Feature identification in complex fluid flows by convolutional neural networks

Recent advancements have established machine learning's utility in predicting nonlinear fluid dynamics,with predictive accuracy being a central motivation for employing neural networks.However,the pattern recognition central to the networks function is equally valuable for enhancing our dynamical insight into the complex fluid dynamics.In this paper,a single-layer convolutional neural network(CNN)was trained to recognize three qualitatively different subsonic buffet flows(periodic,quasi-periodic and chaotic)over a high-incidence airfoil,and a near-perfect accuracy was obtained with only a small training dataset.The convolutional kernels and corresponding feature maps,developed by the model with no temporal information provided,identified large-scale coherent structures in agreement with those known to be associated with buffet flows.Sensitivity to hyperparameters including network architecture and convolutional kernel size was also explored.The coherent structures identified by these models enhance our dynamical understanding of subsonic buffet over high-incidence airfoils over a wide range of Reynolds numbers.

Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing

A tremendous amount of vendor invoices is generated in the corporate sector.To automate the manual data entry in payable documents,highly accurate Optical Character Recognition(OCR)is required.This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement.For text localization,the maximally stable extremal region is used,which extracts a word or digit chunk from an invoice.This chunk is later passed to the deep learning model,which performs text recognition.The deep learning model utilizes both convolution neural networks and long short-term memory(LSTM).The convolution layer is used for extracting features,which are fed to the LSTM.The model integrates feature extraction,modeling sequence,and transcription into a unified network.It handles the sequences of unconstrained lengths,independent of the character segmentation or horizontal scale normalization.Furthermore,it applies to both the lexicon-free and lexicon-based text recognition,and finally,it produces a comparatively smaller model,which can be implemented in practical applications.The overall superior performance in the experimental evaluation demonstrates the usefulness of the proposed model.The model is thus generic and can be used for other similar recognition scenarios.

Fusion of Spiral Convolution-LSTM for Intrusion Detection Modeling

Aiming at the problems of low accuracy and slow convergence speed of current intrusion detection models,SpiralConvolution is combined with Long Short-Term Memory Network to construct a new intrusion detection model.The dataset is first preprocessed using solo thermal encoding and normalization functions.Then the spiral convolution-Long Short-Term Memory Network model is constructed,which consists of spiral convolution,a two-layer long short-term memory network,and a classifier.It is shown through experiments that the model is characterized by high accuracy,small model computation,and fast convergence speed relative to previous deep learning models.The model uses a new neural network to achieve fast and accurate network traffic intrusion detection.The model in this paper achieves 0.9706 and 0.8432 accuracy rates on the NSL-KDD dataset and the UNSWNB-15 dataset under five classifications and ten classes,respectively.

Classification of Arrhythmia Based on Convolutional Neural Networks and Encoder-Decoder Model

As a common and high-risk type of disease,heart disease seriously threatens people’s health.At the same time,in the era of the Internet of Thing(IoT),smart medical device has strong practical significance for medical workers and patients because of its ability to assist in the diagnosis of diseases.Therefore,the research of real-time diagnosis and classification algorithms for arrhythmia can help to improve the diagnostic efficiency of diseases.In this paper,we design an automatic arrhythmia classification algorithm model based on Convolutional Neural Network(CNN)and Encoder-Decoder model.The model uses Long Short-Term Memory(LSTM)to consider the influence of time series features on classification results.Simultaneously,it is trained and tested by the MIT-BIH arrhythmia database.Besides,Generative Adversarial Networks(GAN)is adopted as a method of data equalization for solving data imbalance problem.The simulation results show that for the inter-patient arrhythmia classification,the hybrid model combining CNN and Encoder-Decoder model has the best classification accuracy,of which the accuracy can reach 94.05%.Especially,it has a better advantage for the classification effect of supraventricular ectopic beats(class S)and fusion beats(class F).

基于Convolutional-LSTM的蛋白质亚细胞定位研究

蛋白质亚细胞位置预测研究是目前蛋白质组学和生物信息学研究的重点问题之一。蛋白质的亚细胞定位决定了它的生物学功能,故研究亚细胞定位对了解蛋白质功能非常重要。由于蛋白质结构的序列性,考虑使用序列模型来进行亚细胞定位研究。尝试使用卷积神经网络(convolutional neural network,CNN)、长短期记忆神经网络(long short-term memory,LSTM)两种模型挖掘氨基酸序列所包含的信息,从而进行亚细胞定位的预测。随后构建了基于卷积的长短期记忆网络(Convolutional-LSTM)的集成模型进行亚细胞定位。首先通过卷积神经网络对蛋白质数据进行特征抽取,随后进行特征组合,并将其送入长短期记忆神经网络进行特征表征学习,得到亚细胞定位结果。使用该模型能达到0.816 5的分类准确率,比传统方法有明显提升。

Leucogranite mapping via convolutional recurrent neural networks and geochemical survey data in the Himalayan orogen

Geochemical survey data analysis is recognized as an implemented and feasible way for lithological mapping to assist mineral exploration.With respect to available approaches,recent methodological advances have focused on deep learning algorithms which provide access to learn and extract information directly from geochemical survey data through multi-level networks and outputting end-to-end classification.Accordingly,this study developed a lithological mapping framework with the joint application of a convolutional neural network(CNN)and a long short-term memory(LSTM).The CNN-LSTM model is dominant in correlation extraction from CNN layers and coupling interaction learning from LSTM layers.This hybrid approach was demonstrated by mapping leucogranites in the Himalayan orogen based on stream sediment geochemical survey data,where the targeted leucogranite was expected to be potential resources of rare metals such as Li,Be,and W mineralization.Three comparative case studies were carried out from both visual and quantitative perspectives to illustrate the superiority of the proposed model.A guided spatial distribution map of leucogranites in the Himalayan orogen,divided into high-,moderate-,and low-potential areas,was delineated by the success rate curve,which further improves the efficiency for identifying unmapped leucogranites through geological mapping.In light of these results,this study provides an alternative solution for lithologic mapping using geochemical survey data at a regional scale and reduces the risk for decision making associated with mineral exploration.

Convolutional neural network adaptation and optimization method in SIMT computing mode

For studying and optimizing the performance of general-purpose computing on graphics processing units(GPGPU)based on single instruction multiple threads(SIMT)processor about the neural network application,this work contributes a self-developed SIMT processor named Pomelo and correlated assembly program.The parallel mechanism of SIMT computing mode and self-developed Pomelo processor is briefly introduced.A common convolutional neural network(CNN)is built to verify the compatibility and functionality of the Pomelo processor.CNN computing flow with task level and hardware level optimization is adopted on the Pomelo processor.A specific algorithm for organizing a Z-shaped memory structure is developed,which addresses reducing memory access in mass data computing tasks.Performing the above-combined adaptation and optimization strategy,the experimental result demonstrates that reducing memory access in SIMT computing mode plays a crucial role in improving performance.A 6.52 times performance is achieved on the 4 processing elements case.

Dynamic Resource Allocation in LTE Radio Access Network Using Machine Learning Techniques

Current LTE networks are experiencing significant growth in the number of users worldwide. The use of data services for online browsing, e-learning, online meetings and initiatives such as smart cities means that subscribers stay connected for long periods, thereby saturating a number of signalling resources. One of such resources is the Radio Resource Connected (RRC) parameter, which is allocated to eNodeBs with the aim of limiting the number of connected simultaneously in the network. The fixed allocation of this parameter means that, depending on the traffic at different times of the day and the geographical position, some eNodeBs are saturated with RRC resources (overused) while others have unused RRC resources. However, as these resources are limited, there is the problem of their underutilization (non-optimal utilization of resources at the eNodeB level) due to static allocation (manual configuration of resources). The objective of this paper is to design an efficient machine learning model that will take as input some key performance indices (KPIs) like traffic data, RRC, simultaneous users, etc., for each eNodeB per hour and per day and accurately predict the number of needed RRC resources that will be dynamically allocated to them in order to avoid traffic and financial losses to the mobile network operator. To reach this target, three machine learning algorithms have been studied namely: linear regression, convolutional neural networks and long short-term memory (LSTM) to train three models and evaluate them. The model trained with the LSTM algorithm gave the best performance with 97% accuracy and was therefore implemented in the proposed solution for RRC resource allocation. An interconnection architecture is also proposed to embed the proposed solution into the Operation and maintenance network of a mobile network operator. In this way, the proposed solution can contribute to developing and expanding the concept of Self Organizing Network (SON) used in 4G and 5G networks.

融合自适应周期与兴趣量因子的轻量级GCN推荐

推荐系统在成熟的数据挖掘技术推动下,已能高效地利用评分数据、行为轨迹等显隐性信息,再与复杂而先进的深度学习技术相结合,取得了很好的效果.同时,其应用需求也驱动着对基础数据的深度挖掘与利用,以及对技术要求的减负成为一个研究热点.基于此,提出一种利用GCN(graph convolutional network)方法进行深度信息融合的轻量级推荐模型LG_APIF.该模型结合行为记忆,通过艾宾浩斯遗忘曲线模拟用户兴趣变化过程,采用线性回归等相对轻量的传统方法挖掘项目的自适应周期等深度信息;分析用户当前的兴趣分布,计算项目的兴趣量,以获取用户的潜在兴趣类型;构建用户-类型-项目三元组的图结构,并结合减负后的GCN技术来生成最终的项目推荐列表.实验验证所提方法的有效性,通过与8个经典模型在Last.fm,Douban,Yelp,MovieLens数据集中的对比,表明该方法在Precision,Recall及NDCG指标上都得到良好改善,其中,Precision平均提升2.11%,Recall平均提升1.01%,NDCG平均提升1.48%.

基于CNN-LSTM的机床滚动轴承性能退化趋势和寿命预测

滚动轴承作为机床主轴的关键部件,其剩余寿命预测直接决定着整机设备的剩余寿命。若不能及时地预知滚动轴承的健康状态或损伤情况,不仅会影响维修策略的制定,还会造成级联故障,易造成机床灾难性的事故。针对大数据下滚动轴承振动信号的自适应故障特征提取和智能诊断问题,构建卷积神经网络和长短期记忆网络(CNN-LSTM)相结合的寿命预测模型,它可以避免人工参与的影响,实现网络的互补优势。对滚动轴承的退化状态以及剩余寿命进行预测,并与卷积神经网络(CNN)、长短时记忆神经网络(LSTM)进行对比实验。结果表明:所提方法CNN-LSTM有着较高的预测准确度。

基于多源信息融合和WOA-CNN-LSTM的外脚手架隐患分类预警研究

面对施工现场外脚手架隐患信息的多样性,传统的基于传感器监测的单一信号预警研究存在容错力不佳、含有信息有限等问题。针对施工现场外脚手架“图像+监测”数据,提出一种基于数据层和特征层信息融合的脚手架隐患分类预警方法。首先,利用Revit三维建模软件建立外脚手架实体模型,对不同初始隐患下的外脚手架进行有限元分析,划分隐患预警等级;其次,利用无迹卡尔曼滤波算法(Unscented Kalman Filter,UKF)及卷积长短时记忆网络(Convolutional Neural Network-Long Short Term Memory Network,CNN-LSTM)实现脚手架同类信息数据层融合及异类信息特征层融合;最后,通过实时收集西安市某在建项目落地式双排扣件式钢管脚手架隐患信息,对其进行分类预警,并使用鲸鱼优化算法(Whale Optimization Algorithm,WOA)对CNN-LSTM网络进行参数优化,发现隐藏节点个数为30、学习率为0.0072、正则化系数为1×10^(-4)时分类效果最佳,优化后预警精度达到了91.4526%。通过可视化WOA-CNN-LSTM、CNN-LSTM、CNN-SVM(Support Vector Machine,支持向量机)及CNN-GRU(Gate Recurrent Unit,门控循环单元)分类预警结果,证实了优化后的CNN-LSTM网络在脚手架分类预警方面的优越性。

基于集群辨识和卷积神经网络-双向长短期记忆-时序模式注意力机制的区域级短期负荷预测

为了解决区域级短期电力负荷预测时输入特征过多和负荷时序性较强的问题,提出一种基于集群辨识和卷积神经网络(convolutional neural networks,CNN)-双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)-时序模式注意力机制(temporal pattern attention,TPA)的预测方法。首先,将用电模式和天气作为影响因素,基于二阶聚类算法对区域内的负荷节点进行集群辨识,再从每个集群中挑选代表特征作为深度学习模型的输入,这样既能减少输入特征维度,降低计算复杂度,又能综合考虑预测区域的整体特征,提升预测精度。然后,针对区域电力负荷时序性的特点,用CNN-BiLSTM-TPA模型完成训练和预测,该模型能提取输入数据的双向信息生成隐状态矩阵,并对隐状态矩阵的重要特征加权,从多时间步上捕获双向时序信息用于预测。最后,在美国加利福尼亚州实例上分析验证了所提方法的有效性。

基于ConvLSTM-CNN预测太平洋长鳍金枪鱼时空分布趋势

海洋渔场的变动由空间与环境因子共同驱动,渔场时空演变信息的精准预测是海洋捕捞的关键。本研究利用1995-2018年太平洋海域长鳍金枪鱼(Thunnus alalunga)的渔业生产统计数据,结合同期海洋环境数据包括海表面温度(Sea Surface Temperature,SST)、海表面盐度(Sea Surface Salinity,SSS)、初级生产力(Primary Productivity,PP)和溶解氧浓度(Dissolved Oxygen Concentration,DO),提出了一种融合卷积长短期记忆网络(Convolutional Long Short-Term Memory Networks,ConvLSTM)和卷积神经网络(Convolutional Neural Networks,CNN)的渔场时空分布预测模型。该模型引入特征提取模块,对时空因子进行编码,提取时空特征信息,同时采用CNN提取海洋环境变量的抽象特征,采用ConvLSTM提取渔业数据的高层时空关联信息,最后融合多种特征对渔场时空演变趋势进行预测。结果表明,模型的均方根误差为0.1036,较随机森林、BP神经网络和长短期记忆网络(Long Short Term Memory,LSTM)等传统渔场预报模型的预测误差降低15%~40%,预测的高产渔区与实际作业的高渔获量区匹配度为89%。该研究构建的渔场时空预测模型能够准确地预测出太平洋长鳍金枪鱼的时空分布,为太平洋长鳍金枪鱼的延绳钓渔业提供科学参考依据。

结合算子选择的卷积神经网络显存优化算法

针对卷积神经网络训练中自动算子选择算法在较大的显存压力下性能下降的问题,将卸载、重计算与卷积算子选择统一建模,提出一种智能算子选择算法。该算法权衡卸载和重计算引入的时间开销与更快的卷积算子节省的时间,寻找卸载、重计算和卷积算子选择的调度,解决了自动算子选择算法性能下降的问题.实验结果表明,该智能算子选择算法比重计算-自动算子选择算法缩短了13.53%训练时间,比已有的卸载/重计算-自动算子选择算法缩短了4.36%的训练时间.

结合太阳辐射量计算与CNN-LSTM组合的光伏功率预测方法研究

为了提高模型预测性能,提出一种综合太阳辐射模型及深度学习的光伏功率预测模型。首先,利用太阳辐射机理建立太阳辐射模型(SRM),估算出水平面上总辐射值,再由斜面辐照度转换方法计算出光伏组件所接收的斜面辐射值。其次,通过皮尔逊相关分析法筛选出对光伏功率影响较大的主要因素,将斜面辐射计算值及主要影响因素作为输入,采用卷积神经网络(CNN)和长短期记忆网络(LSTM)建立光伏功率SRM-CNN-LSTM预测模型。分别利用春夏秋冬四季典型日的数据开展对比实验,结果表明:与几种其他方法相比,该文方法具有更好的预测效果。

基于字词向量融合的民航智慧监管短文本分类

为解决民航监管事项所产生的检查记录仅依靠人工进行分类分析导致效率低的问题,提出一种基于数据增强与字词向量融合的双通道特征提取的短文本分类模型,探讨民航监管事项的分类,包括与人、设备设施环境、制度程序和机构职责等相关问题。为解决类别不平衡问题,采用数据增强算法在原始文本上进行变换,生成新的样本,使各个类别的样本数量更加均衡。将字向量和词向量按字融合拼接,得到具有词特征信息的字向量。将字词融合的向量分别送入到文本卷积神经网络(TextCNN)和双向长短期记忆(BiLSTM)模型中进行不同维度的特征提取,从局部的角度和全局的角度分别提取特征,并在民航监管事项检查记录数据集上进行试验。结果表明:该模型准确率为0.9837,F 1值为0.9836。与一些字嵌入模型和词嵌入模型相对比,准确率提升0.4%。和一些常用的单通道模型相比,准确率提升3%,验证了双通道模型提取的特征具有全面性和有效性。

基于VMD-FE-CNN-BiLSTM的短期光伏发电功率预测

为提高光伏功率的预测精度,提出一种变分模态分解(VMD)、模糊熵(FE)、卷积神经网络(CNN)和双向长短期记忆神经网络(BiLSTM)的光伏功率组合预测模型。该方法首先采用VMD将原始光伏序列数据分解成多个子序列,从而减少随机波动分量和噪声干扰对预测模型的影响,通过FE对每个子序列进行重组,使用一维CNN的局部连接及权值共享提取不同分量的特征,将CNN输出的特征融合并输入到BiLSTM模型中;利用BiLSTM模型建立历史数据之间的时间特征关系,得到光伏发电功率预测结果。与BiLSTM、CNN-BiLSTM、EEMD-CNN-BiLSTM、VMD-CNN-BiLSTM这4种模型进行比较,该文提出的VMD-FE-CNN-BiLSTM模型在光伏发电功率预测中具有较高的精确度和稳定性,满足光伏发电短期预测的要求。

基于卷积和长短期记忆网络的地浸开采铀浓度预测研究

文章通过集成经验模态分解(Empirical Mode Decomposition,EMD)、卷积神经网络(Convolutional Neural Networks,CNN)、长短期记忆网络(Long Short-Term Memory,LSTM)、傅里叶变换,提出了一种新型地浸单元浸出液铀浓度预测方法。该方法将浸出液铀浓度监测值时间序列使用EMD进行分解,分解为趋势项、周期项和随机项。通过构建CNN+LSTM网络,并结合傅里叶变换和多项式拟合对铀浓度趋势项、周期项和随机项进行预测,3者预测之和作为铀浓度预测结果。实证结果表明:EMD能够有效分解铀浓度时间序列,模型拟合度比未进行EMD分解的模型提升超50%;基于EMD、CNN+LSTM和傅里叶变换的集成方法预测精度良好,预测的平均绝对百分比误差(Mean Absolute Percentage Error,MAPE)为0.348,与LSTM、反向传播(Back Propagation,BP)和门控循环网络(Gate Recurrent Unit,GRU)等模型相比最高提升超80%。文章提出的集成方法能够准确预测浸采单元铀浓度变化,解决了原有方法和模型无法对非线性、非平稳铀浓度序列进行准确预测的问题,从而为地浸矿山生产规划提供技术支持,并有助于提升中国铀矿山的数字化、信息化程度。

基于注意力机制与LSTM-CCN的月降水量预测

针对现有月降水量预测方法预测准确性不高的问题,提出一种基于注意力机制与LSTM-CCN的月降水量预测方法。首先,利用长短时记忆神经网络(long short-term memory neural network,LSTM)提取气象数据在时间维度的特征分布,从时间相关性方面捕获相邻时间段或长距离气象数据段中的统计分布;其次,利用因果卷积神经网络(causal convolutional network,CCN)将气象数据映射到空间维度,深层次地从空间维度捕获气象数据在空间中的特征统计分布;再次,以并联的方式将时间和空间特征作为交叉注意力网络的输入,构造融合的时空特征;最后,以长短时记忆神经网络构造解码器,并将融合的时空特征作为解码器的输入,预测的月降水量作为输出。选取河南省新乡市2001~2017年数据集进行测试,结果表明:所提出方法的均方根误差仅为13.08 mm,相比主流方法具有更低的预测误差。研究成果可为提高气象预测的准确性和实用性提供参考。

基于句法依赖增强图的方面级情感分析

方面级情感分析旨在分析句子中特定方面的情感极性,现有研究侧重于利用图神经网络建模上下文与方面的依赖信息,忽略了对上下文中情感词及其词性的挖掘和利用。为此,提出一种基于句法依赖的增强图(syntactic dependency enhancement graph, SDEG)模型,在原始句法依赖图上引入情感知识和词性信息,增强情感词权重和相关词性单词在上下文中的作用。使用双向长短期记忆网络和卷积神经网络捕捉句子的重点语义信息,通过图卷积神经网络建模句法依赖增强图,通过交互注意力机制生成特定方面的上下文语义和语法表示以进行情感极性分类。在多个公共基准数据集上的实验结果表明,所提模型在性能上有明显提升。