Wavelet Transform Technique Research Articles

Enhancing Europium Adsorption Effect of Fe on Several Geological Materials by Applying XANES, EXAFS, and Wavelet Transform Techniques.

This study conducted adsorption experiments using Europium (Eu(III)) on geological materials collected from Taiwan. Batch tests on argillite, basalt, granite, and biotite showed that argillite and basalt exhibited strong adsorption reactions with Eu. X-ray diffraction (XRD) analysis also clearly indicated differences before and after adsorption. By combining X-ray absorption near-edge structure (XANES), extended X-ray absorption fine structure (EXAFS), and wavelet transform (WT) analyses, we observed that the Fe2O3 content significantly affects the Eu-Fe distance in the inner-sphere layer during the Eu adsorption process. The wavelet transform analysis for two-dimensional information helps differentiate two distances of Eu-O, which are difficult to analyze, with hydrated outer-sphere Eu-O distances ranging from 2.42 to 2.52 Å and inner-sphere Eu-O distances from 2.27 to 2.32 Å. The EXAFS results for Fe2O3 and SiO2 in argillite and basalt reveal different adsorption mechanisms. Fe2O3 exhibits inner-sphere surface complexation in the order of basalt, argillite, and granite, while SiO2 forms outer-sphere ion exchange with basalt and argillite. Wavelet transform analysis also highlights the differences among these materials.

Wavelet Transform and Thresholding Techniques in Medical Ultrasound Image Speckle Reduction

Wavelet Transform and Thresholding Techniques in Medical Ultrasound Image Speckle Reduction

Biomedical ECG Signal Compression by Combining Wavelet Transform and Unsupervised Autoencoder Techniques

Abstract: Storing biomedical signal data is a significant concern in the field of biomedical signal processing because it requires a substantial amount of storage space. These biomedical signals often contain hours of information, necessitating extensive storage capacity. Researchers employ Electrocardiogram (ECG) signal compression techniques to address this issue. However, compressing the ECG signal could potentially lead to the loss of important features or data. This loss may adversely impact the accurate analysis of heart patient conditions. This paper introduces a novel approach for compressing ECG signals by the combined power of Wavelet Transform (WT) and unsupervised Autoencoder (AE) techniques. ECG signal compression plays a crucial role in reducing data storage and transmission requirements without compromising diagnostic accuracy. The proposed methodology, termed WT-AutoEncoder, integrates Wavelet Transform for signal decomposition and feature extraction, followed by an Autoencoder for efficient encoding and reconstruction of the original signal. Unlike conventional methods, this approach utilizes the strengths of both WT and AE to achieve high compression ratios while preserving signal accuracy. The study conducts comprehensive experiments and evaluations using benchmark MIT-BIH datasets to assess the performance of the WT-AutoEncoder on specific records (101-109) in terms of compression ratio of 32.31, PRDN of 33.26, PRD of 3.06, and QS of 13.49. The final results demonstrate the effectiveness of the proposed model compared with existing methods in compressing ECG signals while maintaining high accuracy, suggesting its potential for secure transmission and storage in various healthcare applications.

Semi-supervised learning for real-time anomaly detection in pulsed transfer wire arc additive manufacturing

In Wire-Arc Additive Manufacturing (WAAM), ensuring the quality and integrity of components is of key importance and performing anomaly detection during production is an efficient strategy for preventing defects and maintaining an acceptable quality with lower costs compared to the development of complex supervised learning techniques. This paper presents an approach based on semi-supervised learning for real-time anomaly detection in the production of Inconel 718 components via the Pulsed Transfer WAAM process. The workflow is based on the use of training data obtained by employing established process parameters, similar to what is done in Welding Procedure Qualification Records (WPQRs), to develop a semi-supervised anomaly detection application. The proposed approach involves depositing material using the already established process parameters and collecting the corresponding welding data in terms of welding current and voltage sensor signals. The collected data are then employed to train an unsupervised algorithm by using solely good deposition data to learn hidden complex patterns of normality and hence detect anomalies based on deviations from these patterns. With this aim, global and local features are extracted from the welding current and voltage sensor signals in both time and time-frequency domains via Wavelet Transform technique and a deep learning approach based on a residual convolutional autoencoder. To showcase the effectiveness of the proposed approach, a case study involving wire arc additive manufacturing of Inconel 718 components was conducted. The proposed algorithm was tested and achieved an F-score of 0.895, representing a 30 % improvement over state-of-the-art methods that rely on time domain features for anomaly detection. This research work contributes to the improvement of anomaly detection methodologies, offering substantial advantages over alternative techniques for quality control and reliability of additive manufacturing processes such as WAAM.

Dynamic inspection data-based analysis of rail base metal irregularities for engineering failure prevention

With the continual increase in train speeds, the requirement for track smoothness has become increasingly critical. Within the railway system of China, the prevalence of short-wave defects, attributed to the rail base metal irregularity, adversely affects the stability of train movement and constitutes a considerable safety risk. However, research into the rail base metal irregularity remains markedly scant, with a conspicuous absence of effective evaluation and identification methodologies. This deficiency critically restricts the capability to mitigate short-wave disturbances arising from such irregularities, negatively impacting the safety and operational stability of railway systems. Therefore, this paper proposes a method for evaluating and identifying the rail base metal irregularity based on dynamic inspection data. Initially, this paper conducts a comprehensive assessment of rail base metal irregularities by analysing wavelength characteristics, frequency amplitude features, and dynamic responses of vehicles. Subsequently, this paper utilises the Synchrosqueezed Wavelet Transform (SWT) technique, which refines the frequency resolution by compressing frequency components within the frequency domain, eliminating cross-frequency interference, and minimising energy dispersion across scale domains, thus accurately delineating the time–frequency attributes of rail base metal irregularity. Building upon the foundation provided by SWT, this paper introduces an identification method employing the Summed Wavelet Power (SWP) across various frequency bands. This approach evaluates the energy variations of different wavelength components relative to changes in mileage, facilitating the identification of track sections with rail base metal irregularity. The efficacy of the proposed methodology is corroborated through the analysis of actual measurement data. The results show that the SWT and SWP techniques can effectively identify rail base metal irregularity, thereby offering substantial support for enhancing the safety and comfort of railway operations.

Enhanced daily streamflow forecasting in Northeastern Algeria: integrating hybrid machine learning with advanced wavelet transformation techniques

Enhanced daily streamflow forecasting in Northeastern Algeria: integrating hybrid machine learning with advanced wavelet transformation techniques

Asymmetric effects of commodity and stock market on Chinese green market: Evidence from wavelet-based quantile-on-quantile approach

This study principally concentrates on evaluating the effects of Chinese commodity and stock market on the green market. The Maximum Overlap Discrete Wavelet Transform (MODWT) technique is utilized for decomposing time series into diverse frequency components. Following this, quantile-on-quantile regression (QQ) is implemented to meticulously demonstrates the dependency structure among the variables under diverse market conditions. Results demonstrate that energy has no short-term impact, while non-ferrous metals, palm oil, sugar, and stock impact the green market at high and low quantiles. Booming gold and declining stock negatively affect the green market. In the medium term, the influence of stocks and most commodities diminishes. Bullish coal and lead prices have a positive impact, while bearish zinc, corn, and palm oil prices have a negative impact. Crude oil does not significantly affect the green market. In the long term, both stock and commodities have a positive impact on the green market, particularly at extreme quantiles. When both markets decline, they negatively affect the green market. Finally, Granger causality test reveals a bi-directional linear and non-linear causality between commodity and stock markets and the green market across all time scales, enabling investors to predict green market returns based on commodity and stock returns.

Emotion Detection Through Electrocardiogram Signal Classification in an IOT Environment with Deep Neural Networks

An ECG detects the health and rhythm of the heart by measuring the electric activity of the heart. It has also been demonstrated that a person's emotions may influence the electrical activity of the heart. As a result, studying the electrical behaviour of the heart may simply determine a person's cardiac state and emotional wellness. IoT is a new technology that is quickly gaining acceptance throughout the world. Anybody, at any time, from anywhere, may connect to any network or service because to the extraordinary power and capacity of IoT. IoT-enabled devices have revolutionized the medical business by providing new capabilities such as remote patient monitoring and self-monitoring. This research proposed an IoT-based ECG monitoring system that employs a heart rate sensor to generate data and an intelligent hybrid classification algorithm to categorize the data. ECG monitoring has become a widely used method for detecting cardiac problems. The following are the primary contributions of this paper: To begin, this paper describes WISE (Wearable IoT-cloud-based health monitoring system), a one-of-a-kind system for real-time personal health monitoring. WISE makes use of the BASN (body area sensor network) technology to provide real-time health monitoring. WISE rapidly transfers data from the BASN to the cloud, and a lightweight wearable LCD may be included to enable quick access to real-time data. This model can address the issue of class imbalance in the ECG dataset, assisting in the development of an IoT-based smart and accurate healthcare system. Pre-processing, feature extraction, and classification are the three steps in any classification technique, whether it is emotion classification or heart health classification. Sensors are used to collect an ECG signal from a person's outside body. The provided ECG signal is first pre-processed using the Butterworth Filtering Method, which effectively reduces noise from the signal. Following pre-processing, the Adaptive Discrete Wavelet Transform technique is used to anticipate the signal's attributes. Lastly, a decision making classification approach based on relational weights is used to determine if the ECG signal is normal or abnormal.

Comparative Performance Analysis of Filtering Methods for Removing Baseline Wander Noise from an ECG Signal

ECG signals play a vital role in the diagnosis of cardiovascular conditions. However, they often suffer from the effects of various noise sources, including baseline wandering, respiratory artifact noise, power line interference and electrode motion artifacts. To overcome these challenges, it is imperative to implement low-frequency signal noise reduction strategies. Such strategies aim to significantly improve the quality of ECG signals, thus promoting more accurate and reliable diagnosis of cardiovascular disorders. This paper conducts a comparative analysis to assess the effectiveness of commonly used filtering and wavelet techniques in reducing Baseline Wander (BW) noise within ECG signals generated by the influence of breathing or electrode movements. It is common to observe the selection and evaluation of only one particular technique in the existing literature. In contrast, this study aims to provide a comprehensive comparative analysis, providing insight into the performance and relative merits of different techniques. Our research uses both filtering and Discrete Wavelet Transform (DWT) techniques in baseline noise removal. In this context, a reference point is established utilizing noise-free signals and a meticulous investigation of the wavelet-based approach that most effectively eliminates the resulting noise is provided. Subsequently, we assess the reference input and output signal via Signal-to-Noise Ratio (SNR) and Kolmogorov–Smirnov statistical test measurements. The most important contribution of this work to the scientific community resides in the comprehensive examination of IIR/FIR-based and wavelet method-based filtering methods capable of yielding the highest SNR levels across various ECG signals with various types of BW noise. Additionally, the effectiveness of the Chebychev-II filter in BW noise removal is highlighted. Our study was conducted using the MATLAB platform and code command lines were shared to facilitate the reproduction of our study by other researchers. It is considered that this study will be an important reference in the selection of effective techniques for removing BW noise within ECG signals.

An adaptive Marine Predator Optimization Algorithm (MPOA) integrated Gated Recurrent Neural Network (GRNN) classifier model for arrhythmia detection

Cardiovascular disorders are typically diagnosed using an Electrocardiogram (ECG). It is a painless method that mimics the cyclical contraction and relaxation of the heart's muscles. By monitoring the electrical activity of the heart, an ECG can be used to identify irregular heartbeats, strokes, cardiac illnesses, or enlarged hearts. Numerous studies and analyses of ECG signals to identify heart problems have been conducted during the past few years. The disadvantages of the current and hybrid approaches include low accuracy, prolonged time, and a dearth of high-quality datasets. The proposed work aims to design a new detection tool for the diagnosis of Arrhythmia from ECG signals. In this study, the wavelet transformation technique is used for preprocessing after collecting the input ECG signal in order to create the quality-improved and normalized signals. The most pertinent characteristics are then selected from the preprocessed output using the cutting-edge Marine Predator Optimization Algorithm (MPOA), which accelerates classifier learning. In addition, the hyper tuned Gated Recurrent Neural Network (Hyp-GRNN) method is used to learn the most effective features and properly classify the normal and arrhythmia-affected signals. The Ebola Optimization Algorithm (EOSA) is used to execute the hyper parameter tweaking in order to enhance the accuracy and training time of the classifier. At the time of evaluation, performance is assessed and compared using a variety of metrics, including accuracy, precision, recall, F-measure and etc.

The Sequence Stratigraphy and Sedimentary Evolution of the Guantao Formation in the Binxian Uplift Area, Bohai Bay Basin, China

Due to the influence of multiple tectonic movements in rift basins, the sequence and sedimentary filling modes of continental petroleum reservoirs are complex, which makes it difficult to establish isochronous stratigraphic frameworks and thus affects the accuracy of subsequent predictions of effective sand bodies. Taking the Guantao Formation of the Binxian uplift and the surrounding areas as an example, this study established the sequence stratigraphic framework of the Guantao Formation and discussed the controlling effect of sequence stratigraphy on sedimentary filling. According to a combination method of seismic data, well log data, the wavelet transform technique (WTT), and Integrated Prediction Error Filter Analysis (INPEFA) methods, the Guantao Formation in the study area can be divided into 1 long-term cycle (LNG), 4 mid-term cycles (MNG1–MNG4, from bottom to top), and 11 short-term cycles (SNG1–SNG11, from bottom to top). Based on comprehensive analysis of geological and seismic data, three sedimentary facies can be classified: alluvial fan facies, braided fluvial facies, and meandering fluvial facies. The sequence stratigraphic style of the study area has a significant controlling effect on sedimentation and sand body distribution. Different levels of cycles have different impacts on sedimentary facies/microfacies types, the development degree of each sedimentary microfacies, and sand body distribution. The long-term cycle controls the distribution of sedimentary facies, while the mid-term and short-term cycles control the distribution of sedimentary microfacies. The bottom interface of the Guantao Formation (T1) served as the dominant migration channel in the study area, connecting the reservoir and source rocks. When the base-level was in the low stage (MNG1), a large amount of sand bodies developed, forming favorable reservoirs for petroleum. The interlayers at the top of the long- and mid-term cycles served as seal layers to prevent oil and gas from escaping. The MNG1 cycle has a good combination of reservoir and seal, resulting in the accumulation of oil and gas in the MNG1 strata, which became the main oil- and gas-producing layer in the area. These study results can provide effective guidance for future prediction of the distribution of sand bodies and high-quality reservoirs.

Vibration analysis using wavelet transformation technique and performance characteristics of a diesel engine fueled with tamarind biodiesel-diesel blends and diverse additives

This study investigates the combustion, performance characteristics, and emission levels of diesel and tamarind biodiesel mixtures (B20, B30, B40, and B100) in a single-cylinder, four-stroke, direct injection (DI) diesel engine. Further, two fuel additives, namely Butylated Hydroxytoluene (BHT) (an antioxidant) and Diethyl ether (DEE) (a cetane improver), were separately added to B20 to enhance the engine characteristics. In addition, Wavelet Transform and Wavelet Packet algorithms were used to investigate the vibration behaviour of the tested fuels. The results demonstrate that the B20 blend improved the performance and generated less emission than the other blends. Moreover, the brake thermal efficiency (BTE) and brake-specific fuel consumption (BSFC) for B20+BHT were 3.8% higher and 4.1% less than B20, respectively. B20+DEE combustion showed lower CO and smoke pollutants by 51% and 19% compared to B20, respectively. The NOx emissions for the blends containing BHT and DEE were decreased by 16% and 9%, respectively. The engine vibration analysis showed almost the same frequency with fuel blends B20 and B30, which were similar to the diesel fuel values. The results concluded the tamarind biodiesel/diesel mixtures with BHT and DEE improved the performance and emissions behaviors of DI diesel engine and could be considered a potential alternative biofuel for such engines. Wavelet analysis of the engine was accurately employed to extract the prominent features of the vibrations.

Harmonizing Cross View Image Transformation Through Local and Global Insights- A Review

Hyperspectral and multispectral information processing systems and technologies have demonstrated their value in enhancing agricultural productivity and practices by providing farmers and crop managers with valuable information on factors influencing crop condition and growth. These technologies play a crucial role in various agricultural applications, such as crop management, crop yield forecasting, crop disease detection, and monitoring soil, water, and land usage. To enhance the process of agriculture through effective crop management and assistance to farmer, using an advanced image transformation techniques the study delves into the exploration of techniques for harmonizing cross-view image transformation, with a focus on integrating both local and global insights. Further the study proposes the application of the 2D-DWT (Two-Dimensional Discrete Wavelet Transform) technique for image data preprocessing and the Extreme Learning Machine (ELM) algorithm for image classification in the context of hyperspectral images. Hyperspectral information sensing allows for the coverage of the electromagnetic spectrum in a single acquisition with several hundred spectral bands, resulting in a data cube containing significant spectral and spatial information ELM is particularly effective for classification problems because of its quick training time and capacity to operate with hidden nodes whose parameters are randomly assigned rather than modified iteratively. This comprehensive review aims to provide valuable insights and a critical analysis of the suggested method, shedding light on its potential contributions to the advancement of image transformation techniques for agricultural development.

Sediment imaging and factorized Haar wavelet transformation techniques for characterizing particle size distributions of granular soils

The particle size distribution (PSD) significantly influences various mechanical behaviors of granular soils. Advances in automation and camera technology allow for a rapid, green, and accurate PSD determination. This study develops a new sediment imaging system, mainly consisting of a sediment column filled with water and a line-scan camera driven by programmable logic controller, which can automatically capture high-resolution soil segregation images. The soil particles settle through the sediment column and segregate by size, which is scanned to generate sediment images. Naturally, the coarsest particles settle at the bottom of the image, while the finest particles are located at the top. Therefore, the analysis of the sediment image enables the determination of PSD. The higher sediment column, despite of poor operationality, yields better particle segregation quality and thus the more accurate PSD result. This study proposed a factorized Haar wavelet transformation (FHWT) algorithm that can account for less perfect segregation of particles by size, enabling to shorten the height of sediment column. The proposed hardware and FHWT are evaluated by six natural sands with different colors, internal textures, and geological origins. An excellent match between image-based PSDs and sieving-based PSDs are observed. Based on FHWT, four sediment columns with different heights of 2.1 m, 1.5 m, 1.0 m, 0.5 m are tested. The results indicate that columns higher than 1.0 m yield acceptable particle segregation results. Furthermore, the proposed FHWT is also adaptable to process high-resolution images captured by advanced cameras.

Application of wavelet transform techniques for corrosion assessment of embedded rebars in RC elements using electromechanical impedance

Corrosion of steel reinforcement embedded in concrete significantly influences the integrity and durability attributes of reinforced concrete structures. Monitoring of the early age corrosion of the reinforcements can contribute to an effective planning of structural maintenance and restoration. This paper demonstrates an innovative approach of monitoring the rate of corrosion (mass loss due to corrosion) of steel bars embedded in concrete by capturing conductance signature using Piezoelectric sensors (PZT) by Electromechanical Impedance Technique. In the recent times Wavelet Transform (WT) based different techniques have been proved to be promising techniques for different structural health monitoring assessment. This study aims to comprehensively use the WT techniques based on EMI signals to assess the corrosion levels in the reinforcement embedded in RC members. Specifically, the study focuses on capturing the progression of corrosion levels using the RMSD index derived from the DWT, CWT and WPT analysis of conductance signatures. WT techniques provides energy values of conductance signatures. Energy level of the signatures decreases with the increase of corrosion levels and deviation of energy level between current and pristine stage is measured using statistical index Root Mean Square Deviation (RMSD). Robust relationship between the RMSD index and the corrosion mass loss ratio through nonlinear curve fitting have been derived and presented using data captured from the 48 specimens of concrete cylinders with embedded reinforcement. At last, the comparison between the actual mass loss ratio and the predicted mass loss ratio derived from these techniques have been made for the validation of the proposed relation of predicted mass loss due to corrosion.

Empowering human-robot interaction using sEMG sensor: Hybrid deep learning model for accurate hand gesture recognition

In this paper, a novel approach using a Henry Gas Solubility-based Stacked Convolutional Neural Network (HGS-SCNN) for hand gesture recognition using surface electromyography (sEMG) sensors is proposed. The stacked architecture of the CNN model helps to capture both low-level and high-level features, enabling effective representation learning. To begin, we generated a dataset comprising 600 samples of hand gestures. Next, we applied the Discrete Wavelet Transform (DWT) technique to extract features from the filtered sEMG signal. This step allowed us to capture both spatial and frequency information, enhancing the discriminative power of the extracted features. Extensive experiments are conducted to evaluate the performance of the proposed HGS-SCNN model. In addition, the obtained results are compared with state-of-the-art techniques, namely AOA-SCNN, GWO-SCNN, and WOA-SCNN. The comparative analysis demonstrates that the HGS-SCNN outperforms these existing methods, achieving an impressive accuracy of 99.3%. The experimental results validate the effectiveness of our proposed approach in accurately detecting hand gestures. The combination of DWT-based feature extraction and the HGS-SCNN model offers robust and reliable hand gesture recognition, thereby opening new possibilities for intuitive human-machine interaction and applications requiring gesture-based control.

Optimized Wavelet and Wavelet Packet Transform Techniques for Assessing Crack Behavior in Curved Segments of Arched Beam Bridges Spanning Rivers

Our study focuses on the dynamic transient analysis of arched beam bridges over rivers, which face unique geohazards and challenges, including vibrations and dynamic loads that can affect structural integrity. The finite element software ANSYS v. 19.3 was employed to assess acceleration time histories at various bridge positions. Using MATLAB, we conducted wavelet packet decomposition to extract insights from the data, specifically isolating river-induced influences. In this article, the introduction of the wavelet packet rate index (WPERI) is presented as a novel metric for the detection of cracks in the curved bridge segments over rivers. The WPERI proves reliable in accounting for the river environment’s impact on structural integrity. Our findings highlight the sensitivity and precision of the WPERI in accurately detecting cracks and vulnerabilities in these riverine bridges. By combining WPT, finite element analysis, and signal decomposition, our research offers insights into tailored crack detection methods for riverine bridges. This study underscores the potential of WPT as a tool for identifying and characterizing cracks in curved bridge elements over rivers. The innovative WPERI provides a holistic approach to addressing structural issues, thus enhancing bridge durability amid changing environmental conditions. It contributes significantly to structural engineering and paves the way for the further exploration of river-specific crack detection techniques.

Multiresolution analysis for tool failure detection in CFRP/Ti6Al4V hybrid stacks drilling in aircraft assembly lines

This article presents a method for identifying Catastrophic Tool Failures during CFRP/Ti6Al4V hybrid stacks drilling in aircraft fuselage components assembly. The study is based on a real industrial case in the production system of a commercial aircraft section, where data was collected from the aircraft assembly line for 17 months. Over 30 different tool configurations were utilized in machining the aircraft section. However, the tool exhibiting the highest breakage rate, specifically a 4.6 mm diameter spiral fluted drill designed for CFRP/Ti6Al4V applications, was chosen for further analysis. This data collection included 69 samples of the selected cutting tool, revealing 19 instances of tool failure. The proposed detection method for spindle consumption signals utilizes Multiresolution Analysis (MRA) with Discrete Wavelet Transform (DWT) and Z-score technique. The research provides a comprehensive analysis of the use of DWT, explaining how the approximation and detail coefficients are obtained, which padding mode is suitable for the industrial case, the influence of the sliding process and filter bank length on the detection of sudden drops in the signal, and how to choose a suitable decomposition level in MRA based on the information provided by the scalograms. The confusion matrix results demonstrate the method's high performance, with Recall and Precision values equal to 1, which means that 100 % of tool failures were detected with no false positives. Additionally, the algorithm running time is 0.65 s, indicating that the method would apply to real-time tool failure detection in industrial machining production systems.

Analysis of Breathing Rate Estimation Using Adaptive Techniques

Breathing rate (BR) is an important physiological factor that is commonly measured in many treatment settings. Though it is still routinely measured by hand. In this study, a novel approach for determining the BR from an ECG, photoplethysmogram, or blood pressure signal is proposed. To extract respiratory signals from time and frequency domain data, the framework employs Discrete Wavelet Transform and Empirical Mode Decomposition techniques. Because we used a Robust Kalman Filter with a Signal Quality Index, our technique worked effectively even when the signals were severely damaged. The output signals have been integrated by state vector fusion, and the BR has been established. Two openly available clinical databases, the MIT-BIH Polysomnographic and the BIDMC datasets are used. The mean absolute percentage error was used to assess performance. The results were very accurate; PPG signals had MAPEs of 7% and BP signals of 5.4%, whereas ECG signals on the two databases had MAPEs of 4% and 4%, respectively.Additionally,the results revealed an astounding robustness to noise at 0 db. In light,this technique may be beneficial for BR monitoring in noisy areas.

On the Migration Origin of the Hercules Moving Group with GAIA, LAMOST, APOGEE, and GALAH Surveys

Using Gaia DR3 data and the wavelet transformation technique, we study the substructures of the Hercules moving group (HMG): Hercules 1 (H1) and Hercules 2 (H2). Spectroscopic survey data from LAMOST, APOGEE, and GALAH are used to obtain metallicities and ages of stars belonging to the HMG. Our analysis leads to several key findings as follows: (a) the HMG is on average richer in metallicity than the Galactic disk, with H2 being metal richer than H1; (b) the HMG likely has a radial metallicity gradient distinct from that of the disk; (c) the HMG is on average older than the disk, with H2 being older than H1; (d) the HMG likely has a radial age gradient distinct from that of the disk; and (e) the metallicity and age distributions of the HMG depend mainly on the Galactic radius but show no dependence on the azimuthal velocity. Taken all together, we conclude that the HMG is composed primarily of stars undergoing radial migration. We suggest that the HMG is associated with a higher-order dynamical resonance of the bar of the Galaxy.