Search Results (1,225)

In this article, the problem of Bayesian detecting rank-one distributed targets under subspace interference and compound Gaussian clutter with inverse Gaussian texture is investigated. Due to the clutter heterogeneity, the training data may be insufficient. To tackle this problem, the clutter speckle covariance matrix (CM) is assumed to obey the complex inverse Wishart distribution, and the Bayesian theory is utilized to obtain an effective estimation. Moreover, the target echo is assumed to be with a known steering vector and unknown amplitudes across range cells. The interference is regarded as a steering matrix that is linearly independent of the target steering vector. By utilizing the generalized likelihood ratio test (GLRT), a Bayesian interference-canceling detector that can work in the absence of training data is derived. Moreover, five interference-cancelling detectors based on the maximum a posteriori (MAP) estimate of the speckle CM are proposed with the two-step GLRT, the Rao, Wald, Gradient, and Durbin tests. Experiments with simulated and measured sea clutter data indicate that the Bayesian interference-canceling detectors show better performance than the competitor in scenarios with limited training data. Full article

When utilizing underwater gliders to observe submerged targets, ensuring the quality and reliability of the acquired target characteristic signals is paramount. However, the signal acquisition process is significantly compromised by noise generated from various motors on the platform, which severely contaminates the authentic target signal characteristics, thereby complicating subsequent research efforts such as target identification. Given the limited capability of wavelet transforms in processing complex non-stationary signals, and considering the non-stationary and non-linear nature of the signals in question, this study focuses on the denoising of hydroacoustic signals and the characteristics of motor noise. Building upon the traditional CEEMDAN-SVD approach, we propose an adaptive noise reduction method that combines the maximum singular value of motor noise with the differential spectrum of singular values. In particular, this paper delves into the symmetry between the noise subspace and the signal subspace in SVD decomposition. By analyzing the symmetric characteristics of their singular value distributions, the process of separating noise from signals is further optimized. The effectiveness of this denoising method is analyzed and validated through simulations and experiments. The results demonstrate that under a signal-to-noise ratio (SNR) of 3 dB, the improved CEEMDAN-SVD method reduces the mean square error by an average of 22.8% and decreases the absolute value of skewness by 27.8% compared to the traditional CEEMDAN-SVD method. These findings indicate that our proposed method exhibits superior noise reduction capabilities under strong non-stationary motor noise interference, effectively enhancing the SNR and reinforcing signal characteristics. This provides a robust foundation for improving the recognition rate of hydroacoustic targets in subsequent research. Full article

This paper describes a Krylov subspace iterative method designed for solving linear systems of equations with a large, symmetric, nonsingular, and indefinite matrix. This method is tailored to enable the evaluation of error estimates for the computed iterates. The availability of error estimates makes it possible to terminate the iterative process when the estimated error is smaller than a user-specified tolerance. The error estimates are calculated by leveraging the relationship between the iterates and Gauss-type quadrature rules. Computed examples illustrate the performance of the iterative method and the error estimates. Full article

Least-squares reverse time migration (LSRTM) is an advanced seismic imaging technique that reconstructs subsurface models by minimizing the residuals between simulated and observed data. Mathematically, the LSRTM inversion of the sub-surface reflectivity is a large-scale, highly ill-posed sparse inverse problem, where conventional inversion methods typically lead to poor imaging quality. In this study, we propose a regularized LSRTM method based on the flexible Krylov subspace inversion framework. Through the strategy of the Krylov subspace projection, a basis set for the projection solution is generated, and then the inversion of a large ill-posed problem is expressed as the small matrix optimization problem. With flexible preconditioning, the proposed method could solve the sparse regularization LSRTM, like with the Tikhonov regularization style. Sparse penalization solution is implemented by decomposing it into a set of Tikhonov penalization problems with iterative reweighted norm, and then the flexible Golub–Kahan process is employed to solve the regularization problem in a low-dimensional subspace, thereby finally obtaining a sparse projection solution. Numerical tests on the Valley model and the Salt model validate that the LSRTM based on Krylov subspace method can effectively address the sparse inversion problem of subsurface reflectivity and produce higher-quality imaging results. Full article

Microwave tomography serves as a promising non-invasive technique for breast imaging, yet accurate reconstruction in noisy environments remains challenging. We propose an adaptive subspace-based two-step iterative shrinkage/thresholding (S-TwIST) algorithm that enhances reconstruction accuracy through two key innovations: a singular value decomposition (SVD) approach for extracting deterministic contrast sources, and an adaptive strategy for optimal singular value selection. Unlike conventional DBIM methods that rely solely on secondary incident fields, S-TwIST incorporates deterministic induced currents to achieve more accurate total field approximation. The algorithm’s performance is validated using both synthetic “Austria” profiles and 45 digital breast phantoms derived from the UWCEM repository. The results demonstrate robust reconstruction capabilities across varying noise levels (0–20 dB SNR), achieving average relative errors of 0.4847% in breast tissue reconstruction without requiring prior noise level knowledge. The algorithm successfully recovers complex tissue structures and density distributions, showing potential for clinical breast imaging applications. Full article

Power transformers are vital for maintaining the reliability and stability of electrical systems. However, their vulnerability to faults, such as partial discharges and winding deformation, poses significant operational risks. Advanced diagnostic techniques are essential for timely fault detection and predictive maintenance. This study investigates the application of machine learning (ML) techniques in transformer fault detection using Frequency Response Analysis (FRA) data. The study aims to evaluate the effectiveness of various ML models, the impact of frequency variations, and the contribution of numerical indices to fault classification accuracy. FRA data, comprising 50 to 70 measurements per transformer, were segmented into eight frequency bands (20 kHz to 12 MHz). A systematic approach utilizing a confusion matrix was applied to classify faults such as partial discharges and winding deformation. The performance of ML models, including Decision Trees and Subspace KNN, was assessed in terms of classification accuracy. Machine learning models achieved fault classification accuracies ranging from 80% to 100% across eight frequency bands (20 kHz to 12 MHz). Decision Tree models excelled in detecting insulation faults, achieving 100% accuracy for faults such as thermal aging (Class A), electrical stress (Class B), and moisture ingress (Class C). Subspace KNN models demonstrated strong performance for core-related faults, with classification accuracies of 100% for core displacement (Class B) and core buckling (Class C), but they faced challenges with lamination deformation, achieving 75% accuracy. Contamination-related faults exhibited a 100% False Negative Rate (FNR), indicating a need for model refinement. Fault detection was consistent across frequency bands, with key diagnostic markers at 7.6 MHz, 8.25 MHz, and 8.7 MHz providing high diagnostic value. Machine learning integration into FRA-based diagnostics enhances the accuracy and reliability of transformer fault detection. While current results are promising, future research should focus on deep learning approaches and enhanced feature extraction to address challenges such as data scarcity and fault diversity. Full article

Local feature descriptors are a critical problem in computer vision; the majority of current approaches find it difficult to achieve a balance between descriptiveness, robustness, compactness, and efficiency. This paper proposes the local discrete feature histogram (LDFH), a novel local feature descriptor, as a solution to this problem. The LDFH descriptor is constructed based on a robust local reference frame (LRF). It partitions the local space based on radial distance and calculates three geometric features, including the normal deviation angle, polar angle, and normal lateral angle, in each subspace. These features are then discretized to generate three feature statistical histograms, which are combined using a weighted fusion strategy to generate the final LDFH descriptor. Experiments on public datasets demonstrate that, compared with the existing methods, LDFH strikes an excellent balance between descriptiveness, robustness, compactness, and efficiency, making it suitable for various scenes and sensor datasets. Full article

Rolling element bearings are key components for reducing friction and supporting rotors. Harsh working conditions contribute to the wear of bearings and consequent breakdown of machines, which leads to economic losses and even catastrophic accidents. Faulty impulses from bearings can excite resonance behavior in a system and produce modulation phenomena. Fault characteristics in modulated signals can be extracted using demodulation analysis methods, significantly improving the reliability and effectiveness of the fault diagnosis of rolling bearings. Optimal demodulation frequency band selection is a primary step for the demodulation-analysis-based fault diagnosis of bearing faults. To exploit the resonant modulation mechanism in the fault diagnosis of rolling element bearings, resonant frequencies identified through stochastic subspace identification are employed to guide the impulsive sparsity measures of an Autogram for bearing fault diagnosis, which combines physical modulation dynamics and data characteristics. The frequency band that not only matches the natural frequencies but also shows highly sparse impulsive characteristics is selected as the optimal demodulation frequency band for bearing fault diagnosis. The results of simulations and experimental data validate the advantages of the proposed method, which exploits physics-guided data processing for optimal demodulation frequency band determination. Full article

Systems may depend on parameters that can be controlled, serve to optimise the system, are imposed externally, or are uncertain. This last case is taken as the “Leitmotiv” for the following discussion.A reduced-order model is produced from the full-order model through some kind of projection onto a relatively low-dimensional manifold or subspace. The parameter-dependent reduction process produces a function mapping the parameters to the manifold.One now wants to examine the relation between the full and the reduced state for all possible parameter values of interest. Similarly, in the field of machine learning, a function mapping the parameter set to the image space of the machine learning model is learned from a training set of samples, typically minimising the mean square error. This set may be seen as a sample from some probability distribution, and thus the training is an approximate computation of the expectation, giving an approximation of the conditional expectation—a special case of Bayesian updating, where the Bayesian loss function is the mean square error. This offers the possibility of having a combined view of these methods and also of introducing more general loss functions. Full article

This paper deals with developing a comparative study of the principal time-domain system identification methods suitable for performing an experimental modal analysis of structural systems. To this end, this work focuses first on analyzing and reviewing the mathematical background concerning the analytical methods and the computational algorithms of interest for this study. The methods considered in the paper are referred to as the AutoRegressive eXogenous (ARX) method, the State-Space ESTimation (SSEST) method, the Numerical Algorithm for Subspace State-Space System Identification (N4SID), the Eigensystem Realization Algorithm (ERA) combined with the Observer/Kalman Filter Identification (OKID) method, and the Transfer Function ESTimation (TFEST) method. Starting from the identified models estimated through the methodologies reported in the paper, a set of second-order configuration-space dynamical models of the structural system of interest can also be determined by employing an estimation method for the Mass, Stiffness, and Damping (MSD) matrices. Furthermore, in practical applications, the correct estimation of the damping matrix is severely hampered by noise that corrupts the input and output measurements. To address this problem, in this paper, the identification of the damping matrix is improved by employing the Proportional Damping Coefficient (PDC) identification method, which is based on the use of the identified set of natural frequencies and damping ratios found for the case study analyzed in the paper. This work also revisits the critical aspects and pitfalls related to using the Model Order Reduction (MOR) approach combined with the Balanced Truncation Method (BTM) to reduce the dimensions of the identified state-space models. Finally, this work analyzes the performance of all the fundamental system identification methods mentioned before when applied to the experimental modal analysis of flexible structures. This is achieved by carrying out an experimental campaign based on the use of a vibrating test rig, which serves as a demonstrative example of a typical structural system. The complete set of experimental results found in this investigation is reported in the appendix of the paper. Full article

Conventional adaptive beamformers usually suffer from serious performance degradation when the receive array is imperfect and unknown sporadic interferences appear. To enhance robustness against array imperfections and simultaneously suppress sporadic interferences, this paper studies robust adaptive beamforming (RAB) with accurate sidelobe level (SLL) control, where the imperfect array steering vector (SV) is expressed as a spherical uncertainty set. Under the maximum signal-to-interference-plus-noise ratio (SINR) criterion and robust SLL constraints, we formulate the resultant RAB into a second-order cone programming problem, which is computationally prohibitive due to numerous robust quadratic SLL constraints. To tackle this issue, we provide a subspace approximation-based method to approximate the whole sidelobe space, thus replacing all robust SLL constraints with a single subspace constraint. Moreover, we leverage the Gauss–Legendre quadrature-based scheme to generate the sidelobe space in a computationally efficient manner. Additionally, we give an explicit approach for determining the norm upper bound of SV uncertainty sets under various imperfection scenarios, addressing the challenge of obtaining this upper bound in practice.Simulation results showed that the proposed subspace approximation-based RAB beamformer had a better SINR performance than typical counterparts and was much more computationally efficient. Full article

Multi-view subspace clustering enhances clustering performance by optimizing and integrating structural information from multiple views. Recently, anchor-based methods have made notable progress in large-scale clustering scenarios by leveraging anchor points to capture data distribution across different views. Although these methods improve efficiency, a common limitation is that they typically select an equal number of anchor points from each view. Additionally, during the graph fusion stage, most existing frameworks use simple linear weighting to construct the final consensus graph, overlooking the inherent structural relationships between the data. To address these issues, we propose a novel and flexible anchor graph fusion framework which selects an appropriate number of anchor points for each view based on its data space, creating suitable anchor graphs. In the graph fusion stage, we introduce a regularization term which adaptively and flexibly combines anchor graphs of varying sizes. Moreover, our approach incorporates both global and local information between views, enabling a more accurate capture of the cluster structure within the data. Furthermore, our method operates with linear time complexity, making it well suited for large-scale datasets. Extensive experiments on multiple datasets demonstrate the superior performance of our proposed algorithm. Full article

Non-invasive temperature monitoring is highly valuable in applications such as microwave hyperthermia treatment, where overheating may damage healthy tissue. This paper presents a subspace-based method for real-time temperature monitoring using a sensor array configuration. The proposed method improves upon the conventional Born approximation (BA) approach by accurately estimating the total field through primary induced currents. The temperature-dependent dielectric properties of breast tissues are modeled using data from porcine tissues, and a sigmoid function is employed to create realistic temperature transition zones in the numerical breast phantom. The method is validated through extensive simulations under noise-free and noisy conditions (SNR = 30 dB and 20 dB). The results demonstrate that our method maintains consistent performance across different temperature levels (38–45 °C), achieving reconstruction accuracy within ±0.2 °C at SNR = 30 dB and ±0.5 °C at SNR = 20 dB. While the computational overhead of calculating primary induced currents slightly increases the overall processing time, it leads to a faster convergence in the cost function minimization. These findings suggest that the proposed method offers a promising solution for real-time temperature monitoring in microwave hyperthermia applications. Full article

Transfer-path analysis (TPA) is a reliable and effective diagnostic tool for determining the dominant vibration transfer paths from the actively vibrating components to the connected passive substructures in complex assemblies. Conventional and component-based TPA approaches achieve this by estimating a set of forces that replicate the operational responses on the passive side of the assembly, requiring separate measurements of the transfer-path admittance and the operational responses, followed by an indirect estimation of the interface forces. This demands significant measurement effort, especially when only the dominant transfer paths are desired. Operational transfer-path analysis (OTPA) overcomes this by identifying transfer-path contributions solely from operational response measurements. However, OTPA is susceptible to measurement errors as minor inaccuracies can result in discrepancies regarding transfer-path characterization. This is especially evident when poor placement of the sensors results in similar response measurements from multiple channels, introducing redundancy and amplifying measurement noise. This is typically resolved using regularization techniques (e.g., singular-value truncation and Tikhonov regularization) that promote vibration transfer related to dominant singular vectors. As an alternative, this paper explores the benefits of using established reduction-based approaches from dynamic substructuring within OTPA. Measured responses are projected onto different dynamic sub-spaces that include the dominant dynamic behavior of the interface between the active and passive sides (i.e., dominant interface modes). In this way, only the vibration transfer related to the interface modes included in the reduction step is evaluated, leaving stiff modes obscured by noise unobserved. This paper proposes using interface-deformation modes and physical modes, demonstrating their feasibility via various experimental setups and comparing them to standard OTPA. Full article

Fuzzy topology has long been celebrated for its ability to address real-world challenges in areas such as information systems and decision making. However, with ongoing technological advancements and the increasing complexity of practical requirements, the focus has gradually shifted toward neutrosophic topology, a broader and more inclusive framework than fuzzy topology. While neutrosophic topology is primarily rooted in neutrosophic open sets, other related families, including neutrosophic pre-open sets, neutrosophic semi-open sets, and neutrosophic beta-open sets, have also proven instrumental in driving progress in this field. This study introduces neutrosophic $\delta$-open sets as a significant enhancement to the current theoretical framework. In addition, we propose a novel category of separation axioms, termed neutrosophic $\delta$-separation axioms, which are derived from the concept of neutrosophic $\delta$-open sets. Moreover, we explore the interplay between these separation properties and their characteristics within subspaces. Our findings confirm that neutrosophic $\delta$-separation axioms are reliably upheld in neutrosophic regular open subspaces. Full article