Decomposition Method Research Articles

Estimation of wind dynamic load linear transfer matrix using proper orthogonal decomposition method and genetic algorithm

ABSTRACT Due to the advantages of Laplace domain in both time and frequency domains, the dynamic analysis and control tools in engineering software like MATLAB and Python have been designed based on it. Dynamic analysis of structures requires determination of the overall transfer matrix, which is obtained by multiplying the structure's transfer matrix (TM) with the load TM. With the input vector of unit intensity white-noise processes and the overall TM, the structural responses can be obtained. Furthermore, having the load TM is crucial for implementation of active control systems. In this article, the TM of along-wind loads was estimated for a tall building using the proper orthogonal decomposition (POD) method and the genetic algorithm (GA). To evaluate the effectiveness of the proposed approach, an example of a tall building was provided. The wind load cross-power spectral matrix (XPSD) was decomposed using POD technique, followed by the truncation of higher loading modes. Subsequently, the loading TM was derived by estimating the target non-linear functions with linear transfer functions through GA technique. Then, the responses of the structure were computed using the loading TM. The results demonstrated the efficacy of the proposed method in acquiring the loading TM and predicting wind-induced responses.

Spectral Decomposition and Transformation for Cross-domain Few-shot Learning

Cross-domain few-shot Learning (CDFSL) is proposed to first pre-train deep models on a source domain dataset where sufficient data is available, and then generalize models to target domains to learn from only limited data. However, the gap between the source and target domains greatly hampers the generalization and target-domain few-shot finetuning. To address this problem, we analyze the domain gap from the aspect of frequency-domain analysis. We find the domain gap could be reflected by the compositions of source-domain spectra, and the lack of compositions in the source datasets limits the generalization. Therefore, we aim to expand the coverage of spectra composition in the source datasets to help the source domain cover a larger range of possible target-domain information, to mitigate the domain gap. To achieve this goal, we propose the Spectral Decomposition and Transformation (SDT) method, which first randomly decomposes the spectrogram of the source datasets into orthogonal bases, and then randomly samples different coordinates in the space formed by these bases. We integrate the above process into a data augmentation module, and further design a two-stream network to handle augmented images and original images respectively. Experimental results show that our method achieves state-of-the-art performance in the CDFSL benchmark dataset.

Artifact feature purification for cross-domain detection of AI-generated images

In the era of AIGC, the fast development of visual content generation technologies, such as diffusion models, brings potential security risks to our society. Existing generated image detection methods suffer from performance drops when faced with out-of-domain generators and image scenes. To relieve this problem, we propose Artifact Purification Network (APN) to facilitate the artifact extraction from generated images through the explicit and implicit purification processes. For the explicit one, a suspicious frequency-band proposal method and a spatial feature decomposition method are proposed to extract artifact-related features. For the implicit one, a training strategy based on mutual information estimation is proposed to further purify the artifact-related features. The experiments are conducted in two settings. Firstly, we perform a cross-generator evaluation, wherein detectors trained using data from one generator are evaluated on data generated by other generators. Secondly, we conduct a cross-scene evaluation, wherein detectors trained for a specific domain of content (e.g., ImageNet) are assessed on data collected from another domain (e.g., LSUN-Bedroom). Results show that for cross-generator detection, the average accuracy of APN is 5.6%∼16.4% higher than the previous 11 methods on the GenImage dataset and 1.7%∼50.1% on the DiffusionForensics dataset. For cross-scene detection, APN maintains its high performance. Via visualization analysis, we find that the proposed method can extract diverse forgery patterns and condense the forgery information diluted in irrelated features. We also find that the artifact features APN focuses on across generators and scenes are global and diverse. The code will be available at https://github.com/RichardSunnyMeng/APN-official-codes.

Alternating generalized projection method applied to phase-only synthesis process of satellite reflectarray antennas

Promising algorithm of alternating generalized projection method (AGP) is proposed for phase-only synthesis process of satellite reflectarray antennas under intersection approach. This promising algorithm is a hybridized algorithm of two specialized algorithms for non-convex sets rescued in the literature: algorithm based on separating hyperplanes and algorithm based on decomposition method in polar cones. Since the sets involved in phase-only synthesis process of satellite reflectarray antennas are non-convex, the conventional von Neumann alternating projection method proposed in the literature does not guarantee convergence to the point in the intersection of the involved sets. In addition, the results of the phase-only synthesis obtained by the different algorithms were compared and promising improvements produced by the proposed hybridized algorithm were shown.

A novel NMF-DiCCA deep learning method and its application in wind turbine blade icing failure identification

Wind turbine blade icing data has the characteristics of multi-source and multi variable. It is difficult to characterize and identify the icing failure with multi-scale features. In this paper, a novel Non-negative Matrix Factorization-Dynamic-inner Canonical Correlation Analysis (NMF-DiCCA )based on GRU and SSAE algorithm is proposed to solve this problem. Firstly, using NMF instead of SVD decomposition method in DiCCA algorithm, the NMF-DiCCA is applied to obtain the dynamic latent variable of time serie. Secondly, the latent structure features S of dynamic latent variable is captured by SSAE. Thirdly, the temporal correlation hidden feature H of dynamic latent variable is extracted by Gated Recurrent Unit (GRU). Finally, the attention weight distribution between latent structure S and temporal correlation hidden feature H is integrated using the attention mechanism, and the fusion feature is reconstructed using the improved SSAE(ISSAE) based on GRU and SSAE.

Robust Ultrafast Projection Pipeline for Structural and Angiography Imaging of Fourier-Domain Optical Coherence Tomography.

The current methods to generate projections for structural and angiography imaging of Fourier-Domain optical coherence tomography (FD-OCT) are significantly slow for prediagnosis improvement, prognosis, real-time surgery guidance, treatments, and lesion boundary definition. This study introduced a robust ultrafast projection pipeline (RUPP) and aimed to develop and evaluate the efficacy of RUPP. RUPP processes raw interference signals to generate structural projections without the need for Fourier Transform. Various angiography reconstruction algorithms were utilized for efficient projections. Traditional methods were compared to RUPP using PSNR, SSIM, and processing time as evaluation metrics. The study used 22 datasets (hand skin: 9; labial mucosa: 13) from 8 volunteers, acquired with a swept-source optical coherence tomography system. RUPP significantly outperformed traditional methods in processing time, requiring only 0.040 s for structural projections, which is 27 times faster than traditional summation projections. For angiography projections, the best RUPP variation took 0.15 s, making it 7518 times faster than the windowed eigen decomposition method. However, PSNR decreased by 41-45% and SSIM saw reductions of 25-74%. RUPP demonstrated remarkable speed improvements over traditional methods, indicating its potential for real-time structural and angiography projections in FD-OCT, thereby enhancing clinical prediagnosis, prognosis, surgery guidance, and treatment efficacy.

Surface crack detection on selective laser melting printed Inconel 718 using a laser generated ultrasound technique and phase space reconstruction

Abstract In the field of metallic additive manufacturing, Selective Laser Melting (SLM) has become a predominant technology due to its advantages of short production cycles, high precision, and low cost. It is frequently employed in the production of complex parts. This paper proposes the use of a scanning laser line source, in conjunction with the singular value decomposition method, to reconstruct phase space and identify surface cracks in SLM specimens. The scanning laser line source addresses the limitations of a single line source, which is often unable to accurately detect tiny cracks. By comparing experimental and simulation data, the results demonstrate that the scanning laser line source can effectively compensate for some of the detection deficiencies of a single line source.

Regularization methods for the inverse initial value problem for the time-fractional diffusion equation with robin boundary condition ★ ★ The project is supported by Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX23_2549) and the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20190578).

In the article, we focus on the inverse initial value problem for the time-fractional diffusion equation with robin boundary condition on a cylindrical symmetric field. The ill-posedness of this problem is proved. We introduce the modified Landweber iteration method(MLIM), the Truncated singular value decomposition(TSVD) method for solving it and propose a new regularization method, named as the TSVD-modified Landweber iteration method(TMLIM). The error estimates between the exact solution and the regularized approximate solution are presented by using two regularization parameter selection rules. Finally, numerical examples are provided to demonstrate the effectiveness and feasibility of the regularization methods.

Novel plasma actuator for mitigation of dynamic stall

A novel plasma actuator, the Linear Counter-flow using a Point Embedded Electrode (LCPEE), is developed for the prevention of dynamic stall for a sinusoidal pitching movement between α = 4° and α = 18°. The LCPEE is implemented on a NACA0012 airfoil and tested at a Reynolds Number of Re c = 2 × 105 and reduced frequency of k = π/16. Prior investigations using a standard linear actuator showed that the exposed electrode introduced perturbations passively which delayed dynamic stall when the actuator was off. For the LCPEE actuator, when turned off, there is least passive delay. When the LCPEE is turned on at St f = 50, the dynamic stall is prevented for the sinusoidal pitching motion of the airfoil. The LCPEE actuator is also tested for the same sinusoidal motion between α = 6° and α = 20°. Four cases are considered for the higher α range of motion: actuator off, actuator on at St f = 50 with a sinusoidal input waveform, actuator on at St f = 50 with a triangular input waveform, and actuator on at St f = 100 with a sinusoidal input waveform. Specifically for the last case, experiment shows no flow reversal demonstrating the efficacy of LCPEE in controlling the dynamic stall. The effects of LCPEE on the flow energy distribution have also been studied by using proper orthogonal decomposition (POD) method.

Novel analysis of nonlinear seventh-order fractional Kaup–Kupershmidt equation via the Caputo operator

In this work, we use two unique methodologies, the homotopy perturbation transform method and Yang transform decomposition method, to solve the fractional nonlinear seventh-order Kaup–Kupershmidt (KK) problem. The physical phenomena that arise in chemistry, physics, and engineering are mathematically explained in this equation, in particular, nonlinear optics, quantum mechanics, plasma physics, fluid dynamics, and so on. The provided methods are used to solve the fractional nonlinear seventh-order KK problem along with the Yang transform and fractional Caputo derivative. The results are significant and necessary for exploring a range of physical processes. This paper uses modern approaches and the fractional operator to develop satisfactory approximations to the offered problem. To solve the fractional KK equation, we first use the Yang transform and fractional Caputo derivative. He’s and Adomian polynomials are useful to manage nonlinear terms. It is shown that the suggested approximate solution converges to the exact one. In these approaches, the results are calculated as convergent series. The key advantage of the recommended approaches is that they provide highly precise results with little computational work. The suggested approach results are compared to the precise solution. By comparing the outcomes with the precise solution using graphs and tables we can verify the efficacy of the offered strategies. Also, the outcomes of the suggested methods at various fractional orders are examined, demonstrating that the findings get more accurate as the value moves from fractional order to integer order. Moreover, the offered methods are innovative, simple, and quite accurate, demonstrating that they are effective for resolving differential equations.

The Decomposition Method of Surface Electromyographic Signals: A Novel Approach for Motor Unit Activity and Recruitment Description.

This review aims to describe a novel method in the field of electromyography (EMG), established and improved upon in the last three decades that is able to observe specific parameters of muscle units (MUs). This concept is called the decomposition method, based on its ability to decompose a surface EMG signal to describe muscle activity on the level of individual muscle units in contrast to the level of the whole muscle, as is customary for regular surface electromyography. We provide a brief overview of its history, constituent parts regarding both hardware and software and possible applications. We also acknowledge the state of the research, regarding the background of the decomposition algorithm, the main software component responsible for identifying individual motor units and their parameters. As a result of the ability to describe the behavior of individual motor units during muscle contractions, key concepts in neuromuscular physiology have been put forward, pertaining to the hierarchy of MUs during their recruitment. Together with the recent application for cyclic contractions and gait, the decomposition method is beginning to open up wider possibilities of enquiry.

A Novel Hybrid Attention-Based Dilated Network for Depression Classification Model from Multimodal Data Using Improved Heuristic Approach

Automatic depression classification from multimodal input data is a challenging task. Modern methods use paralinguistic information such as audio and video signals. Using linguistic information such as speech signals and text data for depression classification is a complicated task in deep learning models. Best audio and video features are built to produce a dependable depression classification system. Textual signals related to depression classification are analyzed using text-based content data. Moreover, to increase the achievements of the depression classification system, audio, visual, and text descriptors are used. So, a deep learning-based depression classification model is developed to detect the person with depression from multimodal data. The EEG signal, Speech signal, video, and text are gathered from standard databases. Four stages of feature extraction take place. In the first stage, the features from the decomposed EEG signals are attained by the empirical mode decomposition (EMD) method, and features are extracted by means of linear and nonlinear feature extraction. In the second stage, the spectral features of the speech signals from the Mel-frequency cepstral coefficients (MFCC) are extracted. In the third stage, the facial texture features from the input video are extracted. In the fourth stage of feature extraction, the input text data are pre-processed, and from the pre-processed data, the textual features are extracted by using the Transformer Net. All four sets of features are optimally selected and combined with the optimal weights to get the weighted fused features using the enhanced mountaineering team-based optimization algorithm (EMTOA). The optimal weighted fused features are finally given to the hybrid attention-based dilated network (HADN). The HDAN is developed by combining temporal convolutional network (TCN) with bidirectional long short-term memory (Bi-LSTM). The parameters in the HDAN are optimized with the assistance of the developed EMTOA algorithm. At last, the classified output of depression is obtained from the HDAN. The efficiency of the developed deep learning HDAN is validated by comparing it with various traditional classification models.

Multi-period optimal capacity expansion planning scheme of regional integrated energy systems considering multi-time scale uncertainty and generation low-carbon retrofit

With increasingly challenging climate crisis, carbon neutrality has become a necessary path for the whole world. However, there still lacks a comprehensive investigation on the optimal capacity expansion planning for a low-carbon transformation while considering multiple uncertainties. In this paper, a multi-period optimal capacity expansion planning scheme for regional integrated energy systems considering multi-time scale uncertainties and generation low-carbon retrofit is proposed. First, to better describe the short-time scale uncertainty caused by renewable energy and load fluctuations, a multi-dimensional tempo-spatial correlated scenario generation method is proposed to more accurately capture stochastic features with the least amounts of representative scenarios. Second, a multi-period optimal planning model considering generation low-carbon retrofit and short-/long-time scale uncertainties is developed. The proposed model combines information gap decision theory and chance constraints to stress both uncertainties simultaneously, which is further linearized to facilitate the computation. Third, an improved bilinear Benders decomposition (IBBD) method is utilized to efficiently solve the proposed large-scale optimal planning problem. Finally, numerical experiments of a real world test system of WF city show: 1) the maximum errors of PDF and CDF between generated scenarios and historical data are 0.108 and 0.016, validating the effectiveness of the proposed scenario generation method; 2) the low-carbon retrofit reduces system carbon emissions by 14.12 × 107 kg, while the total system cost is $956,411,387 with additional energy coupling units and renewables installed, indicating the reliability of the multi-period planning model; 3) the IBBD algorithm demonstrates applicability and efficiency in solving the proposed multi-period optimal expansion planning problem, reducing computation time by over 80.39 % compared to commercial solvers, such as GUROBI.

Carbon pricing policies and renewable energy development: Analysis based on cross-country panel data

While studies have theoretically discussed the impact of carbon pricing on renewable energy, the practical implementation and effectiveness of these policies remain uncertain. This study empirically examines the role of carbon emissions trading and carbon tax in global renewable energy development using panel data from 196 countries and regions and employing the staggered difference-in-differences (DID) model and Bacon decomposition method. The results suggest that: (1) From the perspective of policy shocks, carbon trading has increased non-hydro renewable electricity generation by 73.32%, while carbon tax has increased it by 31.79%. This indicates that the overall impact of carbon trading on renewable energy is greater than that of carbon tax. However, the elasticity coefficients of renewable energy to carbon trading prices and carbon tax rates are 0.1801 and 0.1845, respectively, suggesting a slightly greater marginal effect of carbon tax on renewable energy compared to carbon trading. (2) Both carbon tax and carbon trading have mitigated the growth of fossil electricity and encouraged public investment in renewable energy, thereby fostering its development. (3) The influence of carbon pricing on renewable energy varies by income level; notably, the implementation of these policies in high-income countries has diminished their promotional effect on renewable energy. (4) The contribution of technological innovation to renewable energy development is smaller than that of policies including carbon trading and carbon tax, indicating that renewable energy development during the sample period was predominantly driven by policy measures. The findings indicate that the application of carbon pricing policies should be further promoted to accelerate the energy mix transition.

Dynamic path planning of autonomous bulldozers using activity-value-optimised bio-inspired neural networks and adaptive cell decomposition

Autonomous bulldozers encounter critical challenges in dynamic path planning in complex construction environments such as dynamic obstacles and narrow passages. Bio-inspired neural networks (BINN) are commonly employed for real-time path planning in dynamic environments. However, the algorithm is not feasible enough to calculate the positive input of neighbour cells, thus resulting in unreasonable distribution of activity values in the obstacle-dense regions. Additionally, the use of regular grids results in excessive computation. These drawbacks cause the algorithm to plan irrational paths and reduce computational efficiency. This study proposed an activity-value-optimised BINN and adaptive cell decomposition for the dynamic path planning of autonomous bulldozers. First, an adaptive cell decomposition method was proposed to model a highly intricate and constantly evolving environment, resulting in a reduction in the computational workload of the BINN. Second, the calculation of the activity value is optimised by enhancing the shunt equation of the BINN, which considers both obstacles and grid levels. This improvement enabled more reasonable distribution results. The smoothness of dynamic path planning is refined by incorporating the turning radius, which can be more effectively integrated with local path planning. Compared with the original BINN algorithm and other baseline algorithms, a practical large-scale hydropower project application demonstrated that the proposed method can effectively reduce the number of computational iterations, enrich the travel direction, and improve path quality while ensuring dynamic obstacle avoidance.

Introduction and application of a drive-by damage detection methodology for bridges using variational mode decomposition

In this research, the variational mode decomposition (VMD) method is used for the drive-by health monitoring of bridges. Firstly, the problem of a half-trailer tractor moving over a bridge is formulated. Next, a Finite Element (FE) code is developed and verified against modal analysis results where complete agreement is found. The vehicle's output signals are decomposed through VMD and then analyzed to identify and precisely locate damage in the bridge structure. The range of applicability of this technique is examined from different perspectives by including various road classes, damage severity and location, and noise. The results prove the robustness and reliability of using VMD for drive-by damage detection. The method outcomes indicate that through the VMD method, cracks with a depth of 10% to 20% of the beam height can be detected even in the case of a rough road profile. A comparison of the results of the VMD and the well-known empirical mode decomposition (EMD) method has also been conducted. This comparison reveals that by implementing the VMD, precise damage locations can be determined, whereas the EMD fails to detect any damage under the conditions considered in this study. The effects of noise and moving vehicle speed are also investigated in the research, and it is found that processing the output signals using VMD can yield reliable estimates of the damage location(s).

New fixed-time/preassigned-time stability results of impulsive systems and its application to synchronization of delayed octonion-valued neural networks

In this paper, in order to obtain more accurate settling time estimation and overcome the complexity of the theoretical analysis caused by using decomposition method, the problem of fixed-time (F-T) and preassigned-time (P-T) synchronization of delayed octonion-valued neural networks with impulsive effects is explored by utilizing direct octonion approach. First, a new fixed-time stability theorem for impulsive system is presented by using the comparison principle, average impulsive interval and Bernoulli differential equation theory. Second, to solve the problems of non associative and non commutative laws of octonion, some novel lemmas are introduced, which are the basis of the direct octonion approach. Third, under the improved F-T stability result, a new P-T stability theorem is derived and the settling time is preassigned according to realistic need and is irrelevant with any initial values and any parameters. Then, based on newly established theorems and developed controller strategies, some sufficient conditions are deduced to guarantee F-T and P-T synchronization of the discussed systems. Finally, a numerical simulation is provided to verify the effectiveness of the theoretical results.

Unified method for predicting the fatigue life of pipe–sphere joints in grid structures

Pipe–sphere joints (PSJs), which are commonly used in grid structures, are susceptible to fatigue failure under cyclic loading caused by suspended cranes. This paper presents a unified method for predicting the fatigue life of PSJs, including the weld toes on both the pipe and sphere surfaces, based on equivalent structural stress. A decomposition and recombination fitting (DRF) method was proposed to determine the optimal functional form of stress concentration factors (SCFs). A nonlinear regression analysis was conducted on the calculated results of 61 common PSJs to obtain a semi-analytical expression for the structural stress of the weld toes. Using this unified method, the fatigue life of the weld toes on both the pipe and sphere surfaces was estimated. The results indicated that the logarithmic ratios between the predicted fatigue life and experimental results were typically 0.93–1.08 for weld toes on pipe surfaces and 0.97–1.13 for weld toes on spherical surfaces, confirming the accuracy of the method. This unified method is applicable to predict the fatigue life of PSJs of various sizes and involves concise mathematical calculations independent of finite element analysis, thereby facilitating engineering applications. The DRF method can be used as a reference for fitting SCFs to specific structures. Furthermore, this prediction method enables the identification of the failure modes in PSJs. As the size of the steel pipe increased, the fatigue failure gradually shifted from the pipe surface to the sphere surface.

The picker routing problem in mixed-shelves, multi-block warehouses

Unlike conventional warehouses, where the inventory of an item is concentrated on a single shelf, the inventory in mixed-shelves warehouses is broken down into units and dispersed throughout the warehouse. As a result, the locations from where items are to be retrieved must be chosen when designing the picker tour. The rare research on the picker routing problem (PRP) in mixed-shelves warehouses focuses on (and heavily exploits the properties of) 1-block warehouses, which are warehouses with only two cross aisles. Here, we tackle the more general PRP in mixed-shelves, multi-block warehouses. To solve the problem, we propose a logic-based Benders decomposition method whereby the master problem selects the locations from where each item is retrieved and the subproblem designs the associated picker tour. We introduce tailored optimality cuts and prove their validity. In addition, we propose a set of various techniques to enhance the performance of the logic-based Benders decomposition, including a lower bounding function and valid inequalities. We show the efficiency and effectiveness of the proposed method through extensive computational experiments carried out on both standard instances from the literature (for the 1-block setting) and newly generated instances (for our case). We also leverage our algorithms to generate managerial insights into the benefits of multi-block layouts and mixed-shelves policies in warehousing.

A weighted prior tensor train decomposition method for community detection in multi-layer networks

Community detection in multi-layer networks stands as a prominent subject within network analysis research. However, the majority of existing techniques for identifying communities encounter two primary constraints: they lack suitability for high-dimensional data within multi-layer networks and fail to fully leverage additional auxiliary information among communities to enhance detection accuracy. To address these limitations, a novel approach named weighted prior tensor training decomposition (WPTTD) is proposed for multi-layer network community detection. Specifically, the WPTTD method harnesses the tensor feature optimization techniques to effectively manage high-dimensional data in multi-layer networks. Additionally, it employs a weighted flattened network to construct prior information for each dimension of the multi-layer network, thereby continuously exploring inter-community connections. To preserve the cohesive structure of communities and to harness comprehensive information within the multi-layer network for more effective community detection, the common community manifold learning (CCML) is integrated into the WPTTD framework for enhancing the performance. Experimental evaluations conducted on both artificial and real-world networks have verified that this algorithm outperforms several mainstream multi-layer network community detection algorithms.