Decomposition Technique Research Articles

Study on the accurate detection method of full‐polarimetric ground‐penetrating radar faults in mines based on modified Yamaguchi decomposition

AbstractObtaining information on tectonic tendencies is a prerequisite for intelligent and accurate mining in mines. In the special mine environment, the co‐polarized ground‐penetrating radar can only identify the spatial location of faults, and it is difficult to analyse the inclination information of fault structures. This paper proposes a mine full‐polarimetric ground‐penetrating radar fault tendency detection method based on this. First, based on the stacking characteristics of the coal depositional, this paper analyses the propagation law of the pulse electromagnetic wave in the coal seam and puts forward the assumption of the overlapping echo reflection of the fault structure. The reasonableness of the fault reflection assumption is verified through a numerical simulation study. Second, based on the cutting relationship of the fault to the coal seam, we divided the reflection structure of the fault structure into plane scattering and dihedral angle scattering. We realized the mingled echoes’ decomposition using the improved Yamaguchi decomposition technique. To analyse the applicability of the modified Yamaguchi and Freeman decomposition methods in the identification of fault inclination, we use the upright fault simulation data for the discussion, and we find that the improved Yamaguchi decomposition method is more advantageous in the identification of fault inclination in the mine. The decomposition results based on the simulation data of fault models with different dip angles found that when the dip angle of the fault is less than 90°, the scattering of the fault structure is dominated by planar scattering and dihedral angle scattering; when the dip angle of the fault is greater than or equal to 90°, the scattering of the fault structure is dominated by planar scattering, and the scattering power of the dihedral angle model is zero. By analysing the effect of fault strike on the decomposition results, it is found that the fault strike angle has little effect on the identification of fault tendency. Finally, the application potential of this paper's method is tested by constructing complex numerical models and probing experiments. Therefore, the method proposed in this paper can solve the fault tendency identification under a thick coal seam.

Experimental decomposition of nonlinear hydrodynamic loads and hydroelastic responses in wave–structure interactions of monopile wind turbines

This study explores the nonlinear effects of hydrodynamic loads and hydroelastic responses in monopile-supported offshore wind turbines, with a focus on their harmonic structures. High-quality experimental data were collected in a shallow water basin across various irregular waves. Their harmonic components up to the fourth order were successfully isolated using a novel four-phase decomposition technique, which effectively extracts harmonics from random time series. The results highlight significant contributions of higher-order harmonics across various sea states. Specifically, two physical models were employed: a flexible monopile to capture structural hydroelastic deformations during large wave events and a rigid monopile to accurately estimate hydrodynamic loads without hydroelastic effects. Additionally, numerical results from fully nonlinear potential-flow calculations were compared, revealing that while the potential-flow solver underestimates total wave loading, especially for higher harmonics. In terms of the free-surface elevations and hydrodynamic forces, this study confirms that their higher-order harmonics align well with the scaling of their corresponding linear components in both amplitude and phase. This enables the prediction of higher-order profiles based solely on their linear components. However, this efficient model is inadequate for accurately estimating the higher-order harmonics of monopile's hydroelastic responses during these extreme wave events, highlighting the need for further refinement.

Direct hydroacoustics analyses of pipe orifice using lattice Boltzmann method

Acoustic waves in water pipes pose a structural threat but also offer a valuable tool for non-invasive inspection. Complex pipe geometries such as bends and surface liners create complex fluid boundaries, triggering interactions between fluid flow and acoustics. The lattice Boltzmann method (LBM) excels at capturing these interactions near complex boundaries, in contrast to the finite volume method, which can result in errors. However, the application of LBM in water pipes has been limited by stability problems. This study proposes a two-step (DM-TS) collision operator based on a direct method (LBM-HA) for stable LBM simulations in water pipes. LBM-HA enables direct hydroacoustic predictions for both acoustic and dynamic flow fields in a pipe orifice. A novel acoustic-dynamic complex boundary condition was formulated from the linearized Euler's equation to account for the physical effects of the bounded domain of the LBM-HA analysis. To distinguish between dynamic and acoustic components, wavenumber and frequency decomposition techniques were applied to the pressure data obtained within the pipe. In addition, mode decomposition of the acoustic pressure was conducted to identify the dominant acoustic modes. By comparing the LBM-HA results with experimental data, we highlighted the method's potential for direct hydroacoustic analysis considering the acoustic characteristics of the water pipe.

A Gray Scale Image Compression Using Hierarchical DWT Decomposition and Mixing Quantization Techniques

This paper describes a hybrid grayscale compression system based on the discrete wavelet transform (DWT) and a polynomial coding technique for mixing quantization schemes to increase the compression ratio while maintaining quality. The proposed compression system consists of three main steps: first, decomposing an image using a three-level DWT; second, applying the 2-D linear polynomial coding technique to the approximation sub-band; and third, dividing the detailed sub-bands of each level into the Most Significant Value (MSV) and Least Significant Value (LSV), where the former is compressed using iterative scalar uniform quantization and the latter by soft quantization thresholding. For testing the performance of the suggested compression system, five standard images of size 256×256 pixels were adopted. The suggested technique showed superior performance in terms of reconstructed (decoded) image quality and compression ratio (gain), where the compression ratio is between 21–27 with a PSNR value between 36–38 dB and the compression ratio of JPEG is between 7–20 with a PSNR value between 33–37 dB.

Forecasting of Solar Irradiation Based on the Deep Learning Model Using Complete Ensemble Empirical Mode Decomposition Technique

Since solar energy plants have been developing so quickly in recent years, accurate solar power forecasting plays a significant role in contemporary intelligent grid systems and setup a bilateral contract negotiation between suppliers and customers. This paper introduces a novel three-step method for predicting two-channel solar irradiance that combines cutting-edge techniques like Bidirectional Long Short-Term Memory, Wasserstein Generative adversarial Networks and Complete Ensemble Empirical Mode Decomposition with adaptive noise. This methodology that is being suggested involves a framework of decomposition integration which allows for the effective isolation of the solar output signal into sub-sequences that are simple and can be differentiated based on their unique frequency disparities. This is achieved after the subsequences have been separated, and then they are subjected to individual prediction models. The high subsequences are predicted using the WGAN model, while the low subsequences are forecasted using the Bi-LSTM model. This approach ensures the specific characteristics of each subsequence are captured in the prediction process. The experimental result shows that the proposed model surpasses the performance of the suboptimal model in terms of evaluation metrics: RMSE, MAPE and R2. Compared to the suboptimal model, the RMSE, MAPE of all seasons

Financial Time Series Forecasting Based on Long-Short Term Memory, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Batch Normalization

Long-short term memory (LSTM) is a state-of-art and widely used model to forecast financial time series. However, primitive LSTM networks do not perform well due to over-fitting problems of the deep learning model and nonlinear and non-stationary characteristics of financial time series data. In addition, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is an outstanding data frequency decomposition technique that can decompose original time series into several intrinsic mode functions and a residue. Thus, this paper proposed a novel hybrid network CEEMDAN-LSTM-BN based on LSTM. Specifically, to avoid over-fitting, the modified LSTM-BN network consists of two LSTM layers, two Batch Normalization (BN) layers following each LSTM layer, and a dropout layer. Each of the intrinsic mode functions and the residue would be processed by CEEMDAN-LSTM-BN and the final prediction results are obtained by reconstructing each predictive series. The advantages of the proposed CEEMDANLSTM-BN networks are verified by comparing them to primitive LSTM, other hybrid models, and some famous machine learning models. Moreover, the robustness of the networks is assessed by numerical experiments on different stock indices datasets.

Advanced Diagnosis of Air Gap Eccentricity in Three-Phase Induction Motor Using DWT Decomposition and AI Techniques

Advanced Diagnosis of Air Gap Eccentricity in Three-Phase Induction Motor Using DWT Decomposition and AI Techniques

Reachable Set Estimation for Singular Discrete‐Time Nonlinear Systems With Time‐Varying Delays

ABSTRACTIn this article, the reachable set estimation problems for a class of singular discrete‐time nonlinear systems (SDNS) with time‐varying delay (TVD) are presented. First, we employ a Lyapunov function scheme and utilize zero‐type free matrix equations. Based on this, a criterion is derived in the form of linear matrix inequalities (LMIs) to guarantee that the considered system to be regular, causal, and the state trajectory of the system is bounded. Second, by developing the singular systems decomposition technique into slow and fast subsystems and using the introduced auxiliary lemma, we establish that the state trajectory of the fast subsystem is also bounded, and the reachable set of singular nonlinear system is bounded within the ellipsoid. Finally, Numerical examples are presented to illustrate the effectiveness of the proposed method.

An enhanced method for reconstruction of full SIF spectrum for near-ground measurements

Recently the applications of remotely sensed Solar-Induced chlorophyll Fluorescence (SIF) in the study of photosynthesis, stress conditions, and gross primary production have increased significantly. The full SIF spectrum spans over a spectral region of 650 ∼ 850 nm with two characteristic peaks around 685 nm and 740 nm. Over recent decades, many retrieval algorithms have been developed to estimate SIF at Top-Of-Canopy (TOC) using in-situ measurements of solar irradiance and canopy radiance spectra. Although the majority of retrieval methods retrieve SIF at a narrow spectral window, there exists a potential for retrieval of SIF in the full emission spectrum. Moreover, solar irradiance and canopy radiance spectra should ideally be measured at the same time but are usually measured sequentially with a certain time lag, raising potential errors in SIF retrieval. In this study, an enhanced retrieval algorithm of the full SIF spectrum at TOC is proposed. The proposed algorithm attempts to minimize the errors owing to time mismatch in measurements of solar irradiance and canopy radiance spectra. As an improvement to the previous algorithm (advanced Fluorescence Spectrum Reconstruction, aFSR), this proposed algorithm (aFSR-SVE) models the SIF-free contribution with principal components using the singular value decomposition technique. The optimal parameter settings in the forward model were determined for the experimental data collected by spectrometers used in the study. Firstly, the proposed algorithm was used to reconstruct full SIF spectrum for simulated data. The results were compared with known reference SIF values. After achieving satisfying results from simulated data, the proposed algorithm was compared with retrievals from established algorithms using experimental data. The results show improved SIF retrieval accuracy, without the need to simultaneously measure solar irradiance and canopy radiance spectra. The retrieval values comply with the results of previous algorithms in terms of spectral shape, diurnal trend, and temporal variations. The induced errors in SIF retrievals due to non-simultaneous measurements of solar irradiance and canopy radiance spectra were also investigated and the proposed algorithm was found to be less prone to such errors. Hence, the proposed algorithm is an improvement in reconstructing the full SIF spectrum with near-ground measurements. With the help of the proposed algorithm, field measurements using sequential systems and automated measurements of multiple targets can be performed effectively as it relaxes the requirement of concurrent measurement of solar irradiance and canopy radiance spectra. For future work, the applicability of this method can be investigated under more variable illumination conditions, like high cirrus clouds, passing clouds or persistent thin clouds.

Determinants of rural‑urban child malnutrition disparities in Ethiopia

Child malnutrition is a significant issue in Ethiopia, with high prevalence rates. However, there is limited information on the rural‑urban disparities in child malnutrition of all its forms (stunting, wasting, and underweight) in the country. Therefore, this study aimed to address this gap by analysing data from the 2019 Ethiopian Demographic Health Survey (EDHS). This study examined the rural‑urban child malnutrition gaps in Ethiopia using an extended Oaxaca-Blinder decomposition technique. The analysis reveals significant ruralurban disparities in prevalence rates of wasting, underweight, and stunting. The findings highlighted that children in rural areas are more malnourished than their urban counterparts. Additionally, the study identified key determinants of these disparities, including maternal and parental education, household economic status, women’s age at first birth, childbirth size, and working status of women. The results also indicate that institutional delivery, household size, and dependency ratio contribute to widening the rural‑urban disparities in child malnutrition. This study highlighted the importance of targeted policies and interventions to address these disparities and reduce child malnutrition at the national level.

Identification of ocular artifact in EEG signals using VMD and Hurst exponent.

Electroencephalographic (EEG) readings are usually infected with unavoidable artifacts, especially physiological ones. One such physiological artifact is the ocular artifacts (OAs) that are generally related to eyes and are characterized by high magnitude and a specific spike pattern in the prefrontal region of the brain. During the long-duration EEG acquisition, the retrieval of important information becomes quite complicated in prefrontal regions as ocular artifacts dominate the EEG recorded, making it difficult to discern underlying brain activity. With the progress and development in signal processing techniques, artifact handling has become a progressive field of investigation. This paper presents a framework for the detection and correction of ocular artifacts. This study emphasizes improving the quality and reducing the time complexity by using higher-order statistics (HOS) for artifact identification and variational mode decomposition (VMD) for OA correction. An overall SNR of 14 dB, MAE of 0.09, and PSNR of 33.59 dB has been attained by the proposed framework. It was observed that the proposed HOS-VMD surpassed the state-of-the-art mode decomposition techniques.

Retraction Note: Blind code estimation of multi-antenna direct-spread CDMA with long-code signal using decomposition technique

Retraction Note: Blind code estimation of multi-antenna direct-spread CDMA with long-code signal using decomposition technique

Numerical Solution of External Boundary Conditions Inverse Multilayer Diffusion Problems

The present study is concerned with the numerical solution of external boundary conditions in inverse problems for one-dimensional multilayer diffusion, using the difference method. First, we formulate multispecies parabolic problems with three types of Dirichlet–Neumann–Robin internal boundary conditions that apply at the interfaces between adjacent layers. Then, using the symmetry of the diffusion operator, we prove the well-posedness of the direct (forward) problem in which the coefficients, the right-hand side, and the initial and boundary conditions are given. In inverse problems, instead of external boundary conditions of the first and the last layers, point observations of the solution within the entire domain are posed. Rothe’s semi-discretization of differential problems combined with a symmetric exponential finite difference solution for elliptic problems on each time layer is proposed to develop an efficient semi-analytical approach. Next, using special solution decomposition techniques, we numerically solve the inverse problems for the identification of external boundary conditions. Numerical test examples are discussed.

Imputation based wind speed forecasting technique during abrupt changes in short term scenario

AbstractIt is tough and complex to forecast wind speed due to its intermittent and stochastic nature as well as sudden and abrupt variations in the wind speed. Further, it is required to handle the variety of scenarios e.g. cyber‐attacks, unexpected power device malfunction, communication/sensor outages etc. that can cause the missing data.This paper proposes and employs a de‐noising autoencoder algorithm for wind speed forecasting to ensure the handling of missing data information. At the next step, the data is processed via variational mode decomposition technique to mitigate the noise and improves the model's prediction accuracy. Furthermore, the bi‐directional long‐short term memory deep learning approach is tied with convolution neural network to increase prediction accuracy and anticipating the sudden/abrupt changes in wind speed accurately. Finally, actual wind speed related data is examined to scrutinize meticulousness of projected forecast methodology particularly during sudden/abrupt changes in the wind speed. The parameter indicators of the wind speed forecasting technique exhibit the capability of improved predictions under the diversified conditions.

AnEEG: leveraging deep learning for effective artifact removal in EEG data

In neuroscience and clinical diagnostics, electroencephalography (EEG) is a crucial instrument for capturing neural activity. However, this signal is polluted by different artifacts like muscle activity, eye blinks, environmental interference, etc., which makes it more difficult to retrieve important information from the signal. Deep learning methods have demonstrated the potential to lower these artifacts and enhance the EEG’s quality in recent years. In this work, a novel deep learning method,“AnEEG” is presented for eliminating artifacts from EEG signal. The quantitative matrices NMSE, RMSE, CC, SNR and SAR are calculated to confirm the effectiveness of the proposed model. Through this process, it was found that the suggested model outperformed wavelet decomposition techniques. The model achieves lower NMSE and RMSE values, which indicates better agreement with the original signal. Achieving higher CC values means stronger linear agreement with the ground truth signals. Additionally, the model shows improvements in both SNR and SAR values. Overall, this suggested approach showcases promising results in improving the quality of EEG data by utilizing deep learning.

Decoupling relationship between logistics growth and carbon emissions and driving factors in Chongqing: A novel decomposition framework

A comprehensive understanding of the logistics decoupling status and driving factors is of immense theoretical and practical importance for rationally formulating low-carbon logistics policies and accelerating the realization of high-quality development. Present study introduces the logarithmic mean Divisia index method (LMDI) decomposition technique into Tapio index model, extends traditional decoupling model, and establishes a new analytical framework for the decoupling relationship between logistics growth and carbon emissions. Using long-term data from Chongqing (1997–2021), this study investigated the Chongqing logistics decoupling relationship and driving factors. The study found the following: (1) Chongqing logistics carbon emissions show phased changes and face greater pressure to reduce emissions. (2) The decoupling status of logistics growth and carbon emissions is predominantly expansive and weak decoupling, with an overall evolutionary trend of “expansive decoupling–weak decoupling–strong decoupling. The average decoupling index in 2013–2021 was 0.5523, indicating a decreasing trend; however, there was still a large gap in realizing a strong decoupling goal. (3) The energy consumption intensity effect facilitates logistics carbon decoupling, the economic scale effect has a strong inhibiting effect, and the industrial structure effect and transportation intensity effect are staged. Finally, targeted policy recommendations are proposed to expedite logistics carbon emissions decoupling in Chongqing.

Multi-wing chaotic system based on meminductor and its application in image encryption

Meminductor is a novel type of nonlinear device following the memristor, characterized by its memory properties. Currently, research on meminductors is still in its infancy, with their physical devices yet to be formally realized. Therefore, conducting fundamental research on their nonlinear circuit properties and applications is of great significance. In this paper, a new multi-wing chaotic system is proposed based on the mathematical model of a magnetically controlled meminductor. By varying the values of its parameters, the system can generate two-wing, three-wing, and four-wing chaotic attractors. Various analytical methods are employed to study the dynamical behaviours of the proposed chaotic system. The results demonstrate that the system is highly sensitive to its initial conditions and control parameters, which makes it suitable for image encryption. Based on the new system, we propose a new algorithm for image encryption that combines the newly established four-dimensional multi-wing chaotic system with bit plane decomposition technique, firstly, the high four-bit planes containing 94% image information are disordered by S-type permutation, then the disordered bit planes perform operation of XOR with the random matrix generated by chaotic sequences, and finally, the encrypted image is obtained by merging the bit planes.

Evaluation of Static Displacement Based on Ambient Vibration for Bridge Safety Management.

The evaluation of bridge safety is closely related to structural stiffness, with dynamic characteristics and displacement being key indicators. Displacement is a significant factor as it is a physical phenomenon that bridge users can directly perceive. However, accurately measuring displacement generally necessitates the installation of displacement meters within the bridge substructure and conducting load tests that require traffic closure, which can be cumbersome. This paper proposes a novel method that uses wireless accelerometers to measure ambient vibration data from bridges, extracts mode shapes and natural frequencies through the time domain decomposition (TDD) technique, and estimates static displacement under specific loads using the flexibility matrix. A field test on a 442.0 m cable-stayed bridge was conducted to verify the proposed method. The estimated displacement was compared with the actual displacement measured by a laser displacement sensor, resulting in an error rate of 3.58%. Additionally, an analysis of the accuracy of displacement estimation based on the number of measurement points indicated that securing at least seven measurement points keeps the error rate within 5%. This study could be effective for evaluating the safety of bridges in environments where load testing is difficult or for bridges that require periodic dynamic characteristics and displacement analysis due to repetitive vibrations, and it is expected to be applicable to various types of bridge structures.

Does Ethnicity Moderate the Union Dissolution Penalty for Women? A Register-based Analysis of Changes in Income Components

Union dissolution has severe consequences for women’s economic well-being. Theoretical work links these consequences to ethnic inequality. Ethnic groups vary in terms of separation rates, female employment, repartnering trajectories, kin support, and reliance on welfare benefits. The current study examines whether ethnicity moderates the dissolution penalty. To do so, the authors draw on register data, covering women from five major ethnic groups in the Netherlands: Dutch, Antillean, Surinamese, Moroccan, and Turkish. The authors describe women’s income trajectories from 1 year before to 5 years after union dissolution. Using decomposition techniques, changes in household income are decomposed into changes in six underlying income sources (i.e., earnings, benefits, alimony, partner income, and coresident family income). The results show that ethnicity moderates the dissolution penalty and, especially, the contribution of the various income sources when recovering from dissolution.

Optimizing Retail Inventory Management Through Time Series Analysis

The purpose of this study is to investigate the application of time series analysis in the management of retail inventory, with a particular emphasis on the precision of forecasting as well as the applicability of ARIMA models and seasonal decomposition techniques. This study aimed to evaluate these models' effectiveness in improving the scientific and precise nature of inventory management. This was accomplished by using legitimate retail data. According to the findings, the ARIMA model is effective in managing data that is stable or does not exhibit seasonal patterns. On the other hand, the seasonal decomposition technique demonstrates strong forecasting accuracy in managing data that exhibits seasonal fluctuations. Not only does this research improve the theoretical implementation of time series analysis in the retail sector, but it also provides retailers with practical strategies for mitigating economic losses that are caused by inventory imbalance. When it comes to the management of retail inventory, the findings of the research are significant for improving both overall cost control and customer satisfaction.