Search Results (64)

This study proposes a Swin-ReshoUnet architecture with a three-level enhancement mechanism to address inefficiencies in multi-scale feature extraction and gradient degradation in deep networks for high-precision seismic exploration. The encoder uses a hierarchical convolution module to build a multi-scale feature pyramid, enhancing cross-scale geological signal representation. The decoder replaces traditional self-attention with ORCA attention to enable global context modeling with lower computational cost. Skip connections integrate a residual channel attention module, mitigating gradient degradation via dual-pooling feature fusion and activation optimization, forming a full-link optimization from low-level feature enhancement to high-level semantic integration. Simulated and real dataset experiments show that at decimation ratios of 0.1–0.5, the method significantly outperforms SwinUnet, TransUnet, etc., in reconstruction performance. Residual signals and F-K spectra verify high-fidelity reconstruction. Despite increased difficulty with higher sparsity, it maintains optimal performance with notable margins, demonstrating strong robustness. The proposed hierarchical feature enhancement and cross-scale attention strategies offer an efficient seismic profile signal reconstruction solution and show generality for migration to complex visual tasks, advancing geophysics-computer vision interdisciplinary innovation. Full article

Hyperspectral image classification faces challenges with high-dimensional spectral data and complex dependencies between bands. This paper proposes HyperspectralMamba, a novel architecture for hyperspectral image classification that integrates state space modeling with adaptive recalibration mechanisms. The method addresses limitations in existing techniques through three key innovations: (1) a novel dual-stream architecture that combines SSM global modeling with parallel convolutional local feature extraction, distinguishing our approach from existing single-stream SSM methods; (2) a band-adaptive feature recalibration mechanism specifically designed for hyperspectral data that adaptively adjusts the importance of different spectral band features; and (3) an effective feature fusion strategy that integrates global and local features through residual connections. Experimental results on three benchmark datasets—Indian Pines, Pavia University, and Salinas Valley—demonstrate that the proposed method achieves overall accuracies of 95.31%, 98.60%, and 96.40%, respectively, significantly outperforming existing convolutional neural networks, attention-enhanced networks, and Transformer methods. HyperspectralMamba demonstrates an exceptional performance in small-sample class recognition and distinguishing spectrally similar terrain, while maintaining lower computational complexity, providing a new technical approach for high-precision hyperspectral image classification. Full article

Agricultural pest detection is critical for crop protection and food security, yet existing methods suffer from low computational efficiency and poor generalization due to imbalanced data distribution, minimal inter-class variations among pest categories, and significant intra-class differences. To address the high computational complexity and inadequate feature representation in traditional convolutional networks, this study proposes MA-YOLO, an agricultural pest detection model based on multi-scale fusion and attention mechanisms. The SDConv module reduces computational costs through depthwise separable convolution and dynamic group convolution while enhancing local feature extraction. The LDSPF module captures multi-scale information via parallel dilated convolutions with spatial attention mechanisms and dual residual connections. The ASCC module improves feature discriminability by establishing an adaptive triple-weight system for global, channel, and spatial semantic responses. The MDF module balances efficiency and multi-scale feature extraction using multi-branch depthwise separable convolution and soft attention-based dynamic weighting. Experimental results demonstrate detection accuracies of 65.4% and 73.9% on the IP102 and Pest24 datasets, respectively, representing improvements of 2% and 1.8% over the original YOLOv11s network. These results establish MA-YOLO as an effective solution for automated agricultural pest monitoring with applications in precision agriculture and crop protection systems. Full article

This paper addresses the central issue arising from the (SDAC) of facial expressions, namely, to balance the competing demands of good global features for detection, and fine features for good facial expression classifications by replacing the feature extraction part of the “neck” network in the feature pyramid network in the You Only Look Once X (YOLOX) framework with a novel architecture involving three attention mechanisms—batch, channel, and neighborhood—which respectively explores the three input dimensions—batch, channel, and spatial. Correlations across a batch of images in the individual path of the dual incoming paths are first extracted by a self attention mechanism in the batch dimension; these two paths are fused together to consolidate their information and then split again into two separate paths; the information along the channel dimension is extracted using a generalized form of channel attention, an adaptive graph channel attention, which provides each element of the incoming signal with a weight that is adapted to the incoming signal. The combination of these two paths, together with two skip connections from the input to the batch attention to the output of the adaptive channel attention, then passes into a residual network, with neighborhood attention to extract fine features in the spatial dimension. This novel dual path architecture has been shown experimentally to achieve a better balance between the competing demands in an SDAC problem than other competing approaches. Ablation studies enable the determination of the relative importance of these three attention mechanisms. Competitive results are obtained on two non-aligned face expression recognition datasets, RAF-DB and SFEW, when compared with other state-of-the-art methods. Full article

The complementary dual of entropy has received significant attention in the literature. Due to the emergence of many generalizations and extensions of entropy, the need to generalize the complementary dual of uncertainty arose. This article develops the residual cumulative generalized fractional extropy as a generalization of the residual cumulative complementary dual of entropy. Many properties, including convergence, transformation, bounds, recurrence relations, and connections with other measures, are discussed. Moreover, the proposed measure’s order statistics and stochastic order are examined. Furthermore, the dynamic design of the measure, its properties, and its characterization are considered. Finally, nonparametric estimation via empirical residual cumulative generalized fractional extropy with an application to blood transfusion is performed. Full article

In recent years, with the rapid development of the Internet and mobile devices, the high-resolution video industry has ushered in a booming golden era, making video content the primary driver of Internet traffic. This trend has spurred continuous innovation in efficient video coding technologies, such as Advanced Video Coding/H.264 (AVC), High Efficiency Video Coding/H.265 (HEVC), and Versatile Video Coding/H.266 (VVC), which significantly improves compression efficiency while maintaining high video quality. However, during the encoding process, compression artifacts and the loss of visual details remain unavoidable challenges, particularly in high-resolution video processing, where the massive amount of image data tends to introduce more artifacts and noise, ultimately affecting the user’s viewing experience. Therefore, effectively reducing artifacts, removing noise, and minimizing detail loss have become critical issues in enhancing video quality. To address these challenges, this paper proposes a post-processing method based on Convolutional Neural Network (CNN) that improves the quality of VVC-reconstructed frames through deep feature extraction and fusion. The proposed method is built upon a high-resolution dual-path residual gating system, which integrates deep features from different convolutional layers and introduces convolutional blocks equipped with gating mechanisms. By ingeniously combining gating operations with residual connections, the proposed approach ensures smooth gradient flow while enhancing feature selection capabilities. It selectively preserves critical information while effectively removing artifacts. Furthermore, the introduction of residual connections reinforces the retention of original details, achieving high-quality image restoration. Under the same bitrate conditions, the proposed method significantly improves the Peak Signal-to-Noise Ratio (PSNR) value, thereby optimizing video coding quality and providing users with a clearer and more detailed visual experience. Extensive experimental results demonstrate that the proposed method achieves outstanding performance across Random Access (RA), Low Delay B-frame (LDB), and All Intra (AI) configurations, achieving BD-Rate improvements of 6.1%, 7.36%, and 7.1% for the luma component, respectively, due to the remarkable PSNR enhancement. Full article

Epidemics spread through shipping networks and have dual characteristics as both biological sources of infection and triggers of cascading failures. However, existing resilience models fail to capture this dual and coupled dynamics. To minimize the cascading impacts of epidemics on global shipping networks, this paper proposes an innovative resilience assessment framework that considers the interaction between epidemic transmission and the shipping network cascading failure. First, a weighted shipping network topology is constructed based on route flow characteristics to quantify route frequency, stopping time, and the number of infected people, and the epidemic transmission across ports is modeled with an improved SEIR model, which contains a heterogeneous infectivity function and a dynamic transmission matrix, revealing a dual transmission mechanism inside and outside the ports. Second, a two-stage cascading failure model is developed: a direct failure triggered by infected people exceeding the threshold and an indirect failure triggered by the dynamic redistribution of loads. The load redistribution strategy is optimized to reconcile the residual port capacity and the risk of infection. Finally, a multidimensional resilience assessment framework covering structural destruction resistance, network efficiency, path redundancy, and a cascading failure propagation rate is constructed. Example validation shows that the improved load redistribution strategy reduces the maximum connected subgraph decay rate by 68.2%, reduces the cascading failure rate by 88%, and improves the peak network efficiency by 128.2%. In case of multi-source epidemics, the state of the network collapse can be shortened by 12 days if the following recovery strategy is adopted: initially repair high connectivity hubs (e.g., Port of Shanghai), and then repair high centrality nodes (e.g., Antwerp Port) to achieve a balance between recovery efficiency and network functionality. The research results reduce the risk of systemic disruptions in maritime networks and provide decision-making tools for dynamic port scheduling during pandemics. Full article

Single-pixel imaging has the characteristics of a simple structure and low cost, which means it has potential applications in many fields. This paper proposes an image reconstruction method for single-pixel imaging (SPI) based on deep learning. This method takes the Generative Adversarial Network (GAN) as the basic architecture, combines the dense residual structure and the deep separable attention mechanism, and reduces the parameters while ensuring the diversity of feature extraction. It also reduces the amount of computation and improves the computational efficiency. In addition, dual-skip connections between the encoder and decoder parts are used to combine the original detailed information with the overall information processed by the network structure. This approach enables a more comprehensive and efficient reconstruction of the target image. Both simulations and experiments have confirmed that the proposed method can effectively reconstruct images at low sampling rates and also achieve good reconstruction results on natural images not seen during training, demonstrating a strong generalization capability. Full article

Traditional underwater acoustic reconnaissance technologies are limited in directly detecting underwater acoustic communication signals. This paper proposes a dual-feature ResNet–Transformer model with two innovative breakthroughs: (1) A dual-modal fusion architecture of ResNet and Transformer is constructed using residual connections to alleviate gradient degradation in deep networks and combining multi-head self-attention to enhance long-distance dependency modeling. (2) The time–frequency representation obtained from the smooth pseudo-Wigner–Ville distribution is used as the first input branch, and higher-order statistics are introduced as the second input branch to enhance phase feature extraction and cope with channel interference. Experiments on the Danjiangkou measured dataset show that the model improves the accuracy by 6.67% compared with the existing Convolutional Neural Network (CNN)–Transformer model in long-distance ranges, providing an efficient solution for modulation recognition in complex underwater acoustic environments. Full article

In the realm of natural language processing (NLP), text classification constitutes a task of paramount significance for large language models (LLMs). Nevertheless, extant methodologies predominantly depend on the output generated by the final layer of LLMs, thereby neglecting the wealth of information encapsulated within neurons residing in intermediate layers. To surmount this shortcoming, we introduce LENS (Linear Exploration and Neuron Selection), an innovative technique designed to identify and sparsely integrate salient neurons from intermediate layers via a process of linear exploration. Subsequently, these neurons are transmitted to downstream modules dedicated to text classification. This strategy effectively mitigates noise originating from non-pertinent neurons, thereby enhancing both the accuracy and computational efficiency of the model. The detection of telecommunication fraud text represents a formidable challenge within NLP, primarily attributed to its increasingly covert nature and the inherent limitations of current detection algorithms. In an effort to tackle the challenges of data scarcity and suboptimal classification accuracy, we have developed the LENS-RMHR (Linear Exploration and Neuron Selection with RoBERTa, Multi-head Mechanism, and Residual Connections) model, which extends the LENS framework. By incorporating RoBERTa, a multi-head attention mechanism, and residual connections, the LENS-RMHR model augments the feature representation capabilities and improves training efficiency. Utilizing the CCL2023 telecommunications fraud dataset as a foundation, we have constructed an expanded dataset encompassing eight distinct categories that encapsulate a diverse array of fraud types. Furthermore, a dual-loss function has been employed to bolster the model’s performance in multi-class classification scenarios. Experimental results reveal that LENS-RMHR demonstrates superior performance across multiple benchmark datasets, underscoring its extensive potential for application in the domains of text classification and telecommunications fraud detection. Full article

Maintenance of the exteriors of buildings with convex façades, such as skyscrapers, is in high demand in urban centers. However, manual maintenance is inherently dangerous due to the possibility of accidental falls. Therefore, research has been conducted on cleaning robots as a replacement for human workers, e.g., the dual ascension robot (DAR), which is an underactuated rope-driven robot, and the rope-riding mobile anchor (RMA), which is a rope-riding robot. These robots are equipped with a convex-façade-cleaning system. The DAR and RMA are connected to each other by a rope that enables vibration transmission between them. It also increases the instability of the residual vibration that occurs during the operation of the DAR. This study focused on reducing the residual vibrations of a DAR to improve the stability of the overall system. Because it is a rope-on-rope (ROR) system, we assumed it to be a simplified serial spring–damper system and analyzed its kinematics and dynamics. An input-shaping technique was applied to control the residual vibrations in the DAR. We also applied a disturbance observer to mitigate factors contributing to the system uncertainty, such as rope deformation, slip, and external forces. We experimentally validated the system and assessed the effectiveness of the control method, which consisted of the input shaper and disturbance observer. Consequently, the residual vibrations were reduced. Full article

Paint loss is one of the major forms of deterioration in ancient murals and color paintings, and its detection and segmentation are critical for subsequent restoration efforts. However, existing methods still suffer from issues such as incomplete segmentation, patch noise, and missed detections during paint loss extraction, limiting the automation of paint loss detection and restoration. To tackle these challenges, this paper proposes PLDS-YOLO, an improved model based on YOLOv8s-seg, specifically designed for the detection and segmentation of paint loss in ancient murals and color paintings. First, the PA-FPN network is optimized by integrating residual connections to enhance the fusion of shallow high-resolution features with deep semantic features, thereby improving the accuracy of edge extraction in deteriorated areas. Second, a dual-backbone network combining CSPDarkNet and ShuffleNet V2 is introduced to improve multi-scale feature extraction and enhance the discrimination of deteriorated areas. Third, SPD-Conv replaces traditional pooling layers, utilizing space-to-depth transformation to improve the model’s ability to perceive deteriorated areas of varying sizes. Experimental results on a self-constructed dataset demonstrate that PLDS-YOLO achieves a segmentation accuracy of 86.2%, outperforming existing methods in segmentation completeness, multi-scale deterioration detection, and small target recognition. Moreover, the model maintains a favorable balance between computational complexity and inference speed, providing reliable technical support for intelligent paint loss monitoring and digital restoration. Full article

As a non-associative connective in fuzzy logic, the analysis and research of overlap functions have been extended to many generalized cases, such as interval-valued and intuitionistic fuzzy overlap functions (IFOFs). However, overlap functions face challenges in the Pythagorean fuzzy (PF) environment. This paper first extends overlap functions to the PF domain by proposing PF overlap functions (PFOFs), discussing their representable forms, and providing a general construction method. It then introduces a new PF similarity measure which addresses issues in existing measures (e.g., the inability to measure the similarity of certain PF numbers) and demonstrates its effectiveness through comparisons with other methods, using several examples in fractional form. Based on the proposed PFOFs and their induced residual implication, new generalized PF rough sets (PFRSs) are constructed, which extend the PFRS models. The relevant properties of their approximation operators are explored, and they are generalized to the dual-domain case. Due to the introduction of hesitation in IF and PF sets, the approximate accuracy of classical rough sets is no longer applicable. Therefore, a new PFRS approximate accuracy is developed which generalizes the approximate accuracy of classical rough sets and remains applicable to the classical case. Finally, three multi-criteria decision-making (MCDM) algorithms based on PF information are proposed, and their effectiveness and rationality are validated through examples, making them more flexible for solving MCDM problems in the PF environment. Full article

In the quest to improve energy efficiency in residential environments, home energy management systems (HEMSs) have emerged as an effective solution, leveraging artificial intelligence (AI) technologies to improve energy efficiency. This study proposes a deep learning-based approach employing Long Short-Term Memory (LSTM) neural networks to predict household energy usage based on power consumption data from common appliances, such as lamps, fans, air conditioners, televisions, and computers. The model comprises two interrelated submodels: one predicts the individual energy consumption and usage time of each device, while the other estimates the total energy consumption of connected appliances. This dual structure enhances accuracy by capturing both device-specific consumption patterns and overall household energy use, facilitating informed decision-making at multiple levels. Following a systematic methodology that includes model building, training, and evaluation, the LSTM model achieved a low test set loss and mean squared error (MSE), with values of 0.0163 for individual consumption and usage time and 0.0237 for total consumption. Additionally, the predictive performance was strong, with MSE values of 1.0464 × 10⁻⁶ for usage time, 0.0163 for individual consumption, and 0.0168 for total consumption. The analysis of scatter plots and residuals revealed a high degree of correspondence between predicted and actual values, validating the model’s accuracy and reliability in energy forecasting. This study represents a significant advancement in intelligent home energy management, contributing to improved efficiency and promoting sustainable consumption practices. Full article

Audio tagging, as a fundamental task in acoustic signal processing, has demonstrated significant advances and broad applications in recent years. Spiking Neural Networks (SNNs), inspired by biological neural systems, exploit event-driven computing paradigms and temporal information processing, enabling superior energy efficiency. Despite the increasing adoption of SNNs, the potential of event-driven encoding mechanisms for audio tagging remains largely unexplored. This work presents a pioneering investigation into event-driven encoding strategies for SNN-based audio tagging. We propose the SATRN (Spiking Audio Tagging Robust Network), a novel architecture that integrates temporal–spatial attention mechanisms with membrane potential residual connections. The network employs a dual-stream structure combining global feature fusion and local feature extraction through inverted bottleneck blocks, specifically designed for efficient audio processing. Furthermore, we introduce an event-based encoding approach that enhances the resilience of Spiking Neural Networks to disturbances while maintaining performance. Our experimental results on the Urbansound8k and FSD50K datasets demonstrate that the SATRN achieves comparable performance to traditional Convolutional Neural Networks (CNNs) while requiring significantly less computation time and showing superior robustness against noise perturbations, making it particularly suitable for edge computing scenarios and real-time audio processing applications. Full article