Mode Functions Research Articles

A study on rolling bearing fault diagnosis using RIME-VMD

To address the challenges of feature extraction in Variational Mode Decomposition (VMD) for rolling bearing fault diagnosis, this paper proposes a feature extraction method optimized by the RIME algorithm, called RIME-VMD. First, under various rolling bearing fault conditions, the RIME algorithm is employed to determine the optimal combination of decomposition components and penalty factors in VMD. Next, the kurtosis values of each decomposed Intrinsic Mode Function (IMF) are calculated, and the component with the most prominent fault features is selected for noise reduction through reconstruction. Finally, the sample entropy of the reconstructed signal is calculated as a fault feature and input into a Support Vector Machine (SVM) for rapid identification and diagnosis of various rolling bearing fault types. Simulation results indicate that, compared to the Whale Optimization Algorithm optimized VMD (WOA-VMD), the RIME algorithm optimized VMD (RIME-VMD) achieves shorter search times and higher search efficiency. It facilitates faster identification of decomposition parameters under various fault conditions, enhancing the robustness of fault signal detection and enabling rapid, efficient identification of rolling bearing faults. The findings of this study offer guidance and reference for future research on rolling bearing fault diagnosis.

Machinery structural crack damage detection based on acoustic signal with ICEEMDAN and sensitive IMF fuzzy entropy

Abstract With the increasingly urgent demand on the reliability of mechanical equipment, in the process of production and service, it is of vital importance to apply precise and efficient crack damage detection on the critical structures. To overcome the shortcomings of existing damage detection methods and meet the urgent needs of engineering practice, by analyzing acoustic signal, a novel machinery structural crack damage detection method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and sensitive intrinsic mode function (IMF) fuzzy entropy is proposed in this paper. Firstly, redundant second-generation wavelet denoising strategy based on neighborhood correlation is applied on the raw acoustic signal. Then, the pre-denoised acoustic signal is decomposed by ICEEMDAN to obtain a set of IMFs, and the fuzzy entropies of the first eight IMFs are calculated to reflect the structural crack damage states. Finally, with distance evaluation technique, the most sensitive IMF fuzzy entropy is selected and defined as the damage index to assess the structural crack damage levels. Effectiveness of the proposed method is validated by two case studies as for the crack damage detection of different machinery structures. The results show that the defined damage index is not only sensitive to the occurrence of structural crack damage, but also decreases obviously with the increasing damage level and is not affected by damage location.

High-Resistance Grounding Fault Location in High-Voltage Direct Current Transmission Systems Based on Deep Residual Shrinkage Network

Due to the precision limitations of traditional fault location methods in identifying grounding faults in High-Voltage Direct Current (HVDC) transmission systems and considering the high occurrence probability of high-resistance grounding faults in practical engineering scenarios coupled with the sampling accuracy constraints of actual equipment, this article introduces a novel approach for high-resistance grounding fault location in HVDC transmission lines. This method integrates Variational Mode Decomposition (VMD) and Deep Residual Shrinkage Network (DRSN). Initially, VMD is employed to decompose double-ended high-resistance grounding fault signals, extracting the corresponding Intrinsic Mode Functions (IMF). These IMF signals are then preprocessed to construct the input data for the DRSN model. Upon training, the model outputs the precise fault location. To validate the effectiveness of the proposed method, a ±800 kV bipolar HVDC transmission system model is established using PSCAD/EMTDC version 4.6.2 software for simulating high-resistance grounding faults. The sampling accuracy of the model’s output signals is set to 10 kHz, aligning closely with actual engineering equipment specifications. Comprehensive simulation experiments and anti-interference analyses are conducted on the DRSN model. The results demonstrate that the fault location method based on the DRSN exhibits high accuracy in locating high-resistance grounding faults, with a maximum error of less than 1.5 km, even when considering factors such as engineering sampling frequency, fault types, and signal noise.

An efficient sparse code shrinkage technique for ECG denoising using empirical mode decomposition

Accurate denoising of Electrocardiogram (ECG) signals is essential for reliable cardiac diagnostics, but traditional methods often struggle with high-frequency noise and artifacts, leading to potential misinterpretations. It is often impeded by interference such as power line interference (PLI) and Gaussian noise. To address this challenge, we suggest a novel ECG denoising technique that combines empirical mode decomposition (EMD) with wavelet domain sparse code shrinking. Our approach first decomposes the noisy ECG signal into Intrinsic Mode Functions (IMFs) using EMD. These IMFs are then transformed into the wavelet domain, where a sparse code shrinking function is applied to effectively reduce both Gaussian noise and PLI while preserving the integrity of the original signal. The effectiveness of the technique is assessed on the MIT-BIH database, where it shows marked improvements in Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), and Percentage Root Mean Square Difference (PRD). The suggested approach demonstrates improved SNR and reduced MSE when compared to prior approaches, which suggests that the ECG signals are clearer and more precise. This method presents a rather effective approach to enhancing ECG analysis as it is important for diagnosis and interpretation. At 10 dB SNR, the suggested technique achieves an MSE of 0.005, which is much less than the 0.076 and 0.0025 MSEs obtained by EMD wavelet adaptive thresholding and soft thresholding correspondingly. This indicates that the proposed approach effectively eliminates noise while preserving significant signal characteristics, leading to an improved and less erroneous signal reconstruction. Furthermore, the proposed method outperformed conventional techniques and demonstrated improved noise reduction and signal clarity, achieving an SNR of 19.24 and a PRD of 20.38 at 10 dB SNR.

Abstract TP371: Effects of Empagliflozin on Ischemic Stroke in a Rat Model: Time-Frequency Electroencephalogram Features and Cerebral Infarction Size

Background: Emerging evidence suggests that empagliflozin (EMPA) may offer additional benefits beyond its primary use in diabetes. Ischemic stroke is known to cause distinctive alterations in EEG signals. However, the influences of EMPA on the stroke-induced EEG changes has not been investigated. Here, we study such influences via EEG time-frequency features using the Hilbert-Huang Transform (HHT) and examine how these features correlate with the cerebral infarction. Methods: n= 47 male SD rats were randomized into 3 groups: 1] Control (n = 16, regular diet); 2] Acute EMPA treatment (n = 16, EMPA 10 mg/kg, IV given at 10 mins prior to middle cerebral artery occlusion (MCAO) and 1 min prior to reperfusion); 3] Chronic EMPA treatment (n = 15, EMPA by food, 20 mg/kg for 7 days before MCAO). To induce ischemic stroke, standard MCAO was performed for 1 hour followed by 3 hours of reperfusion. Post-surgery, triphenyltetrazolium chloride (TTC) staining was used to measure volume of cerebral infarction. EEG signals were continuously recorded throughout the procedure. We applied the Hilbert-Huang Transform (HHT) to analyze the EEG signals. HHT includes 2 steps: 1] empirical mode decomposition (EMD) to decompose the EEG signal into a set of intrinsic mode functions (IMFs); 2] the Hilbert Transform is applied to each IMF to compute analytic signals. Such analytic signals are represented in the complex plane, where they often form circular or elliptical contours (Fig. 1). The area of these contours in the complex plane contains unique information about variations in the signal properties. We computed the HHT area metric for the first four IMFs by using EEG recordings at 3 hours post-reperfusion (duration: 2 minute). Results and Conclusions: Our results showed the control group demonstrates higher correlations between the HHT area metric and the cerebral infarction size, compared to acute or chronic EMPA treatments (Fig. 2), with an especially significant correlation for IMF1in controls (R= -0.76). No significant difference was found in the HHT area metric between control, acute, and chronic EMPA (Fig. 3). It revealed that while EMPA treatments do not alter the EEG time-frequency features compared to controls, control group exhibits a stronger correlation between EEG features and cerebral infarction size. This indicates that EMPA's neuroprotective effects might not be directly reflected in EEG changes, highlighting the need for further investigation of its mechanisms.

Preterm birth prediction from electrohysterogram using multivariate empirical mode decomposition.

Electrohysterogram (EHG) is an electrophysiological signal describing uterine contractions that can be non-invasively measured on maternal abdominal surface. This signal contains vital physiological and pathological information for assessing delivery abnormalities, such as preterm birth. However, extracting information that effectively characterizes the association with abnormal delivery from the weak EHG signal is challenging. We present a preterm birth predicting method using multivariate empirical mode decomposition (MEMD) algorithm that adaptively decomposes multichannel EHG signals into different intrinsic mode functions (IMFs). MEMD maintains spectral consistency across channels and avoids mode-mixing problems across IMFs due to its powerful fine-grained signal structure decoupling capability. On this basis, a total of 180 features were extracted from the IMFs and the final eight features were chosen using a two-step feature selection algorithm. A support vector machine (SVM) classifier was employed for decision-making. Specifically, cost-sensitive algorithm was used to solve the data imbalance problem. The proposed method was evaluated using 300 EHG recordings in TPEHG database. The results show that our method outperforms other state-of-the-art methods in terms of sensitivity (85.16%), specificity (96.54%), (91.04%), accuracy (94.36%), and AUC (97.31%). This study provides a powerful tool with wide applications for preterm birth risk diagnosis in clinical obstetric.

A dual decomposition integration and error correction model for carbon price prediction.

Accurately predicting carbon prices is crucial for effective government decision-making and maintenance the stable operation of carbon markets. However, the instability and nonlinearity of carbon prices, driven by the complex interaction between economic, environmental, and political factors, often result in inaccurate predictions. To confront this challenge, this paper proposed a carbon price prediction model that integrates dual decomposition integration and error correction. Firstly, the variational mode decomposition optimized by the sparrow search algorithm (SVMD) is used to decompose carbon price series into intrinsic mode functions (IMFs). Secondly, a classification-prediction module is constructed to classify IMFs on complexity using fuzzy entropy. The long short-term memory networks optimized by the whale optimization algorithm (WLSTM) is employed to capture temporal dynamics and long-term dependencies within data. Conversely, lower complexity IMFs characterized by smoother trends and less erratic behavior are predicted using computationally efficient extreme learning machines (ELM). To further refine the prediction accuracy, ensemble empirical mode decomposition (EEMD) is introduced to decompose the initially predicted error series into IMFs and then predicted by classification-prediction module. Reconstruct the initial prediction IMFs and the error prediction IMFs to obtain the final prediction results. Finally, the proposed model was validated using real carbon price data from three Chinese carbon exchanges. Compared with the 15 comparison models, the performance indicators RMSE, MAE, MAPE, and R2 of the proposed model have promoted at least 19.89%, 25.11%, 25.01%, and 0.79% on average. These results underscore the effectiveness and superiority in predicting carbon prices, providing a robust tool for carbon market stakeholders and climate change policymakers.

An experimental setup to study the collision force between brittle impacting bodies

An experimental setup to study the collision force between brittle impacting bodies

Looseness detection system of bolted joints using a VMD-based nonlinear transformation approach with deep residual network

Abstract Bolted structures are subject to various vibrations, external forces and environmental factors, all of which can reduce their structural stability and compromise the integrity of bolted connections. Detecting bolt loosening in advance is crucial, as these effects often cause bolts to become loose, potentially leading to structural failure or collapse. However, identifying looseness in complex or large structures poses significant challenges, particularly when there is insufficient prior information about the loose-fit condition. To address this issue, the present study proposes a novel detection system for bolted joint looseness, employing a variational mode decomposition (VMD)-based nonlinear transformation (NT) approach integrated with a deep residual neural network, under several underlying assumptions. The proposed method utilizes VMD to decompose transverse vibrational modes into intrinsic mode functions (IMFs), selectively extracting signals with desired modes. The NT method is then applied to scale and shift the extracted signals, transforming them into a form that facilitates approximate classification. Image-based spectrograms are generated from the differences between transformed and reference signals, which are subsequently analyzed by the deep residual network. To validate the proposed method, several plates with bolted joints are considered.

207. Ex vivo Assessment of Cefepime (FEP) Clearance during Continuous Renal Replacement Therapy (CRRT) and Clinical Validation of Optimized Dosing Regimens

Abstract Background Optimal antibiotic dosing in critically ill patients receiving CRRT is crucial. Drug clearance (CL) can be affected by multiple factors such as CRRT mode, effluent rate (ER) or filter type. We investigated FEP transmembrane CL (CLTM) in an ex vivo CRRT model to inform optimal dosing regimens and validate these regimens by re-simulation in a clinical cohort of CRRT patients. Methods CLTM was determined ex vivo in hemofiltration and hemodialysis modes with M100 and HF1400 filters. Bovine blood spiked with FEP was sampled from pre-, post-filter and effluent fluids at 10, 30, and 60 minutes in duplicate runs at ER of 1, 2 and 3 L/h. CLTM was calculated via sieving (SC) and saturation coefficients (SA). A multiple linear regression was performed to describe CLTM as a function of ER, filter, and mode. Monte Carlo Simulations (MCS; n=1000) using the equation CL= non-renal CL + CLTM and distribution parameters from a published population pharmacokinetic (popPK) model were executed for ascending ER (1-5 L/h). The resulting FEP regimens were selected by probability of target attainment (PTA) for 70%fT > MIC (MIC = 8mg/L CLSI susceptible breakpoint) and mean AUC within the 25th-75th percentile of a non-CRRT cohort. Selected regimens were validated by re-simulating fT > MIC and AUC using popPK defined Bayesian parameters in Pmetrics for R from 10 CRRT patients receiving FEP in clinical practice. Results Mean (SD) ex vivo SC/SA were 1.01 (0.08). ER was the main predictor (p< 0.001) for CLTM (CLTM= 0.139+0.841*ER) with no effect from mode or filter type. MCS of FEP 1g and 2g q8h provided 100% PTA at 8 mg/L with AUCs similar to the non-CRRT comparator for ER of 1-2.4 and 2.5-5 L/h respectively. Mean (SD) popPK parameter estimates of the validation cohort were CL, 3.8 (1.1); Vc, 17.3 (15.3) L; k12, 5.0 (3.1) h-1; and k21, 6.4 (7.6) h-1. Selected regimens tested in the validation cohort demonstrated good performance with the algorithm (Figure 1). Conclusion These are the first data to translate the findings of ex vivo CRRT CLTM in a dosing algorithm based on CRRT ER to achievable exposure in patients receiving FEP. For CRRT ER of 1-2.4 L/h and 2.5-5 L/h FEP 1g and 2g 8h as 0.5h infusions achieve 70% fT > MIC, respectively, while retaining AUC in range with safe exposures in non-CRRT patients. Disclosures Christina Konig, PhD, Gilead Inc.: Honoraria|Pfizer Inc.: Honoraria|Shionogi Inc.: Honoraria David P. Nicolau, PharmD, CARB-X: Grant/Research Support|Innoviva: Grant/Research Support|Innoviva: Honoraria|Merck: Advisor/Consultant|Merck: Grant/Research Support|Merck: Honoraria|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Pfizer: Honoraria|Shionogi: Advisor/Consultant|Shionogi: Grant/Research Support|Shionogi: Honoraria|Venatorx: Grant/Research Support Joseph L. Kuti, PharmD, Abbvie: Advisor/Consultant|bioMerieux: Grant/Research Support|Merck: Grant/Research Support|Pfizer: Grant/Research Support|Shionogi Inc: Advisor/Consultant|Shionogi Inc: Grant/Research Support|Shionogi Inc: Honoraria|Venatorx: Grant/Research Support

A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model

In the field of Structural Health Monitoring (SHM), complete datasets are fundamental for modal identification analysis and risk prediction. However, data loss due to sensor failures, transmission interruptions, or hardware issues is a common problem. To address this challenge, this study develops a method combining Variational Mode Decomposition (VMD) and Sparrow Search Algorithm (SSA)-optimized Gate Recurrent Unit (GRU) for recovering structural response data. The methodology initially employs Variational Mode Decomposition (VMD) to preprocess and decompose the existing data from the target sensor into Intrinsic Mode Functions (IMFs) and residuals. Subsequently, the Gated Recurrent Unit (GRU) network utilizes data from other sensors to reconstruct the IMFs and residuals, ultimately producing the data reconstruction results. During this process, Singular Spectrum Analysis (SSA) is used to optimize the hyperparameters of the GRU network. To validate the effectiveness of this method, we utilized one month of monitoring data collected from a certain project and a publicly available dataset. On the public dataset, we tested performance at different data loss rates. Results show that, compared to a standalone GRU model and a VMD + GRU model, the VMD + SSA + GRU model’s reconstruction data root mean squared error is reduced by 46.61% and 32.57% on average, respectively, while the coefficient of determination increases by 38.74% and 18.50%. The data reconstruction method proposed in this study can accurately capture trends in missing data, without the need for manual hyperparameter tuning, and the reconstruction results are highly consistent with the real data.

Pulse engineering via projection of response functions

We present an iterative optimal control method of quantum systems, aimed at an implementation of a desired operation with optimal fidelity. The update step of the method is based on the linear response of the fidelity to the control operators, and its projection onto the mode functions of the corresponding operator. Our method extends methods such as gradient-ascent pulse engineering (GRAPE) and variational quantum algorithms, by determining the fidelity gradient in a hyperparameter-free manner, and using it for a multiparameter update, capitalizing on the multimode overlap of the perturbation and the mode functions. This directly reduces the number of dynamical trajectories that need to be evaluated in order to update a set of parameters. We demonstrate this approach, and compare it to the standard GRAPE algorithm, for the example of a quantum gate on two qubits, demonstrating a clear improvement in convergence and optimal fidelity of the generated protocol. Published by the American Physical Society 2025

Prediction for Coastal Wind Speed Based on Improved Variational Mode Decomposition and Recurrent Neural Network

Accurate and comprehensive wind speed forecasting is crucial for improving efficiency in offshore wind power operation systems in coastal regions. However, raw wind speed data often suffer from noise and missing values, which can undermine the prediction performance. This study proposes a systematic framework, termed VMD-RUN-Seq2Seq-Attention, for noise reduction, outlier detection, and wind speed prediction by integrating Variational Mode Decomposition (VMD), the Runge–Kutta optimization algorithm (RUN), and a Sequence-to-Sequence model with an Attention mechanism (Seq2Seq-Attention). Using wind speed data from the Shidao, Xiaomaidao, and Lianyungang stations as case studies, a fitness function based on the Pearson correlation coefficient was developed to optimize the VMD mode count and penalty factor. A comparative analysis of different Intrinsic Mode Function (IMF) selection ratios revealed that selecting a 50% IMF ratio effectively retains the intrinsic information of the raw data while minimizing noise. For outlier detection, statistical methods were employed, followed by a comparative evaluation of three models—LSTM, LSTM-KAN, and Seq2Seq-Attention—for multi-step wind speed forecasting over horizons ranging from 1 to 12 h. The results consistently showed that the Seq2Seq-Attention model achieved superior predictive accuracy across all forecast horizons, with the correlation coefficient of its prediction results greater than 0.9 in all cases. The proposed VMD-RUN-Seq2Seq-Attention framework outperformed other methods in the denoising, data cleansing, and reconstruction of the original wind speed dataset, with a maximum improvement of 21% in accuracy, producing highly accurate and reliable results. This approach offers a robust methodology for improving data quality and enhancing wind speed forecasting accuracy in coastal environments.

Multi-Scale Analysis of Knee Joint Acoustic Signals for Cartilage Degeneration Assessment

This study focuses on the diagnostic analysis of cartilage damage in the knee joint based on acoustic signals generated by the joint. The research utilizes a combination of advanced signal processing techniques, specifically empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA), alongside convolutional neural networks (CNNs) for classification and detection tasks. Acoustic signals, often reflecting the mechanical behavior of the joint during movement, serve as a non-invasive diagnostic tool for assessing the cartilage condition. EEMD is applied to decompose the signals into intrinsic mode functions (IMFs), which are then analyzed using DFA to quantify the scaling properties and detect irregularities indicative of cartilage damage. The separation of individual frequency components allows for multi-scale analysis of the signals, with each of the functions resulting from the analysis reflecting local variations in the amplitude and frequency over time and allowing for effective removal of noise present in the signal. The CNN model is trained on features extracted from these signals to accurately classify different stages of cartilage degeneration. The proposed method demonstrates the potential for early detection of knee joint pathology, providing a valuable tool for preventive healthcare and reducing the need for invasive diagnostic procedures. The results suggest that the combination of EEMD-DFA for feature extraction and CNN for classification offers a promising approach for the non-invasive assessment of cartilage damage.

A novel hybrid framework for heterogeneous measurement data prediction of cable-stayed bridges using ACMD-PE-BiLSTM

Abstract Bridge heterogeneous measurement data prediction is a crucial aspect of structural analysis of bridge engineering, which serves as an important reference for the early warning and decision-making of the bridges. However, the nonlinearity and nonstationarity of the measurement data are common occurrences in daily monitoring operations, which affects the accuracy of the prediction of bridge health. In this context, this paper proposes a hybrid data-driven deep learning approach for predicting bridge multi-source heterogeneous data to tackle the challenges posed by the complexity and nonstationarity of cable-stayed bridge monitoring data and enhance the accuracy and efficiency of predicting measurement data. This approach leverages adaptive chirp mode decomposition (ACMD), permutation entropy (PE), and Bi-directional long short-term memory (BiLSTM). Firstly, the ACMD algorithm decomposes the bridge monitoring data into a discrete number of intrinsic mode functions (IMFs) to produce clearer signals. Then, the PE algorithm is applied to each IMF to optimize the number of IMFs and construct the new components. Finally, a BiLSTM network is present for each component to establish the prediction model, and the final prediction results are obtained by synthesizing the predictions. The effectiveness and feasibility of the proposed method are extensively evaluated using measured wind speed, displacement, and acceleration data from a cable-stayed bridge. Evaluation indicators are used to evaluate the performance, and a comparative analysis with other benchmark models is further conducted to systematically validate the reliability of the proposed approach. The proposed prediction method offers several advantages, with its stability and accuracy particularly noteworthy. The results suggest that the proposed method is superior to the compared models regarding cable-stayed bridge heterogeneous measurement data and can provide reliable results for real-world bridge engineering.

Combined Prediction of PM10 Concentration at Smart Construction Sites Based on Quadratic Mode Decomposition and Deep Learning

The accurate prediction of PM10 concentrations at smart construction sites is crucial for improving urban air quality, protecting public health, and advancing sustainable development in the construction industry. PM10 concentrations at construction sites are influenced by the interaction of construction intensity and environmental meteorological factors, resulting in nonlinear and volatile data. To improve prediction accuracy, this paper presents a two-stage mode decomposition method that integrates Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD). This method is combined with a Bidirectional Long Short-Term Memory (BiLSTM) neural network, optimized using the Sparrow Search Algorithm (SSA), to establish a hybrid model for forecasting PM10 concentrations at construction sites. Initially, CEEMDAN decomposes the original sequence into several Intrinsic Mode Functions (IMFs). The sample entropy of each component is then calculated, and K-means clustering is used to group them. VMD is applied to further decompose the high-frequency components obtained after clustering. SSA is then employed to optimize the parameters of the BiLSTM network, which models all the components with the optimized predictive model. The predicted values of all components are aggregated to generate the final forecast. Real-time monitoring data from Construction Site A in Nanjing are used for case study validation. The empirical results demonstrate that the proposed hybrid prediction model outperforms comparison models on all evaluation metrics, offering a scientific foundation for sustainable and automated dust reduction decision-making at smart construction sites, thereby facilitating the shift toward greener, smarter, and more digitized construction practices.

An Analysis of Meteorological Anomalies in Kamchatka in Connection with the Seismic Process

This study investigates the hypothesis that meteorological anomalies may precede earthquake events. Long-term time series of observations for air temperature, atmospheric pressure and precipitation at a meteorological station in Kamchatka are considered. Time series are subjected to Huang decomposition into sequences of levels of empirical oscillation modes (intrinsic mode functions—IMFs), forming a set of orthogonal components with decreasing average frequency. For each IMF level, the instantaneous amplitudes of envelopes are calculated using the Hilbert transform. A comparison with the earthquake sequence is made using a parametric model of the intensity of two interacting point processes, which allows one to quantitatively estimate the “measure of the lead” of the time instants of the compared sequences. For each IMF level, the number of time moments of the largest local maxima of instantaneous amplitudes which is equal to the number of earthquakes is selected. As a result of the analysis, it turned out that for the sixth IMF level (periods of 8–16 days), the “lead measure” of the instantaneous amplitude maxima of meteorological parameters in comparison with earthquake time moments significantly exceeds the inverse lead, which confirms the existence of prognostic changes in meteorological parameters in the problem of “atmosphere–lithosphere” interaction. This study reveals that certain meteorological anomalies can be a precursor for seismic activity.

Bearing Fault Feature Extraction Method Based on Adaptive Time-Varying Filtering Empirical Mode Decomposition and Singular Value Decomposition Denoising

Aiming to address the difficulty in extracting the early weak fault features of bearings under complex operating conditions, a fault diagnosis method is proposed based on the adaptive fusion of time-varying filtering empirical mode decomposition (TVF-EMD) modal components and singular value decomposition (SVD) noise reduction. First, the snake optimization (SO) technique is used to optimize the TVF-EMD algorithm in order to determine the optimal parameters that match the input signal. Then, the bearing signal is divided into a number of intrinsic mode functions (IMFs) using TVF-EMD in order to reduce the nonlinearity and non-stationary characteristics of the fault signal. An index for the envelope fault information energy ratio (EFIER) is created to overcome the drawback of there being too many IMF components after TVF-EMD decomposition. The IMF components are ranked in descending order according to the EFIER, and they are fused according to the maximum principle of the energy ratio of envelope fault information until the optimal fusion component is determined. Finally, the fault feature is extracted when the optimal fusion component is denoised using SVD. Two measured bearing fault signals and simulation signals are used to validate the performance of the proposed method. The experimental findings demonstrate that the approach has good sensitive feature screening, fusion, and noise reduction capabilities. The proposed method can more precisely extract the early fault features of bearings and accurately identify fault types.

Forecasting China’s Short-Term Energy Futures Price Using a Novel Secondary Decomposition-Optimized System

AbstractEnergy futures price forecasting is challenging due to the nonlinear and fluctuant characteristics. Existing literature mainly uses decomposition and ensemble method which neglects the intrinsic mode function obtained by the first decomposition could be irregular and thus reduces the prediction accuracy. To fill the research gap, a novel secondary decomposition-optimized-KELM-ensemble forecasting system is proposed to perform short-term forecasting in this study, which synthesizes two-stage data decomposition method, Sparrow search optimization algorithm, and extreme learning machine with kernel. We test the method with two energy futures prices in China, demonstrating that both one-day and three-day ahead forecasting results obtained are more accurate and stable compared to existing models in the literature, such as BPNN (improved by 58.42% on one-day ahead and 56.44% on three-day ahead by MAE) and KELM (improved by 56.40% on one-day ahead and 49.04% on three-day ahead by MAE). Therefore, the forecasting system introduced in this paper can provide useful implications for both policy makers and financial practitioners in the energy sector.

A taxonomy for augmented reality use cases in the AECO industry

Purpose A significant challenge arises from the inconsistent terminology used to describe augmented reality (AR) technology, leading to confusion and hindered communication. The purpose of this paper is to address the absence of a comprehensive taxonomy to define AR use cases in the architecture, engineering, construction and operation (AECO) domain and present a structured approach to developing one. Design/methodology/approach A literature review was conducted to identify AR use cases and use case taxonomies in the AECO and other industry domains in the Compendex database. This review resulted in the identification of 315 AR use cases. From the identified taxonomies, one was selected based on its comprehensiveness, relevance and applicability to the AECO industry. Leveraging this taxonomy from the manufacturing domain, this study validated, refined and added classes to the taxonomy through a content analysis of the existing AECO AR use cases. Additional critical categories were identified from existing taxonomies to enhance the taxonomy. A subset of 63 use cases was then used to validate the refined taxonomy. Findings The resulting taxonomy comprises two main dimensions: context-related and technology-related. The context-related dimension encompasses six classes, including the field of application, effect level, manual action category, context awareness capability, collaboration mode and interaction functions. The technology-related dimension encompasses the aim of augmentation, proximity to reality, hardware, location, content positioning, time and scale. Originality/value The taxonomy provides a comprehensive framework for categorizing and understanding AR use cases in the AECO industry using the domain language. By providing a structured framework for exploring AR applications, the proposed taxonomy may not only facilitate standardized communication but also foster creativity when designing an AR use case.