基于LP与小波包的复杂机电系统监测序列混沌降噪方法

针对流程工业生产系统监测数据存在强噪声和混沌性的特点,提出了一种局部投影方法(Local Projection Method)与小波包方法相结合的信号降噪方法。该方法先利用局部投影方法从动力学系统嵌入流形的角度进行多次迭代降噪,并根据关联维数来判定迭代终止;再利用小波包方法从频率的角度进行降噪,抑制高频噪声的干扰,取得了较好的降噪效果。用Lorenz时间序列进行仿真验证,对仿真时间序列加入不同程度的噪声,对比分析小波包、局部投影与该方法降噪后的相空间、SNR值和最大Lyapunov指数,证明了该方法对于中高强度噪声具有更好的降噪效果。并将该方法用于某压缩机组的实际监测数据降噪,评估三种方法的降噪效果,进一步验证了该方法的优越性。     

面向噪声丢包感知的无线网络拥塞算法

针对无线网络链路干扰大、误码率高等特点，以及TCP Westwood算法（TCPW）存在估算带宽时过度依赖包的反馈，缺乏区分传输过程中丢包类型的缺点等问题，提出一种TCPW拥塞控制优化算法--TCPW-F。该算法利用发送速率等构建拥塞因子[F]作为判断丢包类型的依据，同时对判定发生噪声丢包时的拥塞窗口进一步调整，避免噪声丢包引起的窗口下降，提高该情况下窗口的发送效率。仿真结果表明，TCPW-F算法在时延性能方面表现更优，单位时间抖动趋于稳定的速度更快。在同一信道带宽下增大包生成速率，改进算法的实时吞吐量明显高于原算法，具备一定的噪声丢包感知能力，无线网络的TCP传输质量获得较大改善。     

Packet loss detection in networked control systems

One of the main issues related to the reliable operation of network control systems concerns the design of mechanisms able to detect anomalies in the functioning of the communication network through which the control loops are closed. In this article, we address the problem of detecting the occurrence of packet losses and design a mechanism that can detect the occurrence of packet losses directly from process sensor data. Specifically, we propose a moving‐horizon estimation scheme that permits to detect if the packet‐loss rate exceeds prescribed thresholds, which are representative of stability and performance of the control system. We discuss theoretical properties of the proposed solution along with an approximation scheme with reduced computational burden. A numerical example is discussed to substantiate the analysis.     

Q值正演模拟约束下的零偏移距VSP资料Q值估算

随着油气勘探目的层逐步走向深部,球面扩散、透射损失、环境噪声等使得VSP资料的初至子波发生改变,导致常规的Q值估算方法质量难以得到保证。为此,利用塔里木盆地托甫台地区TP327井零偏移距VSP资料,综合考虑透射损失和球面扩散的影响,提出一种Q值正演模拟约束下的零偏移距VSP资料Q值估算方法。该方法通过研究区的纵波速度、横波速度和密度建立层状地质模型,利用射线追踪的方法,用大地吸收介质模型的Q值正演模拟结果,对频谱比法和经验公式估算的初始Q值进行修正,使得Q值正演模拟结果与实际地震波的衰减记录在频谱特征上吻合,从而得到更为精确的Q模型。TP327井Q值估算结果表明,该方法与李庆忠经验公式得到的结果在浅层比较接近,但在中层和深层,两者差异较大;Q值正演模拟结果约束的子波与实际VSP资料子波的频谱比较吻合,证实用Q值正演模拟约束得到的层间Q值更为可靠。利用Q值正演模拟约束方法得到的Q模型对研究区地震资料进行反Q滤波能显著提高地震资料分辨率,有助于提高碳酸盐岩地层中缝洞的识别精度。     

砂砾岩体沉积期次划分及其与物性的关系——以东营凹陷北部陡坡带Y920区块沙四上亚段为例

砂砾岩体储层岩性复杂、非均质性强，且扇体多期叠置，沉积期次划分困难。以东营凹陷北部陡坡带Y920区块沙四上亚段砂砾岩体为研究对象，建立以岩心、FMI图像为基础，以测井信息小波变换技术为核心，以三维地震为约束的砂砾岩扇体沉积期次划分方法。基于岩心、FMI图像可直观识别沉积期次界面，进而标定常规测井资料；经测井信息小波变换后表现出周期性振荡特征的小波系数曲线可反映沉积界面响应特征，结合功率谱筛选尺度因子，可定量对单井沉积期次进行划分；通过井-震结合，实现全区域期次界面的等时对比，将Y920区块砂砾岩体划分为8个沉积期次。沉积期次的发育控制着沉积相及岩性的分布，与物性展布也密切相关。研究结果表明，研究区块地层自下而上（8至1沉积期次）表现出退积沉积初始阶段（扇根—扇中）沉积期次中上部物性较好，沉积中间阶段（扇中）整个沉积期次物性均较好，而沉积末期阶段（扇中—扇端）沉积期次中下部物性较好，进而统计单井各期次有效储层的厚度，分期次刻画有效储层平面展布特征，发现3，4，5沉积期次的西北部为Y920区块有利储层展布范围。研究结果为东营凹陷北部陡坡带砂砾岩体油藏的精细勘探开发提供了依据。     

Location Related Signals with Satellite Image Fusion Method Using Visual Image Integration Method

Investigations were performed on a group utilizing (General Purpose Unit) GPU and executions were evaluated for the utilization of the created parallel usages to process satellite pictures from satellite Landsat7.The usage on a realistic group gives execution change from 2 to 18 times. The nature of the considered techniques was assessed by relative dimensionless global error in synthesis (ERGAS) and Quality Without Reference (QNR) measurements. The outcomes demonstrate execution picks ups and holding of value with the bunch of GPU contrasted with the outcomes and different analysts for a CPU and single GPU. The errand of upgrading the view of a scene by combining data caught from various picture sensors is usually known as multisensor picture combination. This paper displays a territory based picture combination calculation to consolidate SAR (Synthetic Aperture Radar) and optical pictures. The co-enlistment of the two images is first led utilizing the proposed enrollment method prior to picture combination. The paper displays a parallel execution of existing picture combination techniques on a graphical group. Parallel executions of techniques in view of discrete wavelet changes are created. Division into dynamic and motionless regions is then executed on the SAR surface picture for particular injection of the SAR picture into panchromatic (PAN) picture. An integrated image in view of these two pictures is produced by the novel region based combination plot, which forces diverse combination rules for each fragmented region. At long last, this picture is melded into a multispectral(MS) picture through the half breed skillet honing technique proposed in past research. Exploratory outcomes exhibit that the proposed strategy demonstrates preferred execution over different fusion algorithms and can possibly be connected to the multisensory combination of SAR and optical pictures.     

基于S变换的高速列车小幅蛇行识别方法

针对现有的高速列车监测方法没有考虑高速列车振动信号的非平稳特性,从而使得列车出现蛇形失稳现象的问题，提出一种采用S变换的方法对高速列车转向架信号进行处理，进而提取高速列车运行特征。采用最小二乘支持向量机（LS-SVM）对特征进行训练和识别，结果证明，基于S变换的特征提取方法的识别准确率达到了100%，优于基于小波变换的特征提取方法，从而可以及时预测高速列车的运行状态，保障列车的运行安全。     

面向数据流结构的指令映射优化方法

在高性能计算领域,数据流是一类重要的计算结构,也在很多实际场景表现出很好的性能和适用性。在数据流计算模式中,程序是以数据流图来表示的,数据流计算中一个关键的问题是如何将数据流图映射到多个执行单元上。通过分析现有数据流结构的指令映射方法及其不足,提出了基于数据流结构的新型指令映射优化方法。主要是根据多地址共享数据包的特性对指令映射方法进行优化,延迟多地址共享数据路由包的拆分,减少网络拥堵。     

Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings

Rolling element bearings (REBs) play an essential role in modern machinery and their condition monitoring is significant in predictive maintenance. Due to the harsh operating conditions, multi-fault may co-exist in one bearing and vibration signal always exhibits low signal-to-noise ratio (SNR), which causes difficulties in detecting fault. In the previous studies, maximum correlated kurtosis deconvolution (MCKD) has been validated as an efficient method to extract fault feature in the fault signals. Nonetheless, there are still some challenges when MCKD is applied to fault detection owing to the rigorous requirements of multiple input parameters. To overcome limitation, a multi-objective iterative optimization algorithm (MOIOA) for multi-fault diagnosis is proposed. In this method, correlated kurtosis (CK) is taken as a criterion to select optimal Morlet wavelet filter using the whale optimization algorithm (WOA). Meanwhile, to further eliminate the effect of the inaccurate period on CK, the update process of period is incorporated. After that, the simulated and experimental signals are utilized to testify the validity and superiority of the MOIOA for multiple faults detection by the comparison with MCKD. The results indicate that MOIOA is efficient to extract weak fault features even with heavy noise and harmonic interferences.     

Self‐similarity analysis of vehicle driver's electrodermal activity

This paper characterizes stress levels via a self‐similarity analysis of the electrodermal activity (EDA) collected in a real‐world driving context. To characterize the EDA richness over scales, the fractional Brownian motion (FBM) process and its corresponding exponent H, estimated via a wavelet‐based approach, are used. Specifically, an automatic scale range selection is proposed in order to detect the linearity in a log scale diagram. The procedure is applied to the EDA signals, from the open database drivedb, originally captured on the foot and the hand of the drivers during a real‐world driving experiment, designed to evoke different levels of arousal and stress. The estimated Hurst exponent H offers a distinction in stress levels when driving in highway versus city, with a reference to restful state of minimal stress level. Specifically, the estimated H values tend to decrease when the driving environmental complexity increases. In addition, the estimated H values on the foot EDA signals allow a better characterization of the driving task than that of hand EDA. The self‐similarity analysis was applied to various physiological signals in literature but not to the EDA so far, a signal which was found to correlate most with human affect. The proposed analysis could be useful in real‐time monitoring of stress levels in urban driving spaces, among other applications.