Search Results (10)

With the rapid growth of residential electric vehicles, synchronized charging during peak periods can induce severe load ramping and exceed distribution network capacity limits. To mitigate these issues, governments have promoted a unified build-and-operate community model that enables centralized coordination of community charging and ensures real-time responsiveness to grid dispatch signals. Targeting this emerging operational paradigm, a dual-dimensional compensation mechanism for real-time electric vehicle (EV) demand response is proposed. The mechanism integrates two types of compensation: power regulation compensation, which rewards users for providing controllable power flexibility, and state-of-charge (SoC) loss compensation, which offsets energy deficits resulting from demand response actions. This dual-layer design enhances user willingness and long-term engagement in community-level coordination. Based on the proposed mechanism, a bi-level optimization framework is developed to realize efficient real-time regulation: the upper level maximizes the active response capacity under budget constraints, while the lower level minimizes the aggregator’s total compensation cost subject to user response behavior. Simulation results demonstrate that, compared with conventional fair-share curtailment and single-compensation approaches, the proposed mechanism effectively increases active user participation and reduces incentive expenditures. The study highlights the mechanism’s potential for practical deployment in unified build-and-operate communities and discusses limitations and future research directions. Full article

The irregular and highly non-uniform spatial distribution inherent to dynamic three-dimensional (3D) point clouds (DPCs) severely hampers the extraction of reliable temporal context, rendering inter-frame compression a formidable challenge. Inspired by two-dimensional (2D) image and video compression methods, existing approaches attempt to model the temporal dependence of DPCs through a motion estimation/motion compensation (ME/MC) framework. However, these approaches represent only preliminary applications of this framework; point consistency between adjacent frames is insufficiently explored, and temporal correlation requires further investigation. To address this limitation, we propose a hierarchical ME/MC framework that adaptively selects the granularity of the estimated motion field, thereby ensuring a fine-grained inter-frame prediction process. To further enhance motion estimation accuracy, we introduce a dual-attention-based KNN block-matching (DA-KBM) network. This network employs a bidirectional attention mechanism to more precisely measure the correlation between points, using closely correlated points to predict inter-frame motion vectors and thereby improve inter-frame prediction accuracy. Experimental results show that the proposed DPC compression method achieves a significant improvement (gain of 70%) in the BD-Rate metric on the 8iFVBv2 dataset. compared with the standardized Video-based Point Cloud Compression (V-PCC) v13 method, and a 16% gain over the state-of-the-art deep learning-based inter-mode method. Full article

Establishing scientifically sound and equitable compensation standards is crucial for effective ecological compensation. This study focuses on the quantitative assessment of ecological compensation standards in the water-source areas of the South-to-North Water Diversion Project. Based on the dual perspective of cost and benefit, we embed a three-dimensional dynamic adjustment coefficient—water volume, water quality, and payment capacity—and fully considered spillover effects. Using the AHP-Entropy Method, the allocation ratio of the water-receiving area was scientifically divided, achieving differentiated distribution and dynamic adaptation of the compensation mechanism. The compensation allocation ratio for water-receiving areas was determined, ensuring differentiated distribution and dynamic adaptability in the compensation mechanism. The results show the following: (1) In 2023, the ecological compensation amount for Yangzhou, based on the cost method and the equivalent factor method, ranges from CNY 1.21 billion to 2.53 billion. The amount of compensation after the dynamic game between both parties can avoid the waste of resources caused by over-compensation, and at the same time make up for the shortcomings of under-compensation due to the current water price. (2) Ecological compensation is measured only from the single perspective of the water-source area, without considering the differences in the receiving area. This paper uses the AHP-entropy value method to combine and assign weights, and calculates the apportionment ratio of the main water-receiving areas of Shandong Province in the east line of the South-to-North Water Diversion: for the Jiaodong line, these are Qingdao 20.97% and Jinan 14.53%, and for the North Shandong line, they are Dongying 23.98%, Dezhou 13.68%, Liaocheng 9.47%, and Binzhou 17.37%. (3) The dynamic correction coefficient and game model can effectively balance the cost of protecting the water-source area and the receiving area’s ability to pay, and combination with the empowerment method enhances the regional difference in suitability. Full article

This study addresses the challenges of manual measurement bias and low robustness in detecting small, occluded targets in complex marine environments during real-time onboard sorting of Portunus trituberculatus. We propose YOLO-DFAM, an enhanced YOLOv11n-based model that replaces the global average pooling in the Focal Modulation module with a spatial–channel dual-attention mechanism and incorporates the ASF-YOLO cross-scale fusion strategy to improve feature representation across varying target sizes. These enhancements significantly boost detection, achieving an mAP@50 of 98.0% and precision of 94.6%, outperforming RetinaNet-CSL and Rotated Faster R-CNN by up to 6.3% while maintaining real-time inference at 180.3 FPS with only 7.2 GFLOPs. Unlike prior static-scene approaches, our unified framework integrates attention-guided detection, scale-adaptive tracking, and lightweight weight estimation for dynamic marine conditions. A ByteTrack-based tracking module with dynamic scale calibration, EMA filtering, and optical flow compensation ensures stable multi-frame tracking. Additionally, a region-specific allometric weight estimation model (R² = 0.9856) reduces dimensional errors by 85.7% and maintains prediction errors below 4.7% using only 12 spline-interpolated calibration sets. YOLO-DFAM provides an accurate, efficient solution for intelligent onboard fishery monitoring. Full article

Under the “Dual Carbon” strategy, collaborative governance of port atmospheric pollutants and carbon emissions is critical for low-carbon transformation. Focusing on Ningbo-Zhoushan Port (48% regional ship emissions), this study examines government, port enterprises, and public interactions. A tripartite evolutionary game model with numerical simulation reveals dynamic patterns and key factors. The results show the following: (1) A substitution effect exists between government incentive costs and penalty intensity—increased environmental governance budgets reduce the probability of government incentives, whereas higher public reporting rewards accelerate corporate emission reduction convergence. (2) Public supervision exhibits cyclical fluctuations due to conflicts between individual rationality and collective interests, with excessive reporting rewards potentially triggering free-rider behavior. (3) The system exhibits two stable equilibria: a low-efficiency equilibrium (0,0,0) and a high-efficiency equilibrium (1,1,1). The latter requires policy cost compensation, corporate emission reduction gains exceeding investments, and a supervision benefit–cost ratio greater than 1. Accordingly, the study proposes a three-dimensional “Incentive–Constraint–Collaboration” governance strategy, recommending floating penalty mechanisms, green financial instrument innovation, and community supervision network optimization to balance environmental benefits with fiscal sustainability. This research provides a dynamic decision-making framework for multi-agent collaborative emission reduction in ports, offering both methodological innovation and practical guidance value. Full article

This paper presents the design of an airborne DMD-based dual-path multi-target imaging spectrometer that is capable of achieving instantaneous imaging over a two-dimensional large field of view and the simultaneous spectral analysis of thousands of targets. It also offers advantages such as high spatial resolution, high spectral resolution, high timeliness, and low platform requirements. However, its working mechanism inherently causes misalignment errors in the dual-path images that it obtains due to exposure time differences. To address this issue, we propose a dual-path exposure time difference compensation method based on a velocity vector field model, enabling dynamic and precise matching of the dual paths. For target image points that move beyond the field of view, we propose an attitude compensation method based on optimal angular velocity coordination. Monte Carlo simulation results show that the maximum root mean square error of the compensation method across the entire field of view is 0.9792 pixels in the x-direction and 0.7130 pixels in the y-direction. Experimental results demonstrate the effectiveness of the method, which meets the requirements for practical applications and provides a reliable foundation for the real-world implementation of dual-path multi-target imaging spectrometers. Full article

Accurate current sensing in rectangular conductors is challenged by mechanical deformations, including eccentricity (X/Y-axis shifts) and inclination (Z-axis tilt), which distort magnetic field distributions and induce measurement errors. To address this, we propose a bio-inspired error compensation strategy integrating an elliptically configured Hall sensor array with a hybrid Grey Wolf Optimizer (GWO)-enhanced backpropagation neural network. The eccentric displacement and tilt angle of the conductor are quantified via a three-dimensional magnetic field reconstruction and current inversion modeling. A dual-stage optimization framework is implemented: first, establishing a BP neural network for real-time conductor state estimations, and second, leveraging the GWO’s swarm intelligence to refine network weights and thresholds, thereby avoiding local optima and enhancing the robustness against asymmetric field patterns. The experimental validation under extreme mechanical deformations (X/Y-eccentricity: ±8 mm; Z-tilt: ±15°) demonstrates the strategy’s efficacy, achieving a 65.07%, 45.74%, and 76.15% error suppression for X-, Y-, and Z-axis deviations. The elliptical configuration reduces the installation footprint by 72.4% compared with conventional circular sensor arrays while maintaining a robust suppression of eccentricity- and tilt-induced errors, proving critical for space-constrained applications, such as electric vehicle powertrains and miniaturized industrial inverters. This work bridges bio-inspired algorithms and adaptive sensing hardware, offering a systematic solution to mechanical deformation-induced errors in high-density power systems. Full article

At present, the monitoring technology of the growth status of sika deer antlers faces many challenges in a complex breeding environment (such as light change, object occlusion, etc.). More importantly, an effective method for the segmentation of sika deer antlers is still lacking, which hinders the development of subsequent quality classification of sika deer antlers. In order to fill the research gap and lay a foundation for future sika deer antler quality classification, this paper proposed an improved semantic segmentation model based on U-Net, named SDAS-Net. In order to improve the segmentation accuracy and generalization ability of the model in a complex environment, we introduced a two-dimensional discrete wavelet transform module (2D-DWT) in the encoder head to reduce noise interference and enhance the ability to capture features. In order to compensate for the loss of feature information caused by 2D-DWT, we embedded the Star Blocks module in the encoder. In addition, the efficient mixed channel attention (EMCA) module was introduced to adaptively enhance key feature channels in the decoder, and the dual cross-attention mechanism (DCA) module was used to fuse high-dimensional features in skip connections. To verify the validity of the model, we constructed a 1055-image sika deer antler dataset (SDR). The experimental results show that compared with the baseline model, the performance of the SDAS-Net model is significantly improved, reaching 92.12% in MIoU and 93.63% in the PA index, and the number of parameters is only increased by 6.9%. The results show that the SDAS-Net model can effectively deal with the task of sika deer antler segmentation in a complex breeding environment while maintaining high precision. Full article

Detecting small ships in optical RSIS is challenging. Due to resolution limitations, the texture and edge information of many ship targets are blurred, making feature extraction difficult and thereby reducing detection accuracy. To address this issue, we propose a novel dual-path convolutional self-attention network, DPCSANet, for ship detection. The model first incorporates a dual-path convolutional self-attention module to enhance its ability to extract local and global features and strengthen target features. This module integrates two parallel branches to process features extracted by convolution and attention mechanisms, respectively, thereby mitigating the potential conflicts between local and global information. Additionally, a high-dimensional hybrid spatial pyramid pooling module is introduced into the model to expand the scale range of feature extraction. This enables the model to fully utilize background contextual features to compensate for weak feature representations of the target. To further improve the detection accuracy for small ships, we developed a focal complete intersection over union loss function. This regression loss guides the model to focus on weak targets during training by increasing the contribution of low-accuracy prediction boxes to the loss. Experimental results demonstrate that the proposed method effectively enhances the model’s detection ability for small ships. On the LEVIR-ship, OSSD, and DOTA-ship datasets, DPCANet achieves an average precision improvement of 0.9% to 11.4% over the baseline, outperforming other state-of-the-art object detection models. Full article

Optimizing agricultural carbon compensation zoning is crucial for establishing robust mechanisms in agricultural carbon compensation management, with significant implications for achieving national “dual carbon” strategic objectives. This study employs K-means and the three-dimensional magic cube approach to construct a novel evaluation index system for comprehensive carbon compensation zoning. By combining spatial land-use zoning, we delineate carbon compensation zones in Guangshui City, Hubei Province, and analyze the spatiotemporal variations of agricultural carbon balance, proposing optimization strategies. The results show that (1) from 2000 to 2021, agricultural carbon emissions and absorption exhibit a trend of increasing followed by decreasing, with spatial patterns of “higher in the northwest, lower in the southeast” and “higher in the southwest, lower in the northeast”; (2) the Gini coefficient of agricultural carbon emissions averages at 0.24, with economic contribution coefficients and ecological carrying coefficients ranging from 0.04–16.1 and 0.39–1.99, respectively, from 2000 to 2021; and (3) in 2021, Guangshui City comprises seven payment zones, four balance zones, and six compensation zones, ultimately forming eight optimized agricultural carbon compensation zones in alignment with regional agricultural carbon balance objectives. This study provides theoretical references for enhancing county-level agricultural carbon comprehensive compensation management mechanisms. Full article