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.     

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.     

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.     

基于Marr小波改进的SIFT算法的遥感影像配准

〖HTH〗通讯作者〖HTSS〗:[ZK（]金〓燕(1991-)，女，山东济宁人，硕士研究生，主要从事图形图像处理，图像与视觉信息计算方面的研究。E\|mail:jyan0529@163.com。[ZK）] 〖ZW)〗〖HT〗 〖AM〗〖HT5SS〗〖MM(〗〖ZZ(S〗〖HT5”〗〖SX(B〗第34卷〓第3期〖〗2019年6月〖SX)〗[KG0.2mm]〖KG7*3〗〖HT〗〖SX(B〗遥〓感〓技〓术〓与〓应〓用〖〗〖WT5，6〗REMOTE SENSING TECHNOLOGY AND APPLICATION〖SX)〗〓〓〓〓〖KG6*2〗〖WT5”BX〗〖SX(B〗Vol.34〓No.3〖〗Jun.2019〖WT〗〖SX)〗〖ZZ)〗〖MM)〗〖HT〗 〖HT2H〗〖JZ(〗〖WTHZ〗〖STHZ〗 基于Marr小波改进的SIFT算法的遥感影像配准 〖STBZ〗〖WTBZ〗〖HT4K〗 张海涛，金〓燕，刘万军 〖HT5K〗 (辽宁工程技术大学 软件学院，辽宁 葫芦岛〓125105) 〖JZ)〗〖ＨＴ５Ｈ〗〖GK2!2〗摘要〖HTK〗: [KG(0.1mm]针对遥感图像配准方法中错误匹配点对过多、配准效率低和其他性能，提出了一种基于小波的遥感图像配准方法。首先，利用尺度空间理论下的Marr小波对参考图像和待配准图像进行特征提取，然后利用欧氏距离对参考图像和待配准图像的特征点进行初配准，再根据随机采样一致法，对初配准结果进行精配准。为了验证方法的有效性，选择无人机实时航拍图像、不同时相变化遥感图像以及遥感不同高度的遥感图像。实验结果表明：该方法与SIFT(Scale Invariant Feature Transform)算法以及其他改进SIFT算法相比可以有效剔除错误匹配点对，提高了配准精度，同时提高配准效率两倍以上。该方法可以应用于不同遥感数据源，能够有效地提高配准精度，降低配准时间。[KG)] 〖ＨＴＨ〗关〓键〓词〖ＨＴ５Ｋ〗: 遥感图像配准;Marr小波;欧氏距离;随机采样一致法 〖ＨＴＨ〗中图分类号〖HTSS〗:TP79〓〓〖HTH〗文献标志码〖HTSS〗:A〓〓〖HTH〗文章编号〖HTSS〗:1004 0323(2019)03 0622 08 〖HK〗〖HT5SS〗     

基于双树复小波的无参考立体图像质量评价

随着3D技术的不断发展，立体图像的使用领域越来越广泛，同时人们对图像的清晰度要求越来越高，因此，立体图像的质量评价成为关注点，基于此，提出了一种基于双树复小波变换的立体图像质量评价算法。使用双树复小波变换对立体图像的左、右视图进行处理，生成纹理结构图像，且根据最小能量误差的原理，获取左右视图的视差图；对纹理结构图像和视差图提取非对称广义高斯分布模型的参数、梯度幅值、相对梯度方向方差和奇异值曲线与坐标轴的面积等特征；使用AdaBoosting BP神经网络，进行训练和预测立体图像的质量得分。在LIVE立体图像数据库上的实验结果表明，新方法预测得分与主观得分有较好的一致性，获得了比较好的实验结果。     

WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.