Search Results (574)

The growing complexity of distribution-level virtual power plants (VPPs) demands a rethinking of how flexible demand is modeled, aggregated, and dispatched under uncertainty. Traditional optimization frameworks often rely on deterministic or homogeneous assumptions about end-user behavior, thereby overestimating controllability and underestimating risk. In this paper, we propose a behavior-aware, two-stage stochastic dispatch framework for VPPs that explicitly models heterogeneous user participation via integrated behavioral economics and social interaction structures. At the behavioral layer, user responses to demand response (DR) incentives are captured using a Prospect Theory-based utility function, parameterized by loss aversion, nonlinear gain perception, and subjective probability weighting. In parallel, social influence dynamics are modeled using a peer interaction network that modulates individual participation probabilities through local contagion effects. These two mechanisms are combined to produce a high-dimensional, time-varying participation map across user classes, including residential, commercial, and industrial actors. This probabilistic behavioral landscape is embedded within a scenario-based two-stage stochastic optimization model. The first stage determines pre-committed dispatch quantities across flexible loads, electric vehicles, and distributed storage systems, while the second stage executes real-time recourse based on realized participation trajectories. The dispatch model includes physical constraints (e.g., energy balance, network limits), behavioral fatigue, and the intertemporal coupling of flexible resources. A scenario reduction technique and the Conditional Value-at-Risk (CVaR) metric are used to ensure computational tractability and robustness against extreme behavior deviations. Full article

Precise oriented object detection of corn provides critical support for automated agricultural tasks such as harvesting, spraying, and precision management. In this work, we address this challenge by leveraging oriented object detection in combination with depth information to estimate corn poses. To enhance detection accuracy while maintaining computational efficiency, we construct a precise annotated oriented corn detection dataset and propose YOLOv11OC, an improved detector. YOLOv11OC integrates three key components: Angle-aware Attention Module for angle encoding and orientation perception, Cross-Layer Fusion Network for multi-scale feature fusion, and GSConv Inception Network for efficient multi-scale representation. Together, these modules enable accurate oriented detection while reducing model complexity. Experimental results show that YOLOv11OC achieves 97.6% mAP@0.75, exceeding YOLOv11 by 3.2%, and improves mAP_50:95 by 5.0%. Furthermore, when combined with depth maps, the system achieves 92.5% pose estimation accuracy, demonstrating its potential to advance intelligent and automated cultivation and spraying. Full article

Surface material recognition is a key component in robotic perception and physical interaction, particularly when leveraging both tactile and visual sensory inputs. In this work, we propose Surformer v1, a transformer-based architecture designed for surface classification using structured tactile features and Principal Component Analysis (PCA)-reduced visual embeddings extracted via ResNet 50. The model integrates modality-specific encoders with cross-modal attention layers, enabling rich interactions between vision and touch. Currently, state-of-the-art deep learning models for vision tasks have achieved remarkable performance. With this in mind, our first set of experiments focused exclusively on tactile-only surface classification. Using feature engineering, we trained and evaluated multiple machine learning models, assessing their accuracy and inference time. We then implemented an encoder-only Transformer model tailored for tactile features. This model not only achieves the highest accuracy, but also demonstrated significantly faster inference time compared to other evaluated models, highlighting its potential for real-time applications. To extend this investigation, we introduced a multimodal fusion setup by combining vision and tactile inputs. We trained both Surformer v1 (using structured features) and a Multimodal CNN (using raw images) to examine the impact of feature-based versus image-based multimodal learning on classification accuracy and computational efficiency. The results showed that Surformer v1 achieved 99.4% accuracy with an inference time of 0.7271 ms, while the Multimodal CNN achieved slightly higher accuracy but required significantly more inference time. These findings suggest that Surformer v1 offers a compelling balance between accuracy, efficiency, and computational cost for surface material recognition. The results also underscore the effectiveness of integrating feature learning, cross-modal attention and transformer-based fusion in capturing the complementary strengths of tactile and visual modalities. Full article

Background: To overcome the esthetic limitations of dental monolithic zirconia restorations, multichromatic systems were developed to combine improved structural integrity with a natural shade gradient that mimics the optical properties of natural teeth. In response to the clinical demand for time-efficient, i.e., chairside fabrication of zirconia restorations, rapid sintering protocols have become necessary to adjust clinical efficiency along with material performance. This study addresses the challenges of a rapid sintering protocol related to optical performance and phase transformation of the final restoration and the zirconia–cell interaction. Methods: The influence of a rapid sintering protocol on the color stability of the final dental restoration was evaluated by the CIE L*a*b* color space. Phase transformation was assessed through X-ray diffraction analysis. Cellular behavior was evaluated by measuring wettability, the material’s surface energy, and a cell mitochondrial activity assay on human gingival fibroblasts. Results: Optical measurements demonstrated that the total color change in all layers after rapid sintering was above the perceptibility threshold (ΔE* > 1.2), while only the polished enamel layer (ΔE* = 3.01) exceeded the acceptability threshold (ΔE* > 2.7), resulting in a clinically perceptible mismatch. Results of X-ray diffraction analysis, performed for fixed occupancy at Z_0.935Y_0.065O_0.984, revealed that rapid sintering caused a decrease in the cubic (C-) phase and an increase in the total amount of tetragonal (T-) phases. Conventionally sintered zirconia consists of 54% tetragonal (T-) and 46% cubic (C-) phase, whereas in the speed-sintered specimens, an additional T1 phase was detected (T = 49%; T1 = 27%), along with a reduced cubic fraction (C = 24%). Additionally, a small amount of the monoclinic (M) phase is noticed. Although glazing as a surface finishing procedure resulted in increased hydrophilicity, both polished and glazed surface-treated specimens showed statistically comparable cell adhesion and proliferation (p > 0.05). Conclusions: Rapid sintering induced perceptible color changes only in the enamel layer of multichromatic zirconia, suggesting that even layer-specific alterations may have an impact on the overall esthetic outcome of the final prosthetic restoration. Five times higher heating and cooling rates caused difficulty in reaching equilibrium, leading to changes in lattice parameters and the formation of the metastable T1 phase. Full article

Underwater images frequently exhibit color distortion, detail blurring, and contrast degradation due to absorption and scattering by the underwater medium. This study proposes a progressive color correction strategy integrated with a vision-inspired image enhancement framework to address these issues. Specifically, the progressive color correction process includes adaptive color quantization-based global color correction, followed by guided filter-based local color refinement, aiming to restore accurate colors while enhancing visual perception. Within the vision-inspired enhancement framework, the color-adjusted image is first decomposed into a base layer and a detail layer, corresponding to low- and high-frequency visual information, respectively. Subsequently, detail enhancement and noise suppression are applied in the detail pathway, while global brightness correction is performed in the structural pathway. Finally, results from both pathways are fused to yield the enhanced underwater image. Extensive experiments on four datasets verify that the proposed method effectively handles the aforementioned underwater enhancement challenges and significantly outperforms state-of-the-art techniques. Full article

Lidar point clouds are crucial for autonomous driving, but their sparsity and scale variations pose challenges for instance segmentation. In this paper, we propose LCPSNet, a Light Detection and Ranging (LiDAR) channel-aware point segmentation network designed to handle distance-dependent sparsity and scale variation in point clouds. A top-down FPN is adopted, where high-level features are progressively upsampled and fused with shallow layers. The fused features at 1/16, 1/8, and 1/4 are further aligned to a common BEV/polar grid and processed by the Local Perception Module (LPM), which applies cross-scale, position-dependent weighting to enhance intra-object coherence and suppress interference. The Inter-Channel Correlation Module (ICCM) employs ball queries to model spatial and channel correlations, computing an inter-channel similarity matrix to reduce redundancy and highlight valid features. Experiments on SemanticKITTI and Waymo show that LPM and ICCM effectively improve local feature refinement and global semantic consistency. LCPSNet achieves 70.9 PQ and 77.1 mIoU on SemanticKITTI, surpassing mainstream methods and reaching state-of-the-art performance. Full article

Communications in intelligent transportation systems (ITS) face explosive data growth from applications such as autonomous driving, remote diagnostics, and real-time monitoring, imposing severe challenges on limited spectrum, bandwidth, and latency. Reliable semantic image reconstruction under noisy channel conditions is critical for ITS perception tasks, since noise directly impacts the recognition of both static infrastructure and dynamic obstacles. Unlike traditional approaches that aim to transmit all image data with equal fidelity, effective ITS communication requires prioritizing task-relevant dynamic elements such as vehicles and pedestrians while filtering out largely static background features such as buildings, road signs, and vegetation. To address this, we propose an Offline Knowledge Base and Attention-Driven Semantic Communication (OKBASC) framework for image-based applications in ITS scenarios. The proposed framework performs offline semantic segmentation to build a compact knowledge base of semantic masks, focusing on dynamic task-relevant regions such as vehicles, pedestrians, and traffic signals. At runtime, precomputed masks are adaptively fused with input images via sparse attention to generate semantic-aware representations that selectively preserve essential information while suppressing redundant background. Moreover, we introduce a further Bi-Level Routing Attention (BRA) module that hierarchically refines semantic features through global channel selection and local spatial attention, resulting in improved discriminability and compression efficiency. Experiments on the VOC2012 and nuPlan datasets under varying SNR levels show that OKBASC achieves higher semantic reconstruction quality than baseline methods, both quantitatively via the Structural Similarity Index Metric (SSIM) and qualitatively via visual comparisons. These results highlight the value of OKBASC as a communication-layer enabler that provides reliable perceptual inputs for downstream ITS applications, including cooperative perception, real-time traffic safety, and incident detection. Full article

To address issues such as inadequate robustness in dynamic obstacle avoidance, instability in formation morphology, severe resource conflicts in multi-task scenarios, and challenges in global path planning optimization for unmanned aerial vehicles (UAVs) operating in complex airspace environments, this paper examines the advantages and limitations of conventional UAV formation cooperative control theories. A multi-UAV cooperative control strategy is proposed, integrating adaptive game theory and deep reinforcement learning within a unified framework. By employing a three-layer information fusion architecture—comprising the physical layer, intent layer, and game-theoretic layer—the approach establishes models for multi-modal perception fusion, game-theoretic threat assessment, and dynamic aggregation-reconstruction. This optimizes obstacle avoidance algorithms, facilitates interaction and task coupling among formation members, and significantly improves the intelligence, resilience, and coordination of formation-wide cooperative control. The proposed solution effectively addresses the challenges associated with cooperative control of UAV formations in complex traffic environments. Full article

Electrolyte-gated transistors (EGTs) have emerged as a highly promising platform for neuromorphic computing and bioelectronics, offering potential solutions to overcome the limitations of the von Neumann architecture. This comprehensive review examines recent advancements in EGT technology, focusing on three critical dimensions: materials, device configurations, and external field regulation strategies. We systematically analyze the development and properties of diverse electrolyte materials, including liquid electrolyte, polymer-based electrolytes, and inorganic solid-state electrolytes, highlighting their influence on ionic conductivity, stability, specific capacitance, and operational characteristics. The fundamental operating mechanisms of EGTs and electric double layer transistors (EDLTs) based on electrostatic modulation and ECTs based on electrochemical doping are elucidated, along with prevalent device configurations. Furthermore, the review explores innovative strategies for regulating EGT performance through external stimuli, including electric fields, optical fields, and strain fields/piezopotentials. These multi-field regulation capabilities position EGTs as ideal candidates for building neuromorphic perception systems and energy-efficient intelligent hardware. Finally, we discuss the current challenges such as material stability, interfacial degradation, switching speed limitations, and integration density. Furthermore, we outline future research directions, emphasizing the need for novel hybrid electrolytes, advanced fabrication techniques, and holistic system-level integration to realize the full potential of EGTs in next-generation computing and bio-interfaced applications. Full article

As a new generation 3D video coding standard, 3D-HEVC offers highly efficient compression. However, its recursive quadtree partitioning mechanism and frequent rate-distortion optimization (RDO) computations lead to a significant increase in coding complexity. Particularly, intra-frame coding in depth maps, which incorporates tools like depth modeling modes (DMMs), substantially prolongs the decision-making process for coding unit (CU) partitioning, becoming a critical bottleneck in compression encoding time. To address this issue, this paper proposes a fast CU partitioning framework based on hierarchical feature fusion convolutional neural networks (HFF-CNNs). It aims to significantly accelerate the overall encoding process while ensuring excellent encoding quality by optimizing depth map CU partitioning decisions. This framework synergistically captures CU’s global structure and local details through multi-scale feature extraction and channel attention mechanisms (SE module). It introduces the wavelet energy ratio designed for quantifying the texture complexity of depth map CU and the quantization parameter (QP) that reflects the encoding quality as external features, enhancing the dynamic perception ability of the model from different dimensions. Ultimately, it outputs depth-corresponding partitioning predictions through three fully connected layers, strictly adhering to HEVC’s quad-tree recursive segmentation mechanism. Experimental results demonstrate that, across eight standard test sequences, the proposed method achieves an average encoding time reduction of 48.43%, significantly lowering intra-frame encoding complexity with a BDBR increment of only 0.35%. The model exhibits outstanding lightweight characteristics with minimal inference time overhead. Compared with the representative methods under comparison, this method achieves a better balance between cross-resolution adaptability and computational efficiency, providing a feasible optimization path for real-time 3D-HEVC applications. Full article

Reservoirs face growing challenges in safety and sustainable management, requiring systematic approaches that integrate monitoring, analysis, and decision support. To address this need, this study develops an integrated information system framework with a four-layer architecture, encompassing “perception,” “data,” “model,” and “application.” The perception layer establishes a multi-platform monitoring network based on fused multi-sensor data. The data layer manages heterogeneous information through correlation mechanisms at the physics, semantics, and application levels. The model layer supports decision-making through a cross-coupled analytical framework for the coordinated management of water safety, resources, environment, and ecology. Finally, the application layer utilizes virtual-physical mapping and dynamic reasoning to implement a closed-loop management system encompassing forecasting, warning, simulation, and planning. This framework was implemented and validated at the Ye Fan Reservoir in Hubei Province, China. By integrating components like “One Map,” flood dispatching, safety monitoring, early warning, video surveillance, and operational supervision, a three-dimensional perception network was constructed. This deployment significantly improved the precision, reliability, and scientific basis of reservoir operation. The integrated monitoring and management system presented in this paper, driven by heterogeneous sensor networks, provides an effective and generalizable solution for modern reservoir management, with the potential for extension to broader water resource and infrastructure systems. Full article

To address the challenges of user behavior sparsity and insufficient utilization of course semantics on MOOC platforms, this paper proposes a personalized recommendation method that integrates user behavioral sequences with course textual semantic features. First, shallow word-level features from course titles are extracted using FastText, and deep contextual semantic representations from course descriptions are obtained via a fine-tuned BERT model. The two sets of semantic features are concatenated to form a multi-level semantic representation of course content. Next, the fused semantic features are mapped into the same vector space as course ID embeddings through a linear projection layer and combined with the original course ID embeddings via an additive fusion strategy, enhancing the model’s semantic perception of course content. Finally, the fused features are fed into an improved SASRec model, where a multi-head self-attention mechanism is employed to model the evolution of user interests, enabling collaborative recommendations across behavioral and semantic modalities. Experiments conducted on the MOOCCubeX dataset (1.26 million users, 632 courses) demonstrated that the proposed method achieved NDCG@10 and HR@10 scores of 0.524 and 0.818, respectively, outperforming SASRec and semantic single-modality baselines. This study offers an efficient yet semantically rich recommendation solution for MOOC scenarios. Full article

Friction contact regulation has been widely acknowledged, yet research on micro-textured meshing interfaces appears to have reached an impasse. Conventional wisdom holds that the similarity of micro-element configurations is the key factor contributing to textured interface issues. The traditional perception is transcended, and a novel method for presetting the optimal parameters of gradientized micro-textured interface elements is proposed. The study has analyzed the Interface Enriched Lubrication (IEL) performance and meshing Anti-Scuffing Load-Bearing Capacity (ASLBC) of periodic symmetrical and continuously gradient micro-elements. By actively regulating IEL behavior through geometric constraint effects, dynamic micro-cavity lubrication storage units are formed, thereby extending the retention time of medium film layers. The textured edges induce micro-vortices, delaying scuffing failures induced by load-bearing. Validation analyses demonstrate that optimal micro-element configurations can distribute contact stress to achieve stress homogenization, with the maximum contact stress reduced by 21%. The localized hydrodynamic effect of micro-textured elements increases interfacial meshing stiffness by 5.32% while decreasing friction torque by 27.3%. This investigation reveals a synergistic mechanism between IEL performance and meshing ASLBC under heavy loads conditions. The findings confirm that gradient-based micro-textured element configuration presetting offers an effective solution to reconcile the inherent trade-off between lubrication and load-bearing performance in heavy loads applications. Full article

Biological swarms communicate through decentralized, adaptive behaviors shaped by local interactions, selective attention, and symbolic signaling. These principles of animal communication enable robust coordination without centralized control or persistent connectivity. This work presents a proof of concept that identifies, evaluates, and translates biological communication strategies into a generative visual language for unmanned aerial vehicle (UAV) swarm agents operating in radio-frequency (RF)-denied environments. Drawing from natural exemplars such as bee waggle dancing, white-tailed deer flagging, and peacock feather displays, we construct a configuration space that encodes visual messages through trajectories and LED patterns. A large language model (LLM), preconditioned using retrieval-augmented generation (RAG), serves as a generative translation layer that interprets perception data and produces symbolic UAV responses. Five test cases evaluate the system’s ability to preserve and adapt signal meaning through within-modality fidelity (maintaining symbolic structure in the same modality) and cross-modal translation (transferring meaning across motion and light). Covariance and eigenvalue-decomposition analysis demonstrate that this bio-agentic approach supports clear, expressive, and decentralized communication, with motion-based signaling achieving near-perfect clarity and expressiveness (0.992, 1.000), while LED-only and multi-signal cases showed partial success, maintaining high expressiveness (~1.000) but with much lower clarity (≤0.298). Full article

Underground mining operations face increasing challenges due to their complex and hazardous environments. One key difficulty is ensuring real-time safety monitoring and disaster prevention. Traditional monitoring systems often suffer from delayed data acquisition and rely heavily on cloud-based processing. These factors limit their responsiveness during emergencies. To address these limitations, this study presents an underground Internet of Things (IoT) monitoring system based on edge computing. The system architecture is composed of three layers: a perception layer for real-time sensing, an edge gateway layer for local data processing and decision-making, and a cloud service layer for storage and analytics. By shifting computation closer to the data source, the system significantly reduces latency and enhances response efficiency. The system is tailored to actual mine-site conditions. It integrates pressure monitoring for artificial expandable pillars and roof subsidence detection in stopes. It has been successfully deployed in a field environment, and the data collected during commissioning demonstrate the system’s feasibility and reliability. Results indicate that the proposed system meets real-world demands for underground safety monitoring. It enables timely warnings and improves the overall automation level. This approach offers a practical and scalable solution for enhancing mine safety and provides a valuable reference for future smart mining systems. Full article