Search Results (5,447)

Accurate fault diagnosis of rotating machinery in complex industrial environments demands an optimal trade-off between feature representation capability and computational efficiency. Existing single-modality models relying solely on 1D time-series signals or heavy 2D time-frequency images often fail to simultaneously capture high-frequency transient impacts and long-range degradation trends. CLiST (Complementary Lightweight Spatiotemporal Network), a novel lightweight multimodal framework driven by time-frequency fusion, was proposed to overcome this limitation. The architecture of CLiST employs a synergistic dual-stream design: a LightTS module efficiently extracts global operational trends from 1D vibration signals with linear complexity, while a structurally pruned LiteSwin integrated with Triplet Attention captures local high-frequency textures from 2D continuous wavelet transform (CWT) images. This mechanism establishes explicit cross-dimensional dependencies, effectively eliminating feature blind spots without excessive computational overhead. The experimental results show that CLiST not only achieves perfect accuracy on the fundamental CWRU benchmark but also exhibits exceptional spatial generalization when independently evaluated on non-dominant sensor axes of the XJTUGearbox dataset. Furthermore, validation on the real-world dataset (Guangzhou port) proves that the framework has excellent robustness to the attenuation of the signal transmission path and reduces the performance fluctuation between remote measurement points. Ultimately, CLiST delivers highly reliable AI-driven image and signal-processing solutions for vibration monitoring in industrial equipment. Full article

In this work, a flexible and sustainable radar-absorbing material (RAM) based on porous carbon derived from raw Kraft black liquor was developed. The porous carbon filler was synthesized through a simple, eco-friendly one-pot polymerization route, thereby avoiding lignin extraction, purification, and chemical activation steps. Macroporosity was introduced by using poly(methyl methacrylate) microspheres as a hard template, yielding a lightweight carbon material with a foam-like morphology, low density, and high porosity. The carbon filler was incorporated into a silicone rubber matrix at different loadings (5–25 wt.%) to produce flexible composites. The structural, morphological, and textural properties of porous carbon were investigated by SEM, EDX, Raman spectroscopy, nitrogen adsorption, and mercury porosimetry. The electromagnetic properties of composites were measured in the X-band (8.2–12.4 GHz) using a vector network analyzer. The mechanical behavior was evaluated through Young’s modulus. The results show that increasing filler content enhances dielectric losses and attenuation capability. Among all composites, the sample containing 20 wt.% of porous carbon exhibited the best electromagnetic performance, achieving a reflection loss of −42.3 dB at 10.97 GHz with a thickness of 2.43 mm, corresponding to an absorption efficiency of 99.99%. This performance is attributed to a favorable combination of impedance matching and quarter-wavelength cancellation effects. The developed sustainable, lightweight, and flexible composites demonstrate potential as low-cost RAM for aerospace and electromagnetic interference mitigation applications. Full article

The rapid development of quantum computing poses severe threats to traditional blockchain security mechanisms, while existing full-quantum blockchains face challenges regarding high hardware costs and limited scalability. To address these issues, this paper proposes a secure and practical semi-quantum blockchain system. Specifically, a Semi-Quantum Delegated Proof of Stake consensus mechanism is constructed by integrating an adapted semi-quantum voting protocol with the Borda count method and a malicious behavior penalty model. Furthermore, a lightweight transaction verification framework is designed based on semi-quantum key distribution, enabling classical users with limited quantum capabilities to participate securely. Theoretical analysis demonstrates that the system achieves unconditional security against quantum attacks while maintaining high throughput. These results indicate that the proposed asymmetric resource design significantly lowers hardware barriers compared to full-quantum schemes, effectively balancing security, practicality, and cost-effectiveness for post-quantum blockchain networks. Full article

Early detection of battery degradation is essential for ensuring the safety and reliability of electric vehicle (EV) systems under real-world operating variability. This paper proposes a physics-guided multi-sensor learning framework, termed SensorFusion-Former (SFF), for early warning of short-term EV battery performance degradation. The proposed approach integrates a physics-based baseline model for operational normalization, a multi-sensor fusion attention mechanism to model cross-modality interactions, and a lightweight transformer architecture for efficient temporal representation learning. Weak supervision is derived from physics-consistent residual analysis with temporal smoothing, enabling scalable training without dense manual annotations. To support reliable deployment, evidential uncertainty modeling and conformal calibration are incorporated to obtain statistically controlled decision thresholds. Experiments conducted on a real driving cycle dataset from IEEE DataPort demonstrate that SFF consistently outperforms classical machine learning methods, deep neural networks, and standard transformer models in terms of early-warning lead time, false alarm rate, and inference efficiency while maintaining competitive discriminative performance. Cross-scenario evaluations under diverse thermal conditions further confirm the robustness and generalization capability of the proposed framework. Full article

The application of 3D data in pavement inspection represents an emerging trend. Acquiring and measuring the 3D information of pavement distress enables a more comprehensive assessment of severity, thereby allowing for accurate monitoring and evaluation of the pavement’s technical condition. Existing methods face challenges in high-cost pavement scanning and insufficient research on automated 3D distress segmentation. This study employed a consumer-grade action camera for data acquisition and constructed an engineering-aligned 3D point cloud dataset of pavements. Then a long-tail class imbalance mitigation strategy was introduced, integrating adaptive re-sampling with a weighted fusion loss function, effectively balancing minority class representation. The proposed network, named PointPaveSeg, was a dedicated point cloud processing architecture. A dual-stream feature fusion module was designed for the encoder layer, which decoupled geometric and semantic features to improve distress extraction capability. The network incorporated a hierarchical feature propagation structure enhanced by edge reinforcement, global interaction, and residual connections. Experimental results demonstrated that PointPaveSeg achieved an mIoU of 78.45% and an accuracy of 95.43%. In the field evaluation, post-processing and geometric information extraction were performed on the segmented point clouds. The results showed high consistency with manual measurements. Testing confirmed the method’s practical applicability in real-world projects, offering a new lightweight alternative for intelligent pavement monitoring and maintenance systems. Full article

Artificial light at night (ALAN) data is widely used in urban function analysis and socio-economic activity monitoring, but its application at the micro-scale of cities still faces challenges. This study utilizes high spatial resolution SDGSAT-1 nighttime light data to explore the spatial heterogeneity of ALAN at the street scale in two representative Chinese cities—Beijing and Guangzhou. By integrating multi-source data (such as building vector data, road networks, and point of interest data), a multi-dimensional indicator system covering urban morphology, functional structure, and transportation accessibility is constructed. Based on this, the study employs a Geographically Weighted Random Forest (GWRF) model combined with the Shapley Additive Explanations (SHAP) method to deeply analyze the non-linear relationships between ALAN intensity and multiple driving factors, as well as their spatial variability. Results demonstrate the superiority of the GWRF model over global models in capturing spatial non-stationarity, with R² values of 0.67 for Beijing and 0.74 for Guangzhou, compared to 0.62 and 0.71 for the random forest models, respectively. Road density is the dominant factor influencing nighttime light intensity in both Beijing and Guangzhou. However, the relationship between ALAN and its driving factors varies across these cities. In Beijing, a balanced multi-factor model is observed, whereas in Guangzhou, ALAN intensity is primarily driven by road density, with secondary influences from other factors like sky view factor. This study validates SDGSAT-1 for micro-scale analysis, offering a scientific basis for differentiated urban lighting planning. Full article

Object detection in unmanned aerial vehicle (UAV) imagery remains a crucial yet challenging task due to complex backgrounds, large scale variations, and the prevalence of small objects. Visible-spectrum images lack robustness under all-weather and all-illumination conditions; by contrast, multispectral sensing provides complementary cues (e.g., thermal signatures) that improve detection robustness. However, existing multispectral solutions often incur high computational costs and are therefore difficult to deploy on resource-constrained UAV platforms. To address these issues, SG-YOLO is proposed, a lightweight and efficient multispectral object detection framework that aims to balance accuracy and efficiency. First, a Spectral Gated Downsampling Stem (SGDS) is designed, in which grouped convolutions and a gating mechanism are employed at the early stage of the network to extract band-specific features, thereby maximizing spectral complementarity while minimizing redundancy. Second, a Spectral–Spatial Iterative Attention Fusion (SSIAF) module is introduced, in which spectral-wise (channel) attention and spatial-wise attention are iteratively coupled and cascaded in a multi-scale manner to jointly model cross-band dependencies and spatial saliency, thereby aggregating high-level semantic information while suppressing redundant spectral responses. Finally, a Spatial–Channel Synergistic Fusion (SCSF) module is designed to enhance multi-scale and cross-channel feature integration in the neck. Experiments on the MODA dataset show that SG-YOLOs achieves 72.4% mAP₅₀, outperforming the baseline by 3.2%. Moreover, compared with a range of mainstream one-stage detectors and multispectral detection methods, SG-YOLO delivers the best overall performance, providing an effective solution for UAV object detection while maintaining a favorable trade-off between model size and detection accuracy. Full article

Multi-UAV collaborative mapping in communication-constrained indoor environments is often hampered by a trade-off between overall map refinement and the timely completion of safety-relevant shared regions. In high-density or unmapped areas, network congestion can delay the updates that matter most for close-proximity coordination, because standard bandwidth allocation does not distinguish between general map refinement and hotspot-related spatial data. To address this issue, we propose a resilient scheduling framework that prioritizes globally useful map updates while improving safety-relevant hotspot completeness under unreliable links. At its core is a Safety Reserve allocation strategy for “hotspot” submaps—areas where UAV trajectories overlap or approach unknown frontiers. By enforcing this reserve, the system directs a limited uplink budget to hotspot-related updates earlier during congestion. To remain useful under packet loss, we implement a prefix-decodable hierarchical data structure over a lightweight stateless protocol, allowing immediate fusion of valid partial updates. The framework identifies hotspots using feedback from a Lambda-Field risk model and a truncated least squares solver with graduated non-convexity (TLS–GNC) pose-graph optimizer. Experiments on S3DIS and ScanNet under partition-based two-agent emulation show that the proposed method improves hotspot-band completeness and progressive mapping quality over the tested baselines, especially under packet loss. Full article

Visual speech recognition systems encounter significant challenges in multilingual nations such as the Philippines, where numerous regional languages, including Cebuano and Ilocano, feature distinct phonetic-visual characteristics. Deep learning models such as the Lip Reading Network and the Lightweight Crowd Segmentation Network have demonstrated strong performance with 3D Convolutional Neural Networks (CNNs). However, their substantial computational requirements restrict deployment on portable edge devices. We introduce a more efficient alternative that integrates a 2D Residual Network 50 architecture with a Long Short-Term Memory network and Connectionist Temporal Classification for lip-reading classification of Philippine regional languages. The proposed model is deployed on the Jetson Orin Nano, a high-performance edge device optimized for real-time inference through Compute Unified Device Architecture acceleration. Using a dataset of 2000 annotated videos encompassing 10 lexicons each for Cebuano and Ilocano, the model’s effectiveness was evaluated. Results achieved a regional language classification accuracy of 90%, with lexicon-level accuracies of 74% for Cebuano and 66% for Ilocano. This work represents a step toward developing accessible and scalable communication aids for deaf communities in linguistically diverse environments, leveraging transfer learning on pretrained models. Full article

Reliable semantic perception is crucial for service robots operating in complex public indoor environments. However, existing semantic mapping approaches often face the dual challenges of high computational overhead and semantic redundancy in maps. To address these limitations, this paper proposes a low-resource semantic mapping framework based on improved instance segmentation and dynamic constraints from consecutive frames. First, we design the lightweight model MS-YOLO, which adopts MobileNetV4 as its backbone network and incorporates the SHViT neck module, effectively optimizing the balance between detection accuracy and computational cost. Second, we propose a consecutive frame dynamic constraint method that eliminates redundant object annotations through consecutive frame stability verification. Experimental results relating to both fusion and custom datasets demonstrate that compared to YOLOv8n-seg, MS-YOLO achieves improvements in accuracy, recall, and mAP@0.5, while reducing the number of parameters by 11.7% and floating-point operations (FLOPs) by 32.2%. Furthermore, compared to YOLOv11n-seg and YOLOv5n-seg, its FLOPs are reduced by 17.2% and 25.5%, respectively. Finally, the successful deployment and field validation of this system on the Jetson Orin NX platform demonstrate its real-time capability and engineering practicality for edge computing in public indoor service robots. Full article

The extensive cultivation scale of sugar beet seedlings has resulted in the necessity for accurate identification and monitoring of the seedling count, a task which has become crucial and highly challenging in the sugar industry. However, sugar beet seedlings in UAV aerial photography scenarios are mostly small targets with complex backgrounds. Existing general detection models not only have insufficient detection accuracy, but also struggle to balance computational efficiency and resource consumption. To meet the practical needs of field monitoring, this paper proposes the LDH-RTDETR, a sugar beet seedling detection model that balances high accuracy and light weight. This model uses LSNet for feature extraction to reduce size, adds a deformable attention (DAttention) module to capture fine-grained seedling features, and adopts HS-FPN to improve multi-scale feature fusion in the neck network. Experimental results show that the improved model significantly outperforms the original RT-DETR model, with a 3.6% increase in accuracy, a 2.1% increase in mAP50, a recall rate of 86.0%, and a final model size of only 43.3 MB, thus achieving an effective balance between accuracy and model size. This study’s improved model offers an efficient solution for large-area identification and counting of sugar beet seedlings, and is highly significant for advancing the automation of sugar crop field management and agricultural digital transformation. Full article

Ensuring the structural reliability of power transmission networks is a fundamental prerequisite for the stable operation of modern energy systems. To address the challenges posed by complex weather interference and the small scale of insulator defects during power line inspections, this paper proposes LID-YOLO, a lightweight insulator defect detection network. First, to mitigate image feature degradation caused by weather interference, we design the C3k2-CDGC module. By leveraging the input-adaptive characteristics of dynamic convolution and the spatial preservation properties of coordinate attention, this module enhances feature extraction capabilities and robustness in complex weather scenarios. Second, to address the detection challenges arising from the significant scale disparity between insulators and defects, we propose Detect-LSEAM, a detection head featuring an asymmetric decoupled architecture. This design facilitates multi-scale feature fusion while minimizing computational redundancy. Subsequently, we develop the NWD-MPDIoU hybrid loss function to balance the weights between distribution metrics and geometric constraints dynamically. This effectively mitigates gradient instability arising from boundary ambiguity and the minute size of insulator defects. Finally, we construct a synthetic multi-weather condition insulator defect dataset for training and validation. Compared to the baseline, LID-YOLO improves precision, recall, and mAP@0.5 by 1.7%, 3.6%, and 4.2%, respectively. With only 2.76 M parameters and 6.2 G FLOPs, it effectively maintains the lightweight advantage of the baseline, achieving an optimal balance between detection accuracy and computational efficiency for insulator inspections under complex weather conditions. This lightweight and robust framework provides a reliable algorithmic foundation for automated grid monitoring, supporting the continuous and resilient operation of modern energy systems. Full article

Unmanned Aerial Vehicle (UAV) swarms operating in contested environments face a critical “semantic gap” between raw, high-velocity network traffic and high-level mission security constraints, compounded by the risk of privacy leakage during collaborative learning. Existing deep learning (DL)-based Network Intrusion Detection Systems (NIDSs) suffer from opacity, prohibitive resource consumption, and vulnerability to gradient leakage attacks in federated settings, while traditional rule-based systems fail to handle encrypted payloads and evolving attack patterns. To bridge this gap, we present NeSySwarm-IDS (Neuro-Symbolic Swarm Intrusion Detection System), an end-to-end differentiable neuro-symbolic framework that simultaneously achieves high accuracy, strong privacy guarantees, and built-in interpretability under resource constraints. NeSySwarm-IDS integrates an extremely lightweight 1D convolutional neural network with a differentiable Łukasiewicz fuzzy logic reasoner incorporating attack-specific rules. By aggregating only low-dimensional logic rule weights with calibrated differential privacy noise, we drastically reduce communication overhead while providing $(ϵ,\delta )$-DP guarantees with negligible utility loss. Extensive experiments on the UAV-NIDD dataset and our self-collected dataset demonstrate that NeSySwarm-IDS achieves near-perfect detection accuracy, significantly outperforming traditional machine learning baselines despite using limited training data. A detailed case study on GPS spoofing confirms the interpretability of our approach, providing axiomatic explanations suitable for autonomous mission verification. These results establish that end-to-end neuro-symbolic learning can effectively bridge the semantic gap in UAV swarm security while ensuring privacy and interpretability, offering a practical pathway for deploying trustworthy AI in contested environments. Full article

In complex traffic environments such as low light, strong glare, occlusion and at night, systems that rely solely on visible light single sensors for pedestrian detection have drawbacks such as low detection accuracy and poor robustness. Based on the YOLOv8 convolutional network, this paper adopts a dual-branch structure to process visible light and infrared images simultaneously, fully utilizing feature information at different scales to effectively detect pedestrian targets in complex and changeable environments. To address the issues of insufficient interaction of modal feature information and fixed fusion weights, a cross-modal feature interaction and enhancement mechanism was introduced. A modal-channel interaction block (MCI-Block) was designed, in which residual connection structures and weight interaction were added within the module to achieve feature enhancement and filter out noise information. Introduce a dynamic weighted feature fusion strategy, adaptively adjusting the contribution ratio of different modal features in the fusion process, aiming to enhance the discrimination ability of the key pedestrian area. The training and testing of the network designed in this paper were completed on the visible light and infrared pedestrian detection dataset LLVIP and Kaist. At the same time, the test results of the dual-branch model and the model designed in this paper were further verified in actual traffic scenarios. The results show that the dual-branch YOLOv8 network for visible light and infrared images, which was constructed in this paper, can reliably enhance the detection performance of pedestrian targets in complex traffic environments, including accuracy, recall rate, and mAP@0.5, etc., thereby improving the robustness of pedestrian detection. Full article

Global climate change and increasingly complex geological conditions have led to more frequent landslides in the red-bed sedimentary layers of western Sichuan, China, posing severe threats to human safety and hindering progress toward regional Sustainable Development Goals (SDGs), particularly those related to disaster risk reduction and ecological protection. To address this challenge and advance sustainable disaster management, this study proposes a lightweight hybrid model, termed Transformer–Attention–LSTM, which integrates the global attention mechanism of Transformers with the local time-series modeling capabilities of Long Short-Term Memory networks. Focusing on the Kuyaogou landslide, the model achieves an optimal balance between parameter scale, sequence length, and prediction accuracy. The mean Coefficient of Determination (R²) values for the test samples in the X, Y, and Z directions reached 0.948, representing enhancements of 9.9%, 4.2%, and 2.3%, respectively, compared to the suboptimal Attention–LSTM model. Concurrently, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were reduced to 9.23 mm and 7.17 mm, respectively. Based on these displacement predictions, the landslide evolution stage was determined by calculating the tangent angle, indicating that the Kuyaogou landslide will remain in a stable creep phase over the ensuing ten-day period with low overall risk of rapid movement, though localized instability requires continued monitoring. This research provides a ‘small, fast, and accurate’ paradigm for red-bed landslide displacement prediction, offering scientific support for disaster prevention and emergency decision-making. The framework demonstrates potential for broader application in monitoring other geological hazards, thereby contributing to the implementation of sustainable development strategies in geohazard-prone regions. Full article