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The Lamb wave-based damage imaging technique offers a novel approach for structural health monitoring. However, enhancing the accuracy of damage detection continues to pose a significant challenge. To improve the precision and reliability of defect detection and localisation in composite plates, this paper introduces and investigates the Empirical Mode Decomposition-based Adaptive Time Reversal Method (EMD-ATRM). This method integrates adaptive Tukey window inverse filtering with a data-driven advanced technique, empirical mode decomposition, to effectively mitigate noise and accurately extract the primary modes. Moreover, the enhanced time reversal method is coupled with the Improved Reconstruction Algorithm for Probabilistic Inspection of Damage (IRAPID) to achieve precise computation of damage index values and efficient execution of time-reversed imaging. Experimental results demonstrate that, compared to traditional Time Reversal Method (TRM), EMD-ATRM exhibits superior defect localisation accuracy, clearer damage images, a reduced false alarm rate, and enhanced capability in identifying damage paths and regions. This study not only provides innovative methods and tools for the structural health monitoring of composite materials but also highlights the potential applications of EMD-ATRM in industrial settings, guiding future research directions. [ABSTRACT FROM AUTHOR]

The state of the air-changing environment is a significant problem in urban areas that can cause serious health problems and economic performance. Accurate forecasting of air quality levels is a critical factor influencing public health and economic decisions. Accurate prediction of these variables can help with policy development and informed decision-making. This paper introduces a novel hybrid approach for air quality prediction, combining empirical mode decomposition, long short-term memory networks, and optimization techniques, namely random search and bayesian optimization. Empirical mode decomposition is used for decomposing the actual series into a subseries to reduce the data complexity and use long short-term memory (LSTM) networks to predict time series and employ a bayesian optimization and random search optimization approach to tune hyperparameters of LSTM. The hybrid model EMD-LSTM-Bayesian exhibits the lowest MAE value of 0.385 and the lowest RMSE value of 0.533, in contrast to the LSTM Model, which has the highest MAE value of 0.593 and the highest RMSE value of 0.804. The experimentation results suggest that the proposed hybrid method achieves higher accuracy as compared to other state-of-the-art methods. The percentage improvement of proposed hybrid model EMD-LSTM-Bayesian in terms of MAE%, 35.07, 20.94, 4.22, 27.35 for the comparison models LSTM, LSTM-Random Search, LSTM-Bayesian, and EMD-LSTM, respectively, for the dataset 2013 year. [ABSTRACT FROM AUTHOR]

The reaction of asset prices to monetary and macroeconomic news announcements represents a rich source of information which is important to better understand the financial market impact of economic fundamentals. Given the significant connection of energy to other sectors in the economy, this study attempts to address to what extent the views that a central bank expresses in its communications get reflected in the energy stock market. The GARCH with Jump intensity approach is used to analyse the volatility of energy stock prices and to measure their reactions to anticipated and unanticipated policy news. Moreover, we assess whether central bank communications exert differential impacts on changes in energy stock prices under various time-horizons. Focusing on the European Union and the UK energy stocks, the paper finds evidence of a time-varying market responsiveness. For instance, a more hawkish stance is associated with an increase in the energy stock price jumps for the EU in the short- and medium-terms, whereas it only holds for the UK in the short-run. Although central bank policies do not target the exchange rate itself, we find that the exchange rate channel is an important part of the monetary policy transmission mechanism for the energy market. [ABSTRACT FROM AUTHOR]

This study integrates Long Short-Term Memory (LSTM) networks with Multi-Criteria Decision-Making (MCDM) methods to improve the accuracy of stock market forecasts. Drawing on a dataset from G7 stock markets spanning June 2018 to June 2023, the study aggregates fifteen performance metrics to generate a diverse parameter pool with randomly assigned values. These parameters are evaluated using decision matrices applied through the MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methods. The models are assessed under both equal-weighted criteria and criteria weighted via the CRITIC (Criteria Importance through Intercriteria Correlation) method. Additionally, the study examines Empirical Mode Decomposition-based LSTM (EMD-LSTM) models, revealing that those optimized using the MARCOS method substantially outperform those optimized through TOPSIS, particularly in forecasting accuracy. The integration of MCDM techniques with LSTM models yields a hit rate of up to 75%, demonstrating the effectiveness of this approach in parameter selection and overall model enhancement. This study not only underscores the potential of MCDM methods in refining LSTM models but also provides a robust framework for improving predictive accuracy in financial markets. [ABSTRACT FROM AUTHOR]

Crude oil is a raw and natural, but nonrenewable, resource. It is one of the world's most important commodities, and its price can have ripple effects throughout the broader economy. Accurately predicting crude oil prices is vital for investment decisions but it remains challenging. Due to the deficiencies neglecting residual factors when forecasting using conventional combination models, such as the autoregressive moving average and the long short-term memory for prediction, the variational mode decomposition (VMD)-empirical mode decomposition (EMD)-Transformer model is proposed to predict crude oil prices in this study. This model integrates a second decomposition and Transformer model-based machine learning method. More specifically, we employ the VMD technique to decompose the original sequence into variational mode filtering (VMF) and a residual sequence, followed by using EMD to decompose the residual sequence. Ultimately, we apply the Transformer model to predict the decomposed modal components and superimpose the results to produce the final forecasted prices. Further empirical test results demonstrate that the proposed quadratic decomposition composite model can comprehensively identify the characteristics of WTI and Brent crude oil futures daily price series. The test results illustrate that the proposed VMD–EMD–Transformer model outperforms the other three models—long short-term memory (LSTM), Transformer, and VMD–Transformer in forecasting crude oil prices. Details are presented in the empirical study part. [ABSTRACT FROM AUTHOR]

Biomedical signal processing is crucial in many sectors that save lives. Artificial intelligence improvement in signal collection and conditioning boosted this application's adaptability to varied bodily circumstances. In this study, a novel method is put forth for predicting the type of stroke in the human brain based on the observation of the Electroencephalography (EEG) signal. The signal is the first condition for removing undesirable frequencies by passing through a lowpass filter. To accurately extract the signal features, the signal is first transformed into a 1-second frame format and then normalised. Certain statistical and frequency domain aspects are highlighted to increase taxonomic accuracy. Under the wavelet packet transform, the empirical mode decomposition approach is utilised to recover the most information feasible from the signal. After training on extracted characteristics, the extreme learning machine is regarded to conduct classification. These work achieves 94.95 of Sensitivity, 84.95 of Specificity, 93.74 of Precision, 96.96 of Accuracy, 96.12 of F1 Score. Compared to the standard procedures, the proposed techniques have a greater accuracy rate of about 98%. [ABSTRACT FROM AUTHOR]

We propose a stellar spectral noise reduction algorithm named Ces. The Hermitian interpolation algorithm is utilized to replace the cubic spline interpolation module in the empirical mode decomposition algorithm. Additionally, the shape of both upper and lower envelopes are constrained by continuous spectrum information from celestial spectra. The specific steps involved are: (i) The continuous spectrum of stellar spectra is fitted using a 10th-order polynomial. (ii) Calculate the derivative of the continuous spectrum at the extreme point of the stellar spectrum. (iii) Input the calculated derivative into the Hermite interpolation algorithm, utilizing it as a fitting parameter to generate upper and lower envelopes. Ces algorithm firstly decomposes and reconstructs stellar spectra to achieve initial noise reduction effect. The singular value decomposition algorithm is used to process the initial noise reduction data again to further remove the noise and recover part of the spectral line information. [ABSTRACT FROM AUTHOR]

Accurate identification and monitoring of aerosol properties is crucial for understanding their sources and impacts on human health and the environment. Therefore, we propose a feature extraction and attribute recognition method from in situ light-scattering measurements based on Bayesian Optimization, wavelet scattering transform, and long short-term memory neural network (BO-WST-LSTM), with empirical mode decomposition (EMD) and independent component analysis (ICA) algorithm for signal preprocessing. In this study, an experimental platform was utilized to gather light-scattering signals from particles with varying characteristics. The signals are then processed using the EMD-ICA noise reduction technique. Then, the wavelet scattering network is used to realize the adaptive extraction of the characteristics of the particle light-scattering signal, and the Bayesian Optimization model is used to optimize the hyperparameters of the LSTM neural network. The extracted scattering coefficient matrix is input into the LSTM for iterative training. Finally, the SoftMax layer's probability classification method is applied to the identification of particle attributes. The results show that the multi-angle particle light-scattering signal detection system designed and built in this study performs well and is capable of effectively collecting particle light-scattering signals. At the same time, the proposed new method for particle property recognition demonstrates good classification performance for six different types of particles with a particle size of 2 μm, achieving a classification accuracy of 98.83%. This proves its effectiveness in recognizing particle properties and provides a solid foundation for particle identification. [ABSTRACT FROM AUTHOR]

Speech signals often contain different forms of background and environmental noise. For the development of an efficient speech recognition system, it is essential to preprocess noisy speech signals to reduce the impact of these disturbances. Notably, prior research has paid limited attention to pink and babble noises. This gap in knowledge inspired us to develop and implement hybrid algorithms tailored to handle these specific noise types. We introduce a hybrid method that combines the Bionic Wavelet transform with Adaptive Filtering to enhance signal strength. The performance of this method is assessed using various metrics, including Mean Squared Error, Signal-to-Noise Ratio, and Peak Signal-to-Noise Ratio. Notably, our findings indicate that SNR and PSNR metrics are especially effective in enhancing the handling of pink and babble noises. [ABSTRACT FROM AUTHOR]

Anchorage performance monitoring plays an important role in ensuring the safety of prestressed structures. Over the past decades, traditional structural health monitoring (SHM) methods, such as vibration, magnetoelastic, and acoustoelastic methods, have been plagued by low sensitivity and high susceptibility to environmental and temperature interference. Lead-Zirconate-Titanate (PZT)-enabled active sensing method has proven its effectiveness in prestress monitoring. The main contribution of this paper is the development of a new device that utilises the relationship between energy transfer and contact stress at the interface. This device addresses the existing problem of signal saturation and offers higher sensitivity for practical implementation. First, the time reversal (TR) method was used to overcome the problem of low signal-to-noise ratio (SNR) in traditional methods. Based on this, the empirical mode decomposition (EMD) method was employed to process the acquired signals, thereby improving the stability of the data. A quantitative index for monitoring prestress loss was proposed by normalising the peak value of the reconstructed signals. Finally, several repeated experiments were conducted to verify the accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid transmission line fault localization method combining wavelet packet decomposition with TKEO is proposed. Aiming at the problems of modal aliasing and endpoint effect in previous decomposition methods, the empirical modal decomposition is improved by using SSSC to extract the fault current component information; moreover, the instantaneous energy of the fault traveling wave head is calculated by using TKEO for localization, which solves the problem of traveling wave head uncertainty after wavelet packet decomposition. The experimental results show that the method proposed in this paper can effectively locate the faults at different fault types, different ground resistances and different distances with an error of about 10 m, and the accuracy is higher than that of other algorithms. The fault location algorithm has also been evaluated for the effects of shunt FACTS devices, CT saturation, CVT transient, DC offset, cross-country faults, evolving faults, sampling frequency and power swing. [ABSTRACT FROM AUTHOR]

Fault diagnosis plays a pivotal role in ensuring the reliability and efficiency of industrial machinery. While various machine/deep learning algorithms have been employed extensively for diagnosing faults in bearings and gears, the scarcity of data and the limited availability of labels have become a major bottleneck in developing data-driven diagnosis approaches, restricting the accuracy of deep networks. To overcome the limitations of insufficient labeled data and domain shift problems, an intelligent, data-driven approach based on the Triplex Transfer Long Short-Term Memory (TTLSTM) network is presented, which leverages transfer learning and fine-tuning strategies. Our proposed methodology uses empirical mode decomposition (EMD) to extract pertinent features from raw vibrational signals and utilizes Pearson correlation coefficients (PCC) for feature selection. L2 regularization transfer learning is utilized to mitigate the overfitting problem and to improve the model's adaptability in diverse working conditions, especially in scenarios with limited labeled data. Compared with traditional transfer learning approaches, such as TCA, BDA, and JDA, which demonstrate accuracies in the range of 40–50%, our proposed model excels in identifying machinery faults with minimal labeled data by achieving 99.09% accuracy. Moreover, it performs significantly better than classical methods like SVM, RF, and CNN-based networks found in the literature, demonstrating the improved performance of our approach in fault diagnosis under varying working conditions and proving its applicability in real-world applications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Pearl River is the property of Pearl River Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, a new method for denoising signals is developed that is based on variational mode decomposition (VMD) and a novel metric using detrended fluctuation analysis (DFA). The proposed method first decomposes the signal into band-limited intrinsic mode functions (BLIMFs) using VMD. Then, a DFA-based developed metric is employed to identify the 'noisy' BLIMFs (based on their DFA-based scaling exponent and frequency content). The existing DFA-based methods use a single-slope threshold to detect noise, assuming all signals have the same noise pattern and ignoring their unique characteristics. In contrast, the proposed DFA-based metric sets adaptive thresholds for each mode based on their specific frequency and correlation properties, making it more effective for diverse signals and noise types. These predominantly noisy BLIMFs are then denoised using shrinkage techniques in the framework of stationary wavelet transform (SWT). This step allows efficient denoising of components, mainly the noisy BLIMFs identified by the adaptive threshold, without losing important signal details. Extensive computer simulations have been carried out for both synthetic and real electrocardiogram (ECG) signals. It is demonstrated that the proposed method outperforms the state-of-the-art denoising methods and with a comparable computational complexity. [ABSTRACT FROM AUTHOR]

The electric load demand is thriving faster day-to-day which is highly nonlinear, complex and noisy in nature because of its partial dependency on weather condition (temperature, humidity, pressure and etc.). This makes the load prediction extremely difficult. In this work, the performance of Empirical Mode Decomposition (EMD) based optimized Extreme Learning Machine (OELM) is demonstrated for day ahead load forecasting of Chhattisgarh state of India. EMD method is used to disintegrate the nonlinear load data into some simple and stationary dataset by which the prediction competency of machine learning algorithm can be enhanced. Each decomposed dataset is predicted individually by dedicated OELM. OELM learning algorithm is established by optimizing the parameters of ELM (weights and biases) using Crow Search Algorithm (CSA). Further, the competency of OELM is improved by proposing Craziness based CSA (CCSA) with upgraded potential to equipoise between investigation and exploitation. The consolidation of EMD method and proposed CCSA is applied to achieve better efficacy in load demand prediction. The predicted demand of proposed predictive model is demonstrated by using performance measures and hypotheses tests. The superiority of proposed predictive model over recently published work is substantiated for load demand forecasting of three different cities of Australia. The simulation results contribute the indication that the proposed EMD CCSA OELM model can be taken as a better tool for load forecasting. [ABSTRACT FROM AUTHOR]

This research paper introduces an innovative approach to classify heart rate variability (HRV) time series into paced and spontaneous breathing patterns to reflect changes in the autonomic nervous system. This type of classification is beneficial in wearable devices for stress/relaxation level detection and in deciding therapeutic interventions. The "Multi-Domain Approach" methodology integrates three different techniques: standard HRV features, fuzzy recurrence plot (FRP)-based FRP_GLCM, and empirical mode decomposition-based IMF_FRP_GLCM. The study concentrates on analyzing HRV time series within shorter data segments, aligning with the requirements of contemporary wearable health devices and biofeedback systems. HRV data collected during spontaneous and slow-paced breathing were analyzed across data segments of 5, 4, 3, 2, and 1 min, incorporating feature selection and reduction methods. Results demonstrated that standard HRV features yielded optimal performance for 5-min segments, achieving an average accuracy of 90%. Interestingly, IMF_FRP features achieved comparable accuracy even for 1-min segments. As segment duration decreased, standard HRV feature accuracy declined while IMF_FRP accuracy stayed intact, eventually matching 5-min segment accuracy levels. The study underscores the surging demand for shorter data segment HRV analysis, driven by advancements in wearable smart watches technology and mobile applications for monitoring health and managing stress. [ABSTRACT FROM AUTHOR]

The processing and recognition of electroencephalogram (EEG) signal is the most important part of brain-computer interface (BCI) system, and the quality of signal processing and recognition is directly related to the effectiveness of BCI system. Aiming at the problems of incomplete removal of artifacts and inadequate retention of active components in EEG signal, a fusion method of wavelet transform (WT) and Fast Independent Component Analysis (FastICA) is utilized to preprocess the raw EEG signals. The fusion method can remove noise artifacts while preserving effective information. Aiming at the problems of poor time-frequency resolution and low classification accuracy of the traditional feature extraction method, a feature extraction algorithm on the basis of hybrid Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) is proposed. Firstly, DWT is used to analyze the pre-processed EEG signal to obtain a series of sub-band signals. Then, EMD decomposition is applied to subband signal and eigenmode function is extracted to complete feature integration. Finally, the feature extraction results are input into the Support Vector Machine (SVM) for classification. Comparative experiments show that the classification accuracy of the proposed method reaches 91.32 %, which is significantly higher than other algorithms.

This study introduces a novel approach for detecting mental workload and stress, utilizing ECG and Fingertip-PPG data from the MAUS dataset. The dataset includes physiological recordings such as ECG, Fingertip-PPG, Wrist-PPG, and GSR signals from 22 participants exposed to varying levels of mental workload and stress conditions, serving as a valuable resource for developing mental workload assessment systems. Focusing on ECG and Fingertip-PPG signals, we evaluated their effectiveness in detecting mental workload and stress. Early detection of mental workload and stress is vital for preventing adverse health outcomes, emphasizing the importance of timely intervention. In this study, by applying Empirical Mode Decomposition (EMD) for feature extraction and machine learning classifiers, we achieved high performance with accuracy rates of 88.64% for ECG and 80.68% for Fingertip-PPG Signal. Combining both data types (ECG and Fingertip-PPG data) further improved recall to 94.87% and boosted overall accuracy to 87.50%, marking an improvement of approximately 13%. SHAP analysis identified key features contributing to this performance, revealing key features such as the waveform length of the first Intrinsic Mode Function (IMF) for the ECG signal, the mean of the second IMF for fingertip-PPG, and the waveform length of the second IMF for combined ECG and fingertip-PPG signals. These results highlight the potential of combining physiological signals and EMD with machine learning for detecting mental workload and stress.

Wind energy has become a vital component of the power system. Due to the stochastic and intermittent characteristics of wind speed, thus enhancing accuracy and stability in short-term wind speed prediction is imperative and important for effectively harnessing wind energy. This paper proposes a novel hybrid model combing Legendre multiwavelet transform, Gated recurrent unit and Elman neural network (LMWT-GRU-ENN) for short-term wind speed prediction. More precisely, the rich properties, especial various regularities of LMW bases are utilized to effectively match the non-linearity and larger non-stationary features of short-term wind speed corresponding to multi-resolution level and multi-wavelet bases. GRU model is used to predict the low frequency components, and ENN model is implemented to predict the high frequency components obtained by LMWT, which can effectively improve the prediction performance by thoroughly making use of their advantages. Finally, massive experiments are conducted on two short-term wind speed datasets, and the experimental results demonstrate the proposed method attains the excellent performance of in both accuracy and stability compared with other state-of-the-art methods.

Based on historical flood data (1764—) and measured flood data (1953—2024), the consistency of floods at Feilaixia Station in Beijiang River was tested using time series diagrams, sliding means, M-K method, and Hurst index. According to the P-III frequency analysis method, historical flood data were used to re-rank the flood series for flood frequency analysis. The frequency calculation method based on empirical mode decomposition (EMD) and synthesis was used to analyze and calculate the flood frequency. The results indicate that the extreme floods in Beijiang River have shown a trend of increasing frequency and intensity since the mid-1990s, and significant variations have occurred after testing. The flood design values for each frequency obtained using traditional hydrological frequency calculation methods increase by 500~600 m3/s compared to the original design values. By using the frequency calculation method based on EMD and synthesis, the design values of each frequency for the 2020 level year increase by more than 3 500 m3/s, and for the 2030 level year, the design values of each frequency increase by more than 3 800 m3/s. The flood frequency calculation method based on EMD reconstruction can respond to changing environments in a timely manner and has certain promotion and application value. The hydrological frequency calculation results of Feilaixia Station in Beijiang River have undergone significant changes and need to be re-evaluated as soon as possible.

To ensure the safe and efficient extraction of coal mines and reduce sudden roof accidents, this study proposes a novel multivariate long-term sequence mine pressure prediction model—the Empirical Mode Decomposition Linear Model (EMD–Mixer). Unlike most fixed-length single-feature mine pressure prediction models, this model first introduces the Empirical Mode Decomposition (EMD) method to separate periodic and trend components from the mine pressure signals. It then combines these with the Long-Term Forecasting Linear Layer (LTSF–Linear) to form a module for extracting temporal features. Additionally, a time and channel mixing strategy is designed, utilizing a channel feature module to handle multivariate mine pressure data and process nonlinear relationships. The final prediction results are obtained by adding the residuals of the time and channel module outputs to the input data. In the experiments, the historical window was set to 36 time units, and tests were conducted for prediction lengths of 24, 36, 48, and 60 time units. The results indicate that the EMD–Mixer model exhibits excellent performance and stability across short to medium-term prediction ranges. The model was compared with the LTSF–Linear, MTS–Mixer, and the commonly used Long Short-Term Memory (LSTM) model in mine pressure prediction. Four evaluation metrics were used to assess the prediction results: Mean Absolute Error (EMAE), Mean Squared Error (EMSE), Symmetric Mean Absolute Percentage Error (EsMAPE), and R-squared (R2). The results show that the EMD–Mixer model demonstrates higher prediction accuracy and stability across all metrics. The EMD–Mixer model is simple in structure, has strong generalization capabilities, and can more effectively adapt to multivariate mine pressure data of different levels in various scenarios. This provides an important research approach for the safe and efficient production of coal mines and the early warning of roof accidents.

Abstract Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage.

Abstract The frictional properties of pavement are contingent on its surface texture, which consists of multiple scales that each contribute differently to friction generation at the tire-surface interface. Consequently, this study applied three adaptive decomposition algorithms of the Hilbert–Huang Transformation (HHT) to extract essential texture parameters from surface profiles, aiming to improve understanding of how pavement texture impacts friction properties. The decomposition algorithms used are Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD) and Complete EEMD with Adaptive Noise (CEEMDAN). A Circular Track Meter (CTMeter) was used to measure the surface texture profiles of twelve asphalt mixture slabs with high macrotexture, while friction properties were assessed using a dynamic friction tester (DFT). Additionally, sixteen texture parameters related to height, spacing, and shape were computed to characterize the surface texture of both the original profile and the derived intrinsic mode functions (IMFs). The statistical analysis revealed that while the third IMF (IMF3) obtained by the EEMD exhibited the strongest correlation with the original profile, reaching up to 0.69, the IMFs obtained by EMD demonstrated the highest correlation with the DFT results. Under different polishing conditions and DFT speeds, the friction-texture correlation varied significantly, with the highest correlation observed at the peak values of DFT results and DFT values at 20 km/h (DFT20). Among the texture indicators, in addition to the mean profile depth (MPD), the root mean square (Rq), root mean square wavelength (λq), and two points slope variance (SV2) were recommended for characterizing both the original texture and IMFs profiles in the correlation analysis of the friction-texture relationship.

The electricity sector deregulation has led to the formation of short‐term power markets where the consumers can purchase electricity by bidding at the electricity market. The electricity market price is volatile and changes are due to change in electricity demand and the bid price at different span of time during the day. The availability of the electricity price forecast is essential for the electricity market participants to make informed decisions. In this paper, the modified LSTM approach, wavelet‐LSTM, and Hilbert‐LSTM are proposed to predict the electricity price for bidding in the short‐term electricity market. The objective is to improve the precision and adaptability of electricity price predictions by utilizing the temporal dependence identification capability of LSTM and the multiresolution analysis capability of the transforms. The proposed models combine these two effective methods in order to capture both the long‐term trends and short‐term variations present in electricity price time series data. In this approach, the 8‐year dataset is used for training the models, and based on this the day‐ahead price is calculated and compared with the testing data. The proposed techniques show better performance in terms of rank correlation, mean square error, and root mean square error compared to the existing algorithms of LSTM and CNN‐LSTM. The prediction results achieved with wavelet‐LSTM and Hilbert‐LSTM (1‐month dataset of 8 years) are rank correlation 0.9746 and 0.9749, MSE 0.2962 and 0.1363, and RMSE 0.5443 and 0.3692, respectively. The results achieved with the proposed methods are better than the existing forecasting models, and the RMSE for Hilbert‐LSTM and wavelet‐LSTM techniques is improved by 61% and 43%, respectively, compared to the LSTM method. Also, results are calculated for the complete 8 years all 12 months with Hilbert‐LSTM, and the results achieved are rank correlation 0.9645, MSE 0.3876, and RMSE 0.6225. The results achieved with the proposed models are improved in terms of performance parameters compared to the conventional approaches. The proposed models can be used in the day‐ahead electricity price forecasting to bid for electricity accurately in the day‐ahead electricity market. [ABSTRACT FROM AUTHOR]

Deep learning models, such as recurrent neural network (RNN) models, are suitable for modeling and forecasting non-stationary time series but are not interpretable. A prediction model with interpretability and high accuracy can improve decision makers' trust in the model and provide a basis for decision making. This paper proposes a double decomposition strategy based on wavelet decomposition (WD) and empirical mode decomposition (EMD). We construct a prediction model of high-order fuzzy cognitive maps (HFCM), called the WE-HFCM model, which considers interpretability and strong reasoning ability. Specifically, we use the WD and EDM algorithms to decompose the time sequence signal and realize the depth extraction of the signal's high-frequency, low-frequency, time-domain, and frequency domain features. Then, the ridge regression algorithm is used to learn the HFCM weight vector to achieve modeling prediction. Finally, we apply the proposed WE-HFCM model to stationary and non-stationary datasets in simulation experiments. We compare the predicted results with the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models.For stationary time series, the prediction accuracy of the WE-HFCM model is about 45% higher than that of the ARIMA, about 35% higher than that of the SARIMA model, and about 16% higher than that of the LSTM model. For non-stationary time series, the prediction accuracy of the WE-HFCM model is 69% higher than that of the ARIMA and SARIMA models. [ABSTRACT FROM AUTHOR]

The rising use of 3D digital products has increased the demand for copyright protection. In this paper, we propose a novel and robust 3D watermarking method with high imperceptibility based on EMD (empirical mode decomposition) on surfaces. It first defines a normalized modulus signal on a 3D host model so as to involve EMD into 3D watermarking effectively. And then, it extracts different scale features of the defined signal by using EMD to locate the proper embedding positions. After this, the watermark signal is embedded repeatedly and cyclically into the 3D host model to enhance the robustness. The embedding strength is optimized according to a predefined fidelity parameter to control the imperceptibility of the watermark. Many experiment results show that the proposed method can obtain good results against various attacks while maintaining high invisibility, such as pseudo-random noise, Laplacian smoothing, simplification, subdivision, cropping, and similarity transformation. Furthermore, it is very competitive with the current state-of-the-art 3D watermarking techniques in terms of robustness and invisibility. [ABSTRACT FROM AUTHOR]

Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage. [ABSTRACT FROM AUTHOR]

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Structural assessment of buried energy pipelines is often hindered by the abundance of external vibrations resulting in nebulous noises. Effective and secure nondestructive approaches need to be devised to efficiently reduce noise in multidimensional magnetic anomaly signals collected from a pipeline. This study focuses on the mechanism by which a measured source signal can be broken down into low- and high-frequency constituents known as intrinsic mode functions (IMFs). By doing so, a well-defined set of instantaneous frequencies is obtained utilizing improved complete ensemble empirical mode decomposition (ICEEMDAN) algorithms. These IMFs contain useful structural evidence across multiple scales that can be extracted for effective identification of the defect location. To accomplish this objective, first, the signal gradients are calculated using dual-density complex wavelet transform to diminish the influence of the geomagnetic field. The multiscale variance fusion (MVF) algorithm is then adopted to quantize the fluctuations occurring in each individual IMF. The output signals generated by computing the variances provide sufficient information about the location and severity of the pipeline defects. Numerical simulations for a buried pipeline model have been presented to validate the authenticity of the proposed technique. Indoor laboratory implantation on a pipeline test sample with prefabricated defects justifies the effectiveness of the ICEEMDAN-MVF model, to localize hidden structural flaws in energy pipelines without physical contact and even in more complex environments with multiple sources of magnetic interference. [ABSTRACT FROM AUTHOR]

Identifying and understanding various causal relations are fundamental to climate dynamics for improving the predictive capacity of Earth system modeling. In particular, causality in Earth systems has manifest temporal periodicities, like physical climate variabilities. To unravel the characteristic frequency of causality in climate dynamics, we develop a data‐analytic framework based on a combination of causality detection and Hilbert spectral analysis, using a long‐term temperature and precipitation dataset in the contiguous United States. Using the Huang–Hilbert transform, we identify the intrinsic frequencies of cross‐regional causality for precipitation and temperature, ranging from interannual to interdecadal time scales. In addition, we analyze the spectra of the physical climate variabilities, including El Niño‐Southern Oscillation and Pacific Decadal Oscillation. It is found that the intrinsic causal frequencies are positively associated with the physics of the oscillations in the global climate system. The proposed methodology provides fresh insights into the causal connectivity in Earth's hydroclimatic system and its underlying mechanism as regulated by the characteristic low‐frequency variability associated with various climatic dynamics. [ABSTRACT FROM AUTHOR]

Fatigue cracks, especially at their initial stage, can lead to a repetitive crack open-close breathing-like phenomenon in the vibration response of structural elements. As such, regularities, bi-linearity, or perturbations in the vibration response can arise. Entropy can be used to quantify the irregularity or bi-linearity in these responses since there is an apparent variation of entropy values on the two sides of a breathing crack. Here, we present a new breathing crack localisation method based on a spatially distributed entropy approach coupled with the empirical mode decomposition technique. To enhance the robustness, a pre-selection mechanism is proposed to select the most suitable entropy method. The proposed method is then employed to localise the breathing crack in a beam in a laboratory setup. It is concluded that the proposed approach can be effectively used for breathing crack localisation in a structural element. [ABSTRACT FROM AUTHOR]

Vehicle engine vibration signals acquired using MEMS sensors are crucial in the diagnosis of engine malfunctions, notably misfires due to unwanted signals and external noises in the recorded vibration dataset. In this study, the ADXL1002 accelerometer interfaced with the Beaglebone Black microcontroller is employed to capture vibration signals emitted by the vehicle engine across various operational states, including unloaded, loaded, and misfire conditions at 1500 RPMs, 2500 RPMs, and 3000 RPMs. In conjunction with the acquisition of this raw vibration data, frequency-domain signal processing techniques are employed to meticulously analyze and diagnose the distinct signatures of misfire occurrences across various engine speeds and loads. These techniques encompass the fast Fourier transform (FFT), envelope spectrum (ES), and empirical mode decomposition (EMD), each tailored to discern and characterize the nuanced vibration patterns associated with misfire events at different operational conditions. [ABSTRACT FROM AUTHOR]

Studies have analyzed U.S. tornado variability and correlated F1–F5 tornado occurrence with various natural climate oscillations and anthropogenic factors. Using a relatively new empirical mode decommission (EMD) method that extracts time-frequency modes adaptively without priori assumptions like traditional time-series analysis methods, this study decomposes U.S. tornado variability during 1954–2022 into intrinsic modes on specific temporal scales. Correlating the intrinsic mode functions (IMFs) of EMD with climate indices found that 1. the U.S. overall tornado count is negatively (positively) correlated with the Atlantic Multidecadal Oscillation (AMO) index (the Southern Oscillation Index (SOI)); 2. the negative (positive) correlation tends to be more prevalent in the western (eastern) U.S.; 3. the increase in weak (F1–F2) and decrease in strong (F3–F5) tornadoes after around 2000, when both the AMO and the Pacific Decadal Oscillation (PDO) shifted phases, are likely related to their secular trends and low-frequency IMFs; and 4. the emerging Dixie Tornado Alley coincides with an amplifying intrinsic mode of the SOI that correlates positively with the eastern U.S. and Dixie Alley tornadoes. The long-term persistence of these climate indices can offer potential guidance for future planning for tornado hazards. [ABSTRACT FROM AUTHOR]

Accurate monitoring of lithium-ion battery temperature is essential to ensure these batteries' efficient and safe operation. This paper proposes a relevance-based reconstruction-oriented EMD-Informer machine learning model, which combines empirical mode decomposition (EMD) and the Informer framework to estimate the surface temperature of 18,650 lithium-ion batteries during charging and discharging processes under complex operating conditions. Initially, based on 9000 data points from the U.S. NASA Prognostics Center of Excellence's random battery-usage dataset, where each data point includes three features: temperature, voltage, and current, EMD is used to decompose the temperature data into intrinsic mode functions (IMFs). Subsequently, the IMFs are reconstructed into low-, medium-, and high-correlation components based on their correlation with the original data. These components, along with voltage and current data, are fed into sub-models. Finally, the model captures the long-term dependencies among temperature, voltage, and current. The experimental results show that, in single-step prediction, the mean squared error, mean absolute error, and maximum absolute error of the model's predictions are 0.00095, 0.02114, and 0.32164 °C; these metrics indicate the accurate prediction of the surface temperature of lithium-ion batteries. In multi-step predictions, when the prediction horizon is set to 12 steps, the model achieves a hit rate of 93.57% where the maximum absolute error is within 0.5 °C; under these conditions, the model combines high predictive accuracy with a broad predictive range, which is conducive to the effective prevention of thermal runaway in lithium-ion batteries. [ABSTRACT FROM AUTHOR]