CT IDENTIFICATION OF THE MECHANIC CHARACTERISTICS OF DAMAGE PROPAGATION OF ROCK

The computerized tomography technique is applied to study the damage propagation of rock for the first time in this paper. CT values and their distribution regularity of damage propaga-tion of rock are analyzed in detail. The relation between CT values and stresses (strains) of the damage propagation of rock is then discussed. This provides the foundation for establishing the constitutive relation of damage propagation of rock.

Extended Overlapping Community Detection Algorithm by Label Propagation for Internet Application Identification

The increased capacity and availability of the Intemet has led to a wide variety of applications. Intemet traffic characterization and application identification is important for network management. In this paper, based on detailed flow data collected from the public networks of Intemet Service Providers, we construct a flow graph to model the interactions among users. Considering traffic from different applications, we analyze the community structure of the flow graph in terms of cormmunity size, degree distribution of the community, community overlap, and overlap modularity. The near linear time community detection algorithm in complex networks, the Label Propagation Algorithm （LPA）, is extended to the flow graph for application identification. We propose a new initialization and label propagation and update scheme. Experimental results show that the proposed algorithm has high accuracy and efficiency.

COMBINATION OF DISTRIBUTED KALMAN FILTER AND BP NEURAL NETWORK FOR ESG BIAS MODEL IDENTIFICATION

By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets of multi-measurements of the same ESG in different noise environments are ＂mapped＂ into a sensor network,and DKF with embedded consensus filters is then used to preprocess the data sets. After transforming the preprocessed results into the trained input and the desired output of neural network,BPNN with the learning rate and the momentum term is further utilized to identify the ESG bias. As demonstrated in the experiment,the proposed approach is effective for the model identification of the ESG bias.

New pattern recognition system in the e-nose for Chinese spirit identification

This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance（QCM） principle,and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value（A）,root-mean-square value（RMS）, shape factor value（S_f）, crest factor value（C_f）, impulse factor value（I_f）, clearance factor value（CL_f）, kurtosis factor value（K_v） are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis（PCA） method. Finally the back propagation（BP） neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively.

Model identification with BPNN on restrictive ecological factors of SRB for sulfate-reduction

The model of back-propagation neural network (BPNN) was presented to demonstrate the effect of restrictive ecological factors, COD/SO 4 2- ratio, pH value, alkalinity (ALK) and SO 4 2- loading rate (Ns), on sulfate reduction of Sulfate Reducing Bacteria (SRB) in an acidogenic sulfate reducing reactor supplied with molasses as sole organic carbon source and sodium sulfate as electron acceptor. The compare of experimental results and computer simulation was also discussed. It was shown that the method of BPNN had a powerful ability to analyze the ecological characteristic of acidogenic sulfate reducing ecosystem quantitatively.

Neural network identification for underwater vehicle motion control system based on hybrid learning algorithm

Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm （GA） and error back propagation algorithm （BP） is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle （UV） ＇ s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification nrecision.

Classification and Identification of Nuclear, Biological or Chemical Agents Taken from Remote Sensing Image by Using Neural Network

In the context of new risks and threats associated to nuclear, biological and chemical （NBC） attacks, and given the shortcomings of certain analytical methods such as principal component analysis （PCA）, a neural network approach seems to be more accurate. PCA consists in projecting the spectrum of a gas collected from a remote sensing system in, firstly, a three-dimensional space, then in a two-dimensional one using a model of Multi-Layer Perceptron based neural network. It adopts during the learning process, the back propagation algorithm of the gradient, in which the mean square error output is continuously calculated and compared to the input until it reaches a minimal threshold value. This aims to correct the synaptic weights of the network. So, the Artificial Neural Network （ANN） tends to be more efficient in the classification process. This paper emphasizes the contribution of the ANN method in the spectral data processing, classification and identification and in addition, its fast convergence during the back propagation of the gradient.

Identification of Mine Water Inrush Source Based on PCA-BP Neural Network

It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify the source of water inrush, so as to reduce casualties and economic losses and prevent and control water inrush disasters. Taking Ca2+, Mg2+, Na+ + K+, , , Cl-, pH value and TDS as discriminant indexes, the principal component analysis method was used to reduce the dimension of data, and the identification model of mine water inrush source based on PCA-BP neural network was established. 96 sets of data of different aquifers in Panxie mining area were selected for prediction analysis, and 20 sets of randomly selected data were tested, with an accuracy rate of 95%. The model can effectively reduce data redundancy, has a high recognition rate, and can accurately and quickly identify the water source of mine water inrush.

基于3D激光感知的隧道爆破参数优化设计方法

为解决隧道爆破质量控制困难的问题,探究新型爆破参数优化方法,提出一种3D激光扫描技术与BP神经网络相结合的实时优化方法,对隧道爆破超欠挖感知及其后续钻爆参数进行实时修正,构建现场扫描方法、数据处理、点云提取与超欠挖图像等工作程序,建立BP神经网络模型、算法并确定模型参数。通过试验验证表明:1)将3D激光扫描技术应用于隧道爆破超欠挖质量感知中切实可行,与人工测量结果吻合较好,具有快速、实时和准确的优点;2)采用3D激光扫描方法能够对隧道爆破后的断面轮廓线进行精确评测,在获取爆破超欠挖的精细数据后,通过样本学习完成模型训练能推理出较为合理的爆破参数,改进后的优化方案使平均超挖降低54.8%。

锂离子电池分数阶模型的多参数在线辨识方法

锂离子电池分数阶模型由于含有表征老化机理的参数,被用于电池老化研究并期望据此实现在线的电池老化过程探究。在电池的有限使用工况和产品级检测条件下实现对该模型尽可能多的老化参数的在线辨识,将有助于该模型的在线应用。基于此,提出1种基于反向传播神经网络的多参数在线辨识方法。首先通过分析典型工况下参数灵敏度确定在线辨识的参数集,然后基于电池老化规律设计网络以及网络训练算法以提高辨识速度和准确性,同时设计验证方法以保证辨识参数的收敛性,仿真和实验结果验证在线辨识方法的辨识速度和准确性。

悬空波导环境下AIS信号超视距传播特性分析

大气波导可陷获无线电波信号,使其发生超视距传播现象,影响无线电系统信号传播预测和性能评估应用。本文针对由探空数据诊断的悬空波导个例,利用对流层电波传播确定性方法模拟岸-船自动识别系统(automatic identification system,AIS)传播链路,定量研究了不同悬空波导环境影响下AIS信号的超视距传播特性。结果表明:波导强度50 M单位且底高约7 m的悬空波导环境下,AIS信号发生明显超视距传播现象;波导底高约700 m且强度较大的悬空波导环境也会对AIS信号传播产生影响;相比于单一悬空波导模型,复合悬空波导(文中指两个悬空波导层结构组成)环境对AIS信号传播影响更大。本文研究成果可为大气波导监测反演、无线电系统设计、装备效应评估等应用提供重要理论参考。

基于IRML-STAMP的舰船加油系统风险因素识别方法

为研究舰船加油系统遭受外界干扰后的系统功能状态变化情况和系统自身恢复能力,基于IRML-STAMP方法对舰船加油系统风险因素进行识别,定量评估舰船加油系统动态弹性,并与基于传统STAMP方法的评估结果进行比较。结果表明:利用基于IRML-STAMP的舰船加油系统风险因素识别方法可以精确找出失效后导致舰船加油系统动态弹性损失最多的STAMP模型的关键节点;以舰船加油系统加油作业设定故障场景为例,压力表等仪表是该系统的关键节点,压力表等仪表故障中断后,系统动态弹性大幅下降0.161;当故障传播路径中不包含关键节点时,系统动态弹性最大,当干扰出现在关键节点时,系统动态弹性最小,差值为0.101;与传统STAMP方法相比,IRML-STAMP方法评估结果可以准确定位系统风险,提高系统动态弹性。该研究结果可以帮助提高舰船加油系统的抗干扰性,对现场舰船加油系统的风险因素识别具有指导意义。

基于改进GA-BP算法的RFID天线参数优化方法

为了提高算法对天线参数的预测精度,提出了一种基于Adagrad优化器的改进遗传算法-反向传播(GA-BP)算法。通过在迭代过程中引入Adagrad优化器与阈值策略,对发生退化的种群最优个体的位置信息进行重新引导,解决了GA-BP算法局部寻优能力不足等问题,大幅度减小了误差损失并且加快了收敛速度。利用该方法对射频识别(RFID)标签天线的印刷品质和电磁参数进行了建模与分析。结果表明,改进GA-BP算法在稳步搜索极值的同时可以避免陷入局部极值陷阱,在误差和收敛效率方面均优于传统的反向传播(BP)算法与GA-BP算法,能够得到较高的预测精度,实现了RFID标签天线印刷品质的优化控制以及S_(11)特征曲线的预测。相比于BP算法与GA-BP算法,改进GA-BP算法在用于优化RFID标签天线的印刷品质时,平均绝对误差分别降低了91.92%和85.64%。在电磁参数预测应用时,分别降低了13.77%和13.19%。

基于线性结构因果模型的服务故障传播路径识别

在云计算环境中,当一个服务发生故障时,故障会随着服务之间的交互行为不断传播,导致产生大规模服务失效的风险。复杂且动态变化的服务调用关系加大了识别服务故障传播路径的难度。针对该问题,提出一种基于因果图的服务故障传播路径识别方法。监测并收集服务的运行数据,通过服务运行数据对服务故障事件完成度量;根据因果图模型推断服务故障事件之间的因果关系并构建服务故障传播图;利用服务故障传播图确定故障传播路径。实验结果表明,该方法能够有效识别服务故障传播路径。

基于深度学习的DRFM信号识别

针对数字射频存储器(DRFM)产生信号与源信号之间无法有效区分的问题,运用基于小波变换的同步压缩变换将时域的雷达信号转换为时频图,运用深度学习强大的图像识别能力,实现了基于深度学习的源信号与DRFM信号识别,从而解决了在雷达信号处理中无法有效区分回波信号和DRFM欺骗信号以及在雷达干扰识别中基于DRFM的欺骗干扰难以识别的问题。为了验证深度学习过程的可靠性,通过神经网络可解释性算法对训练结果进行了验证和分析。实验结果表明,相比于识别原始信号,识别DRFM信号神经网络需要用到更多的特征,神经网络判断准确率达到了96.33%,识别精度良好。

A reliable NLOS error identification method based on LightGBM driven by multiple features of GNSS signals

In complicated urban environments,Global Navigation Satellite System(GNSS)signals are frequently affected by building reflection or refraction,resulting in Non-Line-of-Sight(NLOS)errors.In severe cases,NLOS errors can cause a ranging error of hundreds of meters,which has a substantial impact on the precision and dependability of GNSS positioning.To address this problem,we propose a reliable NLOS error identification method based on the Light Gradient Boosting Machine(LightGBM),which is driven by multiple features of GNSS signals.The sample data are first labeled using a fisheye camera to classify the signals from visible satellites as Line-of-Sight(LOS)or NLOS signals.We then analyzed the sample data to determine the correlation among multiple features,such as the signal-to-noise ratio,elevation angle,pseudorange consistency,phase consistency,Code Minus Carrier,and Multi-Path combined observations.Finally,we introduce the LightGBM model to establish an effective correlation between signal features and satellite visibility and adopt a multifeature-driven scheme to achieve reliable identification of NLOSs.The test results show that the proposed method is superior to other methods such as Extreme Gradient Boosting(XGBoost),in terms of accuracy and usability.The model demonstrates a potential classification accuracy of approximately 90%with minimal time consumption.Furthermore,the Standard Point Positioning results after excluding NLOSs show the Root Mean Squares are improved by 47.82%,56.68%,and 36.68%in the east,north,and up directions,respectively,and the overall positioning performance is significantly improved.

水平井水力压裂裂缝扩展诱发垂直邻井光纤应变演化特征

水力压裂过程中很难准确获取裂缝高度扩展信息,利用分布式光纤可准确评价裂缝扩展信息。采用有限元耦合内聚力单元方法,建立水平井水力压裂裂缝扩展诱发垂直邻井光纤应变的正演模型,进行垂直邻井光纤应变数值模拟,分析光纤应变演化特征;根据压裂施工参数及垂直邻井光纤布设位置,判定水力压裂裂缝的光纤有效监测范围;利用大型真三轴压裂实验与分布式光纤感测设备,进行光纤实时监测水平井水力压裂物理实验。结果表明:裂缝高度扩展诱发光纤应变演化分为应变增强、张应变扩展、应变直线状汇聚及应变弱化4个阶段,光纤应变演化特征表现为中间部分出现张应变汇聚带,两侧出现压应变汇聚带。当水力压裂裂缝在光纤有效监测范围内时,垂直邻井光纤可有效监测裂缝高度扩展情况。垂直邻井的光纤应变演化结果与光纤应变正演模计算结果验证正演模型的正确性。利用裂缝高度扩展诱发垂直邻井光纤应变演化特征可评价裂缝高度扩展状态,为油田压裂设计提供参考。

Quality control in multistage machining processes based on a machining error propagation event-knowledge graph

In multistage machining processes(MMPs),a clear understanding of the error accumulation,propagation,and evolution mechanisms between different processes is crucial for improving the quality of machining products and achieving effective product quality control.This paper proposes the construction of a machining error propagation event-knowledge graph(MEPEKG)for quality control in MMPs,inspired by the application of knowledge graphs to data,information,and knowledge organization and utilization.Initially,a cyber-physical system(CPS)-based production process data acquisition sensor network is constructed,and process flow-oriented process monitoring is achieved through the radio frequency identification(RFID)production event model.Secondly,the process-related quality feature and working condition data are preprocessed;features are extracted from the distributed CPS nodes;and the production event model is used to achieve the dynamic mapping and updating of feature data under the guidance of the MEPEKG schema layer.Moreover,the mathematical model of machining error propagation based on the second-order Taylor expansion is used to quantitatively analyze the quality control in MMPs based on the support of MEPEKG data.Finally,the efficacy and reliability of the MEPEKG for error propagation analysis and quality control of MMPs were verified using a case study of a specially shaped rotary component.

恶劣传播环境下导航雷达个体识别方法研究

由于港口等区域内存在大量建筑物,导致个体识别设备侦收的导航雷达信号受严重多径效应影响。为解决上述问题,文中提出了雷达基准时钟周期特征提取方法,并基于该特征在恶劣传播环境下对导航雷达进行个体识别。针对两部某S波段导航雷达的四种量程工作模式,进行了雷达重复间隔特征提取有效性验证,并构造了线性分类模型进行识别有效性验证,验证了基于基准时钟周期特征的个体识别方法应对恶劣传播环境的有效性。

基于电子鼻和BP神经网络对‘黑珍珠’鲜食玉米产地的区分和识别

以‘黑珍珠’鲜食玉米为研究对象,采用电子鼻技术分别测定了黑龙江、陕西两大产区共计200个鲜食玉米样品的气味传感器响应值原始数据,通过主成分分析(Principal component analysis,PCA)、判别因子分析(Discriminant function analysis,DFA)对不同产地鲜食玉米的挥发性风味进行了区分,采用软独立建模分析(Soft independent modeling class analogy,SIMCA)建立了黑龙江‘黑珍珠’鲜食玉米的判定模型,并通过Pytorch软件建立了反向传播神经网络(Back propagation neural network,BP神经网络)模型,对不同产地的‘黑珍珠’鲜食玉米进行鉴别区分。结果表明,不同产地的‘黑珍珠’鲜食玉米的挥发性风味虽有相似之处但具有明显的产地特征,SIMCA模型可实现对未知样品是否来自黑龙江产区的有效识别(正确率为97%),BP神经网络模型则可对未知产地的‘黑珍珠’鲜食玉米样品进行产地预测及鉴别,平均正确率达99.44%。采用电子鼻技术结合BP神经网络模型可以准确的区分和识别‘黑珍珠’鲜食玉米产地。