Advances in Adaptive Data Analysis

This work proposes a feature extraction method from Hilbert–Huang-transform- or HHT-based data analysis of built-in test (BIT) signals, which are sampled on-site and without reference signals for fault diagnosis. The proposed method fully utilizes self-adaptation of the HHT method in characterizing the envelope amplitude and instantaneous frequency for the intrinsic mode function (IMF), so as to single out the features with most irregular characteristics. Simulations are carried out on steering gear feedback voltage signal of target drone aircraft, and the extracted features show great potential for the improvement in built-in fault diagnosis.

MIAO ZHANG

YI SHEN

XIAO-LEI ZHANG

ZHI-BO WANG

YE ZHANG

BUILT-IN TEST SIGNAL FEATURE EXTRACTION METHOD BASED ON HILBERT–HUANG TRANSFORM

基于希尔伯特-黄变换的内建测试信号特征提取方法

This paper presents a data-adaptive technique of cardiovascular disease diagnosis by analyzing electrocardiogram (ECG) signals. The separation of high-frequency (HF) and low-frequency (LF) components are performed by employing empirical mode decomposition (EMD) designed for analyzing nonstationary and non-linear signals. The EMD is used to decompose ECG signal into a finite set of band-limited AM–FM signals termed as intrinsic mode functions (IMFs). Then the LF and HF components of ECG signals are obtained by partial reconstruction based on the energy distribution of IMFs. The extracted HF and LF signals of the ECG are analyzed separately to make the remarks for better diagnosis of the cardiovascular diseases. The experimental results are also illustrated using some ECG signals of normal and abnormal subjects.

MD. RABIUL ISLAM

SOMLAL DAS

KEIKICHI HIROSE

MD. KHADEMUL ISLAM MOLLA

ANALYSIS OF ECG SIGNALS USING DATA-ADAPTIVE TIME DOMAIN FILTERING FOR CARDIOVASCULAR DISEASE DIAGNOSIS

基于数据自适应时域滤波的心电信号分析用于心血管疾病诊断

In the process of photoacoustic imaging (PAI), the photoacoustic signals are polluted by a strong background white noise, which is caused by many factors such as the system thermal noise or short noise, the tissue reflecting or scattering interference, and the impedance match lack between the transducer and tissue. The inevitable noise can degrade the contrast sensitivity of photoacoustic images seriously. In this paper, based on the energy window, a CMSE-EMD denoising method is employed to photoacoustic image reconstruction. Results of the simulation demonstrate that it can eliminate the image artifacts more effectively and achieve great improvement in image quality.

MINGJIAN SUN

NAIZHANG FENG

XIANGLI SHEN

JIANGANG LI

PHOTOACOUSTIC SIGNALS DENOISING BASED ON EMPIRICAL MODE DECOMPOSITION AND ENERGY-WINDOW METHOD

基于经验模态分解和能量窗方法的光声信号去噪

The requirements for inspection and management of track irregularity have to be more rigorous due to safety and serviceability concerns as the increment of the train speed of high-speed train. The wavelength of track irregularity significantly affects running safety and riding comfort. To improve inspection, operation, efficiency, and address, the inability of the mid-chord offset method currently used on track inspection cars to identify some irregularity wavelengths, a measurement and assessment method is presented in this paper. Nonstationary and nonlinear vehicle dynamic response data caused by irregularities were measured by accelerometers mounted on the axle-box of a vehicle. The Hilbert–Huang transform (HHT) technique was applied to analyze the data. Components of specific irregularity wavelength could be effectively identified and accurately matched to the data measured by the track inspection car. Additionally, to obtain the complete waveform of the track irregularity, we propose a restoration method of track irregularity that combines the characteristics of the measurement magnification in the chord-based measuring system and the HHT signal-processing techniques.

Hsin Chu Tsai

Chung Yue Wang

Norden E. Huang

FAST INSPECTION AND IDENTIFICATION TECHNIQUES FOR TRACK IRREGULARITIES BASED ON HHT ANALYSIS

基于HHT分析的轨道不平顺快速检测与识别技术

This paper deals with Bayesian estimation of parameters in the proportional hazards model of random censorship for the Weibull distribution under different loss functions. We consider both the informative and noninformative priors on the model parameters to obtain the Bayes estimates using Gibbs sampling scheme. Maximum likelihood estimates are also obtained for comparison purposes. A simulation study is carried out to observe the behavior of the proposed estimators for different sample sizes and for different censoring parameters. One real data analysis is performed for illustration.

MUHAMMAD YAMEEN DANISH

MUHAMMAD ASLAM

BAYESIAN ESTIMATION IN RANDOM CENSORSHIP MODEL FOR WEIBULL DISTRIBUTION UNDER DIFFERENT LOSS FUNCTIONS

贝叶斯估计在不同损失函数下的威布尔分布随机删失模型

Significant portions of the United States (U.S.) property, commerce and ecosystem assets are located at or near the coast, making them vulnerable to sea level variability and change, especially relative rises. Although global mean sea level (MSL) and sea level rise (SLR) are fundamental considerations, regional mean sea level (RSL) variability along the boundaries of U.S. along the two ocean basins are critical, particularly if the amplitudes of seasonal to annual to inter-annual variability is high. Of interest is that the conventional wisdom of the U.S. agencies, the National Aeronautics and Space Administration (NASA) and the National Oceanic and Atmospheric Administration (NOAA) which both contend that the sources of sea level rise are related principally to heat absorption and release by the ocean(s) to the atmosphere and vice versa, and by Polar glacier melting and freshwater input into the ocean(s). While these phenomena are of great importance to SLR and sea level variability (SLV), we assess a suite of climate factors and the Gulf Stream, for evidence of correlations and thus possible influences; though causality is beyond the scope of this study. In this study, climate factors related to oceanic and atmospheric heat purveyors and reservoirs are analyzed and assessed for possible correlations with sea level variability and overall trends on actionable scales (localized as opposed to global scale). The results confirm that oceanic and atmospheric temperature variability and the disposition of heat accumulation or the lack thereof, are important players in sea level variability and rise, but also that the Atlantic Multi-Decadal Oscillation, the El Niño-Southern Oscillation, the Pacific Decadal Oscillation, the Arctic Oscillation, the Quasi-Biennial Oscillation, the North Atlantic Oscillation, Solar Irradiance, the Western Boundary Current-Gulf Stream, and other climate factors, can have strong correlative and perhaps even causal, modulating effects on the monthly to seasonal to annual to inter-annual to decadal to multi-decadal sea level variability at the community level.

Leonard J. Pietrafesa

Shaowu Bao

Tingzhuang Yan

Michael Slattery

Paul T. Gayes

On Sea Level Variability and Trends in United States Coastal Waters and Relationships with Climate Factors

美国沿海水域海平面变化趋势及其与气候因素的关系

This study uses the Hilbert–Huang transform (HHT), a signal analysis method for nonlinear and non-stationary processes, to separate signals of varying frequencies in a nonlinear system governed by the Lorenz equations. Similar to the Fourier series expansion, HHT decomposes a data time series into a sum of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Unlike an infinite number of Fourier series terms, the EMD always yields a finite number of IMFs, whose sum is equal to the original time series exactly. Using the HHT approach, the properties of the Lorenz attractor are interpreted in a time–frequency frame. This frame shows that: (i) the attractor is symmetric for [Formula: see text] (i.e. invariant for [Formula: see text]), even though the signs on [Formula: see text] and [Formula: see text] are changed; (ii) the attractor is sensitive to initial conditions even by a small perturbation, measured by the divergence of the trajectories over time; (iii) the Lorenz system goes through “windows” of chaos and periodicity; and (iv) at times, a system can be both chaotic and periodic for a given [Formula: see text] value. IMFs are a finite collection of decomposed quasi-periodic signals, starting from the highest to lowest frequencies, providing detection of the lower frequency signals that may have otherwise been “hidden” by their higher frequency counterparts. EMD decomposes the original signal into a family of distinct IMF signals, the Hilbert spectra are a “family portrait” of time–frequency–amplitude interplay of all IMF members. Together with viewing the IMF energy, it is easy to discern where each IMF resides in the spectra in relation to one another. In this study, the majority of high amplitude signals appear at low frequencies, approximately 0.5–1.5. Although our numerical experiments are limited to only two specific cases, our HHT analyses of time–frequency, marginal spectra, and energy and quasi-periodicity of each IMF provide a novel approach to exploring the profound and phenomena-rich Lorenz system.

Gregori J. Clarke

Samuel S. P. Shen

Hilbert–Huang Transform Approach to Lorenz Signal Separation

希尔伯特-黄变换在洛伦兹信号分离中的应用

In this paper, we developed a new approach for the analysis of physiological time series. An iterative convolution filter is used to decompose the time series into various components. Statistics of these components are extracted as features to characterize the mechanisms underlying the time series. Studies have shown that many normal physiological systems involve irregularity, while the decrease of irregularity usually implies abnormality. This motivates the use of the statistics for “outliers” in the components as features measuring irregularity. Support vector machines are used to select the most relevant features that are able to differentiate the time series from normal and abnormal systems. This new approach is successfully used in the study of congestive heart failure by heart beat interval time series.

Dong Mao

Yang Wang

Qiang Wu

A New Approach for Physiological Time Series

生理时间序列的新方法

Maximum difference (MaxDiff) is a discrete choice modeling approach widely used in marketing research for finding utilities and preference probabilities among multiple alternatives. It can be seen as an extension of the paired comparison in Thurstone and Bradley–Terry techniques for the simultaneous presenting of three, four or more items to respondents. A respondent identifies the best and the worst ones, so the remaining are deemed intermediate by preference alternatives. Estimation of individual utilities is usually performed in a hierarchical Bayesian (HB)-multinomial-logit (MNL) modeling. MNL model can be reduced to a logit model by the data composed of two specially constructed design matrices of the prevalence from the best and the worst sides. The composed data can be of a large size which makes logistic modeling less precise and very consuming in computer time and memory. This paper describes how the results for utilities and choice probabilities can be obtained from the raw data, and instead of HB methods the empirical Bayes techniques can be applied. This approach enriches MaxDiff and is useful for estimations on large data sets. The results of analytical approach are compared with HB-MNL and several other techniques.

Stan Lipovetsky

Michael Conklin

MaxDiff Priority Estimations with and without HB-MNL

MaxDiff优先级估计有和没有HB-MNL

We propose using a rolling ball algorithm, which moves a ball along a time series signal, to sort the local extrema within a signal according to a geometric tangibility criterion. Letting the ball always roll above or below the signal, we are able to classify the signal’s extrema according to their tangibility: touched or not touched by the ball. Applying this ball-tangibility information to select an extremum in the empirical mode decomposition (EMD) algorithm, we are able to prevent the mode mixing problem in analyzing intermittent signals and decompose mode functions satisfying bandpass filtering properties.

Publication Information	Publisher: World Scientific Publishing Co. Pte Ltd，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2009，Proportion of original research papers: ，Self Citation Rate:， Gold OA Rate:

Advances in Adaptive Data Analysis

Journal Citation Format

A journal article with 1 author

A journal article with 2 authors

A journal article with 3 authors

A journal article with 5 or more authors

Books Citation Format

Thesis Citation Format

Web sites Citation Format

Patent Citation Format

Staying up late manually editing references? ivySCI automatically matches journals and helps you generate references with a single click.

Share Submission Experience