Wavelet Decomposition Method Research Articles

Improved monthly runoff time series prediction using the SOA–SVM model based on ICEEMDAN–WD decomposition

Abstract In runoff prediction, the prediction accuracy is often affected by the non-linear and non-stationary characteristics of the runoff series. In this study, a coupled forecasting model is proposed that decomposes the original runoff series by an improved complete ensemble Empirical Mode Decomposition (EMD) (ICEEMDAN) combined with a wavelet decomposition (WD) and then forecasts the monthly runoff using a support vector machine (SVM) optimized by the seagull optimization algorithm (SOA). In this method, a series of Intrinsic Mode Function (IMF) and a Residual (Res) are obtained by decomposing the original runoff series with ICEEMDAN. The WD method is used to perform quadratic decomposition of high-frequency components decomposed by the ICEEMDAN method to make the runoff series as smooth as possible. Then the decomposed components are input into the SOA-SVM model for prediction. Finally, the prediction results of each component are superimposed and reconstructed to obtain the final monthly runoff prediction results. RMSE, Mean Absolute Percentage Error (MAPE), Nash-Sutcliffe Efficiency Coefficient (NSEC), and R are selected to evaluate the prediction results and the model is compared with SOA-SVM model, EMD-SOA-SVM model and CEEMDAN-SOA-SVM model other models. The proposed model is applied to the monthly runoff forecast of the Hongjiadu and Manwan Reservoirs. When compared with other benchmarking models, the ICEEMDAN-WD-SOA-SVM model attains the smallest Root Mean Square Error (RMSE) and MAPE and the largest NSEC and R. The ICEEMDAN-WD-SOA-SVM model has the best prediction effect, the highest prediction accuracy, and the lowest prediction error.

Filtering and Compression of Signals by the Method of Discrete Wavelet Decomposition into One-Dimensional Series

Filtering and Compression of Signals by the Method of Discrete Wavelet Decomposition into One-Dimensional Series

Improving the accuracy of bathymetry using the combined neural network and gravity wavelet decomposition method with altimetry derived gravity data

The wide range of bathymetry models can be estimated using the marine gravity information derived from satellite altimetry. However, due to nonlinear factors influences such as isostasy effects, the bathymetry estimated by gravity anomaly and vertical gravity gradient is not satisfactory. Therefore, to improve the accuracy of bathymetry estimation, a combined neural network and gravity information wavelet decomposition (CNNGWD) method is proposed based on wavelet decomposition and correlation analysis. Next, the bathymetry of the Manila Trench area is estimated using the CNNGWD method and multilayer neural network (MNN) method, respectively. Then, the shipborne sounding data and international bathymetric models such as ETOPO1 and GEBCO_2021 are separately used to evaluate the accuracy of the inversion models. The results show that the root mean square errors (RMSE) of the difference between the bathymetric model one (BM1) estimated by CNNGWD method and the shipborne sounding data is 59.90 m, the accuracy is improved by 12.45%, 64.70% and 28.68% compared with the bathymetric model two (BM2) which estimated by MNN, ETOPO1 and GEBCO, respectively. Finally, by analyzing the bathymetric accuracy shift with depth, the BM1 has lower RMSE at depths ranging from 1000 m to 3000 m. Furthermore, BM1 shows dominance in flat troughs and rugged ridge regions.

Research on Leakage Location of Pipeline Based on Module Maximum Denoising

Leak detection and location of water supply pipelines is an important area of research, and it is especially important to find the leakage location in time and repair them. In view of the problem, that a large amount of noise is mixed in the detection signal when the pipeline leaks, it will inevitably affect the detection and positioning effect. In this paper, a denoising algorithm based on improved module maximum is proposed. Firstly, a discrete binary wavelet transform is carried out on the noisy signal, and the module maximum point corresponding to the wavelet transform coefficients, on each scale, is obtained. Secondly, different thresholds are used for the module maximum of different scale layers and the wavelet coefficients are reconstructed according to the retained module maximum and their extremums. Thirdly, the alternative projection algorithm is used to effectively suppress the false oscillations in the reconstructed signal, improve the quality of the reconstructed signal, and obtain the noise reduction signal. Finally, according to the theory of the negative pressure wave, the inflection point of the negative pressure wave is identified by the wavelet decomposition method, and the location of leakage point is determined. In order to verify the effectiveness of the proposed algorithm, a leakage simulation experiment system of water supply pipeline is built. The analysis of the results shows that, compared with the wavelet denoising method and the EMD-based method, the method proposed in this paper achieves a better denoising effect, obtains a smoother pressure signal, retains the signal waveform characteristics, and identifies the obvious inflexion point of the negative pressure wave. The minimum relative error of leakage point location is 0.9%, and the maximum relative error is 2.5%.

Wind Velocity Field Simulation of the Large-Span Spatial Structure Based on the Wavelet Decomposition Method

In this study, the wavelet decomposition method is combined with the vector AR model of the linear filter method to present a new method for simulating the fluctuating wind field of a large-span spatial structure. This method performs wavelet expansion on the autoregressive coefficients of the vector AR model in adequate space and adopts the least square method to estimate the autoregressive coefficients of the AR model. The implementation steps of this method are to simulate the wind field of large-span structures are given. The method is applied to the wind velocity field simulation of a large-span spatial structure, and the comparison between the simulation result and the target value and the comparison with the simulation result of the vector AR model are given. The results prove that the proposed method can reduce the information loss of wind velocity time-series analysis in the frequency domain, can accurately simulate the wind velocity time series of large-span spatial structures, and has high computational efficiency.

Influence of boundary conditions on acoustic emission propagation characteristics of Zelkova schneideriana

To study the propagation characteristics of acoustic emission signals in Zelkova schneideriana under different boundary conditions, three types of boundary conditions were generated by applying aluminum plates and sound-absorbing cotton on the surface of Zelkova schneideriana specimens. Firstly, the sudden and continuous acoustic emission (AE) sources were simulated by PLB (pencil–lead break) tests and signal generator on the specimen surface, and the AE signals were collected by 5 sensors equally spaced on the surface of the specimen, and the sampling frequency was set to 500 kHz. Then, the detailed signals of different frequency bands were obtained by wavelet decomposition, and TDOA (the time difference of arrival) and correlation analysis method were used to calculate the time difference of longitudinal wave and surface transverse wave and the corresponding propagation velocity, respectively. Finally, the pulse trains with different energy levels generated by the signal generator were used as AE sources to study the attenuation law of AE signal energy with distance under different boundary conditions. The results show that the boundary changes can lead to a significant increase in the surface transverse wave velocity, and have no significant effect on the longitudinal wave velocity. At the same time, the energy attenuation of surface and longitudinal waves is faster after the aluminum plate and sound-absorbing cotton are affixed, and the distance of longitudinal waves attenuation to 90% is reduced from 186 to 139 mm, and the distance of surface transverse waves propagation is reduced from 312 to 226 mm.

Prediction Method for Office Building Energy Consumption Based on an Agent-Based Model Considering Occupant–Equipment Interaction Behavior

Traditional building energy consumption prediction methods lack the description of occupant behaviors. The interactions between occupants and equipment have great influence on building energy consumption, which cause a large deviation between the predicted results and the actual situation. To address this problem, a two-part prediction model, consisting of a basic part related to the building area and a variable part related to stochastic occupant behaviors, is proposed in this study. The wavelet decomposition and reconstruction method is firstly used to split the energy consumption. A relationship between the low frequency energy consumption data and the building area is discovered, and an area-based index is used to fit the basic part of the prediction model. With a quantitative description of the occupant–equipment interaction by classifying the equipment into environmentally relevant and environmentally irrelevant equipment, an agent-based model is established in the variable part. According to the validation given by two case office buildings, the prediction error can be controlled to 2.8% and 10.1%, respectively, for the total and the hourly building energy consumption. Compared to the prediction method which does not consider occupant–equipment interactions, the proposed model can improve prediction accuracy by 55.8%.

A hybrid forecasting model based on the group method of data handling and wavelet decomposition for monthly rivers streamflow data sets

Abstract The natural streamflow of the River is encouraged to forecast through multiple methods. The impartiality of this study is the comparison of the forecast accuracy rates of the time-series (TS) hybrid model with the conventional model. The behavior of the natural monthly statistical chaotic streamflow to use in the forecasting models has been compiled by projecting two distinguished rivers, the Indus and Chenab of Pakistan. Therefore, this article is based on the monthly streamflow forecast analysis that has been reported using the group method of data handling with wavelet decomposition (WGMDH) as a new forecasting attribute. Discrete wavelets decompose the perceived data into sub-series and forecast hydrological variables; these fittingly have been endorsed as inputs in the hybrid model. The forecast efficiency and estimations of the hybrid model are measured by the appropriate statistical techniques such as mean absolute error (RME), root mean square error (RMSE), and correlation coefficients (R) and compared to the group method of data handling (GMDH), least-square support vector machine and artificial neural network conventional models. The comparative analysis shows that the hybrid WGMDH model is more stable and more potent for forecasting river flow than other predictive models and significantly proved that the hybrid model is a robust alternate forecasting tool for TS data sets.

Spillovers and diversification benefits between oil futures and ASEAN stock markets

This study combines copula functions, wavelet decomposition and conditional VaR methods to examine spillovers and diversification benefits between oil futures and ASEAN stock markets (Indonesia, Philippines, Malaysia, Singapore, Vietnam and Thailand). The results show zero tail dependence between oil and stock returns at the short term. In contrast, we find a lower tail independence and an upper tail dependence at the long term. Our results highlight that oil futures serve as hedge assets at short term and a safe haven asset at the long term. Furthermore, we find significant and asymmetric risk spillovers from oil to ASEAN markets. The downside and upside spillovers are higher at the long term than short term and increase during the GFC, the recent oil crisis, and COVID-19 periods. Finally, we show that an equally weighted portfolio provides highest diversification benefits at both lower and medium tail distributions with the exception of Malaysian market. The diversification benefits of oil are sizeable for less coupling markets and fall during times of GFC and oil crisis.

Energy Flow Analysis of Excavator System Based on Typical Working Condition Load

Accurate energy flow results are the premise of excavator energy-saving control research. Only through an accurate energy flow analysis based on operating data can a practical excavator energy-saving control scheme be proposed. In order to obtain the excavator’s accurate energy flow, the excavator components’ performance and operating data requirements are obtained, and the experimental schemes are designed to collect it under typical working conditions. The typical working condition load is reconstructed based on wavelet decomposition, harmonic function, and theoretical weighting methods. This paper analyzes the excavator system’s energy flow under the typical working condition load. In operation conditions, the output energy of the engine only accounts for 50.21% of the engine’s fuel energy, and the actuation and the swing system account for 9.33% and 4%, respectively. In transportation conditions, the output energy of the engine only accounts for 49.80% of the engine’s fuel energy, and the torque converter efficiency loss and excavator driving energy account for 15.09% and 17.98%, respectively. The research results show that the energy flow analysis method based on typical working condition load can accurately obtain each excavator component’s energy margin, which provides a basis for designing energy-saving schemes and control strategies.

Short‐term natural gas consumption prediction based on wavelet transform and bidirectional long short‐term memory optimized by Bayesian network

AbstractIn the energy structure, natural gas is an important clean energy, and its consumption forecast is of great significance for energy policy formulation, pipeline network planning design, and peak shaving capacity determination. In the field of forecasting, wavelet transform (WT) is widely used to process time series, especially natural gas consumption. However, in the application of the WT, the wavelet's orders and levels are randomly fixed. To solve this problem, this study proposes a hybrid model, which is based on bidirectional long short‐term memory (BiLSTM). In addition, Bayesian networks are used to optimize the hyperparameters of BiLSTM. And the different wavelets' orders and levels are utilized to deal with the daily natural gas consumption (DNLGSCN). When each level of Coiflets wavelets' four and five orders are used to process the DNLGSCN, the fifth level of Coiflets wavelets' four and five orders have the highest prediction accuracy, respectively, which is 0.3318 and 0.1773, taking U1 as an example. Using Symflets wavelet to decompose the DNLGSCN also gives the same result. Compared with the decomposition method (e.g., ensemble empirical mode decomposition), the prediction performance of the above four wavelet decomposition methods is better. The main conclusions are that decomposing the DNLGSCN with the fifth level of Coiflets wavelet's five orders has the best prediction performance.

Research on the Sound Quality Evaluation Method Based on Artificial Neural Network

For the improvement of the traditional evaluation effect of the automobile sound quality, an evaluation model of automobile sound quality is constructed based on BP neural network. The first is to introduce the basic principle of the BP neural network in detail. The second is to use the MGC parameters to establish the vehicle interior sound conversion model. The converted sound characteristic parameters are taken into the WORLD model to synthesize the new sound signals. Furthermore, the wavelet decomposition method is used to remove noise from the synthesized sound signals. Finally, a sound evaluation model based on BP neural network is established. The sound quality of automobiles can be better evaluated by carrying out the ABX test and MOS test in the field of sound conversion. For the newly synthesized sound signal and the target sound signal, it can be seen that the newly synthesized sound signal is more inclined to the target sound signal, and the sound quality is better. In addition, the sound quality is tested through loudness, roughness, sharpness, and level A in the field of sound quality evaluation. The final results show that the quality of newly synthesized sound is better, and the average errors of sound signals meet the sound standard. Therefore, the constructed sound conversion model and the sound evaluation model are feasible and effective.

Analysis of rock microseismic signal based on blind source wavelet decomposition algorithm

At present, microseismic technology is a widely used method for monitoring the rock burst phenomenon during the construction of deep-buried tunnels. The rock fracture in the tunnel will generate seismic waves. The seismic wave has strong randomness and low energy and is usually mixed with environmental noise, which is called the microseismic signal. The microseismic signal received by the geophone will contain other noises. So, there is an urgent need for an algorithm that can quickly decompose the mixed signal. To solve this problem, this paper proposes a blind source wavelet algorithm to extract the rock fracture signal. First, the signal is preprocessed, and the mixed signal matrix is established. Second, the blind source decomposition algorithm is used to process the signal, and the effective signal is reconstructed. Finally, the wavelet algorithm is used to further remove the noise to enhance the microseismic signal. The proposed method is compared with the wavelet decomposition method and the empirical mode decomposition (EMD) method through the laboratory simulation data and the actual signal of Baihetan Hydropower Station. It is concluded that the proposed algorithm can effectively decompose and remove the noise in the mixed signal, and the decomposition accuracy is better than the wavelet decomposition method and the EMD method. The proposed algorithm has certain practical significance for the identification of microseismic signals of rock fracture and for rock burst early warning.

Daily streamflow forecasting using hybrid long short-term memory model

Characterized by heterogeneity, complexity and non-stationary, streamflow forecasting has always been a challenge in hydrological sciences. In this study, a multiscale wavelet decomposition method with long short-term memory model (WLSTM) is developed to handle the daily streamflow forecasting. Discrete wavelet transform (DWT) is employed to extract multiscale features, which are then simulated by long short-term memory models (LSTMs), respectively. The outputs of the different scales LSTMs are finally reconstructed toward the forecasting results. Seven years daily streamflow sequences of three tributaries and one mainstream in the upper reaches of the Yangtze River are investigated by the WLSTM models. For comparison, standard LSTM, and traditional ANN are chosen for the same streamflow forecasting task. Experimental results show that the proposed hybrid model is better than other comparison models in terms of evaluation indicators. Considering that large floods often occur in the Yangtze River Basin, the performance of flood forecasting is also analyzed and the result shows that forecasting errors of WLSTM are more concentrated, which means WLSTM outperforms traditional ANN and LSTM for the extreme flood forecasting.

EPSPatNet86: eight-pointed star pattern learning network for detection ADHD disorder using EEG signals

Objective. The main objective of this work is to present a hand-modelled one-dimensional signal classification system to detect Attention-Deficit Hyperactivity Disorder (ADHD) disorder using electroencephalography (EEG) signals. Approach. A novel handcrafted feature extraction method is presented in this research. Our proposed method uses a directed graph and an eight-pointed star pattern (EPSPat). Also, tunable q wavelet transforms (TQWT), wavelet packet decomposition (WPD), statistical extractor, iterative Chi2 (IChi2) selector, and the k-nearest neighbors (kNN) classifier have been utilized to develop the EPSPat based learning model. This network uses two wavelet decomposition methods (TQWT and WPD), and 85 wavelet coefficient bands are extracted. The proposed EPSPat and statistical feature creator generate features from the 85 wavelet coefficient bands and the original EEG signal. The learning network is termed EPSPatNet86. The main purpose of the presented EPSPatNet86 is to detect abnormalities of the EEG signals. Therefore, 85 wavelet subbands have been generated to extract features. The created 86 feature vectors have been evaluated using the Chi2 selector and the kNN classifier in the loss value calculation phase. The final features vector is created by employing a minimum loss-valued eight feature vectors. The IChi2 selector selects the best feature vector, which is fed to the kNN classifier. An EEG signal dataset has been used to demonstrate the presented model’s EEG signal classification ability. We have used an ADHD EEG dataset since ADHD is a commonly seen brain-related ailment. Main results. Our developed EPSPatNet86 model can detect the ADHD EEG signals with 97.19% and 87.60% accuracy using 10-fold cross and subject-wise validations, respectively. Significance. The calculated results demonstrate that the presented EPSPatNet86 attained satisfactory EEG classification ability. Results show that we can apply our developed EPSPatNet86 model to other EEG signal datasets to detect abnormalities.

A flexible and accurate method for electroencephalography rhythms extraction based on circulant singular spectrum analysis.

Rhythms extraction from electroencephalography (EEG) signals can be used to monitor the physiological and pathological states of the brain and has attracted much attention in recent studies. A flexible and accurate method for EEG rhythms extraction was proposed by incorporating a novel circulant singular spectrum analysis (CiSSA). The EEG signals are decomposed into the sum of a set of orthogonal reconstructed components (RCs) at known frequencies. The frequency bandwidth of each RC is limited to a particular brain rhythm band, with no frequency mixing between different RCs. The RCs are then grouped flexibly to extract the desired EEG rhythms based on the known frequencies. The extracted brain rhythms are accurate and no mixed components of other rhythms or artifacts are included. Simulated EEG data based on the Markov Process Amplitude EEG model and experimental EEG data in the eyes-open and eyes-closed states were used to verify the CiSSA-based method. The results showed that the CiSSA-based method is flexible in alpha rhythms extraction and has a higher accuracy in distinguishing between the eyes-open and eyes-closed states, compared with the basic SSA method, the wavelet decomposition method, and the finite impulse response filtering method.

A Mediterranean Sea Offshore Wind classification using MERRA-2 and machine learning models

This paper uses a Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) re-analysis to identify long-term Mediterranean Sea Offshore Wind (OW) classification possible locations. In particular, an OW classification based on the last 40-years period OW speeds highlighted the best areas for potential Offshore Wind Turbine Generators (OWTG) installations in the Mediterranean basin. Preliminary, long-term OW classification results show that several Mediterranean basin zones in the Aegean Sea, Gulf of Lyon, the Northern Morocco and Tunisia regions have attractive OW potential. Secondly, a combined forecasting model based on the wavelet decomposition method and long-term memory neural network has been developed to predict the short-term wind speed considering the last ten years of hourly data for Mediterranean areas. The results of the proposed model for wind speed prediction have been compared with other single models, Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), highlighting a higher level of accuracy. Finally, three Weibull fitting algorithms have been provided to analyze the wind energy potential in the Mediterranean basin.

A deep convolutional neural network for estimating hemodynamic response function with reduction of motion artifacts in fNIRS

Objective. Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique for monitoring hemoglobin concentration changes in a non-invasive manner. However, subject movements are often significant sources of artifacts. While several methods have been developed for suppressing this confounding noise, the conventional techniques have limitations on optimal selections of model parameters across participants or brain regions. To address this shortcoming, we aim to propose a method based on a deep convolutional neural network (CNN). Approach. The U-net is employed as a CNN architecture. Specifically, large-scale training and testing data are generated by combining variants of hemodynamic response function (HRF) with experimental measurements of motion noises. The neural network is then trained to reconstruct hemodynamic response coupled to neuronal activity with a reduction of motion artifacts. Main results. Using extensive analysis, we show that the proposed method estimates the task-related HRF more accurately than the existing methods of wavelet decomposition and autoregressive models. Specifically, the mean squared error and variance of HRF estimates, based on the CNN, are the smallest among all methods considered in this study. These results are more prominent when the semi-simulated data contain variants of shapes and amplitudes of HRF. Significance. The proposed CNN method allows for accurately estimating amplitude and shape of HRF with significant reduction of motion artifacts. This method may have a great potential for monitoring HRF changes in real-life settings that involve excessive motion artifacts.

Using wavelet decomposition method to retrieve the solar and the global air temperature signals from Greenland, Andes and East Antarctica δ18O ice core records.

The surface global air temperature (SGAT) and the solar activity presented near similar trends until approximately the 1980's decade, when they start to diverge significantly. This divergence in both time series is attributed to the impact of addition players acting in the climate system as the greenhouse gas emissions and a more active ENSO (El Niño Southern Oscillation). For the period before this "turning point" we have made an exploratory investigation on the imprint of both SGAT and the solar activity (represented by the Sunspot Number - SSN) at δ18O isotopic ratios retrieved from ice cores, a proxy commonly used to describe past climate changes. In this work δ18O isotopic ratios, dated from 1861 to 1997, from three distinct global sites: (1) the Greenland Ice Sheet Project 2 (GISP2); (2) Quelccaya ice cap/Andes (Peru); and (1) Dronning Maud Land (DML)/East Antarctica were investigated. The wavelet decomposition method and regressions applied to these databases successfully allowed the isotopic reconstructions from both SGAT and SSN. We found that the reconstructions differ significantly depending on the geographical site.

Partial Discharge Detection in Pressboards Immersed in Mineral Insulation Oil With Quantum Well Hall Effect Magnetic Field Sensors

Insulation degradation may cause inefficient and faulty operation of transformers. The insulation failures in transformers mostly start with a Partial Discharge (PD) event. For both operational and cost reasons to ensure the best performance and functionality of transformers, early detection of PD events is of great importance. In this paper presents a novel PD detection technique by using a highly sensitive Quantum Well Hall Effect (QWHE) magnetic field sensor and compare the findings with an off-the-shelf silicon magnetic field transducer. The investigation of the QWHE for high voltage engineering problem such as PD detection is given first time in this paper. The aim of the study is to detect PD activity in pressboards immersed in mineral insulation oil experimentally using a new QWHE sensor. The measured experimental data from both sensors are decomposed by Empirical Mode Decomposition (EMD) and Wavelet Decomposition (WD) methods, and PD signals are analyzed comparatively. The results show that QWHE sensors provide more accurate and noise free measurements allowing early and more accurate PD detections.