Prediction of rock mass classification in tunnel boring machine tunneling using the principal component analysis (PCA)-gated recurrent unit (GRU) neural network

Due to the complexity of underground engineering geology,the tunnel boring machine(TBM)usually shows poor adaptability to the surrounding rock mass,leading to machine jamming and geological hazards.For the TBM project of Lanzhou Water Source Construction,this study proposed a neural network called PCA-GRU,which combines principal component analysis(PCA)with gated recurrent unit(GRU)to improve the accuracy of predicting rock mass classification in TBM tunneling.The input variables from the PCA dimension reduction of nine parameters in the sample data set were utilized for establishing the PCA-GRU model.Subsequently,in order to speed up the response time of surrounding rock mass classification predictions,the PCA-GRU model was optimized.Finally,the prediction results obtained by the PCA-GRU model were compared with those of four other models and further examined using random sampling analysis.As indicated by the results,the PCA-GRU model can predict the rock mass classification in TBM tunneling rapidly,requiring about 20 s to run.It performs better than the previous four models in predicting the rock mass classification,with accuracy A,macro precision MP,and macro recall MR being 0.9667,0.963,and 0.9763,respectively.In Class II,III,and IV rock mass prediction,the PCA-GRU model demonstrates better precision P and recall R owing to the dimension reduction technique.The random sampling analysis indicates that the PCA-GRU model shows stronger generalization,making it more appropriate in situations where the distribution of various rock mass classes and lithologies change in percentage.

Classification of Coalbed Methane Enrichment Units of Qinshui Basin Based on Geological Dynamical Conditions

Coalbed methane enrichment will be controlled by many good macro geological dynamical conditions; there is evident difference of enrichment grade in different area and different geological conditions.This paper has studied tectonic dynamical conditions, thermal dynamical conditions and hydraulic conditions, which affect coalbed methane enrichment in Qinshui basin.Coalbed methane enrichment units have been divided based on tectonic dynamical conditions of Qinshui basin,combined with thermal dynamical conditions and hydraulic conditions.

Classification and Denominationof Flow Units for Clastic Reservoirsof Continental Deposit

On the basis of other researchers' achievements and the authors' understanding of flow units, a proposal on classification and denomination of flow units for clastic reservoirs of continental deposit is put forward according to the practical need of oilfield development and relevant theories. The specific implications of development and geology are given to each type of flow units, which has provided a scientific basis for oil development.

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

BSTFNet:An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features

While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.

Clastic compaction unit classification based on clay content and integrated compaction recovery using well and seismic data

Compaction correction is a key part of paleogeomorphic recovery methods. Yet, the influence of lithology on the porosity evolution is not usually taken into account. Present methods merely classify the lithologies as sandstone and mudstone to undertake separate porositydepth compaction modeling. However, using just two lithologies is an oversimplification that cannot represent the compaction history. In such schemes, the precision of the compaction recovery is inadequate. To improve the precision of compaction recovery, a depth compaction model has been proposed that involves both porosity and clay content. A clastic lithological compaction unit classification method, based on clay content, has been designed to identify lithological boundaries and establish sets of compaction units. Also, on the basis of the clastic compaction unit classification, two methods of compaction recovery that integrate well and seismic data are employed to extrapolate well-based compaction information outward along seismic lines and recover the paleo-topography of the clastic strata in the region. The examples presented here show that a better understanding of paleo-geomorphology can be gained by applying the proposed compaction recovery technology.

Three-dimensional Extension of the Unit-Feature Spatial Classification Method for Cloud Type

We describe how the Unit-Feature Spatial Classification Method(UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently.By using a combination of Interactive Data Language(IDL) and Visual C++(VC) code in combination to extend the technique in three dimensions(3-D),this paper provides an efficient method to implement interactive computer visualization of the 3-D discrimination matrix modification,so as to deal with the bi-spectral limitations of traditional two dimensional(2-D) UFSCM.The case study of cloud-type classification based on FY-2C satellite data (0600 UTC 18 and 0000 UTC 10 September 2007) is conducted by comparison with ground station data, and indicates that 3-D UFSCM makes more use of the pattern recognition information in multi-spectral imagery,resulting in more reasonable results and an improvement over the 2-D method.

Comparative study on the performance of ConvLSTM and ConvGRU in classification problems-taking early warning of short-duration heavy rainfall as an example

卷积长短期记忆单元ConvLSTM和卷积门控循环单元ConvGRU是两种广泛应用的深度学习单元,通过将循环机制与卷积运算相结合,常常用于时空序列的预测.为了明确上述两种模型的收敛速度和分类能力,需要使用相同的模型架构对相同的分类问题进行预测.本研究将北京短时强降水区级预警问题看作深度学习中的二分类问题,使用京津冀雷达网的组合反射率数据和北京区域内的自动气象站降雨数据进行深度学习模型的训练和评估.结果表明,ConvGRU的收敛速度比ConvLSTM快约25%.ConvLSTM和ConvGRU的预警性能随地区,时间,降雨强度的变化趋势相似,但大部分ConvLSTM的得分较高,少数情况下ConvGRU的得分较高.

Green Architecture for Dense Home Area Networks Based on Radio-over-Fiber with Data Aggregation Approach

The high-density population leads to crowded cities. The future city is envisaged to encompass a large-scale network with diverse applications and a massive number of interconnected heterogeneous wireless-enabled devices. Hence, green technology elements are crucial to design sustainable and future-proof network architectures. They are the solutions for spectrum scarcity, high latency, interference, energy efficiency, and scalability that occur in dense and heterogeneous wireless networks especially in the home area network （HAN）. Radio-over-fiber （ROF） is a technology candidate to provide a global view of HAN＇s activities that can be leveraged to allocate orthogonal channel communications for enabling wireless-enabled HAN devices transmission, with considering the clustered-frequency-reuse approach. Our proposed network architecture design is mainly focused on enhancing the network throughput and reducing the average network communications latency by proposing a data aggregation unit （DAU）. The performance shows that with the DAU, the average network communications latency reduces significantly while the network throughput is enhanced, compared with the existing ROF architecture without the DAU.

Statistical Tools for Estimation of Threshold Values at Data Classification Task Solution

The paper contains a summary of some results of original research total aggregates. The main idea is determining the boundaries of the groups for classification of fuzzy and threshold aggregates using the method of decomposing a mixture of probability distributions. The article presents the experience of partitions of a real aggregate as a finite mixture of probability distributions on private aggregates. Threshold value defined by the boundaries of private aggregates, will match the value of the phenomenon at the intersection of the curves of probability distributions, which extracted from the mixture. The proposed scheme of identification threshold aggregates has found practical application in the research of aggregate of Russian employees by level of payroll and establishing the optimal minimum value monthly wage. The official data of the Federal State Statistics Service were used.

Multi⁃Scale Dilated Convolutional Neural Network for Hyperspectral Image Classification

Aiming at the problem of image information loss,dilated convolution is introduced and a novel multi⁃scale dilated convolutional neural network(MDCNN)is proposed.Dilated convolution can polymerize image multi⁃scale information without reducing the resolution.The first layer of the network used spectral convolutional step to reduce dimensionality.Then the multi⁃scale aggregation extracted multi⁃scale features through applying dilated convolution and shortcut connection.The extracted features which represent properties of data were fed through Softmax to predict the samples.MDCNN achieved the overall accuracy of 99.58% and 99.92% on two public datasets,Indian Pines and Pavia University.Compared with four other existing models,the results illustrate that MDCNN can extract better discriminative features and achieve higher classification performance.

基于层次特征增强的细粒度点云分类

针对粗粒度点云分类方法在细粒度数据集中局部特征提取不足的问题,提出了一种基于层次特征增强的三维细粒度点云分类网络(HFE-Net)。基于Veronese映射的点特征增强模块(V-PE)对点云数据进行数据增强,辅助网络学习法线和姿态高阶信息;基于多尺度上下文感知的簇内特征增强模块(CA-IntraCE),利用不同尺度的K近邻(K-nearest neighbors,KNN)算法以及交叉注意力实现不同尺度特征的增强,以消除最大池化带来的信息丢失;基于分组稀疏采样的簇间特征增强模块(GSS-InterCE),利用最远点采样(FPS)算法获得稀疏点,并采用交叉注意力实验不同簇间的特征增强,从而提高网络的细粒度判别能力。在FG3D数据集Airplane、Car和Chair 3个类别上的实验结果显示,HFE-Net的总体准确率分别达97.40%,80.53%和83.83%,已超过现有最优方法DC-Net、FGPNet的分类框架,说明HFE-Net的分类性能具有一定的优越性。

基于滑动窗决策树的加氢裂化装置过渡状态识别

加氢裂化生产装置处于多工况运行状态,而不同工况间切换存在过渡状态,操作员会根据装置所处状态进行相应的操作和调整。然而,装置所处的过渡状态难以识别,需要长期操作学习并积累经验。为此,提出了一种基于滑动窗决策树的加氢裂化装置过渡状态识别方法。加氢裂化装置工业数据经去噪、降维等预处理后,使用滑动窗口保留窗口内的数据局部动态时序特征,并建立特征矩阵,再利用精细决策树发掘复杂过程变量之间的关系,可视化地描述了决策树结构,体现其可解释的优势,最终实现加氢裂化装置过渡态的快速、准确识别。基于F1分数,对比了高斯朴素贝叶斯、精细高斯支持向量机、粗略树、中等树、精细树、可优化决策树对加氢裂化装置过渡态的综合识别性能,10次五折交叉验证后,基于精细树的F1分数均值可达0.9896,训练时间均值为3.028 s。

基于语义分类的物联网固件中第三方组件识别

为扩大物联网固件中第三方组件识别范围,从软件供应链层面研究物联网固件安全,提出一种基于语义短文本分类的第三方组件识别方法。通过固件解压提取内部第三方组件和模拟组件运行的方式获取组件语义输出数据,利用Skip-gram将语义输出转化为词嵌入表示,通过卷积神经网络和双向门控循环单元分别提取语义信息局部特征和全局特征,经过多头注意力机制区分关键语义特征,输入到Softmax分类器中实现可用于识别组件的语义信息分类。通过在10个流行的物联网生产商发布的5453个固件上进行实验,验证了该方法可有效识别第三方组件。

多功能浮动港应用研究

自20世纪20年代以来,不断有对海上大型浮体的概念设想以及具体的开发建设实践,在总结归纳现有多功能浮动港开发应用实例基础上,分析多功能浮动港的应用场景和主要需求,包括解决城市空间矛盾、助力岛礁开发和保护、为海洋资源开采提供支撑、为水上活动提供服务等。总结多功能浮动港主要特征,提出按水平投影面积将浮动港分为6个规模等级,以便在建设开发中进行归类管控,按功能将浮动港划分为商业型、公共服务型、居住型、生产服务型、特殊服务型等5种类型。经过技术、政策、经济方面初步适应性分析,结构单元模块的介绍,以及主要技术问题的分析,认为在我国应用尚处于起步阶段的浮动港契合未来海洋经济的发展需求,具备较为广阔的应用前景。

Aggregator-based demand response mechanism for electric vehicles participating in peak regulation in valley time of receiving-end power grid

With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this problem while considering the potential of the large-scale charge load of electric vehicles(EVs), an aggregator-based demand response(DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study. In this aggregator-based DR mechanism, the profits for the power grid’s operation and the participation willingness of the EV owners are considered. Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling, a rolling unit commitment model with the DR is established to maximize the social welfare. In addition, to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator(ISO) and the aggregators, the clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.

An Air Mass Based Approach to the Establishment of Spring Season Synoptic Characteristics in the Northeast United States

The Northeast United States spring is indicative of major meteorological and biological change though the seasonal boundaries are difficult to define and may even be changing with global climate warming. This research aims to obtain a synoptic meteorological definition of the spring season through an assessment of air mass frequency over the past 60 years. The validity of recent speculations that the onset and termination of spring have changed in recent decades with global change is also examined. The Spatial Synoptic Classification is utilized to define daily air masses over the region. Annual and seasonal baseline frequencies are identified and their differences are acquired to characterize the season. Seasonal frequency departures of the early and late segments of the period of record are calculated and examined for practical and statistical significance. The daily boundaries of early and late spring are also isolated and assessed across the period of record to identify important changes in the season’s initiation and termination through time. Results indicate that the Northeast spring season is dominated by dry air masses, mainly the Dry Moderate and Dry Polar types. Prior to 1975, more polar air masses are detected while after 1975 more moderate and tropical types are identified. Late spring is characterized by increased variability in all moist air mass frequencies. These findings indicate that, from a synoptic perspective, the season is dry through time but modern springs are also warmer than those of past decades and the initiation of the season is likely arriving earlier. The end of the season represents more variable day-to-day air mass conditions in modern times than detected in past decades.

水泥稳定碎石基层取芯芯样分类与整体性评价技术标准

为了实现水泥稳碎石基层整体性质量客观和公正的评价,消除7 d取芯芯样质量评判的随意性、弥补因不考虑气候条件和公路等级等因素对施工质量评价的局限性,选择高速、一级、二级和三级共5条公路,对7 d龄期的水泥稳定粒料类基层进行了大量的取芯试验,系统地对取芯芯样的形态进行了调研、统计和分析,根据芯样的完整程度,将其分为完整类、残缺类和松散类3类,芯样完整性的差异代表其扩散荷载应力能力的不同,根据芯样的致密程度,将芯样进一步分成8级,芯样致密性的不同体现服役功能性差别,提出了评价路段芯样完整率的计算方法,确定了芯样完整率技术标准的确定原则,对于季冻区宜采用F(Ⅰ+Ⅱ)作为评价指标,对于非季冻区宜采用F(Ⅰ)作为评价指标,并根据回归曲线,提出了不同区域不同等级道路7 d龄期内的芯样完整率技术标准。结果表明:芯样完整率与道路等级呈线性关系,道路等级对其影响幅度为2%~9%,养生模式的影响幅度为10%左右,气候因素的影响幅度为5%左右。研究成果实现了对半刚性基层取芯芯样质量和整体性质量的定量化评价。

融合二连通模体结构信息的节点分类算法

节点表示学习将图结构数据信息编码到低维的潜在空间中,在节点分类、聚类、链路预测等机器学习任务中被广泛应用。在复杂网络中,节点与节点之间不仅存在直接相连的低阶结构,也存在以特殊连接模式形成的高阶结构,称为模体。提出一种融合二连通模体结构信息的节点分类算法(FMI),利用节点间高阶二连通模体信息学习节点表示,完成节点分类任务。首先,统计网络中的二连通模体,利用其中信息提出一个节点重要性的度量指标——模体比值。根据模体比值计算采样概率进行邻域采样;构造一个带权辅助图以融合网络节点连接的低阶关系与高阶关系,对节点进行加权邻域聚合以得到节点表示。在5个数据集Cora、Citeseer、Pubmed、Wiki和DBLP上执行节点分类任务,与5种经典基准算法进行对比,所提算法FMI在准确度和F1-分数等指标上表现良好。

基于改进图卷积神经网络的半监督分类

图卷积神经网络(GCN)是一种用于处理图数据的深度学习模型.在经典的GCN中节点之间的聚合,未考虑节点间相似度的特征信息,影响了分类模型的准确性和模型训练的收敛速度.本文提出了一种改进聚合权重的图卷积神经网络IAW-GCN,通过利用描述节点相似度的曼哈顿距离度量设计了节点聚合权重函数,并用节点距离度量矩阵改进了GCN模型中的特征矩阵,使得IAW-GCN模型在消息传递聚合过程中根据相似度调节节点聚合权重.实验结果表明,在Cora、Citeseer和Pubmed标准引文数据集条件下,IAW-GCN在半监督分类任务中的分类准确率和模型训练收敛速度均优于经典GCN,为解决半监督分类问题提供了一种新方法.