Search Results (312)

In cross-project defect prediction (CPDP), divergence in feature distribution between the source and target often fails when predicting defects across unrelated projects. Such a mismatch is typical rather than exceptional in real deployment scenarios. To address this, the Transfer Component Analysis (TCA) technique projects the source and target into a shared subspace, where Maximum Mean Discrepancy is minimized. However, combining fixed ensemble classifiers with TCA on NASA datasets is yet to be explored. Previous studies either searched ensemble compositions adaptively or conflated alignment with source selection. In this study, we trained a two-layer hybrid ensemble of Bagging and AdaBoost classifiers, alongside a Logistic Regression meta-learner, on TCA-aligned features across 20 directed source–target pairs. We used five PROMISE datasets, each having 21 McCabe and Halstead features. Experiments were conducted, and the findings show that, against an unaligned baseline using the same ensemble, TCA alignment increased mean AUC by 0.131 (0.625 to 0.755), mean F1 by 0.155, and mean MCC by 0.125. Wilcoxon signed-rank tests confirmed significance across all three metrics (p < 0.003, rank-biserial r = 0.714, Cliff’s Delta d ≥ 0.545). A full ablation showed that alignment was the dominant contributor to performance, with TCA improving AUC by +0.131 over the unaligned baseline. In contrast, SMOTE traded a small AUC reduction (−0.014) for substantial F1 gains (+0.134), while the stacking layer provided modest improvements in F1 and MCC. TCA reduced MMD across all 20 source–target pairs by a mean of 83.3%. Sensitivity analysis further showed that the TCA subspace dimensionality parameter, k, exhibited non-monotone, pair-specific AUC sensitivity, with optimal values ranging from 5 to 30. These findings indicate that alignment parameter selection substantially influences CPDP performance and that a fixed global default is inadequate. Full article

Direction-of-arrival (DOA) estimation is a core research topic in array signal processing, and two-dimensional (2-D) DOA estimation can jointly acquire the azimuth and elevation of incoming signals, which bears great practical value. Traditional subspace and sparse reconstruction algorithms are plagued by heavy computation and deteriorated accuracy under imperfect array manifolds, low signal-to-noise ratios (SNRs) and insufficient snapshots. To enhance estimation robustness and inference speed simultaneously, this paper presents a dual-branch convolutional neural network (CNN) for 2-D DOA estimation based on uniform rectangular arrays. The network takes the sample covariance matrix of array received data as input. A shared feature encoder with residual blocks and channel-attention modules extracts common spatial features, followed by two prediction heads with independent parameters for elevation and azimuth estimation. Because each branch has a 61-dimensional output while two sources may be simultaneously present, the angle estimation is formulated as multi-label classification using sigmoid outputs and weighted binary cross-entropy. Simulations covering diverse SNRs, snapshot counts, angular intervals and off-grid cases verify that the proposed network obtains smaller root mean square errors than methods with multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance techniques (ESPRIT) and ordinary CNN methods, with millisecond-level inference latency. This framework offers an efficient, high-precision real-time 2-D DOA estimation scheme for complicated electromagnetic scenes. Full article

The blades directly affect the safety and power generation efficiency of the wind turbines. With the blade size increases, the reliable modal identification becomes important for vibration-based health monitoring. Although operational modal analysis (OMA) technique has been used in condition monitoring for the wind turbine blades, most existing studies focus on investigating a specific single method or under ideal excitation conditions. To overcome this limitation, this study takes the IEA-15MW large wind turbine blade as the research object and compares three OMA methods through numerical simulations, namely covariance-driven stochastic subspace identification (SSI-COV), frequency domain decomposition (FDD), and poly-reference least squares complex frequency domain (PolyMAX). The performance of the modal parameter identification methods is evaluated with respect to different sensor layouts, blade–tower coupling conditions, and environmental excitations. The results indicate that sparse sensor deployment cannot reliably identify the damage-sensitive high-order and complex modes. A nine-channel layout concentrated near second-order deformation regions significantly improves the identification of second-order flapwise frequencies and controls the average error of the first six modes within 3%. PolyMAX shows the best identification stability under different numbers and layouts of the sensors. Blade–tower coupling changes the blade modal characteristics and increases identification difficulty. Under this condition, FDD can still identify both low-order and high-order modes with good stability. Under different real wind conditions, the increasing wind speed causes the aerodynamic load to deviate from the white noise assumption, generally leading to fluctuations in the identification errors, with relatively large local errors occurring at certain medium and high wind speeds. Overall, the three OMA methods show different advantages under different identification conditions. PolyMAX shows the best stability under different sensor layouts and performs best when wind speed increases in the coupled wind turbine model, indicating that it is the most suitable for the actual complex coupling effects and environmental conditions. This research hopefully provides a basis for the subsequent engineering application of vibration-based modal identification of large offshore blades. Full article

Stay cables are critical components of cable-stayed bridges, and their dynamic characteristics provide essential information for vibration assessment and structural health monitoring. This study presents a comparative performance assessment of Fiber Bragg Grating (FBG) sensors and PCB accelerometers for field-based dynamic characterization of stay cables. Field measurements were conducted on selected stay cables of My Thuan Bridge under normal operating conditions. Since PCB accelerometers measure acceleration, whereas FBG sensors capture dynamic strain or wavelength-shift responses, the comparison was performed primarily in the frequency domain rather than through direct time-domain amplitude equivalence. The measured responses were processed using signal preprocessing, power spectral density analysis, and covariance-driven stochastic subspace identification. Dominant dynamic frequencies were identified from both sensing systems and compared using frequency agreement, frequency deviation, spectral peak consistency, and practical field applicability. The results show that the common frequencies identified from FBG and PCB measurements are in good agreement, with an overall mean relative difference of 0.94%. The FBG sensors also detected additional candidate frequency components that showed an approximately regular progression consistent with stay-cable vibration, although these components require further validation. These findings indicate that FBG sensing can provide reliable and complementary frequency-domain information for stay-cable dynamic characterization. The study demonstrates the feasibility of using FBG sensors as an alternative or complementary sensing technique to conventional accelerometers for field-based vibration monitoring of cable-stayed bridges. Full article

Passive acoustic localization in complex shallow waters requires algorithms tailored to specific operational constraints. This paper investigates the adaptability, computational efficiency, and statistical performance boundaries of five matched-field processing (MFP) methods—Bartlett, Minimum Variance Distortionless Response (MVDR), Multiple Signal Classification (MUSIC), Reduced Covariance Matrix (RCM), and Rank and Trace Minimization (RTM)—using the Elba-93 sea trial dataset. Error metrics and processing complexities are systematically evaluated across stationary and maneuvering target scenarios. Rigorous non-parametric statistical tests reveal distinct operational boundaries: under stationary conditions dominated by systemic environmental mismatch, energy-based processors guarantee reliable baseline stability. Conversely, under snapshot-deficient dynamic conditions tracking a receding target, standard high-resolution subspace methods become highly vulnerable to trajectory jumps. In such highly dynamic scenarios, adaptive energy-based processors (specifically MVDR) exhibit the most stable tracking continuity and lowest numerical peak errors. Simultaneously, the operational adaptability of subspace methods is improved via covariance matrix reconstruction (CMR). Specifically, the RCM technique effectively decouples unstructured sensor noise, mitigating maximum trajectory deviations and providing a balanced trade-off between computational efficiency and robustness. Statistical evaluations confirm the fundamental performance boundaries in static environments, while highlighting sample-size limitations in highly dynamic scenarios, thereby establishing a realistic, evidence-based benchmark for marine engineering applications. Full article

Frequency diverse array (FDA)-aided spacetime adaptive processing (STAP) is a useful approach to suppressing range-ambiguous clutter in airborne radars. In general, FDA-STAP schemes based on subspace techniques can provide excellent clutter rejection capability when only limited secondary data are available. However, transmit array gain and phase errors may cause serious performance degradation. To overcome this problem, a robust subspace-based FDA-STAP algorithm is proposed in this work: RSUB-FDA-STAP. Firstly, the influence of transmit array errors on subspace-based FDA-STAP is analyzed, indicating that the errors must be calibrated. Secondly, an error calibration vector is constructed, and an optimization problem is formulated to estimate the errors. Finally, by minimizing the objective function of the optimization problem, the errors are effectively estimated with limited secondary data, and an effective RSUB-FDA-STAP is developed for range-ambiguous clutter suppression. The proposed method effectively suppresses range-ambiguous clutter under transmit array gain and phase errors in comparison with existing robust FDA-STAP techniques. Numerical simulations demonstrate that the developed RSUB-FDA-STAP shows superior robustness against transmit array gain and phase errors. Full article

The new type of multi-airbag hybrid airship is a novel lighter-than-air platform, but its flexible structures pose challenges for accurate modal parameter identification under complex fluid-structure interaction. Traditional methods often fail to capture the dynamic characteristics of such compliant systems. In this paper, a stochastic subspace identification method is proposed to estimate the modal parameters of the three capsule hybrid airship. The method constructs the Hankel matrix using only output response data and extracts the system matrix by singular value decomposition so as to identify the natural frequency and damping coefficient. Moreover, the numerical model of the airship (aspect ratio 2.22) is built, and the simulated response data (first five modes) are used to validate the approach. The results show that the identified frequencies and damping ratios match the theoretical values with a maximum error of 6.35%, demonstrating good accuracy and robustness. The proposed technique can provide a reliable tool for online modal identification of flexible airships, supporting structural health monitoring and vibration control. Full article

Efficient multi-hop prediction over large-scale spatio-temporal knowledge graphs of aircraft taxiing trajectories remains challenging, as existing methods focus either on static multi-hop relations or on accuracy improvement for spatio-temporal single-hop predictions, leading to computational inefficiency. This paper proposes a vector-index-supported multi-hop prediction method. First, a knowledge graph embedding technique that integrates spatio-temporal features maps the trajectory graph into a low-dimensional complex vector space. Then, a hierarchical query acceleration structure based on IndexIVFFlat is constructed. A clustering strategy guided by the distribution of trajectory data partitions the vector space into subspaces, and approximate nearest neighbor search within those subspaces rapidly prunes the candidate set to accelerate multi-hop retrieval. Experiments on real aircraft taxiing trajectory datasets and general benchmarks show that the proposed method substantially improves prediction efficiency while maintaining competitive accuracy. The results demonstrate that the vector index mechanism effectively balances accuracy and efficiency, and the efficiency has been improved by at least 56.65%. This work provides a key technical foundation for real-time analysis and intelligent prediction of large-scale aircraft taxiing trajectories. Full article

This paper studies the problem of controller design for polynomial fuzzy-model-based (PFMB) systems. To make full use of the information of membership functions (MFs), the operating space is partitioned into several subspaces. According to the information of partitions, switched state feedback and output feedback controllers are designed, respectively, for the system. By employing the approximated membership function method and a new relaxation technique, relaxed stabilization criteria in the form of the sum of squares are derived without the need to impose any constraints on system matrices. Simulation examples are provided to illustrate the validity of the presented method. Full article

Air pollution is primarily caused by human activities such as industrial emissions, road traffic, waste incineration, and fossil fuel power plants. Pollution refers to the presence of harmful substances in the air, such as nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), carbon monoxide (CO), and other environmental pollutants. Some pollutants pose health risks even at low doses. Given the critical importance of air quality, monitoring air pollution has become an urgent and essential subject. Air quality monitoring relies on accurate data, so changeable environments and sensor issues make using interval diagnostic techniques for addressing uncertainty in systems interesting. In this article, we focus on three key aspects to achieve precise and efficient results: (1) the use of an accurate fault detection method that accounts for data uncertainty while maintaining model symmetry, (2) the implementation of a reliable detection index invariant to symmetric sensor behaviors, and (3) the combination of both to improve fault localization accuracy. This paper presented a fault detection and localization framework designed for uncertain and nonlinear monitoring environments. A novel fault-sensitive detection index was developed and integrated into an elimination-based localization strategy within a reduced-rank interval kernel PCA (RR-IKPCA) model. By exploiting information contained in modified residual subspaces and explicitly accounting for measurement uncertainty, the proposed approach enhances fault sensitivity while preserving robust localization capability, as validated on the AIRLOR air quality monitoring network. Full article

To address the limitations of traditional structural damage identification methods in terms of reliance on high-fidelity baseline models and sensitivity to minor damage, this paper proposes a novel physics-informed and data-driven approach based on the modal curvature variation coefficient. A damage-sensitive feature derived from the rate of change in the radius of curvature is established, providing a clear mathematical and physical interpretation to reduce model error interference and enhance local damage localization. The effectiveness of the proposed method is validated through a 1:20 scale model experiment of a main truss from a large stadium steel roof. A total of 33 experimental cases were designed, simulating single and multiple damage scenarios with varying severity levels (large, medium, and small). Multi-source monitoring techniques, including millimeter-wave radar interferometry, laser displacement sensors, high-resolution vision-based measurement, and accelerometers, were integrated. Modal parameters were extracted using the Stochastic Subspace Identification (SSI) method, and the finite element model was updated via a high-order response surface methodology. Numerical simulations and experimental results demonstrate that the proposed modal curvature variation coefficient is highly sensitive to local stiffness degradation and accurately locates both single and multiple large/medium damage regions. In cases involving multiple minor damages, the method effectively identifies the damaged areas but exhibits a risk of false positives in undamaged sections. The millimeter-wave radar measurements exhibit strong agreement with laser displacement data, confirming its viability for non-contact structural health monitoring. This research provides a robust technical framework and experimental foundation for condition assessment and early damage warning in large-scale engineering structures. Full article

To reduce the hardware cost of massive multiple-input multiple-output (MIMO) systems, low-bit analog-to-digital converters (ADCs) and sparse arrays are widely used. Compared with traditional 1-bit and 2-bit quantization techniques, 1.5-bit quantization uses two symmetric non-zero thresholds to quantize signal power into three levels, thereby balancing quantization complexity against system performance. However, the quantization loss introduced by 1.5-bit quantization is still significant and leads to degradation in DOA estimation performance. To improve the DOA estimation accuracy of 1.5-bit sparse massive MIMO systems, a covariance matrix estimation method is proposed. This method exploits the Toeplitz property of the covariance matrix of sparse arrays and the relationship between 1.5-bit quantized signals and their unquantized counterparts to transform the covariance matrix estimation problem for 1.5-bit sparse arrays into a non-convex optimization problem with equality constraints. We then further exploit the properties of 1.5-bit quantized signals to relax this problem into a convex problem and solve it via semidefinite programming. Once the covariance is estimated, the DOAs can be recovered by subspace-based methods. Numerical results show that the proposed method achieves higher estimation accuracy than 1.5B-MUSIC and 1-bit covariance-fitting baselines on 1.5-bit sparse arrays, and is competitive with structured covariance-fitting baselines applied to unquantized data, especially on coprime arrays in low-snapshot scenarios. Full article

GPS-based passive bistatic radar (PBR) benefits from global satellite coverage for target surveillance. However, multiple GPS satellites within the PBR mainlobe generate cross-correlation interference (CCI) that severely masks target echoes, reducing the detection probability to zero across significant portions of the surveillance area. Existing reconstruction-based suppression methods rely on iterative frequency estimation, which introduces substantial errors during the convergence stage of the tracking loop, leading to degraded interference suppression performance. This paper proposes an autoregressive steady-state compensation (ARSSC) method to address this limitation. First, a precise carrier frequency estimation model is established to accelerate convergence and improve tracking accuracy. Second, the frequency estimation outputs are partitioned into convergence and steady-state stages, and a p-th order autoregressive (AR) model is fitted to the steady-state estimates. A compensation function is then derived from the AR model to correct the frequency errors in the convergence stage. Finally, the compensated reconstructed CCI signals are used to construct an interference subspace, and a projection-based algorithm suppresses the CCI from the surveillance signal. Simulation results demonstrate that the proposed ARSSC method achieves a maximum interference suppression improvement of 7.4 dB compared to conventional reconstruction approaches. Real-data experiments conducted under different field scenarios further validate the method, yielding a 6.3 dB interference suppression ratio (ISR) improvement over traditional reconstruction techniques in both tested cases. Full article

Mixup is a data augmentation technique that improves prediction accuracy in classification tasks by combining representations of training samples, which makes it particularly effective in settings with limited data and during fine-tuning for downstream tasks. However, representations generated by mixup may appear unnatural, which can negatively affect fine-tuning performance. To address this limitation, we propose a vision transformer (ViT)-aware variant of mixup strategies, Dimensional Swap Mix (DiSMix). DiSMix divides a representation vector into two segments corresponding to subspaces of the original feature space and generates new representations by swapping one segment with that from another sample and concatenating the segments. This allows part of the original representation to remain unchanged, enabling the model to learn from partially preserved features. We evaluate DiSMix by applying several mixup-based methods to fine-tune ViTs on the VTAB-1k benchmark. The findings show that DiSMix improves accuracy on the VTAB-1k Natural split, reaching 80.0%, compared with conventional mixup methods. This suggests that DiSMix is an effective alternative for representation-level data augmentation in fine-tuning scenarios. Full article

In the classical Stochastic Subspace Identification (SSI) method, mode selection is primarily based on frequency stability, damping stability, and mode shape similarity using the Modal Assurance Criterion (MAC). However, these criteria are often insufficient for reliable modal identification in high-noise environments. This study advances beyond the classical approach by introducing a multi-criteria optimization framework for mode evaluation. In addition to the conventional frequency and damping assessments utilized in the classical SSI method, the proposed approach incorporates a range of supplementary structural metrics. These include Density, Cosine Similarity Difference (CSD), Damping Stability (DS), Spatial Roughness (SR), Mode Shape Complexity (MSC), Signal Energy Coherence (SEC), and Normalized Modal Difference (NMD). These metrics are computed within specifically optimized windows on the stabilization diagram. By integrating spatial, phase, and energy-based characteristics of mode shapes alongside traditional metrics such as the MAC, the method enables a more comprehensive and robust mode selection process that surpasses the limitations of relying solely on frequency and damping stability. Compared to the classical SSI, the optimized window approach provides a significant advantage by enabling the reliable selection of consistent modes by considering the continuity and multi-criteria coherence of modes across window transitions. As a result, the elimination of noise modes and the reliable separation of structural modes are established on a more systematic basis. To achieve this, a two-stage optimization strategy is implemented: the first stage determines the optimal frequency window width and minimum mode count threshold, while the second stage utilizes a Multi-Criteria Decision Making (MCDM) framework based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to assign optimized weights to the structural metrics and rank the candidate windows accordingly. As a result, the ideal frequency window is identified based on its TOPSIS score and subsequently validated using the MAC, confirming that the selected window corresponds to reliable structural modes. The framework is validated using long-term in situ measurements from a Roller Compacted Concrete (RCC) dam operating under significant environmental and operational noise. The dataset comprises continuous, high-resolution (200 Hz) vibration recordings collected between 1 July 2023 and 30 October 2024. While the calendar duration is limited to several weeks, the uninterrupted 24 h measurements yield a high-density time-series dataset with substantial information content, enabling a statistically meaningful and robust evaluation of modal identification performance under real-world and noisy conditions. The results reveal that relying solely on traditional selection criteria such as pole density and the MAC can often lead to the identification of spurious modes, particularly in noisy environments. In contrast, the proposed TOPSIS-based multi-criteria decision-making framework incorporates a broader range of structural indicators, balancing frequency, damping, spatial, and energy-related metrics to enhance the consistency and reliability of mode selection. This approach proved effective even under high-noise conditions, successfully distinguishing true structural modes from artificial ones. Application of the TOPSIS method to RCC dam data revealed consistent fundamental frequencies at approximately 5–10 Hz, 10 Hz, and 15 Hz, confirming its robustness and suitability for complex structural monitoring tasks. Full article