关系,来确保网络层与层间最大程度的信息传输。在文本远程监督关系抽取任务中,针对现有神经网络方法使用浅层网络提取特征的局限,设计了一种基于密集连接方式的深度卷积神经网络模型。该模型采用五层卷积神经网络构成的密集连接模块和最大池化层作为句子编码器,通过合并不同层次的词法、句法和语义特征,来帮助网络学习特征,从而获取输入语句更丰富的语义信息,同时减轻深度神经网络的梯度消失现象,使得网络对自然语言的表征能力更强。模型在NYT-Freebase数据集上的平均准确率达到了82.5%,PR曲线面积达到了0.43。实验结果表明,该模型能够有效利用特征,并提高远程监督关系抽取的准确率。     

Effective Latent Representation for Prediction of Remaining Useful Life

AI approaches have been introduced to predict the remaining useful life (RUL) of a machine in modern industrial areas. To apply them well, challenges regarding the high dimension of the data space and noisy data should be met to improve model efficiency and accuracy. In this study, we propose an end-to-end model, termed ACB, for RUL predictions; it combines an autoencoder, convolutional neural network (CNN), and bidirectional long short-term memory. A new penalized root mean square error loss function is included to avoid an overestimation of the RUL. With the CNN-based autoencoder, a high-dimensional data space can be mapped into a lower-dimensional latent space, and the noisy data can be greatly reduced. We compared ACB with five state-of-the-art models on the Commercial Modular Aero-Propulsion System Simulation dataset. Our model achieved the lowest score value on all four sub-datasets. The robustness of our model to noise is also supported by the experiments.     

Intelligent fault recognition framework by using deep reinforcement learning with one dimension convolution and improved actor-critic algorithm

The quality of fault recognition part is one of the key factors affecting the efficiency of intelligent manufacturing. Many excellent achievements in deep learning (DL) have been realized recently as methods of fault recognition. However, DL models have inherent shortcomings. In particular, the phenomenon of over-fitting or degradation suggests that such an intelligent algorithm cannot fully use its feature perception ability. Researchers have mainly adapted the network architecture for fault diagnosis, but the above limitations are not taken into account. In this study, we propose a novel deep reinforcement learning method that combines the perception of DL with the decision-making ability of reinforcement learning. This method enhances the classification accuracy of the DL module to autonomously learn much more knowledge hidden in raw data. The proposed method based on the convolutional neural network (CNN) also adopts an improved actor-critic algorithm for fault recognition. The important parts in standard actor-critic algorithm, such as environment, neural network, reward, and loss functions, have been fully considered in improved actor-critic algorithm. Additionally, to fully distinguish compound faults under heavy background noise, multi-channel signals are first stacked synchronously and then input into the model in the end-to-end training mode. The diagnostic results on the compound fault of the bearing and tool in the machine tool experimental system show that compared with other methods, the proposed network structure has more accurate results. These findings demonstrate that under the guidance of the improved actor-critic algorithm and processing method for multi-channel data, the proposed method thus has stronger exploration performance.     

基于深度自编码器网络的压盖缺陷检测

口服液压盖过程,会出现压盖不良等情况,瓶盖可能会出现划痕、刮花、表面卷曲、压盖破损等缺陷,为保证食品药品安全必须在出厂前进行检测.在基于深度学习的口服液瓶压盖缺陷检测的研究过程中,使用传统卷积神经网络对口服液压盖缺陷数据集进行训练,需要进行人工标注,效率较低.为有效解决上述问题,设计出一种无监督学习的深度卷积去噪自编码...     

基于轻量型卷积神经网络的交通标志识别方法

交通标志识别设备的功耗和硬件性能较低,而现有卷积神经网络模型内存占用高、训练速度慢、计算开销大,无法应用于识别设备.针对此问题,为降低模型存储,提升训练速度,引入深度可分离卷积和混洗分组卷积并与极限学习机相结合,提出两种轻量型卷积神经网络模型:DSC-ELM模型和SGC-ELM模型.模型使用轻量化卷积神经网络提取特征后,将特征送入极限学习机进行分类,解决了卷积神经网络全连接层参数训练慢的问题.新模型结合了轻量型卷积神经网络模型内存占用低、提取特征质量好以及ELM的泛化性好、训练速度快的优点.实验结果表明.与其他模型相比,该混合模型能够更加快速准确地完成交通标志识别任务.     

非对称卷积编码器的聚类算法

基于深度神经网络的非监督学习方法通过联合优化特征表示和聚类指派,大大提升了聚类任务的性能。但大量的参数降低了运行速度,另外,深度模型提取的特征的区分能力也影响聚类性能。为此,提出一种新的聚类算法(asymmetric fully-connected layers convolutional auto-encoder, AFCAE),其中卷积编码器结合非对称全连接进行无监督的特征提取,然后K-means算法对所得特征执行聚类。网络采用3×3和2×2的小卷积核,大大减少了参数个数,降低了算法复杂性。在MNIST上AFCAE获得0.960的聚类精度,比联合训练的DEC(deep embedding clustering)方法(0.840)提高了12个百分点。在6个图像数据集上实验结果表明AFCAE网络有优异的特征表示能力,能出色完成下游的聚类任务。     

Generating Adversarial Samples on Multivariate Time Series using Variational Autoencoders

Classification models for multivariate time series have drawn the interest of many researchers to the field with the objective of developing accurate and efficient models. However, limited research has been conducted on generating adversarial samples for multivariate time series classification models. Adversarial samples could become a security concern in systems with complex sets of sensors. This study proposes extending the existing gradient adversarial transformation network (GATN) in combination with adversarial autoencoders to attack multivariate time series classification models. The proposed model attacks classification models by utilizing a distilled model to imitate the output of the multivariate time series classification model. In addition, the adversarial generator function is replaced with a variational autoencoder to enhance the adversarial samples. The developed methodology is tested on two multivariate time series classification models: 1-nearest neighbor dynamic time warping (1-NN DTW) and a fully convolutional network (FCN). This study utilizes 30 multivariate time series benchmarks provided by the University of East Anglia (UEA) and University of California Riverside (UCR). The use of adversarial autoencoders shows an increase in the fraction of successful adversaries generated on multivariate time series. To the best of our knowledge, this is the first study to explore adversarial attacks on multivariate time series. Additionally, we recommend future research utilizing the generated latent space from the variational autoencoders.      

深度学习在化学流程工业故障诊断的研究进展

化学流程工业故障诊断（chemical process industry fault diagnosis,CPIFD）是智能制造的一个重要分支。近年来,深度学习在特征识别和分类方面显示出独特的优势和潜力,因此,基于深度学习的CPIFD研究受到了学者们的广泛关注。然而,在已发表的研究文献中,关于基于深度学习的CPIFD的论述是有限的,因此,旨在为CPIFD的研究提供最新的参考,并激励学者进一步探讨深度学习在CPIFD中的应用。介绍了CPIFD技术的发展,阐述了在深度学习中具有代表性模型的基本理论,并综述了它们在CPIFD中的应用,这些模型包括卷积神经网络、深度置信网络、堆叠自动编码器、长短期记忆网络和其他新兴神经网络模型;讨论了深度学习在CPIFD中所面临的问题,并对今后值得研究的方向提出了展望。     

注意力机制和自编码器构造的零水印算法

零水印技术为保护图像版权的有效手段之一.然而,现有的许多零水印算法大多采用传统的数学理论进行人工提取特征,在结合神经网络进行图片特征提取的零水印方向上并没有广泛研究.目前神经网络在图像特征提取上已经取得了很好的成绩,充分利用卷积自编码器和注意力机制,提出了一种用于构造零水印的深度注意自编码器模型(attention mechanism and autoencoder, AMAE).首先是利用带有注意力的卷积神经网络构建自编码器,然后对自编码器进行训练;其次,利用训练好的编码器输出的特征构造图像的整体特征;最后,将获得的特征图进行二值模式处理得到特征二值矩阵,再与水印图像异或运算得到零水印,并在知识产权信息数据库进行注册,零水印一旦注册,原图像便处于水印技术的保护下.在训练过程中,借鉴对抗训练的思想,对模型进行加噪训练,这提高了模型的鲁棒性.实验结果表明,本文的零水印算法在旋转、噪声和滤波等攻击下,提取水印图像与原水印图像的归一化系数(normalized correlation, NC)值均超过0.9,证明了提出算法的有效性和优越性.     

基于轻量自动残差缩放网络的图像超分辨率重建

近年来,由于深度卷积神经网络的出色性能,深度学习已成为图像超分辨率领域的研究热点,已经有许多具有很深结构的大型模型被提出。而在实际应用中,普通个人计算机或智能终端的硬件显然不适合大规模深度神经网络模型。提出了一种针对单幅图像超分辨率且具有自动残差缩放功能的轻量级网络(ARSN),与许多基于深度学习的方法相比,它的层和参数更少。此外,该网络中有特殊的残差块和跳跃连接用来进行残差缩放以及全局和局部残差学习。根据测试数据集结果,该网络在重建质量和运行速度上都达到了非常优异的性能。所提出的网络在性能、速度和硬件消耗方面均取得了良好的效果,具有较高的实用价值。     

基于深度学习LSTM的侧信道分析

加密数据的安全性受到加密算法和加密设备的影响,为评估密码硬件的可靠性,能量分析等多种针对加密平台的攻击方法得到广泛应用。深度学习是一种性能良好的数据分析方法,基于深度学习的功耗侧信道攻击方法一经提出便引起关注。提出一种基于深度学习LSTM的侧信道攻击方法,利用相关功耗分析方法确定侧信道功耗数据的兴趣点,通过兴趣点位置选择合适的兴趣区间作为特征向量以搭建神经网络模型。实验结果表明,相比MLP和CNN模型,LSTM网络模型在侧信道攻击中具有较高的攻击效率。     

基于小样本学习和因果干预的ResNeXt对抗攻击

随着深度学习相关技术在计算机视觉、自然语言处理等领域的快速发展和广泛应用,深度学习模型逐渐成为了高价值攻击目标,其固有的易受噪声干扰的安全隐患也逐步暴露出来,如基于生成对抗网络(GAN)或机器学习的方式,通过添加少量特定的噪声来生成对抗样本,导致现有的深度学习模型失效.目前的对抗攻击技术一般针对特定深度学习模型,使用海...     

基于多通道图卷积自编码器的图表示学习

针对基于图卷积的自编码器模型对原始图属性和拓扑信息的保留能力有限、无法学习结构和属性之间深度关联信息等问题,提出基于多通道图卷积自编码器的图表示学习模型。设计拓扑和属性信息保留能力实验,验证了基于图卷积的自编码器模型具备保留节点属性和拓扑结构信息的能力。构建特定信息卷积编码器和一致信息卷积编码器,提取图的属性空间特征、拓扑空间特征以及两者关联特征,生成属性嵌入、拓扑嵌入和一致性嵌入,同时建立与编码器对称的卷积解码器,还原编码器过程。使用重构损失、局部约束和一致性约束,优化各编码器生成的低维嵌入表示。最终将蕴含不同图信息的多种嵌入进行融合,生成各节点的嵌入表示。实验结果表明,该模型在BlogCatalog和Flickr数据集上节点分类的Micro-F1和Macro-F1明显高于基线模型,在Citeseer数据集上节点聚类的精度和归一化互信息相比于表现最优的基线模型提升了11.84%和34.03%。上述实验结果证明了该模型采用的多通道方式能够在低维嵌入中保留更丰富的图信息,提升图机器学习任务的性能表现。     

A spatio-temporal decomposition based deep neural network for time series forecasting

Spatio-temporal problems arise in a broad range of applications, such as climate science and transportation systems. These problems are challenging because of unique spatial, short-term and long-term patterns, as well as the curse of dimensionality. In this paper, we propose a deep learning framework for spatio-temporal forecasting problems. We explicitly design the neural network architecture for capturing various types of spatial and temporal patterns, and the model is robust to missing data. In a preprocessing step, a time series decomposition method is applied to separately feed short-term, long-term and spatial patterns into different components of the neural network. A fuzzy clustering method finds clusters of neighboring time series residuals, as these contain short-term spatial patterns. The first component of the neural network consists of multi-kernel convolutional layers which are designed to extract short-term features from clusters of time series data. Each convolutional kernel receives a single cluster of input time series. The output of convolutional layers is concatenated by trends and followed by convolutional-LSTM layers to capture long-term spatial patterns. To have a robust forecasting model when faced with missing data, a pretrained denoising autoencoder reconstructs the model’s output in a fine-tuning step. In experimental results, we evaluate the performance of the proposed model for the traffic flow prediction. The results show that the proposed model outperforms baseline and state-of-the-art neural network models.