Search Results (1,496)

Electrical networks face operational challenges from power quality-affecting disturbances. Since disturbance signatures directly affect classifier performance, optimized feature selection becomes critical for accurate power quality assessment. The pursuit of robust feature extraction inevitably constrains the dimensionality of the discriminative feature set, but the complexity of the recognition model will be increased and the recognition speed will be reduced if the feature vector dimension is too high. Building upon the aforementioned requirements, in this paper, we propose a feature extraction framework that combines improved symplectic geometric mode decomposition, refined generalized multiscale quantum entropy, and refined generalized multiscale reverse dispersion entropy. Firstly, based on the intrinsic properties of power quality disturbance (PQD) signals, the embedding dimension of symplectic geometric mode decomposition and the adaptive mode component screening method are improved, and the PQD signal undergoes tri-band decomposition via improved symplectic geometric mode decomposition (ISGMD), yielding distinct high-frequency, medium-frequency, and low-frequency components. Secondly, utilizing the enhanced symplectic geometric mode decomposition as a foundation, the perturbation features are extracted by the combination of refined generalized multiscale quantum entropy and refined generalized multiscale reverse dispersion entropy to construct high-precision and low-dimensional feature vectors. Finally, a double-layer composite power quality disturbance model is constructed by a deep extreme learning machine algorithm to identify power quality disturbance signals. After analysis and comparison, the proposed method is found to be effective even in a strong noise environment with a single interference, and the average recognition accuracy across different noise environments is 97.3%. Under the complex conditions involving multiple types of mixed perturbations, the average recognition accuracy is maintained above 96%. Compared with the existing CNN + LSTM method, the recognition accuracy of the proposed method is improved by 3.7%. In addition, its recognition accuracy in scenarios with small data samples is significantly better than that of traditional methods, such as single CNN models and LSTM models. The experimental results show that the proposed strategy can accurately classify and identify various power quality interferences and that it is better than traditional methods in terms of classification accuracy and robustness. The experimental results of the simulation and measured data show that the combined feature extraction methodology reliably extracts discriminative feature vectors from PQD. The double-layer combined classification model can further enhance the model’s recognition capabilities. This method has high accuracy and certain noise resistance. In the 30 dB white noise environment, the average classification accuracy of the model is 99.10% for the simulation database containing 63 PQD types. Meanwhile, for the test data based on a hardware platform, the average accuracy is 99.03%, and the approach’s dependability is further evidenced by rigorous validation experiments. Full article

The most widely used nanowire channel architecture for creating state-of-the-art high-performance transistors is the nanowire wrap-gate transistor, which offers low power consumption, high carrier mobility, large electrostatic control, and high-speed switching. The frequency-dependent capacitance and conductance measurements of triangular-shaped GaN nanowire wrap-gate transistors are measured in the frequency range of 1 kHz–1 MHz at room temperature to investigate carrier trapping effects in the core and at the surface. The performance of such a low-dimensional device is greatly influenced by its surface traps. With increasing applied frequency, the calculated trap density promptly decreases, from 1.01 × 10¹³ cm⁻² eV⁻¹ at 1 kHz to 8.56 × 10¹² cm⁻²eV⁻¹ at 1 MHz, respectively. The 1/f-noise features show that the noise spectral density rises with applied gate bias and shows 1/f-noise behavior in the accumulation regime. The fabricated device is controlled by 1/f-noise at lower frequencies and 1/f²-noise at frequencies greater than ~ 0.2 kHz in the surface depletion regime. Further generation–recombination (G-R) is responsible for the 1/f²-noise characteristics. This process is primarily brought on by electron trapping and detrapping via deep traps situated on the nanowire’s surface depletion regime. When the device works in the deep-subthreshold regime, the cut-off frequency for the 1/f²-noise characteristics further drops to a lower frequency of 30 Hz–10⁴ Hz. Full article

The exponential growth of multi-modal data in the real world poses significant challenges to efficient retrieval, and traditional single-modal methods are no longer suitable for the growth of multi-modal data. To address this issue, hashing retrieval methods play an important role in cross-modal retrieval tasks when referring to a large amount of multi-modal data. However, effectively embedding multi-modal data into a common low-dimensional Hamming space remains challenging. A critical issue is that feature redundancies in existing methods lead to suboptimal hash codes, severely degrading retrieval performance; yet, selecting optimal features remains an open problem in deep cross-modal hashing. In this paper, we propose an end-to-end approach, named Robust Supervised Deep Discrete Hashing (RSDDH), which can accomplish feature learning and hashing learning simultaneously. RSDDH has a hybrid deep architecture consisting of a convolutional neural network and a multilayer perceptron adaptively learning modality-specific representations. Moreover, it utilizes a non-redundant feature selection strategy to select optimal features for generating discriminative hash codes. Furthermore, it employs a direct discrete hashing scheme (SVDDH) to solve the binary constraint optimization problem without relaxation, fully preserving the intrinsic properties of hash codes. Additionally, RSDDH employs inter-modal and intra-modal consistency preservation strategies to reduce the gap between modalities and improve the discriminability of learned Hamming space. Extensive experiments on four benchmark datasets demonstrate that RSDDH significantly outperforms state-of-the-art cross-modal hashing methods. Full article

In the Ultra-High-Definition (UHD) domain, blind image quality assessment remains challenging due to the high dimensionality of UHD images, which exceeds the input capacity of deep learning networks. Motivated by the visual discrepancies observed between high- and low-quality images after down-sampling and Super-Resolution (SR) reconstruction, we propose a SUper-Resolved Pseudo References In Dual-branch Embedding (SURPRIDE) framework tailored for UHD image quality prediction. SURPRIDE employs one branch to capture intrinsic quality features from the original patch input and the other to encode comparative perceptual cues from the SR-reconstructed pseudo-reference. The fusion of the complementary representation, guided by a novel hybrid loss function, enhances the network’s ability to model both absolute and relational quality cues. Key components of the framework are optimized through extensive ablation studies. Experimental results demonstrate that the SURPRIDE framework achieves competitive performance on two UHD benchmarks (AIM 2024 Challenge, PLCC = 0.7755, SRCC = 0.8133, on the testing set; HRIQ, PLCC = 0.882, SRCC = 0.873). Meanwhile, its effectiveness is verified on high- and standard-definition image datasets across diverse resolutions. Future work may explore positional encoding, advanced representation learning, and adaptive multi-branch fusion to align model predictions with human perceptual judgment in real-world scenarios. Full article

Streak cameras, essential for ultrahigh temporal resolution diagnostics in laser-driven inertial confinement fusion, underpin the streak tube imaging LiDAR (STIL) system—a flash LiDAR technology offering high spatiotemporal resolution, precise ranging, enhanced sensitivity, and wide field of view. This study establishes a theoretical model of the STIL system, with numerical simulations predicting limits of temporal and spatial resolutions of ~6 ps and 22.8 lp/mm, respectively. Dynamic simulations of laser backscatter signals from targets at varying depths demonstrate an optimal distance reconstruction accuracy of 98%. An experimental STIL platform was developed, with the key parameters calibrated as follows: scanning speed (16.78 ps/pixel), temporal resolution (14.47 ps), and central cathode spatial resolution (20 lp/mm). The system achieved target imaging through streak camera detection of azimuth-resolved intensity profiles, generating raw streak images. Feature extraction and neural network-based three-dimensional (3D) reconstruction algorithms enabled target reconstruction from the time-of-flight data of short laser pulses, achieving a minimum distance reconstruction error of 3.57%. Experimental results validate the capability of the system to detect fast, low-intensity optical signals while acquiring target range information, ultimately achieving high-frame-rate, high-resolution 3D imaging. These advancements position STIL technology as a promising solution for applications that require micron-scale depth discrimination under dynamic conditions. Full article

Island settlement landscapes exhibit distinctive characteristics, and investigating their spatio–temporal differentiation features and formation mechanisms is crucial for effective landscape conservation. This study selected Qida Village, Beigang Village, and Jingsha Village in Fuzhou City, Fujian Province, China, as representative cases. It constructed an integrated evaluation framework termed “livelihood transformation–two dimensional expansion–three dimensional form” and systematically analyzed the spatio–temporal differentiation characteristics and driving mechanisms of island settlement landscapes under the context of livelihood transformation by integrating multi-source data. Research findings indicate that livelihood transformation significantly affects both the horizontal expansion and vertical evolution of settlement landscapes. Aquaculture-based villages demonstrate a high expansion rate (15.10%) and pronounced vertical differentiation (building height difference ratio of 13.30) due to industrial agglomeration. Tourism service-oriented villages, influenced by policy regulation, exhibit low architectural style heterogeneity (0.35) and a harmonized skyline. Villages experiencing significant out-migration show a high housing vacancy rate (64.70%) and reduced spatial compactness (0.13) due to population decline. The livelihood model drives landscape differentiation through the “population mobility–economic investment–land use” pathway, where capital accumulation and policy constraints emerge as key determinants of spatial form heterogeneity. This study provides a solid theoretical foundation and methodological support for the differentiated governance of island settlement landscapes. Full article

To address the issue of low accuracy in the dynamic modal decomposition (DMD) method used for operational modal analysis (OMA) under noise conditions of aerospace structures, an enhanced identification approach is proposed in this paper, which integrates subspace orthogonal projection with DMD to better determine the modal properties of linear mechanical systems with noisy observations. Subspace orthogonal projection applied to the Hankelized matrix is utilized for denoising observation signals. Compact singular value decomposition (SVD) is employed on the projection matrix in order to acquire the optimal realization of system matrix. Subsequently, DMD is introduced to reduce the dimensionality of the system matrix and extract the dominant modal features. The effectiveness and practicality of the proposed method are confirmed through numerical and experimental examples. The proposed method demonstrates marginally improved identification accuracy in modal frequency and enhanced performance in damping ratios when compared to representative OMA methods under different white noise conditions. Full article

Functional near-infrared spectroscopy (fNIRS) offers a safe and portable signal source for brain–computer interface (BCI) applications, particularly in motor imagery (MI) decoding. However, its low sampling rate and hemodynamic delay pose challenges for temporal modeling and dynamic brain network analysis. To address these limitations in temporal dynamics, static graph modeling, and feature fusion interpretability, we propose TopoTempNet, an innovative topology-enhanced temporal network for biomedical signal decoding. TopoTempNet integrates multi-level graph features with temporal modeling through three key innovations: (1) multi-level topological feature construction using local and global functional connectivity metrics (e.g., connection strength, density, global efficiency); (2) a graph-modulated attention mechanism combining Transformer and Bi-LSTM to dynamically model key connections; and (3) a multimodal fusion strategy uniting raw signals, graph structures, and temporal representations into a high-dimensional discriminative space. Evaluated on three public fNIRS datasets (MA, WG, UFFT), TopoTempNet achieves superior accuracy (up to 90.04% ± 3.53%) and Kappa scores compared to state-of-the-art models. The ROC curves and t-SNE visualizations confirm its excellent feature discrimination and structural clarity. Furthermore, the statistical analysis of graph features reveals the model’s ability to capture task-specific functional connectivity patterns, enhancing the interpretability of decoding outcomes. TopoTempNet provides a novel pathway for building interpretable and high-performance BCI systems based on fNIRS. Full article

Accurately estimating aboveground carbon storage (AGC) of urban vegetation remains a major challenge, due to the heterogeneity and vertical complexity of urban environments, where traditional forest-based remote sensing models often perform poorly. This study integrates multimodal remote sensing data and incorporates two three-dimensional structural features—mean tree height ($H_{mean}$) and canopy cover ratio (CCR)—in addition to conventional spectral and textural variables. To minimize redundancy, the Boruta algorithm was applied for feature selection, and four machine learning models (SVR, RF, XGBoost, and CatBoost) were evaluated. Results demonstrate that under multimodal data fusion, three-dimensional features emerge as the dominant predictors, with XGBoost using Boruta-selected variables achieving the highest accuracy (R² = 0.701, RMSE = 0.894 tC/400 m²). Spatial mapping of AGC revealed a “high-aggregation, low-dispersion” pattern, with the model performing best in large, continuous green spaces, while accuracy declined in fragmented or small-scale vegetation patches. Overall, this study highlights the potential of machine learning with multi-source variable inputs for fine-scale urban AGC estimation, emphasizes the importance of three-dimensional vegetation indicators, and provides practical insights for urban carbon assessment and green infrastructure planning. Full article

Text classification is essential for handling the large volume of user-generated textual content in social networks. Nowadays, dense word representation techniques, especially those yielded by large language models, capture rich semantic and contextual information from text that is useful for classification tasks, but generates high-dimensional vectors that hinder the efficiency and scalability of the classification algorithms. Despite this, limited research has explored effective dimensionality reduction techniques to balance representation quality with computational demands. This study presents a deep learning-based framework for enhancing text classification in social networks, focusing on computational performance, by compressing high-dimensional text representations into a low-dimensional space while retaining essential features for text classification. To demonstrate the feasibility of the proposal, we conduct a benchmarking study using traditional dimensionality reduction techniques on two widely used benchmark datasets. The findings reveal that our approach can substantially improve the efficiency of text classification in social networks without compromising—and, in some cases, enhancing—the predictive performance. Full article

Induction motors are the dominant choice in industrial applications due to their robustness, structural simplicity, and high reliability. However, extended operation under extreme conditions, such as high temperatures, overload, and contamination, accelerates the degradation of internal components and increases the likelihood of faults. These faults are challenging to detect, as they typically develop gradually without clear external indicators. To address this issue, the present study proposes a cost-effective fault diagnosis system utilizing low-cost MEMS acoustic sensors in conjunction with a lightweight multilayer neural network (MNN). The same MNN architecture is employed to systematically compare three types of input feature representations: raw time-domain waveforms, FFT-based statistical features, and PCA-compressed FFT features. A total of 5040 samples were used to train, validate, and test the model for classifying three conditions: normal, rotor fault, and bearing fault. The time-domain approach achieved 90.6% accuracy, misclassifying 102 samples. In comparison, FFT-based statistical features yielded 99.8% accuracy with only two misclassifications. The FFT + PCA method produced similar performance while reducing dimensionality, making it more suitable for resource-constrained environments. These results demonstrate that acoustic-based fault diagnosis provides a practical and economical solution for industrial applications. Full article

With the rapid advancement in laser scanning technologies, the capability to collect massive volumes of data and richer detailed features has been significantly enhanced. However, the differential representation ability of multi-source point clouds in capturing intricate structures within complex scenes, combined with the computational burden imposed by large datasets, presents substantial challenges to current registration methods. The proposed method encompasses two innovative feature point pruning techniques and two closely interconnected progressive processes. First, it identifies structural points that effectively represent the features of the scene and performs a rapid initial alignment of point clouds within the two-dimensional plane. Subsequently, it establishes the mapping relationship between the point clouds to be matched utilizing FPFH descriptors, followed by further screening to extract the maximum consensus set composed of points that meet constraints based on the intensity of graph nodes. Then, it integrates the processes of feature point description and similarity measurement to achieve precise point cloud registration. The proposed method effectively extracts matching primitives from large datasets, addressing issues related to false matches and noise in complex data environments. It has demonstrated favorable matching results even in scenarios with low overlap between datasets. On two public datasets and a self-constructed dataset, the method achieves an effective point set screening rate of approximately 1‰. On the WHU-TLS dataset, our method achieves a registration accuracy characterized by a rotation precision of 0.062° and a translation precision of 0.027 m, representing improvements of 70% and 80%, respectively, over current state-of-the-art (SOTA) methods. The results obtained from real registration tasks demonstrate that our approach attains competitive registration accuracy when compared with existing SOTA techniques. Full article

Steady-State Visual Evoked Potentials (SSVEPs) have emerged as an efficient means of interaction in brain–computer interfaces (BCIs), achieving bioinspired efficient language output for individuals with aphasia. Addressing the underutilization of frequency information of SSVEPs and redundant computation by existing transformer-based deep learning methods, this paper analyzes signals from both the time and frequency domains, proposing a stacked encoder–decoder (SED) network architecture based on an xLSTM model and spatial attention mechanism, termed SED-xLSTM, which firstly applies xLSTM to the SSVEP speller field. This model takes the low-channel spectrogram as input and employs the filter bank technique to make full use of harmonic information. By leveraging a gating mechanism, SED-xLSTM effectively extracts and fuses high-dimensional spatial-channel semantic features from SSVEP signals. Experimental results on three public datasets demonstrate the superior performance of SED-xLSTM in terms of classification accuracy and information transfer rate, particularly outperforming existing methods under cross-validation across various temporal scales. Full article

Hyperspectral images are rich in spectral and spatial information. However, their high dimensionality and complexity pose significant challenges for effective feature extraction. Specifically, the performance of existing models for hyperspectral image (HSI) classification remains constrained by spectral redundancy among adjacent bands, misclassification at object boundaries, and significant noise in hyperspectral data. To address these challenges, we propose WTCMC—a novel hyperspectral image classification network based on wavelet transform combining Mamba and convolutional neural networks. To establish robust shallow spatial–spectral relationships, we introduce a shallow feature extraction module (SFE) at the initial stage of the network. To enable the comprehensive and efficient capture of both spectral and spatial characteristics, our architecture incorporates a low-frequency spectral Mamba module (LFSM) and a high-frequency multi-scale convolution module (HFMC). The wavelet transform suppresses noise for LFSM and enhances fine spatial and contour features for HFMC. Furthermore, we devise a spectral–spatial complementary fusion module (SCF) that selectively preserves the most discriminative spectral and spatial features. Experimental results demonstrate that the proposed WTCMC network attains overall accuracies (OA) of 98.94%, 98.67%, and 97.50% on the Pavia University (PU), Botswana (BS), and Indian Pines (IP) datasets, respectively, outperforming the compared state-of-the-art methods. Full article

Clustering algorithms based on non-negative matrix factorization (NMF) have garnered significant attention in data mining due to their strong interpretability and computational simplicity. However, traditional NMF often struggles to effectively capture and preserve topological structure information between data during low-dimensional representation. Therefore, this paper proposes an autoencoder-like sparse non-negative matrix factorization with structure relationship preservation (ASNMF-SRP). Firstly, drawing on the principle of autoencoders, a “decoder-encoder” co-optimization matrix factorization framework is constructed to enhance the factorization stability and representation capability of the coefficient matrix. Then, a preference-adjusted random walk strategy is introduced to capture higher-order neighborhood relationships between samples, encoding multi-order topological structure information of the data through an optimal graph regularization term. Simultaneously, to mitigate the impact of noise and outliers, the $l_{2,1}$-norm is used to constrain the feature correlation between low-dimensional representations and the original data, preserving feature relationships between data, and a sparse constraint is imposed on the coefficient matrix via the inner product. Finally, clustering experiments conducted on 8 public datasets demonstrate that ASNMF-SRP consistently exhibits favorable clustering performance. Full article