Complex Empirical Mode Decomposition Research Articles

Multi-step ozone concentration prediction model based on improved secondary decomposition and adaptive kernel density estimation

Accurately predicting ozone concentration is crucial for human pollution prevention and health protection. In recent years, decomposition ensemble frameworks have been widely applied to ozone concentration forecasting. However, due to the inefficiencies of secondary decomposition and insufficient emphasis on interval predictions, developing a stable and efficient model for ozone concentration prediction remains a challenging task. Therefore, this study proposes a hybrid model based on an improved secondary decomposition algorithm (ISD) and adaptive kernel density estimation (AKDE). The original sequences are initially decomposed and reconstructed using Complex Empirical Mode Decomposition (CEEMD) and K-means clustering based on sample entropy. Subsequently, Optimal Variational Mode Decomposition (OVMD) is employed to address the strong fluctuations in high-frequency components, followed by establishing individual Long Short-Term Memory (LSTM) prediction models for each component. The error correction model is then constructed by combining ISD and Gated circulation unit (GRU), culminating in uncertainty quantification using AKDE. Empirical analysis of the Beijing and Shanghai, China datasets shows that the average R2 of their multi-step forecasts are 0.9966 and 0.9938, respectively, which are significantly better than the baseline model.

Study on the micro-Doppler separation method of bistatic inverse synthetic aperture radar targets with rotating parts based on complex variational mode decomposition

In bistatic inverse synthetic aperture radar (Bi-ISAR) imaging of targets with rotating components, the micro-Doppler effect generated by rotating components can produce interference bands in azimuth resolution, seriously affecting the imaging results and causing difficulties in target recognition. A unique Bi-ISAR target micro-Doppler separation method with rotating components is proposed by introducing the complex variational mode decomposition (CVMD) method. First, the echo signal is decomposed into different intrinsic mode functions (IMFs) by CVMD for the Bi-ISAR imaging target with rotating parts. Then, IMFs are divided into main and micromotion parts by setting the energy threshold. The IMFs belonging to the main part are extracted, and the imaging is performed using the range-Doppler algorithm. The simulation results show that CVMD outperforms the complex empirical mode decomposition algorithm and the complex local mean decomposition algorithm in the Doppler separation of Bi-ISAR targets with rotating components. In addition, CVMD exhibits good robustness and provides innovative ideas for future research on micro-Doppler separation technology.

Rotational Angular Velocity Estimation of Rotor Target via 3D-OMP-Based Parametric Sparse Representation

As a special feature of rotor target, rotational angular velocity can provide important information for target classification and recognition. In this paper, a rotational angular velocity estimation method is proposed based on complex empirical mode decomposition (CEMD) and three-dimensional orthogonal matching pursuit (3D-OMP)-based parametric sparse representation (PSR) technique. Firstly, the translational radial velocity is estimated from radar echo via Fourier transform, and then the translational compensation is implemented according to the estimated velocity. Secondly, in order to eliminate the influence of the target main body echo on micro-Doppler parameter estimation, CEMD algorithm is adopted for target echo separation. Then, the separated signal of rotor echo is formulated as a jointly sparse signal through a three-dimensional parametric dictionary matrix, which converts the rotational angular velocity estimation into a problem of dynamic representation of jointly sparse signals. Finally, in order to reduce the computational complexity of the estimation method, we propose a 3D-OMP-based parametric sparse representation algorithm to achieve the sparse solution, and the rotational angular velocity can be estimated by minimizing the reconstruction error. The experimental results verify the effectiveness of the proposed algorithm.

EM-Sign: A Non-Contact Recognition Method Based on 24 GHz Doppler Radar for Continuous Signs and Dialogues

We studied continuous sign language recognition using Doppler radar sensors. Four signs in Chinese sign language and American sign language were captured and extracted by complex empirical mode decomposition (CEMD) to obtain spectrograms. Image sharpening was used to enhance the micro-Doppler signatures of the signs. To classify the different signs, we utilized an improved Yolov3-tiny network by replacing the framework with ResNet and fine-tuned the network in advance. This method can remove the epentheses from the training process. Experimental results revealed that the proposed method can surpass the state-of-the-art sign language recognition methods in continuous sign recognition with a precision of 0.924, a recall of 0.993, an F1-measure of 0.957 and a mean average precision (mAP) of 0.99. In addition, dialogue recognition in three daily conversation scenarios was performed and evaluated. The average word error rate (WER) was 0.235, 10% lower than in of other works. Our work provides an alternative form of sign language recognition and a new approach to simplify the training process and achieve a better continuous sign language recognition effect.

Radio Frequency Interference Suppression for HF Surface Wave Radar Using CEMD and Temporal Windowing Methods

A common source of interference in high-frequency (HF) surface wave (HFSW) radars is radio frequency interference (RFI). Its existence inhibits the detection performance of HFSW radars since its amplitude can mask the sea echoes. On the basis of the analysis of RFI characteristics, a new RFI mitigation algorithm based on inverse temporal windowing and complex empirical mode decomposition (CEMD) is proposed in this letter. In this method, echoes containing RFI are decomposed into a number of intrinsic mode functions (IMFs) via CEMD and then the inverse temporal windowing technique is applied to each IMFs in the time domain. Test results show that the proposed method outperforms the conventional method in simulated and practical conditions and can effectively mitigate RFI without losing sea echoes.

Research on Multidomain Fault Diagnosis of Large Wind Turbines under Complex Environment

Under the complicated environment of large wind turbines, the vibration signal of a wind turbine has the characteristics of coupling and nonlinearity. The traditional feature extraction method for the signal is hard to accurately extract fault information, and there is a serious problem of information redundancy in fault diagnosis. Therefore, this paper proposed a multidomain feature fault diagnosis method based on complex empirical mode decomposition (CEMD) and random forest theory (RF). Firstly, this paper proposes a novel method of complex empirical mode decomposition by using the correlation information between two-dimensional signals and utilizing the idea of ensemble empirical mode decomposition (EEMD) by adding white noise to suppress the problem mode mixing in empirical mode decomposition (EMD). Secondly, the collected vibration signals are decomposed into IMFs by CEMD. Then, calculate 11 time domain characteristic parameters and 13 frequency domain characteristic parameters of the vibration signal, and calculate the energy and energy entropy of each IMF components. Make all the characteristic parameters as the multidomain feature vectors of wind turbines. Finally, the redundant feature vectors are eliminated by the importance of each feature vector which has been calculated, and the feature vectors selected are input to the random forest classifier to achieve the fault diagnosis of large wind turbines. Simulation and experimental results show that this method can effectively extract the fault feature of the signal and achieve the fault diagnosis of wind turbines, which has a higher accuracy of fault diagnosis than the traditional classification methods.

Radio Frequency Interference Mitigation for High-Frequency Surface Wave Radar

Radio frequency interference (RFI) is a common source of interference for high-frequency surface wave radar (HFSWR) since the HF band is shared among many radio services. The existence of RFI greatly degrades the detection performance of HFSWR. On the basis of a detailed analysis of the characteristics of RFI, a new RFI mitigation method based on joint fractional Fourier transform (FRFT) and complex empirical mode decomposition (CEMD) is proposed in this letter. In this method, the optimum transform order of the FRFT is obtained through a two-level peak search. Subsequently, echoes that contain RFI are transformed into the fractional Fourier domain and are then decomposed into a number of intrinsic mode functions (IMFs) via CEMD. The RFI is mitigated by implementing detection and localization in each IMF. The reconstructed signal is then transformed into the time domain via an inverse FRFT, and the mitigated signal is finally obtained. Processing results using experimental data indicate that the proposed method can effectively suppress RFI without losing echoes.

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

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.

Radio Frequency Interference Mitigation in High-Frequency Surface Wave Radar Based on CEMD

Radio frequency interference (RFI) is a common interference source in high-frequency surface wave (HFSW) radar. Its existence degrades the performance of HFSW radar greatly and makes it necessary to find an effective method to mitigate the interferences. There are two kinds of RFI in the experimental data. One is transient RFI, which is usually suppressed by temporal processing. The other is nontransient RFI, which is suppressed by adaptive beamforming methods. However, the temporal processing techniques suffer performance loss in nontransient cases, whereas the adaptive beamforming methods need spatially structuring, which is difficult to meet within the coherent integration time (CIT) of a few minutes. The fact that the experimental data are usually interfered by two kinds of RFI over a CIT motivates us to find a unified method for interference mitigation. In this letter, a new method based on complex empirical mode decomposition (CEMD) is proposed. CEMD is a local decomposition algorithm that can decompose the echoes and the RFI including transient and nontransient RFI into different intrinsic mode functions (IMFs). Then, the IMFs that correspond to RFI are processed. Experimental results indicate that the proposed method can effectively mitigate both kinds of RFI, and improve the signal-to-noise ratio without losing echoes.

Random noise attenuation by f-x spatial projection-based complex empirical mode decomposition predictive filtering

The frequency-space (f-x) empirical mode decomposition (EMD) denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function (IMF) results in signal damage and limited denoising. Second, decomposing the real and imaginary parts of complex data may lead to inconsistent decomposition numbers. Thus, we propose a new method named f-x spatial projection-based complex empirical mode decomposition (CEMD) prediction filtering. The proposed approach directly decomposes complex seismic data into a series of complex IMFs (CIMFs) using the spatial projection-based CEMD algorithm and then applies f-x predictive filtering to the stationary CIMFs to improve the signal-to-noise ratio. Synthetic and real data examples were used to demonstrate the performance of the new method in random noise attenuation and seismic signal preservation.

Automatic Pattern Identification Based on the Complex Empirical Mode Decomposition of the Startup Current for the Diagnosis of Rotor Asymmetries in Asynchronous Machines

This paper presents an advanced signal processing method applied to the diagnosis of rotor asymmetries in asynchronous machines. The approach is based on the application of complex empirical mode decomposition to the measured start-up current and on the subsequent extraction of a specific complex intrinsic mode function. Unlike other approaches, the method includes a pattern recognition stage that makes possible the automatic identification of the signature caused by the fault. This automatic detection is achieved by using a reliable methodology based on hidden Markov models. Both experimental data and a hybrid simulation-experimental approach demonstrate the effectiveness of the proposed methodology.

복소 EMD를 이용한 미약한 JEM의 관측 범위에서 JEM 성분의 추출

제트엔진 변조(Jet Engine Modulation: JEM)는 회전하는 제트엔진 터빈으로부터의 전자기 산란에 따른 레이더 신호의 주파수 변조 현상이다. JEM은 표적의 고유한 정보를 제공하여 대표적인 레이더 표적 인식 수단으로 활용되나, JEM 성분이 미약하게 존재하는 레이더 관측 범위에서는 JEM에 의한 레이더 표적 인식 성능이 저하될 수 있다. 이에 본 논문에서는 복소 신호의 경험적인 모드분리법(Complex Empirical Mode Decomposition: CEMD)를 이용하여 레이더 신호를 여러 기본성분인 고유 모드 함수(Intrinsic Mode Function: IMF)로 분리하고, 신호의 이심률을 기반으로 이들 IMF를 조합하는 근거를 제공하여 JEM 성분을 추출하는 기법을 제시한다. 다양한 신호에 대한 적용 결과를 통하여 제안된 기법이 JEM의 명확성을 개선하는 한편, JEM 해석의 유효 관측 범위를 확장시킬 수 있음을 입증하였다. Jet engine modulation(JEM) is a frequency modulation phenomenon of the radar signal induced by electromagnetic scattering from a rotating jet engine turbine. Although JEM can be used as a representative radar target recognition method by providing unique information on the target, its recognition performance may be degraded in the observation range of weakly present JEM. Hence, this paper presents a method for extracting the JEM component by decomposing the radar signal into intrisic mode functions(IMFs) via complex empirical mode decomposition(CEMD) and by combining them based on signal eccentricity. Its application to various signals demonstrated that the proposed method improved the clarity of JEM analysis and could extend the effective observation range of JEM.

항공기 표적의 레이더 반사 신호에서 제트엔진 정보를 추출하기 위한 자동화 알고리즘

제트엔진변조(Jet Engine Modulation: JEM) 식별기법은 제트엔진의 주기적인 회전에 의해 변조되는 레이더 반사 신호로부터 제트엔진의 종류를 식별하는 기법이다. 본 논문에서는 JEM의 새로운 접근법으로서, 제트엔진의 특성을 추출하기 위한 자동화 알고리즘을 제안한다. 먼저 복소 신호의 경험적인 모드 분리법(Complex Empirical Mode Decomposition: CEMD)을 거친 JEM 신호의 자기상관도로부터 제트엔진의 회전 주기를 산출한다. 그 후 DM(Divisor-Multiplier) 규칙 및 'Scoring' 개념을 JEM 스펙트럼 해석에 도입하여 최종적인 날개개수를 추정한다. 시뮬레이션 및 측정 JEM 신호로의 적용 결과를 통해 제안된 알고리즘이 정확하고 자동적인 제트엔진 정보 추출에 효과적임을 입증하였다. Jet engine modulation(JEM) is a technique used to identify the jet engine type from the radar target signature modulated by periodic rotation of the jet engine mounted on the aircraft target. As a new approach of JEM, this paper proposes an automatic algorithm for extracting the jet engine information. First, the rotation period of the jet engine is yielded from auto-correlation of the JEM signal preprocessed by complex empirical mode decomposition(CEMD). Then, the final blade number is estimated by introducing the DM(Divisor-Multiplier) rule and the 'Scoring' concept into JEM spectral analysis. Application results of the simulated and measured JEM signals demonstrated that the proposed algorithm is effective in accurate and automatic extraction of the jet engine information.

A Novel and Hybrid Optimization Mechanism For Denoising And Classification Of Medical Images using DTCWPT And Neuro-Fuzzy Classifiers

Computed tomography (CT) images are usually corrupted by several noises from the measurement process complicating the automatic feature extraction and analysis of clinical data. To attain the best possible diagnosis it is very vital that medical images be clear, sharp, and free of noise and artifacts. In this research paper, we propose a robust technique to denoise, detect and classify the tumour part from CT medical images. Our proposed approach consists of four phases, such as denoising, region segmentation, feature extraction and classification. In the denoising phase Dual Tree Complex Wavelet Packets and Empirical Mode Decomposition are used for removing noise. The performance of the proposed technique is assessed on the five CT images taking into consideration, Gaussian and salt & pepper noise for the parameters, PSNR and SDME. In the segmentation process K-means clustering technique is employed. For the feature extraction, the parameters contrast, energy and gain are extracted. In classification, a modified technique called Cuckoo-Neuro Fuzzy (CNF) algorithm is developed and applied for detection of the tumour region. The cuckoo search algorithm is employed for training the neural network and the fuzzy rules are generated for classification, according to the weights of the training sets. From the obtained outcomes, we can conclude that the proposed denoising technique have shown better values for the SDME of 69.9798, PSNR of 29.8413 for salt & pepper noise and an accuracy of 96.3% which is very superior compared to existing methods

Extraction of jet engine modulation component weakly present in measured signals for enhanced radar target recognition

This study presents a method for extracting the jet engine modulation (JEM) component weakly present in the radar received signal. First, the analytic form of the complicated measured signal is decomposed into intrinsic mode functions (IMFs) via complex empirical mode decomposition (CEMD). Then, the extracted IMFs are combined to reconstruct the JEM component based on the signal eccentricity, which serves as assessing the JEM micro-Doppler contribution to the signal. Applying these techniques to the measured raw signals demonstrate that the proposed extraction method significantly improves the clarity of the JEM analysis and is expected to be useful for enhanced radar target recognition.

Noise assisted signal decomposition method based on complex empirical mode decomposition

The ensemble empirical mode decomposition has been proposed in order to alleviate mode mixing in empirical mode decomposition, but the ensemble average in it can always result in new mode mixing, spectrum losing, and computational cost increasing, which can affect the analysis and extraction of signal physical characteristics. To tackle these problems, a noise-assisted signal decomposition method based on complex empirical mode decomposition is proposed, in which the mode mixing is reduced by taking the projection of intrinsic mode functions decomposed from white noise as basis functions for signal extrema extraction. While the problems result from ensemble average are reduced because the effects of noise projection are eliminated in the process of calculating the envelope barycenter. Simulation results show that our method has further reduced mode mixing, and speeded up the operation rate visibly and alleviated spectrum losing to a certain degree.

EXTENDED HIGH RESOLUTION RANGE PROFILE-JET ENGINE MODULATION ANALYSIS WITH SIGNAL ECCENTRICITY

In this paper, high resolution range proflle-jet engine mod- ulation (HRRP-JEM) analysis is extended by including quantitative estimation of the jet engine location and extraction of the JEM micro- Doppler component. Based on a parametric model of the range cell data, signal eccentricity was introduced for the purpose of determining the jet engine location. Then, complex empirical mode decomposition (CEMD) was employed to extract the embedded JEM component. The signal eccentricity also served as an auxiliary means of CEMD-based micro-Doppler extraction. Application to the simulated HRRP-JEM data demonstrated that the analysis results described in this paper could be useful for advanced radar target recognition with HRRP- JEM.

A Multi-view Image Coding Scheme Based on CEMD and Fusion

In this paper, a novel fusion based coding scheme of multi-view image is proposed. At the encoder, different sources of images are decomposed into IMFs (Intrinsic Mode Factions) and residue using CEMD (Complex Empirical Mode Decomposition). The IMFs and residue are fused respectively, and the fusion results are compressed and encoded based on EMD. At the decoder, the source images can be reconstructed by disassembling the fusion image. Experimental results show, without reducing quality, the proposed scheme can provide better compression ratio than the previous ones, which open doors for future applications.

Narrow-Band Interference Suppression for SAR Based on Complex Empirical Mode Decomposition

Narrow-band interference (NBI) is a common interference source in synthetic aperture radar (SAR) imaging. Its existence will degrade the imaging quality greatly. Based on detailed analysis on the characteristics of NBI, this letter proposes a new NBI suppression algorithm using the complex empirical mode decomposition (CEMD) method. In this algorithm, echoes that include NBI are recognized in the time domain first. Then, these echoes are decomposed into a number of intrinsic mode functions (IMFs) via the CEMD. After that, IMFs that correspond to NBI are subtracted from the echoes by thresholding. Finally, well-focused SAR imagery can be obtained from the separated target echoes using traditional SAR imaging algorithms. The effective data loss in this algorithm is smaller than other NBI suppression approaches. In addition, this algorithm is robust to time-varying NBI. Imaging results of measured data have proved the validity of this algorithm.

Univariate and Bivariate Empirical Mode Decomposition for Postural Stability Analysis

The aim of this paper was to compare empirical mode decomposition (EMD) and two new extended methods of EMD named complex empirical mode decomposition (complex-EMD) and bivariate empirical mode decomposition (bivariate-EMD). All methods were used to analyze stabilogram center of pressure (COP) time series. The two new methods are suitable to be applied to complex time series to extract complex intrinsic mode functions (IMFs) before the Hilbert transform is subsequently applied on the IMFs. The trace of the analytic IMF in the complex plane has a circular form, with each IMF having its own rotation frequency. The area of the circle and the average rotation frequency of IMFs represent efficient indicators of the postural stability status of subjects. Experimental results show the effectiveness of these indicators to identify differences in standing posture between groups.