小波变换用于输油管道漏磁检测信号处理

管道腐蚀是目前输油管道运营中的重要难题。漏磁检测是无损检测中的一种重要方式,采用漏磁法对输油管道进行无损检测时,采集的漏磁信号具有数据量大和附带有大量噪声的特性。采用小波变换中的Mallat算法可以有效地对漏磁信号进行处理,使信号便于储存和分析。     

基于小波变换的磁记忆信号去噪方法研究

在利用小波变换对油库（站）输油管道的磁记忆检测信号进行消噪处理时，主要有三种方法，基于小波变换的非线性方法能够获得较理想的处理效果。本文详细介绍了基于该方法的信号处理原理，并重点介绍了影响消噪效果的三项因素。非线性方法在工程实践中取得了良好的效果。     

基于自适应提升小波的漏磁信号降噪

为了解决在故障诊断中漏磁信号的噪声干扰问题,在研究了提升小波基本理论原理和特点的基础之上,提出了自适应提升小波的改进算法,并在阈值处理时引入了综合门限阈值法。应用该算法对实际采集的钢丝绳漏磁信号进行去噪处理,并将该去噪效果与现用的传统小波去噪效果进行了比较。仿真结果表明,基于自适应提升小波方法的方案比传统小波变换有更好的去噪效果。     

小波变换在雷达物位计信号处理中的应用

雷达物位计信号处理的关键是去除噪声,提取有用信息.通过研究调频连续波FMCW雷达物位计工作原理及信号处理的基本算法,对比传统的采用傅里叶变换进行信号处理的方法,提出了采用小波变换来去除回波的干扰信号.实际应用结果表明,小波分析可以有效地应用于分析物位计回波信号,为提高测量精度等后续工作的开展提供稳定数据.     

An integrated approach for structural damage identification using wavelet neuro-fuzzy model

Structural damage can be identified by processing structural vibration response signals and excitation data, and thus the suitability of signal processing methods is essential to structural damage identification. To explore an intelligent signal processing method for structural damage identification, the paper integrated wavelet real-time filtering algorithm, Adaptive Neruo-Fuzzy Inference System (ANFIS) and interval modeling technique to process structural response signals and excitation data. With Wavelet Transform (WT) algorithm filtering random noise, ANFIS was found to model the structural behavior properly and interval modeling technique to quantify damage index accurately. The rapid identifications of several unknown damages and small damages indicate the efficiency of this integrated method. The comparison of these results and some other signal processing methods shows that, the proposed method can be used to identify both the time and the location when the structural damage occurs unexpectedly.     

Frequency domain subspace-based identification of discrete-time power spectra from nonuniformly spaced measurements

In this paper, we present a new subspace-based algorithm for the identification of multi-input/multi-output, square, discrete-time, linear-time invariant systems from nonuniformly spaced power spectrum measurements. The algorithm is strongly consistent and it is illustrated with one practical example that solves a stochastic road modeling problem.     

Frequency domain subspace-based identification of discrete-time power spectra from uniformly spaced measurements

In this paper, we revisit the problem of identifying multi-input/multi-output linear-time-invariant discrete-time systems from measured power spectrum data on uniform grids of frequencies studied by Van Overschee, De Moor, Dehandschutter, and Swevers (1997). We show that the algorithm proposed by these authors is not consistent. Then, we propose an interpolatory identification algorithm which is strongly consistent when the corruptions in the spectrum measurements have a bounded covariance function. The performance of the proposed algorithm is demonstrated in a simulation example.     

桥梁无线传感系统设计与分析

为满足桥梁监测的要求,对采集的数据实现无线传输,设计了数据无线采集传输系统.以STM微处理器和振弦式传感器为核心元件,对桥梁应变进行监测分析.分析了振弦式传感器的工作原理,对比分析了两种传统的激振原理,并结合两种激振方式的优缺点,提出了对低压扫频激振原理的改进方法,对激振电路进行了优化设计,同时设计了基于NRF905无线传感网络,完成了数据采集的无线发送和接收.实验结果表明:改进后的激振电路测量精度小于0.1％,系统工作稳定,无线传输功能满足桥梁监测要求.     

Wavelet analysis of hyperspectral reflectance data for detecting pitted morningglory (Ipomoea lacunosa) in soybean (Glycine max)

This research determined the potential for wavelet-based analysis of hyperspectral reflectance signals for detecting the presence of early season pitted morningglory when intermixed with soybean and soil. Ground-level hyperspectral reflectance signals were collected in a field experiment containing plots of soybean and plots containing soybean intermixed with pitted morningglory in a conventional tillage system. The collected hyperspectral signals contained mixed reflectances for vegetation and background soil in each plot. Pure reflectance signals were also collected for pitted morningglory, soybean, and bare soil so that synthetically mixed reflectance curves could be generated, evaluated, and the mixing proportions controlled. Wavelet detail coefficients were used as features in linear discriminant analysis for automated discrimination between the soil+soybean and the soil+soybean+pitted morningglory classes. A total of 36 different mother wavelets were investigated to determine the effect of mother wavelet selection on the ability to detect the presence of pitted morningglory. When the growth stage was two to four leaves, which is still controllable with herbicide, the weed could be detected with at least 87% accuracy, regardless of mother wavelet selection. Moreover, the Daubechies 3, Daubechies 5, and Coiflet 5 mother wavelets resulted in 100% classification accuracy. Most of the best wavelet coefficients, in terms of discriminating ability, were derived from the red-edge and the near-infrared regions of the spectrum. For comparison purposes, the raw spectral bands and principal components were evaluated as possible discriminating features. For the two-leaf to four-leaf weed growth stage, the two methods resulted in classification accuracies of 83% and 81%, respectively. The wavelet-based method was shown to be very promising in detecting the presence of early season pitted morningglory in mixed hyperspectral reflectances.     

GPR data analysis using multi-agent and clustering approaches: A tool for technical management of water supply systems

In this paper a combination of the multi-agent paradigm and a very well known clustering technique is used for unsupervised classification of subsoil characteristics working on a collection of ground penetrating radar (GPR) survey files. The main objective is to assess the feasibility of extracting features and patterns from radargrams. By optimizing both the field work and the interpretation of the raw images our target is to obtain visualizations that are automatic, fast, and reliable so to suitably assess the characteristics of the prospected areas and extract relevant information. The architecture of the system may be split into three interrelated processes: (a) pre-processing, (b) hierarchical agglomerative clustering, and (c) retrieval and visualization. The proposed system shows the viability of arranging GPR data from survey files into clusters, thus reducing the amount of information to be dealt with, while preserving its reliability. The system also helps characterize subsoil properties in a very natural and fast way, favors GPR files interpretation by non-highly qualified personnel, and does not require any assumptions about subsoil parameters. A powerful tool to analyze underground components in water supply systems is thus generated that acts in a non-destructive way and supports decision-making in water supply management.     

Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information

Several real-time short-term prediction methods, based on time-series modeling of past continuous glucose monitoring (CGM) sensor data have been proposed with the aim of allowing the patient, on the basis of predicted glucose concentration, to anticipate therapeutic decisions and improve therapy of type 1 diabetes. In this field, neural network (NN) approaches could improve prediction performance handling in their inputs additional information. In this contribution we propose a jump NN prediction algorithm (horizon 30 min) that exploits not only past CGM data but also ingested carbohydrates information. The NN is tuned on data of 10 type 1 diabetics and then assessed on 10 different subjects. Results show that predictions of glucose concentration are accurate and comparable to those obtained by a recently proposed NN approach (Zecchin et al. (2012) [26]) having higher structural and algorithmical complexity and requiring the patient to announce the meals. This strengthen the potential practical usefulness of the new jump NN approach.     

Accuracy analysis of time domain maximum likelihood method and sample maximum likelihood method for errors-in-variables and output error identification

For identifying errors-in-variables models, the time domain maximum likelihood (TML) method and the sample maximum likelihood (SML) method are two approaches. Both methods give optimal estimation accuracy but under different assumptions. In the TML method, an important assumption is that the noise-free input signal is modelled as a stationary process with rational spectrum. For SML, the noise-free input needs to be periodic. It is interesting to know which of these assumptions contain more information to boost the estimation performance. In this paper, the estimation accuracy of the two methods is analyzed statistically for both errors-in-variables (EIV) and output error models (OEM). Numerical comparisons between these two estimates are also done under different signal-to-noise ratios (SNRs). The results suggest that TML and SML have similar estimation accuracy at moderate or high SNR for EIV. For OEM identification, these two methods have the same accuracy at any SNR.     

A computer-based system for the acquisition and analysis of data from a field tillage study

A computer-based data acquisition system is described that enables both continuous real-time control of data collection from some 250 sensors deployed at a remote field site, and immediate conversion from analogue data to the required physical parameter. In addition, data analysis programs can be developed and run concurrently with data collection. Important aspects of the computer operating system are discussed, as is the overall programming strategy for a field experiment. An account is given of the control of scanning and measuring instruments (connected to the computer via a standard parallel-interface bus), and of the technique adopted for utilizing the file management software to store processed data in an easily retrievable form. A FORTRAN program to control the measurement of soil temperatures is discussed; and an assessment is given of the equipment's performance in use.     

Construction of behavioral models for microwave devices from time domain large‐signal measurements to speed up high‐level design simulations

Behavioral models for microwave devices from time domain large‐signal measurements are developed. For the presented examples, the model is defined by representing the terminal currents as a function of the terminal voltages and their derivatives. When using these models as building blocks of higher level designs, the simulation speed is significantly improved. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 54–61, 2003.     

An analysis of sprain and strain injury data for the New Zealand meat processing industry from national and industry injury surveillance databases

Data on musculoskeletal disorders (MSD) in meat processing and the tasks in which they occur is limited in the literature. This paper provides a summary of such data from the New Zealand industry. Despite the high incidence of MSD in meat processing in New Zealand, little research has been undertaken to identify and assess high-risk tasks and develop interventions to address them. This paper reports on the initial stages of a 2-year government funded project to address these issues. Findings are presented from the analysis of data from two injury surveillance databases. Accident Compensation Corporation national data claims assisted in defining the industry and indicated factors for further assessment, including consideration of claimants' gender, ethnicity and geographical region. National Injury Database industry data claims helped to identify specific tasks in which MSD are more likely to occur by departments and for the two main animal species processed. These factors have helped shape the assessment of high-risk tasks currently undertaken in the meat processing industry.