An end-to-end fault diagnostics method based on convolutional neural network for rotating machinery with multiple case studies

Journal of Intelligent Manufacturing - The fault diagnostics of rotating components are crucial for most mechanical systems since the rotating components faults are the main form of failures of...     

An end-to-end convolutional network for joint detecting and denoising adversarial perturbations in vehicle classification

Computational Visual Media - Deep convolutional neural networks (DCNNs) have been widely deployed in real-world scenarios. However, DCNNs are easily tricked by adversarial examples, which present...     

混合神经网络模型与注意力机制的地址匹配算法

中文地名地址的标准化在当前智慧城市的建设中起到至关重要的作用。传统的地名地址标准化技术通常使用基于文本字符层面的相似度计算或规则库匹配的方法,对复杂、特殊或冗余地址的处理效果较差。通过将地址标准化任务转换为针对地址相似的匹配度计算任务,提出了一种融合注意力机制与多层次语义表征的地址匹配算法。首先依据地址文本特殊的语法结构,利用Trie语法树构建标准地址树;而后基于注意力机制,利用Bi-LSTM网络与CNN网络生成地址对的多层次语义表示;最后通过曼哈顿距离计算相似度。在自主构建的数据集上,提出的SGAM模型的匹配准确度（91.22%）相比TextRCNN、FastText、基于注意力的卷积神经网络（ABCNN）等模型提升了4%~10%,表明SGAM模型在地址匹配任务上有着更好的性能表现。     

基于注意力神经网络的糖尿病视网膜病变识别

针对目前糖尿病视网膜病变识别主要依赖于医生的临床经验,病变特征难以用肉眼区分且识别率较低等问题,提出一种基于注意力神经网络的糖尿病视网膜病变分类方法。首先,对原始数据集中的视网膜图像进行归一化、直方图均衡化和数据增强等预处理;其次,调整经典的DenseNet,在避免梯度消失和保证分类精度的前提下,有针对性地减少连接数,提出了2-DenseNet,同时将注意力模块嵌入到2-DenseNet中,指导网络关注视网膜图像中的渗出物、厚血管和微动脉瘤等特征,使用改进后的网络对预处理后的图像进行训练并测试;最后,在公开的Kaggle数据集上对多个网络进行对比,实验结果表明,该网络对糖尿病视网膜病变的分类性能高于其他对比网络。     

An incremental neural network for tissue segmentation in ultrasound images

This paper presents an incremental neural network (INeN) for the segmentation of tissues in ultrasound images. The performances of the INeN and the Kohonen network are investigated for ultrasound image segmentation. The elements of the feature vectors are individually formed by using discrete Fourier transform (DFT) and discrete cosine transform (DCT). The training set formed from blocks of 4x4 pixels (regions of interest, ROIs) on five different tissues designated by an expert is used for the training of the Kohonen network. The training set of the INeN is formed from randomly selected ROIs of 4x4 pixels in the image. Performances of both 2D-DFT and 2D-DCT are comparatively examined for the segmentation of ultrasound images.     

基于迁移学习的分层注意力网络情感分析算法

文本情感分析的目的是判断文本的情感类型。传统的基于神经网络的研究方法主要依赖于无监督训练的词向量,但这些词向量无法准确体现上下文语境关系;常用于处理情感分析问题的循环神经网络（RNN）,模型参数众多,训练难度较大。为解决上述问题,提出了基于迁移学习的分层注意力神经网络（TLHANN）的情感分析算法。首先利用机器翻译任务训练一个用于在上下文中理解词语的编码器;然后,将这个编码器迁移到情感分析任务中,并将编码器输出的隐藏向量与无监督训练的词向量结合。在情感分析任务中,使用双层神经网络,每层均采用简化的循环神经网络结构——最小门单元（MGU）,有效减少了参数个数,并引入了注意力机制提取重要信息。实验结果证明,所提算法的分类准确率与传统循环神经网络算法、支持向量机（SVM）算法相比分别平均提升了8.7%及23.4%。     

基于卷积神经网络的视频流隐藏信息检测方法

为解决现有视频流隐藏信息检测中,人工检测特征设计难度不断加大的问题,提出一种基于卷积神经网络的视频流隐藏信息检测方法。在神经网络中构建残差学习单元,避免深层次卷积神经网络在训练时的梯度消失,利用深层神经网络自动从数据中挖掘检测特征,在此基础上引入量化截断操作,增加检测模型多样性,提升检测性能。使用FFmpeg与x264编码标准CIF序列生成的视频进行实验,实验结果表明,该方法相比现有方法具有更高的检测准确率。     

基于像素注意力的双通道立体匹配网络

针对现有立体匹配算法在弱纹理、重复纹理、反射表面等病态区域误匹配率高的问题,提出一种基于像素注意力的双通道立体匹配卷积神经网络PASNet,该网络包括双通道注意力沙漏型子网络和注意力U型子网络。首先,通过双通道注意力沙漏型子网络提取输入图像的特征图;其次,通过关联层得到特征图的代价矩阵;最后,利用注意力U型子网络对代价矩阵进行代价聚合,输出视差图。在KITTI数据集上的实验结果表明,所提出的网络能有效解决病态区域误匹配率高等问题,提升立体匹配精度。     

基于直觉模糊ART神经网络的群事件检测方法

描述了态势评估系统中的目标编群问题、目标群处理流程和群事件的检测。结合直觉模糊贴近度理论,构造了直觉模糊ART神经网络。设计了网络的运行机制和网络权值向量的学习机制。给出了一个具体实例,检验了直觉模糊ART神经网络的目标编群效果,为群事件检测提供了一条有效途径。     

基于DHT发现端到端多条覆盖网路径的方法

为进一步提高端到端数据传输的吞吐率,提出基于DHT发现多条覆盖网路径的方法.一条覆盖网路径由若干跳构成,而数据吞吐率依赖各跳传输性能的瓶颈.为消除瓶颈,根据数据到达结点的吞吐率选择往返延迟时间较小的若干下一跳结点,使得数据不会在该结点拥塞.结点DHT维护着到各下一跳结点的往返延迟时间,基于DHT可发现端-端多条覆盖路径,从而实现并行数据传输.实验结果表明,该方法可找到适合的多条端到端路径,并行传输可取得比单路径传榆更大的吞吐率.     

基于多图卷积神经网络和注意力机制的学术新星预测方法

从学术新人中发掘出有潜力的学术新星能够为人才引进、项目评审和专家库构建等任务提供决策支持,具有重要的研究意义与应用价值,因此受到学术界的广泛关注。然而现有的学术新星预测方法并没有将学者的合作关系和个体属性信息进行有机结合,导致准确率低下。为解决上述问题,提出了一种基于多图卷积神经网络与注意力机制的学术新星预测方法MGCNA。综合考虑了合作网络与相似网络,基于2种网络使用图卷积神经网络学习作者的特征表示,再利用注意力机制进行信息融合,从而预测潜力较高的学术新星。最后在来自ArnetMiner平台的真实数据集上进行了实验,实验结果表明了MGCNA在预测学术新星任务上的有效性。     

基于残差时域注意力神经网络的交通模式识别算法

交通模式识别是用户行为识别中的一个重要分支,其目的是对用户所处的交通模式进行准确判断.针对现代智慧城市交通系统对在移动设备环境下精准感知用户交通模式的需求,提出了一种基于残差时域注意力神经网络的交通模式识别算法.首先,通过具有较强局部特征提取能力的残差网络提取传感器时序中的局部特征;然后,采用基于通道的注意力机制对不同...     

基于局部信息的复杂网络社团结构发现算法

发现网络中的社团结构有助于更好地理解网络结构和分析网络属性。通过定义边的聚类系数和基于局部信息的方法,提出了一种寻找复杂网络中社团结构的算法。该算法首先在网络的剩余节点中寻找度最大的节点,然后利用该节点的局部信息、边的聚类系数和凝聚的思想,得到复杂网络的社团结构。在两个典型网络上的测试结果表明了该方法的可行性。     

基于细胞神经网络的生物芯片图像实时处理

运用CNN设计了一套生物芯片样点识别算法。算法实现的目标:改善已有方法的缺陷,达到良好的图像质量增强效果;将CNN输出的模拟信号图像转化为样点数据信息,使得后续的信息分析成为可能。最后利用实际CNN芯片参数估算了整套算法的运算时间,结果显示其速度达到实时处理的标准。     

An end-to-end network for irregular printed Mongolian recognition

International Journal on Document Analysis and Recognition (IJDAR) - Mongolian is a language spoken in Inner Mongolia, China. In the recognition process, due to the shooting angle and other...     

Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images

The performance of supervised classification algorithms is highly dependent on the quality of training data. Ambiguous training patterns may misguide the classifier leading to poor classification performance. Further, the manual exploration of class labels is an expensive and time consuming process. An automatic method is needed to identify noisy samples in the training data to improve the decision making process. This article presents a new classification technique by combining an unsupervised learning technique (i.e. fuzzy c-means clustering (FCM)) and supervised learning technique (i.e. back-propagation artificial neural network (BPANN)) to categorize benign and malignant tumors in breast ultrasound images. Unsupervised learning is employed to identify ambiguous examples in the training data. Experiments were conducted on 178 B-mode breast ultrasound images containing 88 benign and 90 malignant cases on MATLAB® software platform. A total of 457 features were extracted from ultrasound images followed by feature selection to determine the most significant features. Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and Mathew's correlation coefficient (MCC) were used to access the performance of different classifiers. The result shows that the proposed approach achieves classification accuracy of 95.862% when all the 457 features were used for classification. However, the accuracy is reduced to 94.138% when only 19 most relevant features selected by multi-criterion feature selection approach were used for classification. The results were discussed in light of some recently reported studies. The empirical results suggest that eliminating doubtful training examples can improve the decision making performance of expert systems. The proposed approach show promising results and need further evaluation in other applications of expert and intelligent systems.     

Traffic identification method based on multiple probabilistic neural network model

Neural Computing and Applications - Traffic identification is currently an important challenge for network management and security. In this paper, we propose a novel application identification...     

Deep convolutional neural network based disease identification in grapevine leaf images

Grapevine (Vitis vinifera L.) is a major fruit crop with commercial importance worldwide. Black rot, Black measles, and Leaf blight are three diseases commonly found in the grapevine. The timely and accurate diagnosis is crucial in preventing the spread of the disease and reducing loss in production. The advancement in deep learning has opened doors for new diagnostic algorithms in the domain of plant disease identification. In this paper, we propose a grapevine disease identification method using a convolutional neural network (CNN). A light weight 6-layer CNN model was designed from scratch and trained using an open repository with 3 disease classes and 1 healthy leaf image dataset. The dataset contained a total of 3423 grapevine leaf images. The model was trained with a 70–30 train-test ratio. Image augmentation and early stopping techniques were used to avoid overfitting of the model. The proposed model achieved 98.4% classification accuracy on the test dataset. Additionally, the key feature of the proposed 6-layer model is that it has lesser number of trainable parameters which reduces its computational complexity as compared to the existing pre-trained models.

     

Face tracking and recognition in video moving images based on convolutional neural network

Multimedia Tools and Applications - In order to accurately track and recognize faces in video moving images, a method for tracking and recognizing faces in video moving images based on...