Computer implemented empirical mode decomposition method, apparatus and article of manufacture

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

1999-01-01

A computer implemented physical signal analysis method is invented. This method includes two essential steps and the associated presentation techniques of the results. All the steps exist only in a computer: there are no analytic expressions resulting from the method. The first step is a computer implemented Empirical Mode Decomposition to extract a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform. The final result is the Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum.

Analyzing the Multiscale Processes in Tropical Cyclone Genesis Associated with African Easterly Waves using the PEEMD. Part I: Downscaling Processes

NASA Astrophysics Data System (ADS)

Wu, Y.; Shen, B. W.; Cheung, S.

2016-12-01

Recent advance in high-resolution global hurricane simulations and visualizations have collectively suggested the importance of both downscaling and upscaling processes in the formation and intensification of TCs. To reveal multiscale processes from massive volume of global data for multiple years, a scalable Parallel Ensemble Empirical Mode Decomposition (PEEMD) method has been developed for the analysis. In this study, the PEEMD is applied to analyzing 10-year (2004-2013) ERA-Interim global 0.750 resolution reanalysis data to explore the role of the downscaling processes in tropical cyclogenesis associated with African Easterly Waves (AEWs). Using the PEEMD, raw data are decomposed into oscillatory Intrinsic Function Modes (IMFs) that represent atmospheric systems of the various length scales and the trend mode that represents a non-oscillatory large scale environmental flow. Among oscillatory modes, results suggest that the third oscillatory mode (IMF3) is statistically correlated with the TC/AEW scale systems. Therefore, IMF3 and trend mode are analyzed in details. Our 10-year analysis shows that more than 50% of the AEW associated hurricanes reveal the association of storms' formation with the significant downscaling shear transfer from the larger-scale trend mode to the smaller scale IMF3. Future work will apply the PEEMD to the analysis of higher-resolution datasets to explore the role of the upscaling processes provided by the convection (or TC) in the development of the TC (or AEW). Figure caption: The tendency for horizontal wind shear for the total winds (black line), IMF3 (blue line), and trend mode (red line) and SLP (black dotted line) along the storm track of Helene (2006).

Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain.

PubMed

Zhuang, Ning; Zeng, Ying; Tong, Li; Zhang, Chi; Zhang, Hanming; Yan, Bin

2017-01-01

This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) automatically. Multidimensional information of IMF is utilized as features, the first difference of time series, the first difference of phase, and the normalized energy. The performance of the proposed method is verified on a publicly available emotional database. The results show that the three features are effective for emotion recognition. The role of each IMF is inquired and we find that high frequency component IMF1 has significant effect on different emotional states detection. The informative electrodes based on EMD strategy are analyzed. In addition, the classification accuracy of the proposed method is compared with several classical techniques, including fractal dimension (FD), sample entropy, differential entropy, and discrete wavelet transform (DWT). Experiment results on DEAP datasets demonstrate that our method can improve emotion recognition performance.

A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine.

PubMed

Wang, Deyun; Wei, Shuai; Luo, Hongyuan; Yue, Chenqiang; Grunder, Olivier

2017-02-15

The randomness, non-stationarity and irregularity of air quality index (AQI) series bring the difficulty of AQI forecasting. To enhance forecast accuracy, a novel hybrid forecasting model combining two-phase decomposition technique and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm is developed for AQI forecasting in this paper. In phase I, the complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose the AQI series into a set of intrinsic mode functions (IMFs) with different frequencies; in phase II, in order to further handle the high frequency IMFs which will increase the forecast difficulty, variational mode decomposition (VMD) is employed to decompose the high frequency IMFs into a number of variational modes (VMs). Then, the ELM model optimized by DE algorithm is applied to forecast all the IMFs and VMs. Finally, the forecast value of each high frequency IMF is obtained through adding up the forecast results of all corresponding VMs, and the forecast series of AQI is obtained by aggregating the forecast results of all IMFs. To verify and validate the proposed model, two daily AQI series from July 1, 2014 to June 30, 2016 collected from Beijing and Shanghai located in China are taken as the test cases to conduct the empirical study. The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.

Signatures of the seismic source in EMD-based characterization of the 1994 Northridge, California, earthquake recordings

USGS Publications Warehouse

Zhang, R.R.; Ma, S.; Hartzell, S.

2003-01-01

In this article we use empirical mode decomposition (EMD) to characterize the 1994 Northridge, California, earthquake records and investigate the signatures carried over from the source rupture process. Comparison of the current study results with existing source inverse solutions that use traditional data processing suggests that the EMD-based characterization contains information that sheds light on aspects of the earthquake rupture process. We first summarize the fundamentals of the EMD and illustrate its features through the analysis of a hypothetical and a real record. Typically, the Northridge strong-motion records are decomposed into eight or nine intrinsic mode functions (IMF's), each of which emphasizes a different oscillation mode with different amplitude and frequency content. The first IMF has the highest-frequency content; frequency content decreases with an increase in IMF component. With the aid of a finite-fault inversion method, we then examine aspects of the source of the 1994 Northridge earthquake that are reflected in the second to fifth IMF components. This study shows that the second IMF is predominantly wave motion generated near the hypocenter, with high-frequency content that might be related to a large stress drop associated with the initiation of the earthquake. As one progresses from the second to the fifth IMF component, there is a general migration of the source region away from the hypocenter with associated longer-period signals as the rupture propagates. This study suggests that the different IMF components carry information on the earthquake rupture process that is expressed in their different frequency bands.

Computer implemented empirical mode decomposition method apparatus, and article of manufacture utilizing curvature extrema

NASA Technical Reports Server (NTRS)

Shen, Zheng (Inventor); Huang, Norden Eh (Inventor)

2003-01-01

A computer implemented physical signal analysis method is includes two essential steps and the associated presentation techniques of the results. All the steps exist only in a computer: there are no analytic expressions resulting from the method. The first step is a computer implemented Empirical Mode Decomposition to extract a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals based on local extrema and curvature extrema. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform. The final result is the Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum.

Analysis of Vibration and Noise of Construction Machinery Based on Ensemble Empirical Mode Decomposition and Spectral Correlation Analysis Method

NASA Astrophysics Data System (ADS)

Chen, Yuebiao; Zhou, Yiqi; Yu, Gang; Lu, Dan

In order to analyze the effect of engine vibration on cab noise of construction machinery in multi-frequency bands, a new method based on ensemble empirical mode decomposition (EEMD) and spectral correlation analysis is proposed. Firstly, the intrinsic mode functions (IMFs) of vibration and noise signals were obtained by EEMD method, and then the IMFs which have the same frequency bands were selected. Secondly, we calculated the spectral correlation coefficients between the selected IMFs, getting the main frequency bands in which engine vibration has significant impact on cab noise. Thirdly, the dominated frequencies were picked out and analyzed by spectral analysis method. The study result shows that the main frequency bands and dominated frequencies in which engine vibration have serious impact on cab noise can be identified effectively by the proposed method, which provides effective guidance to noise reduction of construction machinery.

Palm vein recognition based on directional empirical mode decomposition

NASA Astrophysics Data System (ADS)

Lee, Jen-Chun; Chang, Chien-Ping; Chen, Wei-Kuei

2014-04-01

Directional empirical mode decomposition (DEMD) has recently been proposed to make empirical mode decomposition suitable for the processing of texture analysis. Using DEMD, samples are decomposed into a series of images, referred to as two-dimensional intrinsic mode functions (2-D IMFs), from finer to large scale. A DEMD-based 2 linear discriminant analysis (LDA) for palm vein recognition is proposed. The proposed method progresses through three steps: (i) a set of 2-D IMF features of various scale and orientation are extracted using DEMD, (ii) the 2LDA method is then applied to reduce the dimensionality of the feature space in both the row and column directions, and (iii) the nearest neighbor classifier is used for classification. We also propose two strategies for using the set of 2-D IMF features: ensemble DEMD vein representation (EDVR) and multichannel DEMD vein representation (MDVR). In experiments using palm vein databases, the proposed MDVR-based 2LDA method achieved recognition accuracy of 99.73%, thereby demonstrating its feasibility for palm vein recognition.

Appropriate IMFs associated with cepstrum and envelope analysis for ball-bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Tsao, Wen-Chang; Pan, Min-Chun

2014-03-01

The traditional envelope analysis is an effective method for the fault detection of rolling bearings. However, all the resonant frequency bands must be examined during the bearing-fault detection process. To handle the above deficiency, this paper proposes using the empirical mode decomposition (EMD) to select a proper intrinsic mode function (IMF) for the subsequent detection tools; here both envelope analysis and cepstrum analysis are employed and compared. By virtue of the band-pass filtering nature of EMD, the resonant frequency bands of structure to be measured are captured in the IMFs. As impulses arising from rolling elements striking bearing faults modulate with structure resonance, proper IMFs potentially enable to characterize fault signatures. In the study, faulty ball bearings are used to justify the proposed method, and comparisons with the traditional envelope analysis are made. Post the use of IMFs highlighting faultybearing features, the performance of using envelope analysis and cepstrum analysis to single out bearing faults is objectively compared and addressed; it is noted that generally envelope analysis offers better performance.

Identifying Decadal to Multi-decadal Variability in the Pacific by Empirical Mode Decomposition

NASA Astrophysics Data System (ADS)

Sommers, L. A.; Hamlington, B.; Cheon, S. H.

2016-12-01

Large scale climate variability in the Pacific Ocean like that associated with ENSO and the Pacific Decadal Oscillation (PDO) has been shown to have a significant impact on climate and sea level across a range of timescales. The changes related to these climate signals have worldwide impacts on fisheries, weather, and precipitation patterns among others. Understanding these inter-annual to multi-decadal oscillations is imperative to longer term climate forecasts and understanding how climate will behave, and its effect on changes in sea level. With a 110-year reconstruction of sea level, we examine decadal to multi-decadal variability seen in the sea level fluctuations in the Pacific Ocean. Using empirical mode decomposition (EMD), we break down regional sea level into a series of intrinsic mode functions (IMFs) and attempt attribution of these IMFs to specific climate modes of variability. In particular, and not unexpectedly, we identify IMFs associated with the PDO, finding correlations between the PDO Index and IMFs in the Pacific Ocean upwards of 0.6-0.8 over the 110-year reconstructed record. Perhaps more significantly, we also find evidence of a longer multi-decadal signal ( 50-60 years) in the higher order IMFs. This lower frequency variability has been suggested in previous literature as influencing GMSL, but here we find a regional pattern associated with this multi-decadal signal. By identifying and separating these periodic climate signals, we can gain a better understanding of how the sea level variability associated with these modes can impact sea level on short timescales and serve to exacerbate the effects of long-term sea level change.

A New Method for Nonlinear and Nonstationary Time Series Analysis and Its Application to the Earthquake and Building Response Records

NASA Technical Reports Server (NTRS)

Huang, Norden E.

1999-01-01

A new method for analyzing nonlinear and nonstationary data has been developed. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time that give sharp identifications of imbedded structures. The final presentation of the results is an energy-frequency-time distribution, designated as the Hilbert Spectrum, Example of application of this method to earthquake and building response will be given. The results indicate those low frequency components, totally missed by the Fourier analysis, are clearly identified by the new method. Comparisons with Wavelet and window Fourier analysis show the new method offers much better temporal and frequency resolutions.

Reconstructed phase spaces of intrinsic mode functions. Application to postural stability analysis.

PubMed

Snoussi, Hichem; Amoud, Hassan; Doussot, Michel; Hewson, David; Duchêne, Jacques

2006-01-01

In this contribution, we propose an efficient nonlinear analysis method characterizing postural steadiness. The analyzed signal is the displacement of the centre of pressure (COP) collected from a force plate used for measuring postural sway. The proposed method consists of analyzing the nonlinear dynamics of the intrinsic mode functions (IMF) of the COP signal. The nonlinear properties are assessed through the reconstructed phase spaces of the different IMFs. This study shows some specific geometries of the attractors of some intrinsic modes. Moreover, the volume spanned by the geometric attractors in the reconstructed phase space represents an efficient indicator of the postural stability of the subject. Experiments results corroborate the effectiveness of the method to blindly discriminate young subjects, elderly subjects and subjects presenting a risk of falling.

A Cutting Pattern Recognition Method for Shearers Based on Improved Ensemble Empirical Mode Decomposition and a Probabilistic Neural Network

PubMed Central

Xu, Jing; Wang, Zhongbin; Tan, Chao; Si, Lei; Liu, Xinhua

2015-01-01

In order to guarantee the stable operation of shearers and promote construction of an automatic coal mining working face, an online cutting pattern recognition method with high accuracy and speed based on Improved Ensemble Empirical Mode Decomposition (IEEMD) and Probabilistic Neural Network (PNN) is proposed. An industrial microphone is installed on the shearer and the cutting sound is collected as the recognition criterion to overcome the disadvantages of giant size, contact measurement and low identification rate of traditional detectors. To avoid end-point effects and get rid of undesirable intrinsic mode function (IMF) components in the initial signal, IEEMD is conducted on the sound. The end-point continuation based on the practical storage data is performed first to overcome the end-point effect. Next the average correlation coefficient, which is calculated by the correlation of the first IMF with others, is introduced to select essential IMFs. Then the energy and standard deviation of the reminder IMFs are extracted as features and PNN is applied to classify the cutting patterns. Finally, a simulation example, with an accuracy of 92.67%, and an industrial application prove the efficiency and correctness of the proposed method. PMID:26528985

Optimal Averages for Nonlinear Signal Decompositions - Another Alternative for Empirical Mode Decomposition

DTIC Science & Technology

2014-10-01

nonlinear and non-stationary signals. It aims at decomposing a signal, via an iterative sifting procedure, into several intrinsic mode functions ...stationary signals. It aims at decomposing a signal, via an iterative sifting procedure into several intrinsic mode functions (IMFs), and each of the... function , optimization. 1 Introduction It is well known that nonlinear and non-stationary signal analysis is important and difficult. His- torically

Seismic random noise attenuation method based on empirical mode decomposition of Hausdorff dimension

NASA Astrophysics Data System (ADS)

Yan, Z.; Luan, X.

2017-12-01

Introduction Empirical mode decomposition (EMD) is a noise suppression algorithm by using wave field separation, which is based on the scale differences between effective signal and noise. However, since the complexity of the real seismic wave field results in serious aliasing modes, it is not ideal and effective to denoise with this method alone. Based on the multi-scale decomposition characteristics of the signal EMD algorithm, combining with Hausdorff dimension constraints, we propose a new method for seismic random noise attenuation. First of all, We apply EMD algorithm adaptive decomposition of seismic data and obtain a series of intrinsic mode function (IMF)with different scales. Based on the difference of Hausdorff dimension between effectively signals and random noise, we identify IMF component mixed with random noise. Then we use threshold correlation filtering process to separate the valid signal and random noise effectively. Compared with traditional EMD method, the results show that the new method of seismic random noise attenuation has a better suppression effect. The implementation process The EMD algorithm is used to decompose seismic signals into IMF sets and analyze its spectrum. Since most of the random noise is high frequency noise, the IMF sets can be divided into three categories: the first category is the effective wave composition of the larger scale; the second category is the noise part of the smaller scale; the third category is the IMF component containing random noise. Then, the third kind of IMF component is processed by the Hausdorff dimension algorithm, and the appropriate time window size, initial step and increment amount are selected to calculate the Hausdorff instantaneous dimension of each component. The dimension of the random noise is between 1.0 and 1.05, while the dimension of the effective wave is between 1.05 and 2.0. On the basis of the previous steps, according to the dimension difference between the random noise and effective signal, we extracted the sample points, whose fractal dimension value is less than or equal to 1.05 for the each IMF components, to separate the residual noise. Using the IMF components after dimension filtering processing and the effective wave IMF components after the first selection for reconstruction, we can obtained the results of de-noising.

Applications of Hilbert Spectral Analysis for Speech and Sound Signals

NASA Technical Reports Server (NTRS)

Huang, Norden E.

2003-01-01

A new method for analyzing nonlinear and nonstationary data has been developed, and the natural applications are to speech and sound signals. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time, which give sharp identifications of imbedded structures. This method invention can be used to process all acoustic signals. Specifically, it can process the speech signals for Speech synthesis, Speaker identification and verification, Speech recognition, and Sound signal enhancement and filtering. Additionally, as the acoustical signals from machinery are essentially the way the machines are talking to us. Therefore, the acoustical signals, from the machines, either from sound through air or vibration on the machines, can tell us the operating conditions of the machines. Thus, we can use the acoustic signal to diagnosis the problems of machines.

Empirical mode decomposition for analyzing acoustical signals

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2005-01-01

The present invention discloses a computer implemented signal analysis method through the Hilbert-Huang Transformation (HHT) for analyzing acoustical signals, which are assumed to be nonlinear and nonstationary. The Empirical Decomposition Method (EMD) and the Hilbert Spectral Analysis (HSA) are used to obtain the HHT. Essentially, the acoustical signal will be decomposed into the Intrinsic Mode Function Components (IMFs). Once the invention decomposes the acoustic signal into its constituting components, all operations such as analyzing, identifying, and removing unwanted signals can be performed on these components. Upon transforming the IMFs into Hilbert spectrum, the acoustical signal may be compared with other acoustical signals.

Pipe leak diagnostic using high frequency piezoelectric pressure sensor and automatic selection of intrinsic mode function

NASA Astrophysics Data System (ADS)

Yusop, Hanafi M.; Ghazali, M. F.; Yusof, M. F. M.; Remli, M. A. Pi; Kamarulzaman, M. H.

2017-10-01

In a recent study, the analysis of pressure transient signals could be seen as an accurate and low-cost method for leak and feature detection in water distribution systems. Transient phenomena occurs due to sudden changes in the fluid’s propagation in pipelines system caused by rapid pressure and flow fluctuation due to events such as closing and opening valves rapidly or through pump failure. In this paper, the feasibility of the Hilbert-Huang transform (HHT) method/technique in analysing the pressure transient signals in presented and discussed. HHT is a way to decompose a signal into intrinsic mode functions (IMF). However, the advantage of HHT is its difficulty in selecting the suitable IMF for the next data postprocessing method which is Hilbert Transform (HT). This paper reveals that utilizing the application of an integrated kurtosis-based algorithm for a z-filter technique (I-Kaz) to kurtosis ratio (I-Kaz-Kurtosis) allows/contributes to/leads to automatic selection of the IMF that should be used. This technique is demonstrated on a 57.90-meter medium high-density polyethylene (MDPE) pipe installed with a single artificial leak. The analysis results using the I-Kaz-kurtosis ratio revealed/confirmed that the method can be used as an automatic selection of the IMF although the noise level ratio of the signal is low. Therefore, the I-Kaz-kurtosis ratio method is recommended as a means to implement an automatic selection technique of the IMF for HHT analysis.

Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS

PubMed Central

Kuai, Moshen; Cheng, Gang; Li, Yong

2018-01-01

For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively. PMID:29510569

Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS.

PubMed

Kuai, Moshen; Cheng, Gang; Pang, Yusong; Li, Yong

2018-03-05

For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.

A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis

NASA Astrophysics Data System (ADS)

Grasso, M.; Chatterton, S.; Pennacchi, P.; Colosimo, B. M.

2016-12-01

Health condition analysis and diagnostics of rotating machinery requires the capability of properly characterizing the information content of sensor signals in order to detect and identify possible fault features. Time-frequency analysis plays a fundamental role, as it allows determining both the existence and the causes of a fault. The separation of components belonging to different time-frequency scales, either associated to healthy or faulty conditions, represents a challenge that motivates the development of effective methodologies for multi-scale signal decomposition. In this framework, the Empirical Mode Decomposition (EMD) is a flexible tool, thanks to its data-driven and adaptive nature. However, the EMD usually yields an over-decomposition of the original signals into a large number of intrinsic mode functions (IMFs). The selection of most relevant IMFs is a challenging task, and the reference literature lacks automated methods to achieve a synthetic decomposition into few physically meaningful modes by avoiding the generation of spurious or meaningless modes. The paper proposes a novel automated approach aimed at generating a decomposition into a minimal number of relevant modes, called Combined Mode Functions (CMFs), each consisting in a sum of adjacent IMFs that share similar properties. The final number of CMFs is selected in a fully data driven way, leading to an enhanced characterization of the signal content without any information loss. A novel criterion to assess the dissimilarity between adjacent CMFs is proposed, based on probability density functions of frequency spectra. The method is suitable to analyze vibration signals that may be periodically acquired within the operating life of rotating machineries. A rolling element bearing fault analysis based on experimental data is presented to demonstrate the performances of the method and the provided benefits.

A Noise Reduction Method for Dual-Mass Micro-Electromechanical Gyroscopes Based on Sample Entropy Empirical Mode Decomposition and Time-Frequency Peak Filtering

PubMed Central

Shen, Chong; Li, Jie; Zhang, Xiaoming; Shi, Yunbo; Tang, Jun; Cao, Huiliang; Liu, Jun

2016-01-01

The different noise components in a dual-mass micro-electromechanical system (MEMS) gyroscope structure is analyzed in this paper, including mechanical-thermal noise (MTN), electronic-thermal noise (ETN), flicker noise (FN) and Coriolis signal in-phase noise (IPN). The structure equivalent electronic model is established, and an improved white Gaussian noise reduction method for dual-mass MEMS gyroscopes is proposed which is based on sample entropy empirical mode decomposition (SEEMD) and time-frequency peak filtering (TFPF). There is a contradiction in TFPS, i.e., selecting a short window length may lead to good preservation of signal amplitude but bad random noise reduction, whereas selecting a long window length may lead to serious attenuation of the signal amplitude but effective random noise reduction. In order to achieve a good tradeoff between valid signal amplitude preservation and random noise reduction, SEEMD is adopted to improve TFPF. Firstly, the original signal is decomposed into intrinsic mode functions (IMFs) by EMD, and the SE of each IMF is calculated in order to classify the numerous IMFs into three different components; then short window TFPF is employed for low frequency component of IMFs, and long window TFPF is employed for high frequency component of IMFs, and the noise component of IMFs is wiped off directly; at last the final signal is obtained after reconstruction. Rotation experimental and temperature experimental are carried out to verify the proposed SEEMD-TFPF algorithm, the verification and comparison results show that the de-noising performance of SEEMD-TFPF is better than that achievable with the traditional wavelet, Kalman filter and fixed window length TFPF methods. PMID:27258276

A Noise Reduction Method for Dual-Mass Micro-Electromechanical Gyroscopes Based on Sample Entropy Empirical Mode Decomposition and Time-Frequency Peak Filtering.

PubMed

Shen, Chong; Li, Jie; Zhang, Xiaoming; Shi, Yunbo; Tang, Jun; Cao, Huiliang; Liu, Jun

2016-05-31

The different noise components in a dual-mass micro-electromechanical system (MEMS) gyroscope structure is analyzed in this paper, including mechanical-thermal noise (MTN), electronic-thermal noise (ETN), flicker noise (FN) and Coriolis signal in-phase noise (IPN). The structure equivalent electronic model is established, and an improved white Gaussian noise reduction method for dual-mass MEMS gyroscopes is proposed which is based on sample entropy empirical mode decomposition (SEEMD) and time-frequency peak filtering (TFPF). There is a contradiction in TFPS, i.e., selecting a short window length may lead to good preservation of signal amplitude but bad random noise reduction, whereas selecting a long window length may lead to serious attenuation of the signal amplitude but effective random noise reduction. In order to achieve a good tradeoff between valid signal amplitude preservation and random noise reduction, SEEMD is adopted to improve TFPF. Firstly, the original signal is decomposed into intrinsic mode functions (IMFs) by EMD, and the SE of each IMF is calculated in order to classify the numerous IMFs into three different components; then short window TFPF is employed for low frequency component of IMFs, and long window TFPF is employed for high frequency component of IMFs, and the noise component of IMFs is wiped off directly; at last the final signal is obtained after reconstruction. Rotation experimental and temperature experimental are carried out to verify the proposed SEEMD-TFPF algorithm, the verification and comparison results show that the de-noising performance of SEEMD-TFPF is better than that achievable with the traditional wavelet, Kalman filter and fixed window length TFPF methods.

System and methods for determining masking signals for applying empirical mode decomposition (EMD) and for demodulating intrinsic mode functions obtained from application of EMD

DOEpatents

Senroy, Nilanjan [New Delhi, IN; Suryanarayanan, Siddharth [Littleton, CO

2011-03-15

A computer-implemented method of signal processing is provided. The method includes generating one or more masking signals based upon a computed Fourier transform of a received signal. The method further includes determining one or more intrinsic mode functions (IMFs) of the received signal by performing a masking-signal-based empirical mode decomposition (EMD) using the at least one masking signal.

Empirical mode decomposition-based facial pose estimation inside video sequences

NASA Astrophysics Data System (ADS)

Qing, Chunmei; Jiang, Jianmin; Yang, Zhijing

2010-03-01

We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition (EMD) and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function (IMF) components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions.

Improved CEEMDAN-wavelet transform de-noising method and its application in well logging noise reduction

NASA Astrophysics Data System (ADS)

Zhang, Jingxia; Guo, Yinghai; Shen, Yulin; Zhao, Difei; Li, Mi

2018-06-01

The use of geophysical logging data to identify lithology is an important groundwork in logging interpretation. Inevitably, noise is mixed in during data collection due to the equipment and other external factors and this will affect the further lithological identification and other logging interpretation. Therefore, to get a more accurate lithological identification it is necessary to adopt de-noising methods. In this study, a new de-noising method, namely improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-wavelet transform, is proposed, which integrates the superiorities of improved CEEMDAN and wavelet transform. Improved CEEMDAN, an effective self-adaptive multi-scale analysis method, is used to decompose non-stationary signals as the logging data to obtain the intrinsic mode function (IMF) of N different scales and one residual. Moreover, one self-adaptive scale selection method is used to determine the reconstruction scale k. Simultaneously, given the possible frequency aliasing problem between adjacent IMFs, a wavelet transform threshold de-noising method is used to reduce the noise of the (k-1)th IMF. Subsequently, the de-noised logging data are reconstructed by the de-noised (k-1)th IMF and the remaining low-frequency IMFs and the residual. Finally, empirical mode decomposition, improved CEEMDAN, wavelet transform and the proposed method are applied for analysis of the simulation and the actual data. Results show diverse performance of these de-noising methods with regard to accuracy for lithological identification. Compared with the other methods, the proposed method has the best self-adaptability and accuracy in lithological identification.

EMD-WVD time-frequency distribution for analysis of multi-component signals

NASA Astrophysics Data System (ADS)

Chai, Yunzi; Zhang, Xudong

2016-10-01

Time-frequency distribution (TFD) is two-dimensional function that indicates the time-varying frequency content of one-dimensional signals. And The Wigner-Ville distribution (WVD) is an important and effective time-frequency analysis method. The WVD can efficiently show the characteristic of a mono-component signal. However, a major drawback is the extra cross-terms when multi-component signals are analyzed by WVD. In order to eliminating the cross-terms, we decompose signals into single frequency components - Intrinsic Mode Function (IMF) - by using the Empirical Mode decomposition (EMD) first, then use WVD to analyze each single IMF. In this paper, we define this new time-frequency distribution as EMD-WVD. And the experiment results show that the proposed time-frequency method can solve the cross-terms problem effectively and improve the accuracy of WVD time-frequency analysis.

Analysis of turbine-grid interaction of grid-connected wind turbine using HHT

NASA Astrophysics Data System (ADS)

Chen, A.; Wu, W.; Miao, J.; Xie, D.

2018-05-01

This paper processes the output power of the grid-connected wind turbine with the denoising and extracting method based on Hilbert Huang transform (HHT) to discuss the turbine-grid interaction. At first, the detailed Empirical Mode Decomposition (EMD) and the Hilbert Transform (HT) are introduced. Then, on the premise of decomposing the output power of the grid-connected wind turbine into a series of Intrinsic Mode Functions (IMFs), energy ratio and power volatility are calculated to detect the unessential components. Meanwhile, combined with vibration function of turbine-grid interaction, data fitting of instantaneous amplitude and phase of each IMF is implemented to extract characteristic parameters of different interactions. Finally, utilizing measured data of actual parallel-operated wind turbines in China, this work accurately obtains the characteristic parameters of turbine-grid interaction of grid-connected wind turbine.

Research on Ship-Radiated Noise Denoising Using Secondary Variational Mode Decomposition and Correlation Coefficient.

PubMed

Li, Yuxing; Li, Yaan; Chen, Xiao; Yu, Jing

2017-12-26

As the sound signal of ships obtained by sensors contains other many significant characteristics of ships and called ship-radiated noise (SN), research into a denoising algorithm and its application has obtained great significance. Using the advantage of variational mode decomposition (VMD) combined with the correlation coefficient for denoising, a hybrid secondary denoising algorithm is proposed using secondary VMD combined with a correlation coefficient (CC). First, different kinds of simulation signals are decomposed into several bandwidth-limited intrinsic mode functions (IMFs) using VMD, where the decomposition number by VMD is equal to the number by empirical mode decomposition (EMD); then, the CCs between the IMFs and the simulation signal are calculated respectively. The noise IMFs are identified by the CC threshold and the rest of the IMFs are reconstructed in order to realize the first denoising process. Finally, secondary denoising of the simulation signal can be accomplished by repeating the above steps of decomposition, screening and reconstruction. The final denoising result is determined according to the CC threshold. The denoising effect is compared under the different signal-to-noise ratio and the time of decomposition by VMD. Experimental results show the validity of the proposed denoising algorithm using secondary VMD (2VMD) combined with CC compared to EMD denoising, ensemble EMD (EEMD) denoising, VMD denoising and cubic VMD (3VMD) denoising, as well as two denoising algorithms presented recently. The proposed denoising algorithm is applied to feature extraction and classification for SN signals, which can effectively improve the recognition rate of different kinds of ships.

Empirical Mode Decomposition of Geophysical Well-log Data of Bombay Offshore Basin, Mumbai, India

NASA Astrophysics Data System (ADS)

Siddharth Gairola, Gaurav; Chandrasekhar, Enamundram

2016-04-01

Geophysical well-log data manifest the nonlinear behaviour of their respective physical properties of the heterogeneous subsurface layers as a function of depth. Therefore, nonlinear data analysis techniques must be implemented, to quantify the degree of heterogeneity in the subsurface lithologies. One such nonlinear data adaptive technique is empirical mode decomposition (EMD) technique, which facilitates to decompose the data into oscillatory signals of different wavelengths called intrinsic mode functions (IMF). In the present study EMD has been applied to gamma-ray log and neutron porosity log of two different wells: Well B and Well C located in the western offshore basin of India to perform heterogeneity analysis and compare the results with those obtained by multifractal studies of the same data sets. By establishing a relationship between the IMF number (m) and the mean wavelength associated with each IMF (Im), a heterogeneity index (ρ) associated with subsurface layers can be determined using the relation, Im=kρm, where 'k' is a constant. The ρ values bear an inverse relation with the heterogeneity of the subsurface: smaller ρ values designate higher heterogeneity and vice-versa. The ρ values estimated for different limestone payzones identified in the wells clearly show that Well C has higher degree of heterogeneity than Well B. This correlates well with the estimated Vshale values for the limestone reservoir zone showing higher shale content in Well C than Well B. The ρ values determined for different payzones of both wells will be used to quantify the degree of heterogeneity in different wells. The multifractal behaviour of each IMF of both the logs of both the wells will be compared with one another and discussed on the lines of their heterogeneity indices.

A novel technique for phase synchrony measurement from the complex motor imaginary potential of combined body and limb action

NASA Astrophysics Data System (ADS)

Zhou, Zhong-xing; Wan, Bai-kun; Ming, Dong; Qi, Hong-zhi

2010-08-01

In this study, we proposed and evaluated the use of the empirical mode decomposition (EMD) technique combined with phase synchronization analysis to investigate the human brain synchrony of the supplementary motor area (SMA) and primary motor area (M1) during complex motor imagination of combined body and limb action. We separated the EEG data of the SMA and M1 into intrinsic mode functions (IMFs) using the EMD method and determined the characteristic IMFs by power spectral density (PSD) analysis. Thereafter, the instantaneous phases of the characteristic IMFs were obtained by the Hilbert transformation, and the single-trial phase-locking value (PLV) features for brain synchrony measurement between the SMA and M1 were investigated separately. The classification performance suggests that the proposed approach is effective for phase synchronization analysis and is promising for the application of a brain-computer interface in motor nerve reconstruction of the lower limbs.

Phase correlation of foreign exchange time series

NASA Astrophysics Data System (ADS)

Wu, Ming-Chya

2007-03-01

Correlation of foreign exchange rates in currency markets is investigated based on the empirical data of USD/DEM and USD/JPY exchange rates for a period from February 1 1986 to December 31 1996. The return of exchange time series is first decomposed into a number of intrinsic mode functions (IMFs) by the empirical mode decomposition method. The instantaneous phases of the resultant IMFs calculated by the Hilbert transform are then used to characterize the behaviors of pricing transmissions, and the correlation is probed by measuring the phase differences between two IMFs in the same order. From the distribution of phase differences, our results show explicitly that the correlations are stronger in daily time scale than in longer time scales. The demonstration for the correlations in periods of 1986-1989 and 1990-1993 indicates two exchange rates in the former period were more correlated than in the latter period. The result is consistent with the observations from the cross-correlation calculation.

Image fusion method based on regional feature and improved bidimensional empirical mode decomposition

NASA Astrophysics Data System (ADS)

Qin, Xinqiang; Hu, Gang; Hu, Kai

2018-01-01

The decomposition of multiple source images using bidimensional empirical mode decomposition (BEMD) often produces mismatched bidimensional intrinsic mode functions, either by their number or their frequency, making image fusion difficult. A solution to this problem is proposed using a fixed number of iterations and a union operation in the sifting process. By combining the local regional features of the images, an image fusion method has been developed. First, the source images are decomposed using the proposed BEMD to produce the first intrinsic mode function (IMF) and residue component. Second, for the IMF component, a selection and weighted average strategy based on local area energy is used to obtain a high-frequency fusion component. Third, for the residue component, a selection and weighted average strategy based on local average gray difference is used to obtain a low-frequency fusion component. Finally, the fused image is obtained by applying the inverse BEMD transform. Experimental results show that the proposed algorithm provides superior performance over methods based on wavelet transform, line and column-based EMD, and complex empirical mode decomposition, both in terms of visual quality and objective evaluation criteria.

Defects diagnosis in laser brazing using near-infrared signals based on empirical mode decomposition

NASA Astrophysics Data System (ADS)

Cheng, Liyong; Mi, Gaoyang; Li, Shuo; Wang, Chunming; Hu, Xiyuan

2018-03-01

Real-time monitoring of laser welding plays a very important role in the modern automated production and online defects diagnosis is necessary to be implemented. In this study, the status of laser brazing was monitored in real time using an infrared photoelectric sensor. Four kinds of braze seams (including healthy weld, unfilled weld, hole weld and rough surface weld) along with corresponding near-infrared signals were obtained. Further, a new method called Empirical Mode Decomposition (EMD) was proposed to analyze the near-infrared signals. The results showed that the EMD method had a good performance in eliminating the noise on the near-infrared signals. And then, the correlation coefficient was developed for selecting the Intrinsic Mode Function (IMF) more sensitive to the weld defects. A more accurate signal was reconstructed with the selected IMF components. Simultaneously, the spectrum of selected IMF components was solved using fast Fourier transform, and the frequency characteristics were clearly revealed. The frequency energy of different frequency bands was computed to diagnose the defects. There was a significant difference in four types of weld defects. This approach has been proved to be an effective and efficient method for monitoring laser brazing defects.

Daily air quality index forecasting with hybrid models: A case in China.

PubMed

Zhu, Suling; Lian, Xiuyuan; Liu, Haixia; Hu, Jianming; Wang, Yuanyuan; Che, Jinxing

2017-12-01

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the proposed hybrid models can be used as effective and simple tools for air pollution forecasting and warning as well as for management. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adaptive photoacoustic imaging quality optimization with EMD and reconstruction

NASA Astrophysics Data System (ADS)

Guo, Chengwen; Ding, Yao; Yuan, Jie; Xu, Guan; Wang, Xueding; Carson, Paul L.

2016-10-01

Biomedical photoacoustic (PA) signal is characterized with extremely low signal to noise ratio which will yield significant artifacts in photoacoustic tomography (PAT) images. Since PA signals acquired by ultrasound transducers are non-linear and non-stationary, traditional data analysis methods such as Fourier and wavelet method cannot give useful information for further research. In this paper, we introduce an adaptive method to improve the quality of PA imaging based on empirical mode decomposition (EMD) and reconstruction. Data acquired by ultrasound transducers are adaptively decomposed into several intrinsic mode functions (IMFs) after a sifting pre-process. Since noise is randomly distributed in different IMFs, depressing IMFs with more noise while enhancing IMFs with less noise can effectively enhance the quality of reconstructed PAT images. However, searching optimal parameters by means of brute force searching algorithms will cost too much time, which prevent this method from practical use. To find parameters within reasonable time, heuristic algorithms, which are designed for finding good solutions more efficiently when traditional methods are too slow, are adopted in our method. Two of the heuristic algorithms, Simulated Annealing Algorithm, a probabilistic method to approximate the global optimal solution, and Artificial Bee Colony Algorithm, an optimization method inspired by the foraging behavior of bee swarm, are selected to search optimal parameters of IMFs in this paper. The effectiveness of our proposed method is proved both on simulated data and PA signals from real biomedical tissue, which might bear the potential for future clinical PA imaging de-noising.

Tourism forecasting using modified empirical mode decomposition and group method of data handling

NASA Astrophysics Data System (ADS)

Yahya, N. A.; Samsudin, R.; Shabri, A.

2017-09-01

In this study, a hybrid model using modified Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) model is proposed for tourism forecasting. This approach reconstructs intrinsic mode functions (IMFs) produced by EMD using trial and error method. The new component and the remaining IMFs is then predicted respectively using GMDH model. Finally, the forecasted results for each component are aggregated to construct an ensemble forecast. The data used in this experiment are monthly time series data of tourist arrivals from China, Thailand and India to Malaysia from year 2000 to 2016. The performance of the model is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) where conventional GMDH model and EMD-GMDH model are used as benchmark models. Empirical results proved that the proposed model performed better forecasts than the benchmarked models.

An epileptic seizures detection algorithm based on the empirical mode decomposition of EEG.

PubMed

Orosco, Lorena; Laciar, Eric; Correa, Agustina Garces; Torres, Abel; Graffigna, Juan P

2009-01-01

Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates the energy of each IMF and performs the detection based on an energy threshold and a minimum duration decision. The algorithm was tested in 9 invasive EEG records provided and validated by the Epilepsy Center of the University Hospital of Freiburg. In 90 segments analyzed (39 with epileptic seizures) the sensitivity and specificity obtained with the method were of 56.41% and 75.86% respectively. It could be concluded that EMD is a promissory method for epileptic seizure detection in EEG records.

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

PubMed Central

Lv, Yong; Song, Gangbing

2018-01-01

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal. PMID:29659510

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition.

PubMed

Yuan, Rui; Lv, Yong; Song, Gangbing

2018-04-16

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal.

Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms

PubMed Central

Kwak, Dae-Ho; Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

2014-01-01

This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system. PMID:24368701

Temporal structure of neuronal population oscillations with empirical model decomposition

NASA Astrophysics Data System (ADS)

Li, Xiaoli

2006-08-01

Frequency analysis of neuronal oscillation is very important for understanding the neural information processing and mechanism of disorder in the brain. This Letter addresses a new method to analyze the neuronal population oscillations with empirical mode decomposition (EMD). Following EMD of neuronal oscillation, a series of intrinsic mode functions (IMFs) are obtained, then Hilbert transform of IMFs can be used to extract the instantaneous time frequency structure of neuronal oscillation. The method is applied to analyze the neuronal oscillation in the hippocampus of epileptic rats in vivo, the results show the neuronal oscillations have different descriptions during the pre-ictal, seizure onset and ictal periods of the epileptic EEG at the different frequency band. This new method is very helpful to provide a view for the temporal structure of neural oscillation.

Instantaneous Respiratory Estimation from Thoracic Impedance by Empirical Mode Decomposition.

PubMed

Wang, Fu-Tai; Chan, Hsiao-Lung; Wang, Chun-Li; Jian, Hung-Ming; Lin, Sheng-Hsiung

2015-07-07

Impedance plethysmography provides a way to measure respiratory activity by sensing the change of thoracic impedance caused by inspiration and expiration. This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the need for adjustments as body position changes and making it suitable for long-term or ambulatory monitoring. The empirical mode decomposition (EMD) can decompose a signal into several intrinsic mode functions (IMFs) that disclose nonstationary components as well as stationary components and, similarly, capture respiratory episodes from thoracic impedance. However, upper-body movements usually produce motion artifacts that are not easily removed by digital filtering. Moreover, large motion artifacts disable the EMD to decompose respiratory components. In this paper, motion artifacts are detected and replaced by the data mirrored from the prior and the posterior before EMD processing. A novel intrinsic respiratory reconstruction index that considers both global and local properties of IMFs is proposed to define respiration-related IMFs for respiration reconstruction and instantaneous respiratory estimation. Based on the experiments performing a series of static and dynamic physical activates, our results showed the proposed method had higher cross correlations between respiratory frequencies estimated from thoracic impedance and those from oronasal airflow based on small window size compared to the Fourier transform-based method.

Instantaneous Respiratory Estimation from Thoracic Impedance by Empirical Mode Decomposition

PubMed Central

Wang, Fu-Tai; Chan, Hsiao-Lung; Wang, Chun-Li; Jian, Hung-Ming; Lin, Sheng-Hsiung

2015-01-01

Impedance plethysmography provides a way to measure respiratory activity by sensing the change of thoracic impedance caused by inspiration and expiration. This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the need for adjustments as body position changes and making it suitable for long-term or ambulatory monitoring. The empirical mode decomposition (EMD) can decompose a signal into several intrinsic mode functions (IMFs) that disclose nonstationary components as well as stationary components and, similarly, capture respiratory episodes from thoracic impedance. However, upper-body movements usually produce motion artifacts that are not easily removed by digital filtering. Moreover, large motion artifacts disable the EMD to decompose respiratory components. In this paper, motion artifacts are detected and replaced by the data mirrored from the prior and the posterior before EMD processing. A novel intrinsic respiratory reconstruction index that considers both global and local properties of IMFs is proposed to define respiration-related IMFs for respiration reconstruction and instantaneous respiratory estimation. Based on the experiments performing a series of static and dynamic physical activates, our results showed the proposed method had higher cross correlations between respiratory frequencies estimated from thoracic impedance and those from oronasal airflow based on small window size compared to the Fourier transform-based method. PMID:26198231

High and low frequency unfolded partial least squares regression based on empirical mode decomposition for quantitative analysis of fuel oil samples.

PubMed

Bian, Xihui; Li, Shujuan; Lin, Ligang; Tan, Xiaoyao; Fan, Qingjie; Li, Ming

2016-06-21

Accurate prediction of the model is fundamental to the successful analysis of complex samples. To utilize abundant information embedded over frequency and time domains, a novel regression model is presented for quantitative analysis of hydrocarbon contents in the fuel oil samples. The proposed method named as high and low frequency unfolded PLSR (HLUPLSR), which integrates empirical mode decomposition (EMD) and unfolded strategy with partial least squares regression (PLSR). In the proposed method, the original signals are firstly decomposed into a finite number of intrinsic mode functions (IMFs) and a residue by EMD. Secondly, the former high frequency IMFs are summed as a high frequency matrix and the latter IMFs and residue are summed as a low frequency matrix. Finally, the two matrices are unfolded to an extended matrix in variable dimension, and then the PLSR model is built between the extended matrix and the target values. Coupled with Ultraviolet (UV) spectroscopy, HLUPLSR has been applied to determine hydrocarbon contents of light gas oil and diesel fuels samples. Comparing with single PLSR and other signal processing techniques, the proposed method shows superiority in prediction ability and better model interpretation. Therefore, HLUPLSR method provides a promising tool for quantitative analysis of complex samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Temporal Associations between Weather and Headache: Analysis by Empirical Mode Decomposition

PubMed Central

Yang, Albert C.; Fuh, Jong-Ling; Huang, Norden E.; Shia, Ben-Chang; Peng, Chung-Kang; Wang, Shuu-Jiun

2011-01-01

Background Patients frequently report that weather changes trigger headache or worsen existing headache symptoms. Recently, the method of empirical mode decomposition (EMD) has been used to delineate temporal relationships in certain diseases, and we applied this technique to identify intrinsic weather components associated with headache incidence data derived from a large-scale epidemiological survey of headache in the Greater Taipei area. Methodology/Principal Findings The study sample consisted of 52 randomly selected headache patients. The weather time-series parameters were detrended by the EMD method into a set of embedded oscillatory components, i.e. intrinsic mode functions (IMFs). Multiple linear regression models with forward stepwise methods were used to analyze the temporal associations between weather and headaches. We found no associations between the raw time series of weather variables and headache incidence. For decomposed intrinsic weather IMFs, temperature, sunshine duration, humidity, pressure, and maximal wind speed were associated with headache incidence during the cold period, whereas only maximal wind speed was associated during the warm period. In analyses examining all significant weather variables, IMFs derived from temperature and sunshine duration data accounted for up to 33.3% of the variance in headache incidence during the cold period. The association of headache incidence and weather IMFs in the cold period coincided with the cold fronts. Conclusions/Significance Using EMD analysis, we found a significant association between headache and intrinsic weather components, which was not detected by direct comparisons of raw weather data. Contributing weather parameters may vary in different geographic regions and different seasons. PMID:21297940

Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator.

PubMed

Ma, Jun; Wu, Jiande; Wang, Xiaodong

2018-06-04

Aiming at the problems that the incipient fault of rolling bearings is difficult to recognize and the number of intrinsic mode functions (IMFs) decomposed by variational mode decomposition (VMD) must be set in advance and can not be adaptively selected, taking full advantages of the adaptive segmentation of scale spectrum and Teager energy operator (TEO) demodulation, a new method for early fault feature extraction of rolling bearings based on the modified VMD and Teager energy operator (MVMD-TEO) is proposed. Firstly, the vibration signal of rolling bearings is analyzed by adaptive scale space spectrum segmentation to obtain the spectrum segmentation support boundary, and then the number K of IMFs decomposed by VMD is adaptively determined. Secondly, the original vibration signal is adaptively decomposed into K IMFs, and the effective IMF components are extracted based on the correlation coefficient criterion. Finally, the Teager energy spectrum of the reconstructed signal of the effective IMF components is calculated by the TEO, and then the early fault features of rolling bearings are extracted to realize the fault identification and location. Comparative experiments of the proposed method and the existing fault feature extraction method based on Local Mean Decomposition and Teager energy operator (LMD-TEO) have been implemented using experimental data-sets and a measured data-set. The results of comparative experiments in three application cases show that the presented method can achieve a fairly or slightly better performance than LMD-TEO method, and the validity and feasibility of the proposed method are proved. Copyright © 2018. Published by Elsevier Ltd.

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

PubMed

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P; McDonald-Maier, Klaus D

2015-05-08

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

PubMed Central

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P.; McDonald-Maier, Klaus D.

2015-01-01

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences. PMID:26007714

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

PubMed

Wang, Jinjia; Liu, Yuan

2015-04-01

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

NASA Astrophysics Data System (ADS)

Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

2016-06-01

To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.

PubMed

Wang, Wen-chuan; Chau, Kwok-wing; Qiu, Lin; Chen, Yang-bo

2015-05-01

Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for the effective reservoir management. In this research, an artificial neural network (ANN) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting medium and long-term runoff time series. First, the original runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and a residual series using EEMD technique for attaining deeper insight into the data characteristics. Then all IMF components and residue are predicted, respectively, through appropriate ANN models. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Two annual reservoir runoff time series from Biuliuhe and Mopanshan in China, are investigated using the developed model based on four performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and the proposed EEMD-ANN model can attain significant improvement over ANN approach in medium and long-term runoff time series forecasting. Copyright © 2015 Elsevier Inc. All rights reserved.

New procedure for gear fault detection and diagnosis using instantaneous angular speed

NASA Astrophysics Data System (ADS)

Li, Bing; Zhang, Xining; Wu, Jili

2017-02-01

Besides the extreme complexity of gear dynamics, the fault diagnosis results in terms of vibration signal are sometimes easily misled and even distorted by the interference of transmission channel or other components like bearings, bars. Recently, the research field of Instantaneous Angular Speed (IAS) has attracted significant attentions due to its own advantages over conventional vibration analysis. On the basis of IAS signal's advantages, this paper presents a new feature extraction method by combining the Empirical Mode Decomposition (EMD) and Autocorrelation Local Cepstrum (ALC) for fault diagnosis of sophisticated multistage gearbox. Firstly, as a pre-processing step, signal reconstruction is employed to address the oversampled issue caused by the high resolution of the angular sensor and the test speed. Then the adaptive EMD is used to acquire a number of Intrinsic Mode Functions (IMFs). Nevertheless, not all the IMFs are needed for the further analysis since different IMFs have different sensitivities to fault. Hence, the cosine similarity metric is introduced to select the most sensitive IMF. Even though, the sensitive IMF is still insufficient for the gear fault diagnosis due to the weakness of the fault component related to the gear fault. Therefore, as the final step, ALC is used for the purpose of signal de-noising and feature extraction. The effectiveness and robustness of the new approach has been validated experimentally on the basis of two gear test rigs with gears under different working conditions. Diagnosis results show that the new approach is capable of effectively handling the gear fault diagnosis i.e., the highlighted quefrency and its rahmonics corresponding to the rotary period and its multiple are displayed clearly in the cepstrum record of the proposed method.

A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition

PubMed Central

Wang, Huaqing; Li, Ruitong; Tang, Gang; Yuan, Hongfang; Zhao, Qingliang; Cao, Xi

2014-01-01

A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals’ separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system. PMID:25289644

Fault detection, isolation, and diagnosis of self-validating multifunctional sensors.

PubMed

Yang, Jing-Li; Chen, Yin-Sheng; Zhang, Li-Li; Sun, Zhen

2016-06-01

A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis.

PubMed

Saidi, Lotfi; Ali, Jaouher Ben; Fnaiech, Farhat

2014-09-01

Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always non-stationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it is flat for non-Gaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bi-spectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of IMFs is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Improving EMG based classification of basic hand movements using EMD.

PubMed

Sapsanis, Christos; Georgoulas, George; Tzes, Anthony; Lymberopoulos, Dimitrios

2013-01-01

This paper presents a pattern recognition approach for the identification of basic hand movements using surface electromyographic (EMG) data. The EMG signal is decomposed using Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs) and subsequently a feature extraction stage takes place. Various combinations of feature subsets are tested using a simple linear classifier for the detection task. Our results suggest that the use of EMD can increase the discrimination ability of the conventional feature sets extracted from the raw EMG signal.

Coherent seasonal, annual, and quasi-biennial variations in ionospheric tidal/SPW amplitudes

NASA Astrophysics Data System (ADS)

Chang, Loren C.; Sun, Yan-Yi; Yue, Jia; Wang, Jack Chieh; Chien, Shih-Han

2016-07-01

In this study, we examine the coherent spatial and temporal modes dominating the variation of selected ionospheric tidal and stationary planetary wave (SPW) signatures from 2007 to 2013 FORMOSAT-3/COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) total electron content observations using multidimensional ensemble empirical mode decomposition (MEEMD) from the Hilbert-Huang Transform. We examine the DW1, SW2, DE3, and SPW4 components, which are driven by a variety of in situ and vertical coupling sources. The intrinsic mode functions (IMFs) resolved by MEEMD analysis allows for the isolation of the dominant modes of variability for prominent ionospheric tidal/SPW signatures in a manner not previously used, allowing the effects of specific drivers to be examined individually. The time scales of the individual IMFs isolated for all tidal/SPW signatures correspond to a semiannual variation at equatorial ionization anomaly (EIA) latitudes maximizing at the equinoxes, as well as annual oscillations at the EIA crests and troughs. All tidal/SPW signatures show one IMF isolating an ionospheric quasi-biennial oscillation (QBO) in the equatorial latitudes maximizing around January of odd-numbered years. This total electron content QBO variation is in phase with a similar QBO variation isolated in both the Global Ultraviolet Imager (GUVI) zonal mean column O/N2 density ratio (ΣO/N2) and the F10.7 solar radio flux index around solar maximum, while showing temporal variation more similar to that of GUVI ΣO/N2 during the time around the 2008/2009 extended solar minimum. These results point to both quasi-biennial variations in solar irradiance and thermosphere/ionosphere composition as a generation mechanism for the ionospheric QBO.

On the Hilbert-Huang Transform Theoretical Developments

NASA Technical Reports Server (NTRS)

Kizhner, Semion; Blank, Karin; Flatley, Thomas; Huang, Norden E.; Patrick, David; Hestnes, Phyllis

2005-01-01

One of the main heritage tools used in scientific and engineering data spectrum analysis is the Fourier Integral Transform and its high performance digital equivalent - the Fast Fourier Transform (FFT). Both carry strong a-priori assumptions about the source data, such as linearity, of being stationary, and of satisfying the Dirichlet conditions. A recent development at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), known as the Hilbert-Huang Transform (HHT), proposes a novel approach to the solution for the nonlinear class of spectrum analysis problems. Using a-posteriori data processing based on the Empirical Mode Decomposition (EMD) sifting process (algorithm), followed by the normalized Hilbert Transform of the decomposition data, the HHT allows spectrum analysis of nonlinear and nonstationary data. The EMD sifting process results in a non-constrained decomposition of a source real value data vector into a finite set of Intrinsic Mode Functions (IMF). These functions form a near orthogonal adaptive basis, a basis that is derived from the data. The IMFs can be further analyzed for spectrum interpretation by the classical Hilbert Transform. A new engineering spectrum analysis tool using HHT has been developed at NASA GSFC, the HHT Data Processing System (HHT-DPS). As the HHT-DPS has been successfully used and commercialized, new applications post additional questions about the theoretical basis behind the HHT and EMD algorithms. Why is the fastest changing component of a composite signal being sifted out first in the EMD sifting process? Why does the EMD sifting process seemingly converge and why does it converge rapidly? Does an IMF have a distinctive structure? Why are the IMFs near orthogonal? We address these questions and develop the initial theoretical background for the HHT. This will contribute to the developments of new HHT processing options, such as real-time and 2-D processing using Field Programmable Array (FPGA) computational resources, enhanced HHT synthesis, and broaden the scope of HHT applications for signal processing.

On Certain Theoretical Developments Underlying the Hilbert-Huang Transform

NASA Technical Reports Server (NTRS)

Kizhner, Semion; Blank, Karin; Flatley, Thomas; Huang, Norden E.; Petrick, David; Hestness, Phyllis

2006-01-01

One of the main traditional tools used in scientific and engineering data spectral analysis is the Fourier Integral Transform and its high performance digital equivalent - the Fast Fourier Transform (FFT). Both carry strong a-priori assumptions about the source data, such as being linear and stationary, and of satisfying the Dirichlet conditions. A recent development at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), known as the Hilbert-Huang Transform (HHT), proposes a novel approach to the solution for the nonlinear class of spectral analysis problems. Using a-posteriori data processing based on the Empirical Mode Decomposition (EMD) sifting process (algorithm), followed by the normalized Hilbert Transform of the decomposed data, the HHT allows spectral analysis of nonlinear and nonstationary data. The EMD sifting process results in a non-constrained decomposition of a source real-value data vector into a finite set of Intrinsic Mode Functions (IMF). These functions form a nearly orthogonal derived from the data (adaptive) basis. The IMFs can be further analyzed for spectrum content by using the classical Hilbert Transform. A new engineering spectral analysis tool using HHT has been developed at NASA GSFC, the HHT Data Processing System (HHT-DPS). As the HHT-DPS has been successfully used and commercialized, new applications pose additional questions about the theoretical basis behind the HHT and EMD algorithms. Why is the fastest changing component of a composite signal being sifted out first in the EMD sifting process? Why does the EMD sifting process seemingly converge and why does it converge rapidly? Does an IMF have a distinctive structure? Why are the IMFs nearly orthogonal? We address these questions and develop the initial theoretical background for the HHT. This will contribute to the development of new HHT processing options, such as real-time and 2-D processing using Field Programmable Gate Array (FPGA) computational resources,

Statistical analysis of geomagnetic field intensity differences between ASM and VFM instruments onboard Swarm constellation

NASA Astrophysics Data System (ADS)

De Michelis, Paola; Tozzi, Roberta; Consolini, Giuseppe

2017-02-01

From the very first measurements made by the magnetometers onboard Swarm satellites launched by European Space Agency (ESA) in late 2013, it emerged a discrepancy between scalar and vector measurements. An accurate analysis of this phenomenon brought to build an empirical model of the disturbance, highly correlated with the Sun incidence angle, and to correct vector data accordingly. The empirical model adopted by ESA results in a significant decrease in the amplitude of the disturbance affecting VFM measurements so greatly improving the vector magnetic data quality. This study is focused on the characterization of the difference between magnetic field intensity measured by the absolute scalar magnetometer (ASM) and that reconstructed using the vector field magnetometer (VFM) installed on Swarm constellation. Applying empirical mode decomposition method, we find the intrinsic mode functions (IMFs) associated with ASM-VFM total intensity differences obtained with data both uncorrected and corrected for the disturbance correlated with the Sun incidence angle. Surprisingly, no differences are found in the nature of the IMFs embedded in the analyzed signals, being these IMFs characterized by the same dominant periodicities before and after correction. The effect of correction manifests in the decrease in the energy associated with some IMFs contributing to corrected data. Some IMFs identified by analyzing the ASM-VFM intensity discrepancy are characterized by the same dominant periodicities of those obtained by analyzing the temperature fluctuations of the VFM electronic unit. Thus, the disturbance correlated with the Sun incidence angle could be still present in the corrected magnetic data. Furthermore, the ASM-VFM total intensity difference and the VFM electronic unit temperature display a maximal shared information with a time delay that depends on local time. Taken together, these findings may help to relate the features of the observed VFM-ASM total intensity difference to the physical characteristics of the real disturbance thus contributing to improve the empirical model proposed for the correction of data.[Figure not available: see fulltext.

Signals of dynamical and statistical process from IMF-IMF correlation function

NASA Astrophysics Data System (ADS)

Pagano, E. V.; Acosta, L.; Auditore, L.; Baran, V.; Cap, T.; Cardella, G.; Colonna, M.; De Luca, S.; De Filippo, E.; Dell'Aquila, D.; Francalanza, L.; Gnoffo, B.; Lanzalone, G.; Lombardo, I.; Maiolino, C.; Martorana, N. S.; Norella, S.; Pagano, A.; Papa, M.; Piasecki, E.; Pirrone, S.; Politi, G.; Porto, F.; Quattrocchi, L.; Rizzo, F.; Rosato, E.; Russotto, P.; Siwek-Wilczyńska, K.; Trifiro, A.; Trimarchi, M.; Verde, G.; Vigilante, M.; Wilczyńsky, J.

2017-11-01

In this paper we briefly discuss about a novel application of the IMF-IMF correlation function to the physical case of binary massive projectile-like (PLF) splitting for dynamical and statistical breakup/fission in heavy ion collisions at Fermi energy. Theoretical simulations are also shown for comparisons with the data. These preliminary results have been obtained for the reverse kinematics reaction 124Sn + 64Ni at 35 AMeV that was studied using the forward part of CHIMERA detector. In that reaction a strong competition between a dynamical and a statistical components and its evolution with the charge asymmetry of the binary break up was already shown. In this work we show that the IMF-IMF correlation function can be used to pin down the timescale of the fragments production in binary fission-like phenomena. We also made simulations with the CoMDII model in order to compare to the experimental IMF-IMF correlation function. In future we plan to extend these studies to different reaction mechanisms and nuclear systems and to compare with different theoretical transport simulations.

Ensemble Empirical Mode Decomposition based methodology for ultrasonic testing of coarse grain austenitic stainless steels.

PubMed

Sharma, Govind K; Kumar, Anish; Jayakumar, T; Purnachandra Rao, B; Mariyappa, N

2015-03-01

A signal processing methodology is proposed in this paper for effective reconstruction of ultrasonic signals in coarse grained high scattering austenitic stainless steel. The proposed methodology is comprised of the Ensemble Empirical Mode Decomposition (EEMD) processing of ultrasonic signals and application of signal minimisation algorithm on selected Intrinsic Mode Functions (IMFs) obtained by EEMD. The methodology is applied to ultrasonic signals obtained from austenitic stainless steel specimens of different grain size, with and without defects. The influence of probe frequency and data length of a signal on EEMD decomposition is also investigated. For a particular sampling rate and probe frequency, the same range of IMFs can be used to reconstruct the ultrasonic signal, irrespective of the grain size in the range of 30-210 μm investigated in this study. This methodology is successfully employed for detection of defects in a 50mm thick coarse grain austenitic stainless steel specimens. Signal to noise ratio improvement of better than 15 dB is observed for the ultrasonic signal obtained from a 25 mm deep flat bottom hole in 200 μm grain size specimen. For ultrasonic signals obtained from defects at different depths, a minimum of 7 dB extra enhancement in SNR is achieved as compared to the sum of selected IMF approach. The application of minimisation algorithm with EEMD processed signal in the proposed methodology proves to be effective for adaptive signal reconstruction with improved signal to noise ratio. This methodology was further employed for successful imaging of defects in a B-scan. Copyright © 2014. Published by Elsevier B.V.

Monte Carlo study for physiological interference reduction in near-infrared spectroscopy based on empirical mode decomposition

NASA Astrophysics Data System (ADS)

Zhang, Yan; Sun, JinWei; Rolfe, Peter

2010-12-01

Near-infrared spectroscopy (NIRS) can be used as the basis of non-invasive neuroimaging that may allow the measurement of haemodynamic changes in the human brain evoked by applied stimuli. Since this technique is very sensitive, physiological interference arising from the cardiac cycle and breathing can significantly affect the signal quality. Such interference is difficult to remove by conventional techniques because it occurs not only in the extracerebral layer but also in the brain tissue itself. Previous work on this problem employing temporal filtering, spatial filtering, and adaptive filtering have exhibited good performance for recovering brain activity data in evoked response studies. However, in this study, we present a time-frequency adaptive method for physiological interference reduction based on the combination of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA). Monte Carlo simulations based on a five-layered slab model of a human adult head were implemented to evaluate our methodology. We applied an EMD algorithm to decompose the NIRS time series derived from Monte Carlo simulations into a series of intrinsic mode functions (IMFs). In order to identify the IMFs associated with symmetric interference, the extracted components were then Hilbert transformed from which the instantaneous frequencies could be acquired. By reconstructing the NIRS signal by properly selecting IMFs, we determined that the evoked brain response is effectively filtered out with even higher signal-to-noise ratio (SNR). The results obtained demonstrated that EMD, combined with HSA, can effectively separate, identify and remove the contamination from the evoked brain response obtained with NIRS using a simple single source-detector pair.

A novel hybrid ensemble learning paradigm for tourism forecasting

NASA Astrophysics Data System (ADS)

Shabri, Ani

2015-02-01

In this paper, a hybrid forecasting model based on Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) is proposed to forecast tourism demand. This methodology first decomposes the original visitor arrival series into several Intrinsic Model Function (IMFs) components and one residual component by EMD technique. Then, IMFs components and the residual components is forecasted respectively using GMDH model whose input variables are selected by using Partial Autocorrelation Function (PACF). The final forecasted result for tourism series is produced by aggregating all the forecasted results. For evaluating the performance of the proposed EMD-GMDH methodologies, the monthly data of tourist arrivals from Singapore to Malaysia are used as an illustrative example. Empirical results show that the proposed EMD-GMDH model outperforms the EMD-ARIMA as well as the GMDH and ARIMA (Autoregressive Integrated Moving Average) models without time series decomposition.

Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine.

PubMed

Chen, Lili; Hao, Yaru

2017-01-01

Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

Application of the Hilbert-Huang Transform to Financial Data

NASA Technical Reports Server (NTRS)

Huang, Norden

2005-01-01

A paper discusses the application of the Hilbert-Huang transform (HHT) method to time-series financial-market data. The method was described, variously without and with the HHT name, in several prior NASA Tech Briefs articles and supporting documents. To recapitulate: The method is especially suitable for analyzing time-series data that represent nonstationary and nonlinear phenomena including physical phenomena and, in the present case, financial-market processes. The method involves the empirical mode decomposition (EMD), in which a complicated signal is decomposed into a finite number of functions, called "intrinsic mode functions" (IMFs), that admit well-behaved Hilbert transforms. The HHT consists of the combination of EMD and Hilbert spectral analysis. The local energies and the instantaneous frequencies derived from the IMFs through Hilbert transforms can be used to construct an energy-frequency-time distribution, denoted a Hilbert spectrum. The instant paper begins with a discussion of prior approaches to quantification of market volatility, summarizes the HHT method, then describes the application of the method in performing time-frequency analysis of mortgage-market data from the years 1972 through 2000. Filtering by use of the EMD is shown to be useful for quantifying market volatility.

Bearing damage assessment using Jensen-Rényi Divergence based on EEMD

NASA Astrophysics Data System (ADS)

Singh, Jaskaran; Darpe, A. K.; Singh, S. P.

2017-03-01

An Ensemble Empirical Mode Decomposition (EEMD) and Jensen Rényi divergence (JRD) based methodology is proposed for the degradation assessment of rolling element bearings using vibration data. The EEMD decomposes vibration signals into a set of intrinsic mode functions (IMFs). A systematic methodology to select IMFs that are sensitive and closely related to the fault is proposed in the paper. The change in probability distribution of the energies of the sensitive IMFs is measured through JRD which acts as a damage identification parameter. Evaluation of JRD with sensitive IMFs makes it largely unaffected by change/fluctuations in operating conditions. Further, an algorithm based on Chebyshev's inequality is applied to JRD to identify exact points of change in bearing health and remove outliers. The identified change points are investigated for fault classification as possible locations where specific defect initiation could have taken place. For fault classification, two new parameters are proposed: 'α value' and Probable Fault Index, which together classify the fault. To standardize the degradation process, a Confidence Value parameter is proposed to quantify the bearing degradation value in a range of zero to unity. A simulation study is first carried out to demonstrate the robustness of the proposed JRD parameter under variable operating conditions of load and speed. The proposed methodology is then validated on experimental data (seeded defect data and accelerated bearing life test data). The first validation on two different vibration datasets (inner/outer) obtained from seeded defect experiments demonstrate the effectiveness of JRD parameter in detecting a change in health state as the severity of fault changes. The second validation is on two accelerated life tests. The results demonstrate the proposed approach as a potential tool for bearing performance degradation assessment.

Computational Pipeline for NIRS-EEG Joint Imaging of tDCS-Evoked Cerebral Responses-An Application in Ischemic Stroke.

PubMed

Guhathakurta, Debarpan; Dutta, Anirban

2016-01-01

Transcranial direct current stimulation (tDCS) modulates cortical neural activity and hemodynamics. Electrophysiological methods (electroencephalography-EEG) measure neural activity while optical methods (near-infrared spectroscopy-NIRS) measure hemodynamics coupled through neurovascular coupling (NVC). Assessment of NVC requires development of NIRS-EEG joint-imaging sensor montages that are sensitive to the tDCS affected brain areas. In this methods paper, we present a software pipeline incorporating freely available software tools that can be used to target vascular territories with tDCS and develop a NIRS-EEG probe for joint imaging of tDCS-evoked responses. We apply this software pipeline to target primarily the outer convexity of the brain territory (superficial divisions) of the middle cerebral artery (MCA). We then present a computational method based on Empirical Mode Decomposition of NIRS and EEG time series into a set of intrinsic mode functions (IMFs), and then perform a cross-correlation analysis on those IMFs from NIRS and EEG signals to model NVC at the lesional and contralesional hemispheres of an ischemic stroke patient. For the contralesional hemisphere, a strong positive correlation between IMFs of regional cerebral hemoglobin oxygen saturation and the log-transformed mean-power time-series of IMFs for EEG with a lag of about -15 s was found after a cumulative 550 s stimulation of anodal tDCS. It is postulated that system identification, for example using a continuous-time autoregressive model, of this coupling relation under tDCS perturbation may provide spatiotemporal discriminatory features for the identification of ischemia. Furthermore, portable NIRS-EEG joint imaging can be incorporated into brain computer interfaces to monitor tDCS-facilitated neurointervention as well as cortical reorganization.

Computational Pipeline for NIRS-EEG Joint Imaging of tDCS-Evoked Cerebral Responses—An Application in Ischemic Stroke

PubMed Central

Guhathakurta, Debarpan; Dutta, Anirban

2016-01-01

Transcranial direct current stimulation (tDCS) modulates cortical neural activity and hemodynamics. Electrophysiological methods (electroencephalography-EEG) measure neural activity while optical methods (near-infrared spectroscopy-NIRS) measure hemodynamics coupled through neurovascular coupling (NVC). Assessment of NVC requires development of NIRS-EEG joint-imaging sensor montages that are sensitive to the tDCS affected brain areas. In this methods paper, we present a software pipeline incorporating freely available software tools that can be used to target vascular territories with tDCS and develop a NIRS-EEG probe for joint imaging of tDCS-evoked responses. We apply this software pipeline to target primarily the outer convexity of the brain territory (superficial divisions) of the middle cerebral artery (MCA). We then present a computational method based on Empirical Mode Decomposition of NIRS and EEG time series into a set of intrinsic mode functions (IMFs), and then perform a cross-correlation analysis on those IMFs from NIRS and EEG signals to model NVC at the lesional and contralesional hemispheres of an ischemic stroke patient. For the contralesional hemisphere, a strong positive correlation between IMFs of regional cerebral hemoglobin oxygen saturation and the log-transformed mean-power time-series of IMFs for EEG with a lag of about −15 s was found after a cumulative 550 s stimulation of anodal tDCS. It is postulated that system identification, for example using a continuous-time autoregressive model, of this coupling relation under tDCS perturbation may provide spatiotemporal discriminatory features for the identification of ischemia. Furthermore, portable NIRS-EEG joint imaging can be incorporated into brain computer interfaces to monitor tDCS-facilitated neurointervention as well as cortical reorganization. PMID:27378836

Acoustical Applications of the HHT Method

NASA Technical Reports Server (NTRS)

Huang, Norden E.

2003-01-01

A document discusses applications of a method based on the Huang-Hilbert transform (HHT). The method was described, without the HHT name, in Analyzing Time Series Using EMD and Hilbert Spectra (GSC-13817), NASA Tech Briefs, Vol. 24, No. 10 (October 2000), page 63. To recapitulate: The method is especially suitable for analyzing time-series data that represent nonstationary and nonlinear physical phenomena. The method involves the empirical mode decomposition (EMD), in which a complicated signal is decomposed into a finite number of functions, called intrinsic mode functions (IMFs), that admit well-behaved Hilbert transforms. The HHT consists of the combination of EMD and Hilbert spectral analysis.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xiang, N. B.; Qu, Z. N., E-mail: znqu@ynao.ac.cn

The ensemble empirical mode decomposition (EEMD) analysis is utilized to extract the intrinsic mode functions (IMFs) of the solar mean magnetic field (SMMF) observed at the Wilcox Solar Observatory of Stanford University from 1975 to 2014, and then we analyze the periods of these IMFs as well as the relation of IMFs (SMMF) with some solar activity indices. The two special rotation cycles of 26.6 and 28.5 days should be derived from different magnetic flux elements in the SMMF. The rotation cycle of the weak magnetic flux element in the SMMF is 26.6 days, while the rotation cycle of themore » strong magnetic flux element in the SMMF is 28.5 days. The two rotation periods of the structure of the interplanetary magnetic field near the ecliptic plane are essentially related to weak and strong magnetic flux elements in the SMMF, respectively. The rotation cycle of weak magnetic flux in the SMMF did not vary over the last 40 years because the weak magnetic flux element derived from the weak magnetic activity on the full disk is not influenced by latitudinal migration. Neither the internal rotation of the Sun nor the solar magnetic activity on the disk (including the solar polar fields) causes the annual variation of SMMF. The variation of SMMF at timescales of a solar cycle is more related to weak magnetic activity on the full solar disk.« less

Leak detection in medium density polyethylene (MDPE) pipe using pressure transient method

NASA Astrophysics Data System (ADS)

Amin, M. M.; Ghazali, M. F.; PiRemli, M. A.; Hamat, A. M. A.; Adnan, N. F.

2015-12-01

Water is an essential part of commodity for a daily life usage for an average person, from personal uses such as residential or commercial consumers to industries utilization. This study emphasizes on detection of leaking in medium density polyethylene (MDPE) pipe using pressure transient method. This type of pipe is used to analyze the position of the leakage in the pipeline by using Ensemble Empirical Mode Decomposition Method (EEMD) with signal masking. Water hammer would induce an impulse throughout the pipeline that caused the system turns into a surge of water wave. Thus, solenoid valve is used to create a water hammer through the pipelines. The data from the pressure sensor is collected using DASYLab software. The data analysis of the pressure signal will be decomposed into a series of wave composition using EEMD signal masking method in matrix laboratory (MATLAB) software. The series of decomposition of signals is then carefully selected which reflected intrinsic mode function (IMF). These IMFs will be displayed by using a mathematical algorithm, known as Hilbert transform (HT) spectrum. The IMF signal was analysed to capture the differences. The analyzed data is compared with the actual measurement of the leakage in term of percentage error. The error recorded is below than 1% and it is proved that this method highly reliable and accurate for leak detection.

The emergence of the galactic stellar mass function from a non-universal IMF in clusters

NASA Astrophysics Data System (ADS)

Dib, Sami; Basu, Shantanu

2018-06-01

We investigate the dependence of a single-generation galactic mass function (SGMF) on variations in the initial stellar mass functions (IMF) of stellar clusters. We show that cluster-to-cluster variations of the IMF lead to a multi-component SGMF where each component in a given mass range can be described by a distinct power-law function. We also show that a dispersion of ≈0.3 M⊙ in the characteristic mass of the IMF, as observed for young Galactic clusters, leads to a low-mass slope of the SGMF that matches the observed Galactic stellar mass function even when the IMFs in the low-mass end of individual clusters are much steeper.

Steepest Ascent Low/Non-Low-Frequency Ratio in Empirical Mode Decomposition to Separate Deterministic and Stochastic Velocities From a Single Lagrangian Drifter

NASA Astrophysics Data System (ADS)

Chu, Peter C.

2018-03-01

SOund Fixing And Ranging (RAFOS) floats deployed by the Naval Postgraduate School (NPS) in the California Current system from 1992 to 2001 at depth between 150 and 600 m (http://www.oc.nps.edu/npsRAFOS/) are used to study 2-D turbulent characteristics. Each drifter trajectory is adaptively decomposed using the empirical mode decomposition (EMD) into a series of intrinsic mode functions (IMFs) with corresponding specific scale for each IMF. A new steepest ascent low/non-low-frequency ratio is proposed in this paper to separate a Lagrangian trajectory into low-frequency (nondiffusive, i.e., deterministic) and high-frequency (diffusive, i.e., stochastic) components. The 2-D turbulent (or called eddy) diffusion coefficients are calculated on the base of the classical turbulent diffusion with mixing length theory from stochastic component of a single drifter. Statistical characteristics of the calculated 2-D turbulence length scale, strength, and diffusion coefficients from the NPS RAFOS data are presented with the mean values (over the whole drifters) of the 2-D diffusion coefficients comparable to the commonly used diffusivity tensor method.

Pseudo-fault signal assisted EMD for fault detection and isolation in rotating machines

NASA Astrophysics Data System (ADS)

Singh, Dheeraj Sharan; Zhao, Qing

2016-12-01

This paper presents a novel data driven technique for the detection and isolation of faults, which generate impacts in a rotating equipment. The technique is built upon the principles of empirical mode decomposition (EMD), envelope analysis and pseudo-fault signal for fault separation. Firstly, the most dominant intrinsic mode function (IMF) is identified using EMD of a raw signal, which contains all the necessary information about the faults. The envelope of this IMF is often modulated with multiple vibration sources and noise. A second level decomposition is performed by applying pseudo-fault signal (PFS) assisted EMD on the envelope. A pseudo-fault signal is constructed based on the known fault characteristic frequency of the particular machine. The objective of using external (pseudo-fault) signal is to isolate different fault frequencies, present in the envelope . The pseudo-fault signal serves dual purposes: (i) it solves the mode mixing problem inherent in EMD, (ii) it isolates and quantifies a particular fault frequency component. The proposed technique is suitable for real-time implementation, which has also been validated on simulated fault and experimental data corresponding to a bearing and a gear-box set-up, respectively.

Hilbert-Huang Transformation Based Analyses of FP1, FP2, and Fz Electroencephalogram Signals in Alcoholism.

PubMed

Lin, Chin-Feng; Su, Jiun-Yi; Wang, Hao-Min

2015-09-01

Chronic alcoholism may damage the central nervous system, causing imbalance in the excitation-inhibition homeostasis in the cortex, which may lead to hyper-arousal of the central nervous system, and impairments in cognitive function. In this paper, we use the Hilbert-Huang transformation (HHT) method to analyze the electroencephalogram (EEG) signals from control and alcoholic observers who watched two different pictures. We examined the intrinsic mode function (IMF) based energy distribution features of FP1, FP2, and Fz EEG signals in the time and frequency domains for alcoholics. The HHT-based characteristics of the IMFs, the instantaneous frequencies, and the time-frequency-energy distributions of the IMFs of the clinical FP1, FP2, and Fz EEG signals recorded from normal and alcoholic observers who watched two different pictures were analyzed. We observed that the number of peak amplitudes of the alcoholic subjects is larger than that of the control. In addition, the Pearson correlation coefficients of the IMFs, and the energy-IMF distributions of the clinical FP1, FP2, and Fz EEG signals recorded from normal and alcoholic observers were analyzed. The analysis results show that the energy ratios of IMF4, IMF5, and IMF7 waves of the normal observers to the refereed total energy were larger than 10 %, respectively. In addition, the energy ratios of IMF3, IMF4, and IMF5 waves of the alcoholic observers to the refereed total energy were larger than 10 %. The FP1 and FP2 waves of the normal observers, the FP1 and FP2 waves of the alcoholic observers, and the FP1 and Fz waves of the alcoholic observers demonstrated extremely high correlations. On the other hand, the FP1 waves of the normal and alcoholic observers, the FP1 wave of the normal observer and the FP2 wave of the alcoholic observer, the FP1 wave of the normal observer and the Fz wave of the alcoholic observer, the FP2 waves of the normal and alcoholic FP2 observers, and the FP2 wave of the normal observer and the Fz wave of the alcoholic observer demonstrated extremely low correlations. The IMF4 of the FP1 and FP2 signals of the normal observer, and the IMF5 of the FP1 and FP2 signals of the alcoholic observer were correlated. The IMF4 of the FP1 signal of the normal observer and that of the FP2 signal of the alcoholic observer as well as the IMF5 of the FP1 signal of the normal observer and that of the FP2 signal of the alcoholic observer exhibited extremely low correlations. In this manner, our experiment leads to a better understanding of the HHT-based IMFs features of FP1, FP2, and Fz EEG signals in alcoholism. The analysis results show that the energy ratios of the wave of an alcoholic observer to its refereed total energy for IMF4, and IMF5 in the δ band for FP1, FP2, and Fz channels were larger than those of the respective waves of the normal observer. The alcoholic EEG signals constitute more than 1 % of the total energy in the δ wave, and the reaction times were 0_4, 4_8, 8_12, and 12_16 s. For normal EEG signals, more than 1 % of the total energy is distributed in the δ wave, with a reaction time 0 to 4 s. We observed that the alcoholic subject reaction times were slower than those of the normal subjects, and the alcoholic subjects could have experienced a cognitive error. This phenomenon is due to the intoxicated central nervous systems of the alcoholic subjects.

Mirror-mode structures at Comet 1P/Halley: A comparison between VEGA1 and Giotto Flyby

NASA Astrophysics Data System (ADS)

Volwerk, M.; Glassmeier, K.-H.; Schmid, D.; Delva, M.; Koenders, C.

2014-04-01

The pickup of freshly ionized particles emitted by the cometary nucleus creates a particle distribution in phase-space which is, amongst others, mirror-mode unstable. Many detailed studies have shown the presence of mirror-mode structures in the vicinity of comet 1P/Halley, using data from VEGA1/2 and Giotto. In the current presentation the almost similar flybys of VEGA1 and Giotto are compared with respect to the presence and occurrence rate of mirrormode structures. An automated search on the magnetic field data is performed, using minimum variance analysis, which has proved its usefulness in earlier mirror-mode studies at Earth, Venus and comets. It is found that there is an asymmetry between the two flybys: both missions show many events before closest approach and magnetic pile up region, however, after closest approach and magnetic pile up region the mirror-modes are strongly reduced at Giotto, whereas they increase in number for VEGA1. One source of influence could be the solar wind IMF, which is different: VEGA1 IMF ≈ (0, 0, 15) nT, Vsw ≈ 500 km/s and Giotto IMF ≈ (-3/3, -4, 5) nT (Bx rotating over the passage), Vsw ≈ 370 km/s. In this presentation we will discuss the occurrence rate, sizes and other characteristics of the mirror-mode structures.

Merger-driven evolution of the effective stellar initial mass function of massive early-type galaxies

NASA Astrophysics Data System (ADS)

Sonnenfeld, Alessandro; Nipoti, Carlo; Treu, Tommaso

2017-02-01

The stellar initial mass function (IMF) of early-type galaxies is the combination of the IMF of the stellar population formed in situ and that of accreted stellar populations. Using as an observable the effective IMF αIMF, defined as the ratio between the true stellar mass of a galaxy and the stellar mass inferred assuming a Salpeter IMF, we present a theoretical model for its evolution as a result of dry mergers. We use a simple dry-merger evolution model, based on cosmological N-body simulations, together with empirically motivated prescriptions for the IMF to make predictions on how the effective IMF of massive early-type galaxies changes from z = 2 to z = 0. We find that the IMF normalization of individual galaxies becomes lighter with time. At fixed velocity dispersion, αIMF is predicted to be constant with redshift. Current dynamical constraints on the evolution of the IMF are in slight tension with this prediction, even though systematic uncertainties, including the effect of radial gradients in the IMF, prevent a conclusive statement. The correlation of αIMF with stellar mass becomes shallower with time, while the correlation between αIMF and velocity dispersion is mostly preserved by dry mergers. We also find that dry mergers can mix the dependence of the IMF on stellar mass and velocity dispersion, making it challenging to infer, from z = 0 observations of global galactic properties, what is the quantity that is originally coupled with the IMF.

16S rRNA gene-based association study identified microbial taxa associated with pork intramuscular fat content in feces and cecum lumen.

PubMed

Fang, Shaoming; Xiong, Xingwei; Su, Ying; Huang, Lusheng; Chen, Congying

2017-07-19

Intramuscular fat (IMF) that deposits among muscle fibers or within muscle cells is an important meat quality trait in pigs. Previous studies observed the effects of dietary nutrients and additives on improving the pork IMF. Gut microbiome plays an important role in host metabolism and energy harvest. Whether gut microbiota exerts effect on IMF remains unknown. In this study, we investigated the microbial community structure of 500 samples from porcine cecum and feces using high-throughput 16S rRNA gene sequencing. We found that phylogenetic composition and potential function capacity of microbiome varied between two types of samples. Bacteria wide association study identified 119 OTUs significantly associated with IMF in the two types of samples (FDR < 0.1). Most of the IMF-associated OTUs belong to the bacteria related to polysaccharide degradation and amino acid metabolism (such as Prevotella, Treponema, Bacteroides and Clostridium). Potential function capacities related to metabolisms of carbohydrate, energy and amino acids, cell motility, and membrane transport were significantly associated with IMF content. FishTaco analysis suggested that the shifts of potential function capacities of microbiome associated with IMF might be caused by the IMF-associated microbial taxa. This study firstly evaluated the contribution of gut microbiome to porcine IMF content. The results presented a potential capacity for improving IMF through modulating gut microbiota.

Multi-Fault Detection of Rolling Element Bearings under Harsh Working Condition Using IMF-Based Adaptive Envelope Order Analysis

PubMed Central

Zhao, Ming; Lin, Jing; Xu, Xiaoqiang; Li, Xuejun

2014-01-01

When operating under harsh condition (e.g., time-varying speed and load, large shocks), the vibration signals of rolling element bearings are always manifested as low signal noise ratio, non-stationary statistical parameters, which cause difficulties for current diagnostic methods. As such, an IMF-based adaptive envelope order analysis (IMF-AEOA) is proposed for bearing fault detection under such conditions. This approach is established through combining the ensemble empirical mode decomposition (EEMD), envelope order tracking and fault sensitive analysis. In this scheme, EEMD provides an effective way to adaptively decompose the raw vibration signal into IMFs with different frequency bands. The envelope order tracking is further employed to transform the envelope of each IMF to angular domain to eliminate the spectral smearing induced by speed variation, which makes the bearing characteristic frequencies more clear and discernible in the envelope order spectrum. Finally, a fault sensitive matrix is established to select the optimal IMF containing the richest diagnostic information for final decision making. The effectiveness of IMF-AEOA is validated by simulated signal and experimental data from locomotive bearings. The result shows that IMF-AEOA could accurately identify both single and multiple faults of bearing even under time-varying rotating speed and large extraneous shocks. PMID:25353982

Multiscale Characterization of PM2.5 in Southern Taiwan based on Noise-assisted Multivariate Empirical Mode Decomposition and Time-dependent Intrinsic Correlation

NASA Astrophysics Data System (ADS)

Hsiao, Y. R.; Tsai, C.

2017-12-01

As the WHO Air Quality Guideline indicates, ambient air pollution exposes world populations under threat of fatal symptoms (e.g. heart disease, lung cancer, asthma etc.), raising concerns of air pollution sources and relative factors. This study presents a novel approach to investigating the multiscale variations of PM2.5 in southern Taiwan over the past decade, with four meteorological influencing factors (Temperature, relative humidity, precipitation and wind speed),based on Noise-assisted Multivariate Empirical Mode Decomposition(NAMEMD) algorithm, Hilbert Spectral Analysis(HSA) and Time-dependent Intrinsic Correlation(TDIC) method. NAMEMD algorithm is a fully data-driven approach designed for nonlinear and nonstationary multivariate signals, and is performed to decompose multivariate signals into a collection of channels of Intrinsic Mode Functions (IMFs). TDIC method is an EMD-based method using a set of sliding window sizes to quantify localized correlation coefficients for multiscale signals. With the alignment property and quasi-dyadic filter bank of NAMEMD algorithm, one is able to produce same number of IMFs for all variables and estimates the cross correlation in a more accurate way. The performance of spectral representation of NAMEMD-HSA method is compared with Complementary Empirical Mode Decomposition/ Hilbert Spectral Analysis (CEEMD-HSA) and Wavelet Analysis. The nature of NAMAMD-based TDICC analysis is then compared with CEEMD-based TDIC analysis and the traditional correlation analysis.

Auroral Substorms during Prolonged Northward IMF

NASA Astrophysics Data System (ADS)

Du, Aimin

Multiple observations by satellites and ground-based magnetometers identify the occurrence of substorm events during prolonged northward interplanetary magnetic field (IMF). The func-tion, as an expression of the solar wind energy flow, and the energy dissipation in the ionosphere (UI) are calculated during substorm periods. The delay time of the UI to the function and UI for seven substorm events with AL values of -231 -1500 nT under northward IMF condition are 45 95 min with a mean value of 70.86 min. For comparison, 23 substorm events with the AL index of -316 -1685 nT under southward IMF condition are detected to have the delay time of 21 66 min with a mean value of 42.04 min. The longer delay time for substorms during northward IMF can be presumably attributed to the contribution of IMF By component to merging between IMF and the Earth's magnetic field. A tendency of the decrease of the delay time with increasing absolute values of IMF By is noted. Acknowledgement: This work is supported by NSFC(40774086).

Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification

NASA Astrophysics Data System (ADS)

He, Zhi; Liu, Lin

2016-11-01

Empirical mode decomposition (EMD) and its variants have recently been applied for hyperspectral image (HSI) classification due to their ability to extract useful features from the original HSI. However, it remains a challenging task to effectively exploit the spectral-spatial information by the traditional vector or image-based methods. In this paper, a three-dimensional (3D) extension of EMD (3D-EMD) is proposed to naturally treat the HSI as a cube and decompose the HSI into varying oscillations (i.e. 3D intrinsic mode functions (3D-IMFs)). To achieve fast 3D-EMD implementation, 3D Delaunay triangulation (3D-DT) is utilized to determine the distances of extrema, while separable filters are adopted to generate the envelopes. Taking the extracted 3D-IMFs as features of different tasks, robust multitask learning (RMTL) is further proposed for HSI classification. In RMTL, pairs of low-rank and sparse structures are formulated by trace-norm and l1,2 -norm to capture task relatedness and specificity, respectively. Moreover, the optimization problems of RMTL can be efficiently solved by the inexact augmented Lagrangian method (IALM). Compared with several state-of-the-art feature extraction and classification methods, the experimental results conducted on three benchmark data sets demonstrate the superiority of the proposed methods.

Partial differential equation transform — Variational formulation and Fourier analysis

PubMed Central

Wang, Yang; Wei, Guo-Wei; Yang, Siyang

2011-01-01

Nonlinear partial differential equation (PDE) models are established approaches for image/signal processing, data analysis and surface construction. Most previous geometric PDEs are utilized as low-pass filters which give rise to image trend information. In an earlier work, we introduced mode decomposition evolution equations (MoDEEs), which behave like high-pass filters and are able to systematically provide intrinsic mode functions (IMFs) of signals and images. Due to their tunable time-frequency localization and perfect reconstruction, the operation of MoDEEs is called a PDE transform. By appropriate selection of PDE transform parameters, we can tune IMFs into trends, edges, textures, noise etc., which can be further utilized in the secondary processing for various purposes. This work introduces the variational formulation, performs the Fourier analysis, and conducts biomedical and biological applications of the proposed PDE transform. The variational formulation offers an algorithm to incorporate two image functions and two sets of low-pass PDE operators in the total energy functional. Two low-pass PDE operators have different signs, leading to energy disparity, while a coupling term, acting as a relative fidelity of two image functions, is introduced to reduce the disparity of two energy components. We construct variational PDE transforms by using Euler-Lagrange equation and artificial time propagation. Fourier analysis of a simplified PDE transform is presented to shed light on the filter properties of high order PDE transforms. Such an analysis also offers insight on the parameter selection of the PDE transform. The proposed PDE transform algorithm is validated by numerous benchmark tests. In one selected challenging example, we illustrate the ability of PDE transform to separate two adjacent frequencies of sin(x) and sin(1.1x). Such an ability is due to PDE transform’s controllable frequency localization obtained by adjusting the order of PDEs. The frequency selection is achieved either by diffusion coefficients or by propagation time. Finally, we explore a large number of practical applications to further demonstrate the utility of proposed PDE transform. PMID:22207904

Timescales Of The Influence Of IMF Clock Angle In Controlling The Characteristics Of Magnetospheric Dynamics

NASA Astrophysics Data System (ADS)

Grocott, A.; Milan, S. E.

2013-12-01

We exploit a database of high-latitude ionospheric electric potential patterns, derived from radar observations of plasma convection in the northern hemisphere from the years 2000 - 2006, to investigate the timescales of interplanetary magnetic field (IMF) penetration into the magnetosphere. We parameterise the convection observations by IMF clock angle, θ (the angle between geocentric solar magnetic (GSM) north and the projection of the IMF vector onto the GSM Y-Z plane), and by an IMF timescale, τB (the length of time that a similar clock angle has been maintained prior to the convection observations being made). We find that the nature of the ionospheric convection changes with IMF clock angle, as expected from previous time-averaged studies, and that for τB ~ 30 mins the convection patterns closely resemble their time-averaged counterparts. However, we also find that for certain IMF clock angles, in particular those with a northward BZ component and significant BY (dusk-dawn) component, the patterns evolve with increasing τB to less resemble their time-averaged counterparts, showing a marked enhancement in dusk-dawn asymmetry as τB approaches 10 hours. We discuss these findings in terms of the effects of the persistent penetration of a quasi-steady IMF into the magnetosphere, and its implications for understanding different modes of magnetospheric dynamics.

A hybrid filtering method based on a novel empirical mode decomposition for friction signals

NASA Astrophysics Data System (ADS)

Li, Chengwei; Zhan, Liwei

2015-12-01

During a measurement, the measured signal usually contains noise. To remove the noise and preserve the important feature of the signal, we introduce a hybrid filtering method that uses a new intrinsic mode function (NIMF) and a modified Hausdorff distance. The NIMF is defined as the difference between the noisy signal and each intrinsic mode function (IMF), which is obtained by empirical mode decomposition (EMD), ensemble EMD, complementary ensemble EMD, or complete ensemble EMD with adaptive noise (CEEMDAN). The relevant mode selecting is based on the similarity between the first NIMF and the rest of the NIMFs. With this filtering method, the EMD and improved versions are used to filter the simulation and friction signals. The friction signal between an airplane tire and the runaway is recorded during a simulated airplane touchdown and features spikes of various amplitudes and noise. The filtering effectiveness of the four hybrid filtering methods are compared and discussed. The results show that the filtering method based on CEEMDAN outperforms other signal filtering methods.

Galaxy and Mass Assembly (GAMA): the star formation rate dependence of the stellar initial mass function

NASA Astrophysics Data System (ADS)

Gunawardhana, M. L. P.; Hopkins, A. M.; Sharp, R. G.; Brough, S.; Taylor, E.; Bland-Hawthorn, J.; Maraston, C.; Tuffs, R. J.; Popescu, C. C.; Wijesinghe, D.; Jones, D. H.; Croom, S.; Sadler, E.; Wilkins, S.; Driver, S. P.; Liske, J.; Norberg, P.; Baldry, I. K.; Bamford, S. P.; Loveday, J.; Peacock, J. A.; Robotham, A. S. G.; Zucker, D. B.; Parker, Q. A.; Conselice, C. J.; Cameron, E.; Frenk, C. S.; Hill, D. T.; Kelvin, L. S.; Kuijken, K.; Madore, B. F.; Nichol, B.; Parkinson, H. R.; Pimbblet, K. A.; Prescott, M.; Sutherland, W. J.; Thomas, D.; van Kampen, E.

2011-08-01

The stellar initial mass function (IMF) describes the distribution in stellar masses produced from a burst of star formation. For more than 50 yr, the implicit assumption underpinning most areas of research involving the IMF has been that it is universal, regardless of time and environment. We measure the high-mass IMF slope for a sample of low-to-moderate redshift galaxies from the Galaxy and Mass Assembly survey. The large range in luminosities and galaxy masses of the sample permits the exploration of underlying IMF dependencies. A strong IMF-star formation rate dependency is discovered, which shows that highly star-forming galaxies form proportionally more massive stars (they have IMFs with flatter power-law slopes) than galaxies with low star formation rates. This has a significant impact on a wide variety of galaxy evolution studies, all of which rely on assumptions about the slope of the IMF. Our result is supported by, and provides an explanation for, the results of numerous recent explorations suggesting a variation of or evolution in the IMF.

Hierarchical Bayesian inference of the initial mass function in composite stellar populations

NASA Astrophysics Data System (ADS)

Dries, M.; Trager, S. C.; Koopmans, L. V. E.; Popping, G.; Somerville, R. S.

2018-03-01

The initial mass function (IMF) is a key ingredient in many studies of galaxy formation and evolution. Although the IMF is often assumed to be universal, there is continuing evidence that it is not universal. Spectroscopic studies that derive the IMF of the unresolved stellar populations of a galaxy often assume that this spectrum can be described by a single stellar population (SSP). To alleviate these limitations, in this paper we have developed a unique hierarchical Bayesian framework for modelling composite stellar populations (CSPs). Within this framework, we use a parametrized IMF prior to regulate a direct inference of the IMF. We use this new framework to determine the number of SSPs that is required to fit a set of realistic CSP mock spectra. The CSP mock spectra that we use are based on semi-analytic models and have an IMF that varies as a function of stellar velocity dispersion of the galaxy. Our results suggest that using a single SSP biases the determination of the IMF slope to a higher value than the true slope, although the trend with stellar velocity dispersion is overall recovered. If we include more SSPs in the fit, the Bayesian evidence increases significantly and the inferred IMF slopes of our mock spectra converge, within the errors, to their true values. Most of the bias is already removed by using two SSPs instead of one. We show that we can reconstruct the variable IMF of our mock spectra for signal-to-noise ratios exceeding ˜75.

Water Stage Forecasting in Tidal streams during High Water Using EEMD

NASA Astrophysics Data System (ADS)

Chen, Yen-Chang; Kao, Su-Pai; Su, Pei-Yi

2017-04-01

There are so many factors may affect the water stages in tidal streams. Not only the ocean wave but also the stream flow affects the water stage in a tidal stream. During high water, two of the most important factors affecting water stages in tidal streams are flood and tide. However the hydrological processes in tidal streams during high water are nonlinear and nonstationary. Generally the conventional methods used for forecasting water stages in tidal streams are very complicated. It explains the accurately forecasting water stages, especially during high water, in tidal streams is always a difficult task. The study makes used of Ensemble Empirical Model Decomposition (EEMD) to analyze the water stages in tidal streams. One of the advantages of the EEMD is it can be used to analyze the nonlinear and nonstationary data. The EEMD divides the water stage into several intrinsic mode functions (IMFs) and a residual; meanwhile, the physical meaning still remains during the process. By comparing the IMF frequency with tidal frequency, it is possible to identify if the IMF is affected by tides. Then the IMFs is separated into two groups, affected by tide or not by tide. The IMFs in each group are assembled to become a factor. Therefore the water stages in tidal streams are only affected by two factors, tidal factor and flood factor. Finally the regression analysis is used to establish the relationship between the factors of the gaging stations in the tidal stream. The available data during 15 typhoon periods of the Tanshui River whose downstream reach is in estuary area is used to illustrate the accuracy and reliability of the proposed method. The results show that the simple but reliable method is capable of forecasting water stages in tidal streams.

The First in situ Observation of Kelvin-Helmholtz Waves at High-Latitude Magnetopause during Strongly Dawnward Interplanetary Magnetic Field Conditions

NASA Technical Reports Server (NTRS)

Hwang, K.-J.; Goldstein, M. L.; Kuznetsova, M. M.; Wang, Y.; Vinas, A. F.; Sibeck, D. G.

2012-01-01

We report the first in situ observation of high-latitude magnetopause (near the northern duskward cusp) Kelvin-Helmholtz waves (KHW) by Cluster on January 12, 2003, under strongly dawnward interplanetary magnetic field (IMF) conditions. The fluctuations unstable to Kelvin-Helmholtz instability (KHI) are found to propagate mostly tailward, i.e., along the direction almost 90 deg. to both the magnetosheath and geomagnetic fields, which lowers the threshold of the KHI. The magnetic configuration across the boundary layer near the northern duskward cusp region during dawnward IMF is similar to that in the low-latitude boundary layer under northward IMF, in that (1) both magnetosheath and magnetospheric fields across the local boundary layer constitute the lowest magnetic shear and (2) the tailward propagation of the KHW is perpendicular to both fields. Approximately 3-hour-long periods of the KHW during dawnward IMF are followed by the rapid expansion of the dayside magnetosphere associated with the passage of an IMF discontinuity that characterizes an abrupt change in IMF cone angle, Phi = acos (B(sub x) / absolute value of Beta), from approx. 90 to approx. 10. Cluster, which was on its outbound trajectory, continued observing the boundary waves at the northern evening-side magnetopause during sunward IMF conditions following the passage of the IMF discontinuity. By comparing the signatures of boundary fluctuations before and after the IMF discontinuity, we report that the frequencies of the most unstable KH modes increased after the discontinuity passed. This result demonstrates that differences in IMF orientations (especially in f) are associated with the properties of KHW at the high-latitude magnetopause due to variations in thickness of the boundary layer, and/or width of the KH-unstable band on the surface of the dayside magnetopause.

Strong gravitational lensing and the stellar IMF of early-type galaxies

NASA Astrophysics Data System (ADS)

Leier, Dominik; Ferreras, Ignacio; Saha, Prasenjit; Charlot, Stéphane; Bruzual, Gustavo; La Barbera, Francesco

2016-07-01

Systematic variations of the initial mass function (IMF) in early-type galaxies, and their connection with possible drivers such as velocity dispersion or metallicity, have been much debated in recent years. Strong lensing over galaxy scales combined with photometric and spectroscopic data provides a powerful method to constrain the stellar mass-to-light ratio and hence the functional form of the IMF. We combine photometric and spectroscopic constraints from the latest set of population synthesis models of Charlot & Bruzual, including a varying IMF, with a non-parametric analysis of the lens masses of 18 ETGs from the SLACS survey, with velocity dispersions in the range 200-300 km s-1. We find that very bottom-heavy IMFs are excluded. However, the upper limit to the bimodal IMF slope (μ ≲ 2.2, accounting for a dark matter fraction of 20-30 per cent, where μ = 1.3 corresponds to a Kroupa-like IMF) is compatible at the 1σ level with constraints imposed by gravity-sensitive line strengths. A two-segment power-law parametrization of the IMF (Salpeter-like for high masses) is more constrained (Γ ≲ 1.5, where Γ is the power index at low masses) but requires a dark matter contribution of ≳25 per cent to reconcile the results with a Salpeter IMF. For a standard Milky Way-like IMF to be applicable, a significant dark matter contribution is required within 1Re. Our results reveal a large range of allowed IMF slopes, which, when interpreted as intrinsic scatter in the IMF properties of ETGs, could explain the recent results of Smith et al., who find Milky Way-like IMF normalizations in a few massive lensing ETGs.

Size and Shape of the Distant Magnetotail

NASA Technical Reports Server (NTRS)

Sibeck, D.G.; Lin, R.-Q.

2014-01-01

We employ a global magnetohydrodynamic model to study the effects of the interplanetary magnetic field (IMF) strength and direction upon the cross-section of the magnetotail at lunar distances. The anisotropic pressure of draped magnetosheath magnetic field lines and the inclusion of a reconnection-generated standing slow mode wave fan bounded by a rotational discontinuity within the definition of the magnetotail result in cross-sections elongated in the direction parallel to the component of the IMF in the plane perpendicular to the Sun-Earth line. Tilted cross-tail plasma sheets separate the northern and southern lobes within these cross-sections. Greater fast mode speeds perpendicular than parallel to the draped magnetos heath magnetic field lines result in greater distances to the bow shock in the direction perpendicular than parallel to the component of the IMF in the plane transverse to the Sun-Earth line. The magnetotail cross-section responds rapidly to reconnected magnetic field lines requires no more than the magnetosheath convection time to appear at any distance downstream, and further adjustments of the cross-section in response to the anisotropic pressures of the draped magnetic field lines require no more than 10-20 minutes. Consequently for typical ecliptic IMF orientations and strengths, the magnetotail cross-section is oblate while the bow shock is prolate.

VizieR Online Data Catalog: GalIMF version 1.0.0 (Yan+, 2017)

NASA Astrophysics Data System (ADS)

Yan, Z.; Jerabkova, T.; Kroupa, P.

2017-08-01

GalIMF stands for the Galaxy-wide Initial Mass Function. It is a Python 3 module that allows users to compute galaxy-wide initial stellar mass functions based on locally derived empirical constraints following the IGIMF theory. See the GalIMF homepage https://sites.google.com/view/galimf/home for more information. (1 data file).

Dominant modes of variability in large-scale Birkeland currents

NASA Astrophysics Data System (ADS)

Cousins, E. D. P.; Matsuo, Tomoko; Richmond, A. D.; Anderson, B. J.

2015-08-01

Properties of variability in large-scale Birkeland currents are investigated through empirical orthogonal function (EOF) analysis of 1 week of data from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE). Mean distributions and dominant modes of variability are identified for both the Northern and Southern Hemispheres. Differences in the results from the two hemispheres are observed, which are attributed to seasonal differences in conductivity (the study period occurred near solstice). A universal mean and set of dominant modes of variability are obtained through combining the hemispheric results, and it is found that the mean and first three modes of variability (EOFs) account for 38% of the total observed squared magnetic perturbations (δB2) from both hemispheres. The mean distribution represents a standard Region 1/Region 2 (R1/R2) morphology of currents and EOF 1 captures the strengthening/weakening of the average distribution and is well correlated with the north-south component of the interplanetary magnetic field (IMF). EOF 2 captures a mixture of effects including the expansion/contraction and rotation of the (R1/R2) currents; this mode correlates only weakly with possible external driving parameters. EOF 3 captures changes in the morphology of the currents in the dayside cusp region and is well correlated with the dawn-dusk component of the IMF. The higher-order EOFs capture more complex, smaller-scale variations in the Birkeland currents and appear generally uncorrelated with external driving parameters. The results of the EOF analysis described here are used for describing error covariance in a data assimilation procedure utilizing AMPERE data, as described in a companion paper.

The effects of the initial mass function on the chemical evolution of elliptical galaxies

NASA Astrophysics Data System (ADS)

De Masi, Carlo; Matteucci, F.; Vincenzo, F.

2018-03-01

We describe the use of our chemical evolution model to reproduce the abundance patterns observed in a catalogue of elliptical galaxies from the Sloan Digital Sky Survey Data Release 4. The model assumes ellipticals form by fast gas accretion, and suffer a strong burst of star formation followed by a galactic wind, which quenches star formation. Models with fixed initial mass function (IMF) failed in simultaneously reproducing the observed trends with the galactic mass. So, we tested a varying IMF; contrary to the diffused claim that the IMF should become bottom heavier in more massive galaxies, we find a better agreement with data by assuming an inverse trend, where the IMF goes from being bottom heavy in less massive galaxies to top heavy in more massive ones. This naturally produces a downsizing in star formation, favouring massive stars in largest galaxies. Finally, we tested the use of the integrated Galactic IMF, obtained by averaging the canonical IMF over the mass distribution function of the clusters where star formation is assumed to take place. We combined two prescriptions, valid for different SFR regimes, to obtain the Integrated Initial Mass Function values along the whole evolution of the galaxies in our models. Predicted abundance trends reproduce the observed slopes, but they have an offset relative to the data. We conclude that bottom-heavier IMFs do not reproduce the properties of the most massive ellipticals, at variance with previous suggestions. On the other hand, an IMF varying with galactic mass from bottom heavier to top heavier should be preferred.

[An EMD based time-frequency distribution and its application in EEG analysis].

PubMed

Li, Xiaobing; Chu, Meng; Qiu, Tianshuang; Bao, Haiping

2007-10-01

Hilbert-Huang transform (HHT) is a new time-frequency analytic method to analyze the nonlinear and the non-stationary signals. The key step of this method is the empirical mode decomposition (EMD), with which any complicated signal can be decomposed into a finite and small number of intrinsic mode functions (IMF). In this paper, a new EMD based method for suppressing the cross-term of Wigner-Ville distribution (WVD) is developed and is applied to analyze the epileptic EEG signals. The simulation data and analysis results show that the new method suppresses the cross-term of the WVD effectively with an excellent resolution.

A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier.

PubMed

Zhou, Shenghan; Qian, Silin; Chang, Wenbing; Xiao, Yiyong; Cheng, Yang

2018-06-14

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.

Hilbert-Huang transform analysis of dynamic and earthquake motion recordings

USGS Publications Warehouse

Zhang, R.R.; Ma, S.; Safak, E.; Hartzell, S.

2003-01-01

This study examines the rationale of Hilbert-Huang transform (HHT) for analyzing dynamic and earthquake motion recordings in studies of seismology and engineering. In particular, this paper first provides the fundamentals of the HHT method, which consist of the empirical mode decomposition (EMD) and the Hilbert spectral analysis. It then uses the HHT to analyze recordings of hypothetical and real wave motion, the results of which are compared with the results obtained by the Fourier data processing technique. The analysis of the two recordings indicates that the HHT method is able to extract some motion characteristics useful in studies of seismology and engineering, which might not be exposed effectively and efficiently by Fourier data processing technique. Specifically, the study indicates that the decomposed components in EMD of HHT, namely, the intrinsic mode function (IMF) components, contain observable, physical information inherent to the original data. It also shows that the grouped IMF components, namely, the EMD-based low- and high-frequency components, can faithfully capture low-frequency pulse-like as well as high-frequency wave signals. Finally, the study illustrates that the HHT-based Hilbert spectra are able to reveal the temporal-frequency energy distribution for motion recordings precisely and clearly.

Measuring the High-Mass IMF in Low-Metallicity Dwarf Galaxies

NASA Astrophysics Data System (ADS)

Weisz, Daniel

2017-08-01

We propose to measure the stellar initial mass function above >1-2 Msun in 46 nearby dwarf galaxies with archival HST observations. This novel approach leverages the redundant age information provided by the main sequence and blue core helium burning stars <500 years old to break the well-known degeneracy between the IMF and star formation history (SFH), enabling a direct measurement of the high-mass IMF in dwarf galaxies. We will be able to constrain the high-mass IMF slope to a precision better than 0.1 to 0.3 dex in each galaxy. Our sample spans a factor of 6 in metallicity ( 5-30% Zsun), 4 decades in star formation rate, and 3 decades in both stellar and gas mass, allowing us to explore the IMF over a wide range of extreme environments.Current observational evidence suggests that nearby dwarf galaxies are the most likely candidates to host significant and systematic variations in the high-mass IMF (e.g., Halpha/UV ratios). However, to date there have been no direct measurements of the high-mass IMF in environments with lower star formation rates and/or more metal poor than the Magellanic Clouds. Our program remedies this shortcoming allowing us to (1) make the first-ever measurement of the high-mass IMF in extremely metal-poor environments; (2) empirically quantify environmental the (lack of) variations in the high-mass IMF; (3) directly test the integrated galactic mass initial mass function (IGIMF), which predicts environmental sensitivity of the IMF in dwarf galaxies.

The Effect of Star Formation History on the Inferred Stellar Initial Mass Function

NASA Astrophysics Data System (ADS)

Elmegreen, Bruce G.; Scalo, John

2006-01-01

Peaks and lulls in the star formation rate (SFR) over the history of the Galaxy produce plateaus and declines in the present-day mass function (PDMF) where the main-sequence lifetime overlaps the age and duration of the SFR variation. These PDMF features can be misinterpreted as the form of the intrinsic stellar initial mass function (IMF) if the star formation rate is assumed to be constant or slowly varying with time. This effect applies to all regions that have formed stars for longer than the age of the most massive stars, including OB associations, star complexes, and especially galactic field stars. Related problems may apply to embedded clusters. Evidence is summarized for temporal SFR variations from parsec scales to entire galaxies, all of which should contribute to inferred IMF distortions. We give examples of various star formation histories to demonstrate the types of false IMF structures that might be seen. These include short-duration bursts, stochastic histories with lognormal amplitude distributions, and oscillating histories with various periods and phases. The inferred IMF should appear steeper than the intrinsic IMF over mass ranges where the stellar lifetimes correspond to times of decreasing SFRs; shallow portions of the inferred IMF correspond to times of increasing SFRs. If field regions are populated by dispersed clusters and defined by their low current SFRs, then they should have steeper inferred IMFs than the clusters. The SFRs required to give the steep field IMFs in the LMC and SMC are determined. Structure observed in several determinations of the Milky Way field star IMF can be accounted for by a stochastic and bursty star formation history.

EVIDENCE FOR A CONSTANT IMF IN EARLY-TYPE GALAXIES BASED ON THEIR X-RAY BINARY POPULATIONS

NASA Astrophysics Data System (ADS)

Zepf, Stephen E.; Maccarone, T. J.; Kundu, A.; Gonzalez, A. H.; Lehmer, B.; Maraston, C.

2014-01-01

A number of recent studies have proposed that the stellar initial mass function (IMF) of early type galaxies varies systematically as a function of galaxy mass, with higher mass galaxies having steeper IMFs. These steeper IMFs have more low-mass stars relative to the number of high mass stars, and therefore naturally result in proportionally fewer neutron stars and black holes. In this paper, we specifically predict the variation in the number of black holes and neutron stars in early type galaxies based on the IMF variation required to reproduce the observed mass-to-light ratio trends with galaxy mass. We then test whether such variations are observed by studying the field low-mass X-ray binary populations (LMXBs) of nearby early-type galaxies. These binaries are field neutron stars or black holes accreting from a low-mass donor star. We specifically compare the number of field LMXBs per K-band light in a well-studied sample of elliptical galaxies, and use this result to distinguish between an invariant IMF and one that is Kroupa/Chabrier-like at low masses and steeper at high masses. We discuss how these observations constrain the possible forms of the IMF variations and how future Chandra observations can enable sharper tests of the IMF.

Pediatric mandibular fractures: a free hand technique.

PubMed

Davison, S P; Clifton, M S; Davison, M N; Hedrick, M; Sotereanos, G

2001-01-01

The treatment of pediatric mandibular fractures is rare, controversial, and complicated by mixed dentition. To determine if open mandibular fracture repair with intraoral and extraoral rigid plate placement, after free hand occlusal and bone reduction, without intermaxillary fixation (IMF), is appropriate and to discuss postoperative advantages, namely, maximal early return of function and minimal oral hygiene issues. A group of 29 pediatric patients with a mandibular fracture were examined. Twenty pediatric patients (13 males and 7 females) with a mean age of 9 years (age range, 1-17 years) were treated using IMF. All patients were treated by the same surgeon (G.S.). Surgical time for plating was reduced by 1 hour, the average time to place patients in IMF. The patients who underwent open reduction internal fixation without IMF ate a soft mechanical diet by postoperative day 3 compared with postoperative day 16 for those who underwent IMF. Complication rates related to fixation technique were comparable at 20% for those who did not undergo IMF and 33% for those who did. We believe that free hand reduction is a valuable technique to reduce operative time for pediatric mandibular fractures. It maximizes return to function while minimizing the oral hygiene issues and hardware removal of intermaxillary function.

Intestinal myofibroblast-specific Tpl2-Cox-2-PGE2 pathway links innate sensing to epithelial homeostasis

PubMed Central

Roulis, Manolis; Nikolaou, Christoforos; Kotsaki, Elena; Kaffe, Eleanna; Karagianni, Niki; Koliaraki, Vasiliki; Salpea, Klelia; Ragoussis, Jiannis; Aidinis, Vassilis; Martini, Eva; Becker, Christoph; Herschman, Harvey R.; Vetrano, Stefania; Danese, Silvio; Kollias, George

2014-01-01

Tumor progression locus-2 (Tpl2) kinase is a major inflammatory mediator in immune cell types recently found to be genetically associated with inflammatory bowel diseases (IBDs). Here we show that Tpl2 may exert a dominant homeostatic rather than inflammatory function in the intestine mediated specifically by subepithelial intestinal myofibroblasts (IMFs). Mice with complete or IMF-specific Tpl2 ablation are highly susceptible to epithelial injury-induced colitis showing impaired compensatory proliferation in crypts and extensive ulcerations without significant changes in inflammatory responses. Following epithelial injury, IMFs sense innate or inflammatory signals and activate, via Tpl2, the cyclooxygenase-2 (Cox-2)-prostaglandin E2 (PGE2) pathway, which we show here to be essential for the epithelial homeostatic response. Exogenous PGE2 administration rescues mice with complete or IMF-specific Tpl2 ablation from defects in crypt function and susceptibility to colitis. We also show that Tpl2 expression is decreased in IMFs isolated from the inflamed ileum of IBD patients indicating that Tpl2 function in IMFs may be highly relevant to human disease. The IMF-mediated mechanism we propose also involves the IBD-associated genes IL1R1, MAPK1, and the PGE2 receptor-encoding PTGER4. Our results establish a previously unidentified myofibroblast-specific innate pathway that regulates intestinal homeostasis and may underlie IBD susceptibility in humans. PMID:25316791

Microstructure of the IMF turbulences at 2.5 AU

NASA Technical Reports Server (NTRS)

Mavromichalaki, H.; Vassilaki, A.; Marmatsouri, L.; Moussas, X.; Quenby, J. J.; Smith, E. J.

1995-01-01

A detailed analysis of small period (15-900 sec) magnetohydrodynamic (MHD) turbulences of the interplanetary magnetic field (IMF) has been made using Pioneer-11 high time resolution data (0.75 sec) inside a Corotating Interaction Region (CIR) at a heliocentric distance of 2.5 AU in 1973. The methods used are the hodogram analysis, the minimum variance matrix analysis and the cohenrence analysis. The minimum variance analysis gives evidence of linear polarized wave modes. Coherence analysis has shown that the field fluctuations are dominated by the magnetosonic fast modes with periods 15 sec to 15 min. However, it is also shown that some small amplitude Alfven waves are present in the trailing edge of this region with characteristic periods (15-200 sec). The observed wave modes are locally generated and possibly attributed to the scattering of Alfven waves energy into random magnetosonic waves.

Multivariate EMD and full spectrum based condition monitoring for rotating machinery

NASA Astrophysics Data System (ADS)

Zhao, Xiaomin; Patel, Tejas H.; Zuo, Ming J.

2012-02-01

Early assessment of machinery health condition is of paramount importance today. A sensor network with sensors in multiple directions and locations is usually employed for monitoring the condition of rotating machinery. Extraction of health condition information from these sensors for effective fault detection and fault tracking is always challenging. Empirical mode decomposition (EMD) is an advanced signal processing technology that has been widely used for this purpose. Standard EMD has the limitation in that it works only for a single real-valued signal. When dealing with data from multiple sensors and multiple health conditions, standard EMD faces two problems. First, because of the local and self-adaptive nature of standard EMD, the decomposition of signals from different sources may not match in either number or frequency content. Second, it may not be possible to express the joint information between different sensors. The present study proposes a method of extracting fault information by employing multivariate EMD and full spectrum. Multivariate EMD can overcome the limitations of standard EMD when dealing with data from multiple sources. It is used to extract the intrinsic mode functions (IMFs) embedded in raw multivariate signals. A criterion based on mutual information is proposed for selecting a sensitive IMF. A full spectral feature is then extracted from the selected fault-sensitive IMF to capture the joint information between signals measured from two orthogonal directions. The proposed method is first explained using simple simulated data, and then is tested for the condition monitoring of rotating machinery applications. The effectiveness of the proposed method is demonstrated through monitoring damage on the vane trailing edge of an impeller and rotor-stator rub in an experimental rotor rig.

Evidence for a Constant Initial Mass Function in Early-type Galaxies Based on Their X-Ray Binary Populations

NASA Astrophysics Data System (ADS)

Peacock, Mark B.; Zepf, Stephen E.; Maccarone, Thomas J.; Kundu, Arunav; Gonzalez, Anthony H.; Lehmer, Bret D.; Maraston, Claudia

2014-04-01

A number of recent studies have proposed that the stellar initial mass function (IMF) of early type galaxies varies systematically as a function of galaxy mass, with higher mass galaxies having bottom-heavy IMFs. These bottom-heavy IMFs have more low-mass stars relative to the number of high mass stars, and therefore naturally result in proportionally fewer neutron stars (NSs) and black holes (BHs). In this paper, we specifically predict the variation in the number of BHs and NSs based on the power-law IMF variation required to reproduce the observed mass-to-light ratio trends with galaxy mass. We then test whether such variations are observed by studying the field low-mass X-ray binary (LMXB) populations of nearby early-type galaxies. In these binaries, an NS or BH accretes matter from a low-mass donor star. Their number is therefore expected to scale with the number of BHs and NSs present in a galaxy. We find that the number of LMXBs per K-band light is similar among the galaxies in our sample. These data therefore demonstrate the uniformity of the slope of the IMF from massive stars down to those now dominating the K-band light and are consistent with an invariant IMF. Our results are inconsistent with an IMF which varies from a Kroupa/Chabrier like IMF for low-mass galaxies to a steep power-law IMF (with slope x = 2.8) for high mass galaxies. We discuss how these observations constrain the possible forms of the IMF variations and how future Chandra observations can enable sharper tests of the IMF. Based in part on observations made with the NASA/ESA Hubble Space Telescope, and obtained from the Hubble Legacy Archive, which is a collaboration between the Space Telescope Science Institute (STScI/NASA), the Space Telescope European Coordinating Facility (ST-ECF/ESA) and the Canadian Astronomy Data Centre (CADC/NRC/CSA). The scientific results reported in this article are based in part on data obtained from the Chandra Data Archive and observations made by the Chandra X-ray Observatory and published previously in cited articles.

A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

PubMed Central

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782

A four-stage hybrid model for hydrological time series forecasting.

PubMed

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.

Implications of Galaxy Buildup for Putative IMF Variations in Massive Galaxies

NASA Astrophysics Data System (ADS)

Blancato, Kirsten; Genel, Shy; Bryan, Greg

2017-08-01

Recent observational evidence for initial mass function (IMF) variations in massive quiescent galaxies at z = 0 challenges the long-established paradigm of a universal IMF. While a few theoretical models relate the IMF to birth cloud conditions, the physical driver underlying these putative IMF variations is still largely unclear. Here we use post-processing analysis of the Illustris cosmological hydrodynamical simulation to investigate possible physical origins of IMF variability with galactic properties. We do so by tagging stellar particles in the simulation (each representing a stellar population of ≈ {10}6 {M}⊙ ) with individual IMFs that depend on various physical conditions, such as velocity dispersion, metallicity, or star formation rate, at the time and place in which the stars are formed. We then follow the assembly of these populations throughout cosmic time and reconstruct the overall IMF of each z = 0 galaxy from the many distinct IMFs it is composed of. Our main result is that applying the observed relations between IMF and galactic properties to the conditions at the star formation sites does not result in strong enough IMF variations between z = 0 galaxies. Steeper physical IMF relations are required for reproducing the observed IMF trends, and some stellar populations must form with more extreme IMFs than those observed. The origin of this result is the hierarchical nature of massive galaxy assembly, and it has implications for the reliability of the strong observed trends, for the ability of cosmological simulations to capture certain physical conditions in galaxies, and for theories of star formation aiming to explain the physical origin of a variable IMF.

Magnetic Flux Circulation During Dawn-Dusk Oriented Interplanetary Magnetic Field

NASA Technical Reports Server (NTRS)

Mitchell, E. J.; Lopez, R. E.; Fok, M.-C.; Deng, Y.; Wiltberger, M.; Lyon, J.

2010-01-01

Magnetic flux circulation is a primary mode of energy transfer from the solar wind into the ionosphere and inner magnetosphere. For southward interplanetary magnetic field (IMF), magnetic flux circulation is described by the Dungey cycle (dayside merging, night side reconnection, and magnetospheric convection), and both the ionosphere and inner magnetosphere receive energy. For dawn-dusk oriented IMF, magnetic flux circulation is not well understood, and the inner magnetosphere does not receive energy. Several models have been suggested for possible reconnection patterns; the general pattern is: dayside merging; reconnection on the dayside or along the dawn/dusk regions; and, return flow on dayside only. These models are consistent with the lack of energy in the inner magnetosphere. We will present evidence that the Dungey cycle does not explain the energy transfer during dawn-dusk oriented IMF. We will also present evidence of how magnetic flux does circulate during dawn-dusk oriented IMF, specifically how the magnetic flux reconnects and circulates back.

Constraining the intermediate-mass range of the Initial Mass Function using Galactic Cepheids

NASA Astrophysics Data System (ADS)

Mor, R.; Figueras, F.; Robin, A. C.; Lemasle, B.

2015-05-01

Aims. To use the Besançon Galaxy Model (Robin A.C. et al., 2003) and the most complete observational catalogues of Galactic Cepheids to constrain the intermediate-mass range of the Initial Mass Function (IMF) in the Milky Way Galactic thin disc. Methods. We have optimized the flexibility of the new Besançon Galaxy Model (Czekaj et al., 2014) to simulate magnitude and distance complete samples of young intermediate mass stars assuming different IMFs and Star Formation Histories (SFH). Comparing the simulated synthetic catalogues with the observational data, we studied which IMF reproduces better the observational number of Cepheids in the Galactic thin disc. We analysed three different IMFs: (1) Salpeter, (2) Kroupa-Haywood and (3) Haywood-Robin, all of them with a decreasing SFH from Aumer and Binney, 2009. Results. For the first time the Besançon Galaxy Model is used to characterize the Galactic Cepheids. We find that for most of the cases the Salpeter IMF overestimates the number of observed Cepheids and Haywood-Robin IMF underestimates it. The Kroupa-Haywood IMF, with a slope α=3.2, is the one that best reproduces the observed Cepheids. From the comparison of the predicted and observed number of Cepheids up to V=12, we point that the model might underestimate the scale-height of the young population. The effects of the variation of the model ingredients need to be quantified. Conclusions. In agreement with Kroupa and Weidner (2003), our study shows that the Salpeter IMF (α=2.35) overestimates the star counts in the range 4 ≤ M/M_{⊙} ≤ 10 and supports the idea that the slope of the intermediate and massive stars IMF is steeper than the Salpeter IMF.

Systematic variation of the stellar initial mass function in early-type galaxies.

PubMed

Cappellari, Michele; McDermid, Richard M; Alatalo, Katherine; Blitz, Leo; Bois, Maxime; Bournaud, Frédéric; Bureau, M; Crocker, Alison F; Davies, Roger L; Davis, Timothy A; de Zeeuw, P T; Duc, Pierre-Alain; Emsellem, Eric; Khochfar, Sadegh; Krajnović, Davor; Kuntschner, Harald; Lablanche, Pierre-Yves; Morganti, Raffaella; Naab, Thorsten; Oosterloo, Tom; Sarzi, Marc; Scott, Nicholas; Serra, Paolo; Weijmans, Anne-Marie; Young, Lisa M

2012-04-25

Much of our knowledge of galaxies comes from analysing the radiation emitted by their stars, which depends on the present number of each type of star in the galaxy. The present number depends on the stellar initial mass function (IMF), which describes the distribution of stellar masses when the population formed, and knowledge of it is critical to almost every aspect of galaxy evolution. More than 50 years after the first IMF determination, no consensus has emerged on whether it is universal among different types of galaxies. Previous studies indicated that the IMF and the dark matter fraction in galaxy centres cannot both be universal, but they could not convincingly discriminate between the two possibilities. Only recently were indications found that massive elliptical galaxies may not have the same IMF as the Milky Way. Here we report a study of the two-dimensional stellar kinematics for the large representative ATLAS(3D) sample of nearby early-type galaxies spanning two orders of magnitude in stellar mass, using detailed dynamical models. We find a strong systematic variation in IMF in early-type galaxies as a function of their stellar mass-to-light ratios, producing differences of a factor of up to three in galactic stellar mass. This implies that a galaxy's IMF depends intimately on the galaxy's formation history.

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

NASA Astrophysics Data System (ADS)

Wen-Bo, Wang; Xiao-Dong, Zhang; Yuchan, Chang; Xiang-Li, Wang; Zhao, Wang; Xi, Chen; Lei, Zheng

2016-01-01

In this paper, a new method to reduce noises within chaotic signals based on ICA (independent component analysis) and EMD (empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions (IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor. Project supported by the National Science and Technology, China (Grant No. 2012BAJ15B04), the National Natural Science Foundation of China (Grant Nos. 41071270 and 61473213), the Natural Science Foundation of Hubei Province, China (Grant No. 2015CFB424), the State Key Laboratory Foundation of Satellite Ocean Environment Dynamics, China (Grant No. SOED1405), the Hubei Provincial Key Laboratory Foundation of Metallurgical Industry Process System Science, China (Grant No. Z201303), and the Hubei Key Laboratory Foundation of Transportation Internet of Things, Wuhan University of Technology, China (Grant No.2015III015-B02).

Investigating the low-mass slope and possible turnover in the LMC IMF

NASA Astrophysics Data System (ADS)

Gennaro, Mario

2014-10-01

We propose to derive the Initial Mass Function (IMF) of the field population of the Large Magellanic Cloud (LMC) down to 0.2 solar masses, probing the mass regime where the characteristic IMF turnover is observed in our Galaxy. The power of the HST, using the WFC3 IR channel, is necessary to obtain photometric mass estimates for the faint, cool, dwarf stars with masses below the expected IMF turnover point. Only by probing the IMF down to such masses, it will be possible to clearly distinguish between a bottom-heavy or bottom-light IMF in the LMC. Recent studies, using the deepest available observations for the Small Magellanic Cloud, cannot find clear evidence of a turnover in the IMF for this galaxy, suggesting a bottom-heavy IMF in contrast to the Milky Way. A similar study of the LMC is needed to confirm a possible dependence of the low-mass IMF with galactic environment. Studies of giant ellipticals have recently challenged the picture of a universal IMF, and suggest an enviromental dependence of the IMF, with the most massive galaxies having a larger fraction of low mass stars and no IMF turnover. A study of possible IMF variations from resolved stellar populations in nearby galaxies is of great importance in sheding light on this issue. Our simple approach, using direct evidence from basic star counts, is much less prone to systematic errors with respect to studies of more distant objects which have to rely on the observations of integrated properties.

Computing frequency by using generalized zero-crossing applied to intrinsic mode functions

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2006-01-01

This invention presents a method for computing Instantaneous Frequency by applying Empirical Mode Decomposition to a signal and using Generalized Zero-Crossing (GZC) and Extrema Sifting. The GZC approach is the most direct, local, and also the most accurate in the mean. Furthermore, this approach will also give a statistical measure of the scattering of the frequency value. For most practical applications, this mean frequency localized down to quarter of a wave period is already a well-accepted result. As this method physically measures the period, or part of it, the values obtained can serve as the best local mean over the period to which it applies. Through Extrema Sifting, instead of the cubic spline fitting, this invention constructs the upper envelope and the lower envelope by connecting local maxima points and local minima points of the signal with straight lines, respectively, when extracting a collection of Intrinsic Mode Functions (IMFs) from a signal under consideration.

A Coupling Function Linking Solar Wind /IMF Variations and Geomagnetic Activity

NASA Astrophysics Data System (ADS)

Lyatsky, W.; Lyatskaya, S.; Tan, A.

2006-12-01

From a theoretical consideration we have obtained expressions for the coupling function linking solar wind and IMF parameters to geomagnetic activity. While deriving these expressions, we took into account (1) a scaling factor due to polar cap expansion while increasing a reconnected magnetic flux in the dayside magnetosphere, and (2) a modified Akasofu function for the reconnected flux for combined IMF Bz and By components. The resulting coupling function may be written as Fa = aVsw B^1/2 sina (q/2), where Vsw is the solar wind speed, B^ is the magnitude of the IMF vector in the Y-Z plane, q is the clock angle between the Z axis and IMF vector in the Y-Z plane, a is a coefficient, and the exponent, a, is derived from the experimental data and equals approximately to 2. The Fa function differs primary by the power of B^ from coupling functions proposed earlier. For testing the obtained coupling function, we used solar wind and interplanetary magnetic field data for four years for maximum and minimum solar activity. We computed 2-D contour plots for correlation coefficients for the dependence of geomagnetic activity indices on solar wind parameters for different coupling functions. The obtained diagrams showed a good correspondence to the theoretic coupling function Fa for a »2. The maximum correlation coefficient for the dependence of the polar cap PC index on the Fa coupling function is significantly higher than that computed for other coupling functions used researchers, for the same time intervals.

Transcriptome analysis of mRNA and microRNAs in intramuscular fat tissues of castrated and intact male Chinese Qinchuan cattle

PubMed Central

Wang, Ya-Ning; Wang, Hong-Cheng; Zhang, Song; Hong, Jie-Yun; Guo, Hong-Fang; Chen, Dai; Yang, Yang; Zan, Lin-Sen

2017-01-01

Intramuscular fat (IMF) is known to enhance beef palatability and can be markedly increased by castration. However, there is little understanding of the molecular mechanism underlying the IMF deposition after castration of beef cattle. We hypothesize that genetic regulators function differently in IMF from bulls and steers. Therefore, after detecting serum testosterone and lipid parameter, as well as the contents of IMF at 6, 12, 18 and 24 months, we have investigated differentially expressed (DE) microRNAs (miRNAs) and mRNAs in IMF of bulls and steers at 24 months of age in Qinchuan cattle using next-generation sequencing, and then explored the possible biopathways regulating IMF deposition. Serum testosterone levels were significantly decreased in steers, whereas IMF content, serum total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TGs) were markedly increased in steers. Comparing the results of steers and bulls, 580 upregulated genes and 1,120 downregulated genes in IMF tissues were identified as DE genes correlated with IMF deposition. The upregulated genes were mainly associated with lipid metabolism, lipogenesis and fatty acid transportation signalling pathways, and the downregulated genes were correlated with immune response and intracellular signal transduction. Concurrently, the DE miRNAs—important players in adipose tissue accumulation induced by castration—were also examined in IMF tissues; 52 DE miRNAs were identified. The expression profiles of selected genes and miRNAs were also confirmed by quantitative real-time PCR (qRT-PCR) assays. Using integrated analysis, we constructed the microRNA-target regulatory network which was supported by target validation using the dual luciferase reporter system. Moreover, Ingenuity Pathway Analysis (IPA) software was used to construct a molecular interaction network that could be involved in regulating IMF after castration. The detected molecular network is closely associated with lipid metabolism and adipocyte differentiation, which is supported by functional identification results of bta-let-7i on bovine preadipocytes. These results provided valuable insights into the molecular mechanisms of the IMF phenotype differences between steers and bulls. PMID:29073274

DOE Office of Scientific and Technical Information (OSTI.GOV)

Conroy, Charlie; Van Dokkum, Pieter G.; Villaume, Alexa

It is now well-established that the stellar initial mass function (IMF) can be determined from the absorption line spectra of old stellar systems, and this has been used to measure the IMF and its variation across the early-type galaxy population. Previous work focused on measuring the slope of the IMF over one or more stellar mass intervals, implicitly assuming that this is a good description of the IMF and that the IMF has a universal low-mass cutoff. In this work we consider more flexible IMFs, including two-component power laws with a variable low-mass cutoff and a general non-parametric model. Wemore » demonstrate with mock spectra that the detailed shape of the IMF can be accurately recovered as long as the data quality is high (S/N ≳ 300 Å{sup −1}) and cover a wide wavelength range (0.4–1.0 μ m). We apply these flexible IMF models to a high S/N spectrum of the center of the massive elliptical galaxy NGC 1407. Fitting the spectrum with non-parametric IMFs, we find that the IMF in the center shows a continuous rise extending toward the hydrogen-burning limit, with a behavior that is well-approximated by a power law with an index of −2.7. These results provide strong evidence for the existence of extreme (super-Salpeter) IMFs in the cores of massive galaxies.« less

Statistical against dynamical PLF fission as seen by the IMF-IMF correlation functions and comparisons with CoMD model

NASA Astrophysics Data System (ADS)

Pagano, E. V.; Acosta, L.; Auditore, L.; Cap, T.; Cardella, G.; Colonna, M.; De Filippo, E.; Geraci, E.; Gnoffo, B.; Lanzalone, G.; Maiolino, C.; Martorana, N.; Pagano, A.; Papa, M.; Piasecki, E.; Pirrone, S.; Politi, G.; Porto, F.; Quattrocchi, L.; Rizzo, F.; Russotto, P.; Trifiro’, A.; Trimarchi, M.; Siwek-Wilczynska, K.

2018-05-01

In nuclear reactions at Fermi energies two and multi particles intensity interferometry correlation methods are powerful tools in order to pin down the characteristic time scale of the emission processes. In this paper we summarize an improved application of the fragment-fragment correlation function in the specific physics case of heavy projectile-like (PLF) binary massive splitting in two fragments of intermediate mass(IMF). Results are shown for the reverse kinematics reaction 124 Sn+64 Ni at 35 AMeV that has been investigated by using the forward part of CHIMERA multi-detector. The analysis was performed as a function of the charge asymmetry of the observed couples of IMF. We show a coexistence of dynamical and statistical components as a function of the charge asymmetry. Transport CoMD simulations are compared with the data in order to pin down the timescale of the fragments production and the relevant ingredients of the in medium effective interaction used in the transport calculations.

Integrated ensemble noise-reconstructed empirical mode decomposition for mechanical fault detection

NASA Astrophysics Data System (ADS)

Yuan, Jing; Ji, Feng; Gao, Yuan; Zhu, Jun; Wei, Chenjun; Zhou, Yu

2018-05-01

A new branch of fault detection is utilizing the noise such as enhancing, adding or estimating the noise so as to improve the signal-to-noise ratio (SNR) and extract the fault signatures. Hereinto, ensemble noise-reconstructed empirical mode decomposition (ENEMD) is a novel noise utilization method to ameliorate the mode mixing and denoised the intrinsic mode functions (IMFs). Despite the possibility of superior performance in detecting weak and multiple faults, the method still suffers from the major problems of the user-defined parameter and the powerless capability for a high SNR case. Hence, integrated ensemble noise-reconstructed empirical mode decomposition is proposed to overcome the drawbacks, improved by two noise estimation techniques for different SNRs as well as the noise estimation strategy. Independent from the artificial setup, the noise estimation by the minimax thresholding is improved for a low SNR case, which especially shows an outstanding interpretation for signature enhancement. For approximating the weak noise precisely, the noise estimation by the local reconfiguration using singular value decomposition (SVD) is proposed for a high SNR case, which is particularly powerful for reducing the mode mixing. Thereinto, the sliding window for projecting the phase space is optimally designed by the correlation minimization. Meanwhile, the reasonable singular order for the local reconfiguration to estimate the noise is determined by the inflection point of the increment trend of normalized singular entropy. Furthermore, the noise estimation strategy, i.e. the selection approaches of the two estimation techniques along with the critical case, is developed and discussed for different SNRs by means of the possible noise-only IMF family. The method is validated by the repeatable simulations to demonstrate the synthetical performance and especially confirm the capability of noise estimation. Finally, the method is applied to detect the local wear fault from a dual-axis stabilized platform and the gear crack from an operating electric locomotive to verify its effectiveness and feasibility.

Stellar Initial Mass Function: Trends With Galaxy Mass And Radius

NASA Astrophysics Data System (ADS)

Parikh, Taniya

2017-06-01

There is currently no consensus about the exact shape and, in particular, the universality of the stellar initial mass function (IMF). For massive galaxies, it has been found that near-infrared (NIR) absorption features, which are sensitive to the ratio of dwarf to giant stars, deviate from a Milky Way-like IMF; their modelling seems to require a larger fraction of low mass stars. There are now increasing results looking at whether the IMF varies not only with galaxy mass, but also radially within galaxies. The SDSS-IV/MaNGA integral-field survey will provide spatially resolved spectroscopy for 10,000 galaxies at R 2000 from 360-1000nm. Spectra of early-type galaxies were stacked to achieve high S/N which is particularly important for features in the NIR. Trends with galaxy radius and mass were compared to stellar population models for a range of absorption features in order to separate degeneracies due to changes in stellar population parameters, such as age, metallicity and element abundances, with potential changes in the IMF. Results for 611 galaxies show that we do not require an IMF steeper than Kroupa as a function of galaxy mass or radius based on the NaI index. The Wing-Ford band hints towards a steeper IMF at large radii however we do not have reliable measurements for the most massive galaxies.

Comparing models for IMF variation across cosmological time in Milky Way-like galaxies

NASA Astrophysics Data System (ADS)

Guszejnov, Dávid; Hopkins, Philip F.; Ma, Xiangcheng

2017-12-01

One of the key observations regarding the stellar initial mass function (IMF) is its near-universality in the Milky Way (MW), which provides a powerful way to constrain different star formation models that predict the IMF. However, those models are almost universally 'cloud-scale' or smaller - they take as input or simulate single molecular clouds (GMCs), clumps or cores, and predict the resulting IMF as a function of the cloud properties. Without a model for the progenitor properties of all clouds that formed the stars at different locations in the MW (including ancient stellar populations formed in high redshift, likely gas-rich dwarf progenitor galaxies that looked little like the Galaxy today), the predictions cannot be fully explored nor safely applied to 'live' cosmological calculations of the IMF in different galaxies at different cosmological times. We therefore combine a suite of high-resolution cosmological simulations (from the Feedback In Realistic Environments project), which form MW-like galaxies with reasonable star formation properties and explicitly resolve massive GMCs, with various proposed cloud-scale IMF models. We apply the models independently to every star particle formed in the simulations to synthesize the predicted IMF in the present-day galaxy. We explore models where the IMF depends on Jeans mass, sonic or 'turbulent Bonnor-Ebert' mass, fragmentation with a polytropic equation of state, or where it is self-regulated by protostellar feedback. We show that all of these models, except the feedback-regulated ones, predict far more variation (∼0.6-1 dex 1σ scatter in the IMF turnover mass) in the simulations than is observed in the MW.

THE NON-UNIVERSALITY OF THE LOW-MASS END OF THE IMF IS ROBUST AGAINST THE CHOICE OF SSP MODEL

DOE Office of Scientific and Technical Information (OSTI.GOV)

Spiniello, C.; Trager, S. C.; Koopmans, L. V. E.

2015-04-20

We perform a direct comparison of two state-of-the art single stellar population (SSP) models that have been used to demonstrate the non-universality of the low-mass end of the initial mass function (IMF) slope. The two public versions of the SSP models are restricted to either solar abundance patterns or solar metallicity, too restrictive if one aims to disentangle elemental enhancements, metallicity changes, and IMF variations in massive early-type galaxies (ETGs) with star formation histories different from those in the solar neighborhood. We define response functions (to metallicity and α-abundance) to extend the parameter space for each set of models. Wemore » compare these extended models with a sample of Sloan Digital Sky Survey (SDSS) ETG spectra with varying velocity dispersions. We measure equivalent widths of optical IMF-sensitive stellar features to examine the effect of the underlying model assumptions and ingredients, such as stellar libraries or isochrones, on the inference of the IMF slope down to ∼0.1 M{sub ⊙}. We demonstrate that the steepening of the low-mass end of the IMF based on a non-degenerate set of spectroscopic optical indicators is robust against the choice of the stellar population model. Although the models agree in a relative sense (i.e., both imply more bottom-heavy IMFs for more massive systems), we find non-negligible differences in the absolute values of the IMF slope inferred at each velocity dispersion by using the two different models. In particular, we find large inconsistencies in the quantitative predictions of the IMF slope variations and abundance patterns when sodium lines are used. We investigate the possible reasons for these inconsistencies.« less

Further Constraints on Variations in the Initial Mass Function from Low-mass X-ray Binary Populations

NASA Astrophysics Data System (ADS)

Peacock, Mark B.; Zepf, Stephen E.; Kundu, Arunav; Maccarone, Thomas J.; Lehmer, Bret D.; Maraston, Claudia; Gonzalez, Anthony H.; Eufrasio, Rafael T.; Coulter, David A.

2017-05-01

We present constraints on variations in the initial mass function (IMF) of nine local early-type galaxies based on their low-mass X-ray binary (LMXB) populations. Comprised of accreting black holes and neutron stars, these LMXBs can be used to constrain the important high-mass end of the IMF. We consider LMXB populations beyond the cores of the galaxies (>0.2R e; covering 75%-90% of their stellar light) and find no evidence for systematic variations of the IMF with velocity dispersion (σ). We reject IMFs which become increasingly bottom-heavy with σ, up to steep power laws (exponent, α > 2.8) in massive galaxies (σ > 300 {km} {{{s}}}-1), for galactocentric radii >1/4 R e. Previously proposed IMFs that become increasingly bottom-heavy with σ are consistent with these data if only the number of low-mass stars (<0.5 M ⊙) varies. We note that our results are consistent with some recent work which proposes that extreme IMFs are only present in the central regions of these galaxies. We also consider IMFs that become increasingly top-heavy with σ, resulting in significantly more LMXBs. Such a model is consistent with these observations, but additional data are required to significantly distinguish between this and an invariant IMF. For six of these galaxies, we directly compare with published “IMF mismatch” parameters from the Atlas3D survey, α dyn. We find good agreement with the LMXB population if galaxies with higher α dyn have more top-heavy IMFs—although we caution that our sample is quite small. Future LMXB observations can provide further insights into the origin of α dyn variations.

The unexpectedly large proportion of high-mass star-forming cores in a Galactic mini-starburst

NASA Astrophysics Data System (ADS)

Motte, F.; Nony, T.; Louvet, F.; Marsh, K. A.; Bontemps, S.; Whitworth, A. P.; Men'shchikov, A.; Nguyáën Luong, Q.; Csengeri, T.; Maury, A. J.; Gusdorf, A.; Chapillon, E.; Könyves, V.; Schilke, P.; Duarte-Cabral, A.; Didelon, P.; Gaudel, M.

2018-04-01

Understanding the processes that determine the stellar initial mass function (IMF) is a critical unsolved problem, with profound implications for many areas of astrophysics1. In molecular clouds, stars are formed in cores—gas condensations sufficiently dense that gravitational collapse converts a large fraction of their mass into a star or small clutch of stars. In nearby star-formation regions, the core mass function (CMF) is strikingly similar to the IMF, suggesting that the shape of the IMF may simply be inherited from the CMF2-5. Here, we present 1.3 mm observations, obtained with the Atacama Large Millimeter/submillimeter Array telescope, of the active star-formation region W43-MM1, which may be more representative of the Galactic-arm regions where most stars form6,7. The unprecedented resolution of these observations reveals a statistically robust CMF at high masses, with a slope that is markedly shallower than the IMF. This seriously challenges our understanding of the origin of the IMF.

SDSS-IV MaNGA: Variation of the Stellar Initial Mass Function in Spiral and Early-type Galaxies

NASA Astrophysics Data System (ADS)

Li, Hongyu; Ge, Junqiang; Mao, Shude; Cappellari, Michele; Long, R. J.; Li, Ran; Emsellem, Eric; Dutton, Aaron A.; Li, Cheng; Bundy, Kevin; Thomas, Daniel; Drory, Niv; Lopes, Alexandre Roman

2017-04-01

We perform Jeans anisotropic modeling (JAM) on elliptical and spiral galaxies from the MaNGA DR13 sample. By comparing the stellar mass-to-light ratios estimated from stellar population synthesis and from JAM, we find a systematic variation of the initial mass function (IMF) similar to that in the earlier {{ATLAS}}3{{D}} results. Early-type galaxies (elliptical and lenticular) with lower velocity dispersions within one effective radius are consistent with a Chabrier-like IMF, while galaxies with higher velocity dispersions are consistent with a more bottom-heavy IMF such as the Salpeter IMF. Spiral galaxies have similar systematic IMF variations, but with slightly different slopes and larger scatters, due to the uncertainties caused by the higher gas fractions and extinctions for these galaxies. Furthermore, we examine the effects of stellar mass-to-light ratio gradients on our JAM modeling, and we find that the trends become stronger after considering the gradients.

The unexpectedly large proportion of high-mass star-forming cores in a Galactic mini-starburst

NASA Astrophysics Data System (ADS)

Motte, F.; Nony, T.; Louvet, F.; Marsh, K. A.; Bontemps, S.; Whitworth, A. P.; Men'shchikov, A.; Nguyen Luong, Q.; Csengeri, T.; Maury, A. J.; Gusdorf, A.; Chapillon, E.; Könyves, V.; Schilke, P.; Duarte-Cabral, A.; Didelon, P.; Gaudel, M.

2018-06-01

Understanding the processes that determine the stellar initial mass function (IMF) is a critical unsolved problem, with profound implications for many areas of astrophysics1. In molecular clouds, stars are formed in cores—gas condensations sufficiently dense that gravitational collapse converts a large fraction of their mass into a star or small clutch of stars. In nearby star-formation regions, the core mass function (CMF) is strikingly similar to the IMF, suggesting that the shape of the IMF may simply be inherited from the CMF2-5. Here, we present 1.3 mm observations, obtained with the Atacama Large Millimeter/submillimeter Array telescope, of the active star-formation region W43-MM1, which may be more representative of the Galactic-arm regions where most stars form6,7. The unprecedented resolution of these observations reveals a statistically robust CMF at high masses, with a slope that is markedly shallower than the IMF. This seriously challenges our understanding of the origin of the IMF.

The Low-Mass Stellar Initial Mass Function: Ultra-Faint Dwarf Galaxies Revisited

NASA Astrophysics Data System (ADS)

Platais, Imants

2017-08-01

The stellar Initial Mass Function plays a critical role in the evolution of the baryonic content of the Universe. The form of the low-mass IMF - stars of mass less than the solar mass - determines the fraction of baryons locked up for a Hubble time, and thus indicates how gas and metals are cycled through galaxies. Inferences from resolved stellar populations, where the low-mass luminosity function and associated IMF can be derived from direct star counts, generally favor an invariant and universal IMF. However, a recent study of ultra-faint dwarf galaxies Hercules and Leo IV indicates a bottom-lite IMF, over a narrow range of stellar mass (only 0.55-0.75 M_sun), correlated with the internal velocity dispersion and/or metallicity. We propose to obtain ultra-deep imaging for a significantly closer ultra-faint dwarf, Bootes I, which will allow us to construct the luminosity function down to M_v=+10 (equivalent to 0.35 solar mass). We will also re-analyze the HST archival observations for the Hercules and Leo IV dwarfs using the same updated techniques as for Bootes I. The combined datasets should provide a reliable answer to the question of how variable is the low-mass stellar IMF.

The Mass Function in h+(chi) Persei

NASA Astrophysics Data System (ADS)

Bragg, Ann; Kenyon, Scott

2000-08-01

Knowledge of the stellar initial mass function (IMF) is critical to understanding star formation and galaxy evolution. Past studies of the IMF in open clusters have primarily used luminosity functions to determine mass functions, frequently in relatively sparse clusters. Our goal with this project is to derive a reliable, well- sampled IMF for a pair of very dense young clusters (h+(chi) Persei) with ages, 1-2 × 10^7 yr (e.g., Vogt A& A 11:359), where stellar evolution theory is robust. We will construct the HR diagram using both photometry and spectral types to derive more accurate stellar masses and ages than are possible using photometry alone. Results from the two clusters will be compared to examine the universality of the IMF. We currently have a spectroscopic sample covering an area within 9 arc-minutes of the center of each cluster taken with the FAST Spectrograph. The sample is complete to V=15.4 and contains ~ 1000 stars. We request 2 nights at WIYN/HYDRA to extend this sample to deeper magnitudes, allowing us to determine the IMF of the clusters to a lower limiting mass and to search for a pre-main sequence, theoretically predicted to be present for clusters of this age. Note that both clusters are contained within a single HYDRA field.

Low-end mass function of the Quintuplet cluster

NASA Astrophysics Data System (ADS)

Shin, Jihye; Kim, Sungsoo S.

2016-08-01

The Quintuplet and Arches clusters, which were formed in the harsh environment of the Galactic Centre (GC) a few million years ago, have been excellent targets for studying the effects of a star-forming environment on the initial mass function (IMF). In order to estimate the shape of the low-end IMF of the Arches cluster, Shin & Kim devised a novel photometric method that utilizes pixel intensity histograms (PIHs) of the observed images. Here, we apply the PIH method to the Quintuplet cluster and estimate the shape of its low-end IMF below the magnitude of completeness limit as set by conventional photometry. We found that the low-end IMF of the Quintuplet is consistent with that found for the Arches cluster-Kroupa MF, with a significant number of low-mass stars below 1 M⊙. We conclude that the most likely IMFs of the Quintuplet and the Arches clusters are not too different from the IMFs found in the Galactic disc. We also find that the observed PIHs and stellar number density profiles of both clusters are best reproduced when the clusters are assumed to be at three-dimensional distances of approximately 100 pc from the GC.

What is the Relationship between the Solar Wind and Storms/Substorms?

NASA Technical Reports Server (NTRS)

Fairfield, D. H.; Burlaga, L. F.

1999-01-01

The interplanetary magnetic field (IMF) carried past the Earth by the solar wind has long been known to be the principal quantity that controls geomagnetic storms and substorms. Intervals of strong southward IMF with durations of at least a significant fraction of a day produce storms, while more typical, shorter intervals of less-intense southward fields produce substorms. The strong, long-duration southward fields are generally associated with coronal mass ejections and magnetic clouds or else they are produced by interplanetary dynamics initiated by fast solar wind flows that compress preexisting southward fields. Smaller, short-duration southward fields that occur on most days are related to long period waves, turbulence, or random variations in the IMF. Southward IMF enhances dayside reconnection between the IMF and the Earth's dipole with the reconnected field lines supplementing open field lines of the geomagnetic tail and producing an expanded polar cap and increased tail energy. Although the frequent storage of solar wind energy and its release during substorms is the most common mode of solar wind/magnetosphere interaction, under certain circumstances, steady southward IMF seems to produce intervals of relatively steady magnetosphere convection without substorms. During these latter times, the inner magnetosphere remains in a stressed tail-like state while the more distant magnetotail has larger northward field and more dipolar-like field lines. Recent evidence suggests that enhanced magnetosphere particle densities associated with enhanced solar wind densities allow more particles to be accelerated for the ring current, thus creating larger storms.

Investigation of isolated substorms: Generation conditions and characteristics of different phases

NASA Astrophysics Data System (ADS)

Vorobjev, V. G.; Yagodkina, O. I.; Zverev, V. L.

2016-11-01

Characteristics of isolated substorms selected by variations in the 1-min values of the AL index are analyzed. The substorms were divided into several types with respect to the behavior of the Bz component of the interplanetary magnetic field (IMF) during the expansion phase. The probability of observations of substorms associated with the northward turn of the Bz component of IMF was 19%, while the substorms taking place at Bz < 0 were observed in 53% of cases. A substantial number of events in which no substorm magnetic activity was observed in the auroral zone after a long (>30 min) period of the southward IMF and a following sharp turn of the Bz component of IMF before the north was detected. The data suggest that a northward IMF turn is neither a necessary nor sufficient condition for generating substorms. It has been shown for substorms of the both types that the average duration of the southward IMF to moment T 0 and the average intensity of the magnetic perturbation in the maximum are approximately the same and amount to 80 min and-650 nT, respectively. However, for substorms at Bz < 0, their mean duration, including the expansive and recovery phases, is on average 30 min longer than that at a northward turn of IMF. Correlations between the loading-unloading processes in the magnetosphere in the periods of magnetospheric substorms were investigated with different functions that determine the efficiency of the energy transfer from the solar wind to the magnetosphere. It has been shown that the highest correlation coefficient ( r = 0.84) is observed when the function suggested by Newell et al. (2007) is used. It has been detected that a simple function VB S yields a high correlation coefficient ( r = 0.75).

Variations of the stellar initial mass function in semi-analytical models - II. The impact of cosmic ray regulation

NASA Astrophysics Data System (ADS)

Fontanot, Fabio; De Lucia, Gabriella; Xie, Lizhi; Hirschmann, Michaela; Bruzual, Gustavo; Charlot, Stéphane

2018-04-01

Recent studies proposed that cosmic rays (CRs) are a key ingredient in setting the conditions for star formation, thanks to their ability to alter the thermal and chemical state of dense gas in the ultraviolet-shielded cores of molecular clouds. In this paper, we explore their role as regulators of the stellar initial mass function (IMF) variations, using the semi-analytic model for GAlaxy Evolution and Assembly (GAEA). The new model confirms our previous results obtained using the integrated galaxy-wide IMF (IGIMF) theory. Both variable IMF models reproduce the observed increase of α-enhancement as a function of stellar mass and the measured z = 0 excess of dynamical mass-to-light ratios with respect to photometric estimates assuming a universal IMF. We focus here on the mismatch between the photometrically derived (M^app_{\\star }) and intrinsic (M⋆) stellar masses, by analysing in detail the evolution of model galaxies with different values of M_{\\star }/M^app_{\\star }. We find that galaxies with small deviations (i.e. formally consistent with a universal IMF hypothesis) are characterized by more extended star formation histories and live in less massive haloes with respect to the bulk of the galaxy population. In particular, the IGIMF theory does not change significantly the mean evolution of model galaxies with respect to the reference model, a CR-regulated IMF instead implies shorter star formation histories and higher peaks of star formation for objects more massive than 1010.5 M⊙. However, we also show that it is difficult to unveil this behaviour from observations, as the key physical quantities are typically derived assuming a universal IMF.

A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network.

PubMed

Zhou, Qingping; Jiang, Haiyan; Wang, Jianzhou; Zhou, Jianling

2014-10-15

Exposure to high concentrations of fine particulate matter (PM₂.₅) can cause serious health problems because PM₂.₅ contains microscopic solid or liquid droplets that are sufficiently small to be ingested deep into human lungs. Thus, daily prediction of PM₂.₅ levels is notably important for regulatory plans that inform the public and restrict social activities in advance when harmful episodes are foreseen. A hybrid EEMD-GRNN (ensemble empirical mode decomposition-general regression neural network) model based on data preprocessing and analysis is firstly proposed in this paper for one-day-ahead prediction of PM₂.₅ concentrations. The EEMD part is utilized to decompose original PM₂.₅ data into several intrinsic mode functions (IMFs), while the GRNN part is used for the prediction of each IMF. The hybrid EEMD-GRNN model is trained using input variables obtained from principal component regression (PCR) model to remove redundancy. These input variables accurately and succinctly reflect the relationships between PM₂.₅ and both air quality and meteorological data. The model is trained with data from January 1 to November 1, 2013 and is validated with data from November 2 to November 21, 2013 in Xi'an Province, China. The experimental results show that the developed hybrid EEMD-GRNN model outperforms a single GRNN model without EEMD, a multiple linear regression (MLR) model, a PCR model, and a traditional autoregressive integrated moving average (ARIMA) model. The hybrid model with fast and accurate results can be used to develop rapid air quality warning systems. Copyright © 2014 Elsevier B.V. All rights reserved.

Does the IMF vary with galaxy mass? The X-ray binary population of a key galaxy, NGC7457

NASA Astrophysics Data System (ADS)

Peacock, Mark

2014-09-01

We propose a 100ksec observation of NGC7457. The primary goal of this observation is to test for variations in the initial mass function (IMF). Many recent studies have proposed that the IMF varies systematically as a function of early-type galaxy mass. This has potentially dramatic consequences and must to be confirmed. The number of LMXBs in a galaxy (per stellar luminosity) can be used to provide an independent test of this hypothesis (see Peacock et al. 2014). Unfortunately, only galaxies with intermediate to high masses currently have the data needed to perform this test. The proposed observation of the elliptical galaxy NGC7457 will detect an order of magnitude more LMXBs in a low mass galaxy - hence providing the crucial constraint needed to significantly test for a variable IMF.

Solar wind controls on Mercury's magnetospheric cusp

NASA Astrophysics Data System (ADS)

He, Maosheng; Vogt, Joachim; Heyner, Daniel; Zhong, Jun

2017-06-01

This study assesses the response of the cusp to solar wind changes comprehensively, using 2848 orbits of MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) observation. The assessment entails four steps: (1) propose and validate an approach to estimate the solar wind magnetic field (interplanetary magnetic field (IMF)) for MESSENGER's cusp transit; (2) define an index σ measuring the intensity of the magnetic disturbance which significantly peaks within the cusp and serves as an indicator of the cusp activity level; (3) construct an empirical model of σ as a function of IMF and Mercury's heliocentric distance rsun, through linear regression; and (4) use the model to estimate and compare the polar distribution of the disturbance σ under different conditions for a systematic comparison. The comparison illustrates that the disturbance peak over the cusp is strongest and widest extending in local time for negative IMF Bx and negative IMF Bz, and when Mercury is around the perihelion. Azimuthal shifts are associated with both IMF By and rsun: the cusp moves toward dawn when IMF By or rsun decrease. These dependences are explained in terms of the IMF Bx-controlled dayside magnetospheric topology, the component reconnection model applied to IMF By and Bz, and the variability of solar wind ram pressure associated with heliocentric distance rsun. The applicability of the component reconnection model on IMF By indicates that at Mercury reconnection occurs at lower shear angles than at Earth.