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- 摘要：Aiming at the problems of data security and low management efficiency in the operation of plant-station side intelligent power distribution and utilization terminals, a blockchain-based management system is proposed and designed in this paper. The system applies blockchain technology to construct an Alliance Chain Network (ACN), where the control units of plant-station side intelligent power distribution and utilization terminals are taken as the network nodes. Public key data verification and data submission for intelligent terminals are realized through a Trust Authority (TA). Besides, Smart Contracts (SCs) are adopted to automatically complete the public key registration, update and revocation of terminals, and a random number proof consensus mechanism is designed by combining with trusted chips. Experimental results demonstrate that, in comparison with the traditional management systems, the proposed system reduces the communication latency by 30%, makes the data tampering probability close to 0, and shortens the equipment fault response time by 40%. It effectively improves the information sharing efficiency among control unit nodes and guarantees the security of information transmission in the power distribution and utilization system.关键词：blockchain;Plant-side Intelligent Power Distribution and Utilization Terminal;Terminal Management System;smart contract;Alliance Chain Network6|0|0更新时间：2026-06-16
- 摘要：To address the issues of limited network coverage and weak computing capabilities in maritime environments, a task offloading scheme in the space-air-ground-sea integrated network scenario was introduced. Considering factors such as the offloading success rate of computational tasks, energy consumption constraints, environmental differences between near-shore and offshore scenarios, and the dynamic nature of the integrated space-air-ground-sea environment, a task offloading framework suitable for space-air-ground-sea integrated networks was constructed. A multi-agent collaborative task offloading scheme based on deep reinforcement learning was proposed. Experimental results demonstrate that, compared to offloading schemes based on the MADQN algorithm, the DDPG algorithm, and random policy, the proposed scheme improves the offloading success rate by 3.09%, 18.42%, and 66.42%, respectively, reduces delay by 19.07%, 21.53%, and 65.02%, respectively, and lowers energy consumption by 10.59%, 8.20%, and 8.75%, respectively.关键词：Space-Air-Ground-Sea Integrated Networks;task offloading;Multi Agent;deep reinforcement learning47|6|0更新时间：2026-06-16
- 摘要：To address the problems of high small-object proportion, severe dense occlusion, shallow-detail degradation during downsampling, and limited onboard edge resources in aerial images captured by unmanned aerial vehicles (UAVs), a lightweight small-object detection method for UAV edge devices was proposed. YOLO11s was adopted as the baseline. The original P3/P4/P5 detection structure was reconstructed into a P2/P3/P4 structure, so that high-resolution shallow features could directly participate in tiny-object prediction. Meanwhile, the P5 branch with limited benefit for UAV small-object scenes was removed, and the high-level semantic modeling module was moved to the P4 layer to reduce redundant parameter overhead. A selective guidance block, a P2-guided cross-layer re-injection mechanism, and an adaptive fusion module were further designed to enhance shallow details and alleviate multi-source feature fusion conflicts. Experimental results on the VisDrone validation set showed that mAP (mean average precision)@0.5 and mAP@0.5:0.95 were improved by 4.40 and 3.22 percentage points, respectively, compared with the YOLO11s baseline, while the number of parameters was reduced from 9.46M to 3.82M. When deployed with TensorRT using 32-bit floating-point precision (FP32) on a Jetson Orin NX 16 GB platform, an inference speed of 52.63 frames per second (FPS) was achieved. The results indicate that the proposed method improves detection accuracy while maintaining a compact model size and real-time edge inference capability.关键词：edge intelligence;uav small object detection;yolo11s;feature enhancement14|0|0更新时间：2026-06-15
- 摘要：Accurate prediction of electric vehicle charging demand is crucial for alleviating pressure on the power grid. To address the issues of slow convergence and vulnerability to unreliable nodes in existing prediction methods when processing non-independently and identically distributed (non-IID) data, this paper proposes a Double-Layer Credible Federated Learning (DCFL) strategy for energy demand prediction. The proposed method employs heuristic association rules to automatically mine charging information without requiring additional data collection. It selects benign local updates based on weight loss variation to accelerate convergence and resist interference from unreliable nodes. Furthermore, a multi-channel attention mechanism is introduced to design the loss function and the weighted aggregation scheme of federated learning. Experimental results demonstrate that, compared with traditional federated learning methods, the proposed method reduces training time by 71% and increases convergence speed by 17.6%, showing significant advantages in both prediction accuracy and convergence efficiency.关键词：energy prediction;heuristic correlation rules;electric vehicles networks;privacy protection;multi-channel attention mechanism5|0|0更新时间：2026-06-15
- 摘要：The short message communication service (SMCS) is an integrated communication and navigation (ICAN) advantage service of China's BeiDou Navigation Satellite System (BDS). It not only enables ten million times/h high-capacity SMCS, but also features ten meter level high-precision positioning capability. The next-generation BDS will build a global short message communication service (GSMCS) system combining high and low orbit satellites, which service performance will upgraded from regional high capacity to global high capacity, to better support various Satellite Internet of Things (SIoT) applications. Under the existing frequency resource constraints of SMCS, to address the mutual interference problem of user inbound signals caused by satellite overlapping coverage and frequency sharing, while maintaining the ranging accuracy not less than 8ns, a multi narrowband subcarrier spectrum reuse method is proposed, with two schemes: equal division into 4 subcarriers and equal division into 8 subcarriers. Comparative analysis shows that the low orbit inbound performance using multi-narrowband subcarrier spectrum is significantly better than directly using the existing high orbit inbound spectrum scheme. Under the equal division into 8 subcarriers scheme, the inbound information rate range supported by low orbit SMCS covers the existing inbound information rate categories, which can better accommodate existing users of SMCS, while achieving a maximum increase of 4 times in inbound capacity and a maximum increase of 46.3% in spectrum efficiency.关键词：BeiDou short message communication;frequency reuse;inbound capacity;spectral efficiency11|1|0更新时间：2026-05-20
- 摘要：Based on the 3GPP specifications and ITU-R standardized channel models, in direct-to-satellite smartphone scenarios, the satellite channel is dominated by the energy of the line-of-sight (LoS) path. Consequently, its delay spread is significantly smaller than that in terrestrial non-line-of-sight (NLoS) environments, exhibiting strong sparsity in the discrete delay domain. Due to the lack of structural constraints, the conventional least mean squares algorithm is highly sensitive to noise fluctuations on zero taps in the steady state, resulting in slow convergence and high steady-state error. Therefore, building upon the zero-attracting LMS (ZA-LMS) and reweighted zero-attracting LMS (RZA-LMS), this paper proposes an improved sparse least mean squares algorithm with a rational-function penalty, termed rational least mean squares (Rational-LMS), and derives its stochastic gradient descent-based weight update equation. By exploiting the high-sensitivity gradient of the rational-function penalty near the origin and its rapid attenuation in the large-coefficient region, the proposed algorithm effectively suppresses noise components while ensuring stable convergence of the equalizer coefficients, thereby yielding a regularization effect that is mathematically closer to the ideal
-norm constraint. Furthermore, an adaptive parameter update mechanism is introduced to dynamically adjust the sparsity penalty factor according to the instantaneous error, thereby achieving faster convergence and lower steady-state mean squared error (MSE). Theoretical analysis and simulation results demonstrate that, in sparse channel environments, the proposed Rational-LMS outperforms ZA-LMS and RZA-LMS in terms of both convergence rate and steady-state MSE performance. 关键词：adaptive filtering;sparse channel;-norm;penalty term5|1|0更新时间：2026-05-20 - 摘要：Object recognition in complex scenes is an important task in the Internet of Things (IoT) based edge perception systems. Infrared polarization images show superior foreground–background contrast in security monitoring and camouflage detection. However, due to the limited hardware resources of imaging systems, it is difficult for long-wave infrared division-of-focal-plane (DoFP) polarization imaging systems to achieve high-quality images in practical applications, which adversely affects edge perception performance. To this end, a lightweight joint denoising and demosaicking network for infrared polarization images is developed via spatial-frequency collaborative learning. The proposed method employs a three-stage learning network to enhance the quality of polarization image reconstruction while maintaining low computational complexity. The dual-domain interactive denoising block leverages the statistical properties of the frequency domain to suppress noise while preserving polarization features. After coarse demosaicking, a lightweight fine reconstruction module is adopted to generate the final results. Depthwise grouped convolutions are used to reduce model parameter count and improve reconstruction quality. Extensive experiments are performed on the IR-DoT and IR-DoFP datasets. The results show that the proposed method achieves superior performance compared with other leading approaches while maintaining low computational overhead, making it a low-power, high-quality data preprocessing approach for IoT-based edge devices.关键词：internet of things edge intelligence;infrared polarization imaging;denoising and demosaicking;frequency learning;lightweight model design5|1|0更新时间：2026-05-20
- 摘要：To address the challenges of strong node heterogeneity, frequent dynamic topology changes, and difficult cache coordination in Space-Air-Ground Integrated Vehicular Network (SAGVN), this paper constructs a directed heterogeneous graph model for SAGVNs, designs a distributed caching decision framework that integrates Graph Attention Network (GAT) and Multi-Agent Deep Reinforcement Learning (MADRL), and proposes a caching strategy optimization method based on GAT and MADRL. Experimental results show that, compared to the Random strategy, GA strategy, MADQN strategy, and MADDPG strategy, the proposed strategy reduces the average task delay by 51.61%, 38.12%, 26.83%, and 22.12%, lowers the total system energy consumption by 71.68%, 67.53%, 43.23%, and 24.21%, and improves the cache hit rate by 261.19%, 49.11%, 21.43%, and 6.21%, respectively.关键词：Space-Air-Ground Integrated Vehicular Network;caching strategy;Graph Attention Network;multi-agent deep reinforcement learning8|0|0更新时间：2026-05-20
- 摘要：Wireless radio frequency backscatter is characterized by shared spectrum, low power consumption, and low cost, making it a significant player in the terminal devices and repeater markets of communication and sensing. Mobile communication technology has always been a hotspot of development,the transition from 5G to 6G is not merely about speed but also about evolving from the Internet of Things to the Internet of Intelligent Things. Both mobile communication technology and IoT technology face challenges such as spectrum scarcity and energy efficiency, creating intersections with wireless radio frequency backscatter communication. By exploring research hotspots like mobile communication and IoT technologies, this study introduces wireless radio frequency backscatter communication. Through reviewing seminal papers on environmental backscatter communication, recent research, and fundamental principles, a clear developmental trajectory is mapped out, and the current state is categorized across different dimensions. Based on this developmental trajectory and classification, existing issues and partial solutions are identified. Finally, synthesizing insights from previous sections, this study predicts the next phase of development trends.关键词：backscatter communication;wireless radio frequency;review;mobile communication technology;internet of things53|35|0更新时间：2026-04-10
- 摘要：To address the privacy leakage risks in vision-based gesture sensing and the high sensing costs of other non-visual signals, this paper investigated a gesture reconstruction and recognition method based on ultrasonic signals. The collected ultrasonic echo and gesture image data were processed to construct the Ultrasonic Gesture dataset. Based on this dataset, we proposed a CAMT-Net with high-performance local perception and global modeling capabilities, achieving high-precision end-to-end mapping from ultrasonic signals to 2D gesture keypoint coordinates. Experiments were conducted on the dataset containing six static gestures. The proposed method achieved accuracy close to the metods based on RGB images. Further gesture recognition based on the reconstructed keypoints reached an accuracy of 89%. The results indicate that ultrasonic signals can effectively support fine-grained gesture perception tasks.关键词：ultrasound;sensing;gesture;deep learning54|30|0更新时间：2026-04-10
- 摘要：With the rapid growth of sensitive data in the Internet of Things (IoT), covert communication has become an important security mechanism by hiding the very existence of wireless transmissions. In this context, the detection and communication performance of IoT covert communications were analyzed when a disco reconfigurable intelligent surface (DRIS) was deployed by the warden Willie. Owing to the randomly time-varying reflection coefficients of the DRIS, the effective channel at the receiver was no longer approximately constant within one coherence block, thereby inducing active channel aging (ACA) and causing channel state information mismatch. To address this scenario, a detection rule for Willie tailored to time-varying DRIS-assisted channels was developed. Under non-equiprobable prior probabilities, the total detection error probability was used to characterize the detection performance, while the signal-to-jamming-plus-noise ratio (SJNR) was used to evaluate the communication performance. The optimal detection threshold and the corresponding analytical expressions were further derived. Numerical results showed that, at a transmit power of 5 dBm, compared with the no-DRIS scheme, the DRIS reduced Willie’s total detection error probability by approximately 63.4% while decreasing Bob’s achievable rate by about 41.9%. Although a fixed RIS improved the achievable rate by approximately 5.7%, its total detection error probability was 8.43 times that of the no-DRIS scheme. These results indicate that DRIS can simultaneously enhance Willie’s detection capability and impair the legitimate covert link, thereby posing a serious security threat to IoT covert communications.关键词：Internet-of-Things;covert communication;reconfigurable intelligent surface;signal detection;channel aging34|32|0更新时间：2026-04-10
- 摘要：The low-altitude economy, as a national strategic emerging industry, is accelerating the transformation of Advanced Air Mobility (AAM) systems from traditional ground-based transportation toward comprehensive, high-density low-altitude operational modes. However, factors such as communication interference in complex urban scenarios, insufficient perception accuracy, flight safety hazards, and energy consumption constraints severely limit the practical application effectiveness of AAM systems. This paper addresses the critical challenges of cooperative perception and control for unmanned aerial vehicle (UAV) formations in AAM scenarios by proposing an integrated cooperative formation method that combines perception optimization with energy consumption control. First, we innovatively construct a joint optimization model with the objective function of minimizing formation flight energy consumption, constrained by the Cramér-Rao Lower Bound (CRLB) for cooperative perception and communication signal-to-noise ratio requirements. Based on Lyapunov stability theory, we design a gradient control algorithm to solve for the optimal formation orientation configuration that satisfies perception accuracy requirements. Second, we propose an improved potential field control method that significantly enhances obstacle avoidance capability and flight stability in complex environments by optimizing the effective range of repulsive forces, introducing a relative velocity dynamic adjustment mechanism, designing compensatory forces to escape local minimum traps, and incorporating physical air resistance models. Simulation experiments demonstrate that the proposed method achieves significant optimization of UAV formation energy consumption while meeting collaborative perception accuracy thresholds, simultaneously ensuring cooperative perception capabilities and safe flight operations in complex interference environments.关键词：Advanced Air Mobility;UAV formation-based Cooperative perception;Lyapunov method;Improved potential field method;Flight energy consumption93|35|0更新时间：2026-04-10
- 摘要：In low Earth orbit (LEO) satellite networks, the influence of satellite orbital parameters and the distribution of ground stations can lead to link congestion during large-scale, parallel cross-traffic transmissions, as many data flows simultaneously pass through critical nodes. This congestion may degrade overall system performance or even disrupt normal communication. To address this issue, a virtual topology approach is employed to convert the dynamic trajectory changes of satellites into a series of sequential static topology graphs. Next, each satellite node's importance in each time slot is assessed from the perspective of information transmission, using the node degree and the betweenness centrality of its neighboring edges. A load optimization strategy for LEO satellite communication systems is then proposed, based on critical node analysis. This strategy plans routes according to each node's importance assessment value, alleviating transmission pressure on critical nodes under high load conditions and providing a new approach for addressing node congestion in LEO satellite networks. Finally, the proposed load optimization strategy is validated through simulations on the Hypatia platform. The results indicate that, compared to the conventional shortest path routing method, the proposed strategy effectively utilizes previously idle links and reasonably avoids congestion-prone satellite nodes, enhancing system throughput while reducing end-to-end transmission delay.关键词：LEO;complex networks;load optimization;critical node53|56|0更新时间：2026-03-03
- 摘要：In Unmanned Aerial Vehicle Integrated Sensing and Communication (UAV - ISAC)scenarios, the overall data transmission rate and power consumption are adversely affected by interference. To address this issue, this paper proposes a multi-dimensional energy-efficient scheme based on interference mitigation. Firstly, an interference model is established that comprehensively considers the interactions among communication signals, sensing signals, and perceived signals. The objective of energy efficiency optimization is defined as the ratio of the combined data transmission rates for communication and sensing to the total power consumption. By treating communication weight and sensing weight as dimensional parameters, a multi-dimensional energy efficiency function is formulated. The Particle Swarm Optimization-enhanced Dinkelbach algorithm is employed to optimize these two-dimensional weight parameters, thereby maximizing energy efficiency. The simulation results show that the complexity of the PSO+Dinkelbach algorithm is 44% higher than that of the traditional Dinkelbach algorithm, while its energy efficiency is improved by 63%.关键词：integrated sensing and communication;unmanned aerial vehicle;interference modeling;energy efficiency optimization44|41|0更新时间：2026-03-03
- 摘要：To address the challenges of user mobility, task dependency, and restricted aerial trajectory planning in maritime mobile edge computing, this study proposes a user mobility-aware air-sea collaborative task offloading framework. We establish a three-tier computing architecture comprising user equipment, aerial auxiliary nodes (AANs), and edge servers, formulating a multi-objective optimization model that minimizes system costs (latency and energy consumption) while considering task dependencies, resource allocation constraints, and AAN 3D spatial safety requirements. The technical contributions include: 1) A dynamic K-means-based user clustering mechanism that periodically updates MD-AAN associations according to real-time mobility patterns; 2) A Twin-delayed Deep Deterministic Policy Gradient based Heterogeneous Agent Offloading (TD3-HAO) algorithm enabling joint optimization of dependent sub-task offloading, resource allocation, and 3D AAN trajectory planning through heterogeneous multi-agent coordination. Simulation results demonstrate superior performance over baseline methods, maintaining less than 3% deviation from optimal solutions when scaling to 25 MDs, with 16.94%-38.34% latency reduction. The proposed solution effectively resolves the resource underutilization caused by ignoring task dependencies and agent homogeneity in existing approaches, providing theoretical guidance for air-sea integrated edge computing systems.关键词：Maritime mobile edge computing;Aerial auxiliary nodes;Dependent task offloading;3D trajectory planning;Heterogeneous multi-agent deep reinforcement learning46|27|0更新时间：2026-03-03
- 摘要：The complex environment of construction sites presents a huge challenge for detecting unsafe behaviors among workers. In order to accurately identify the wear of helmets for workers in complex construction environments, and effectively reduce the incidence of safety accidents, a detection algorithm based on deformable convolution for the identification of safety helmets worn by workers is proposed. The algorithm fuses deformable convolutional modules in the YOLOv8 backbone network part, alleviating the limitations of traditional standard convolution layers, which rely on fixed geometric structures for feature extraction. This enhancement improves the model's ability to analyze and identify complex construction environments. By employing image geometric transformations and pixel-level processing, the algorithm enriches the number of training samples, thereby enhancing the generalization capability of the detection model across various site scenarios. The algorithm is deployed on the Jetson TX2, and the target detection software is developed to facilitate on-site detection of the actual construction site environment. The results indicate that the model can effectively detect unsafe behaviors among workers in complex construction environments, achieving a recognition accuracy of 95.2% on a standard dataset. Furthermore, the model demonstrates robustness and can be applied across various construction sites.关键词：YOLOv8;construction site;unsafe behavior detection;deformable convolution;object recognition49|92|0更新时间：2026-03-03
- 摘要：In recent years, deep learning technologies have been widely adopted to the Channel State Information (CSI)-based fingerprinting localization field, demonstrating reliable localization accuracy. However, deep learning-based fingerprinting localization models typically rely on multi-layered network structures to extract distinctive location features, the process accompanied by a large number of model parameters and intensive computational operations, which occupy substantial physical resources on devices. This can be a significant burden, especially for resource-limited smartphones. To this end, this paper proposes a novel lightweight fingerprinting localization model based on smartphone CSI，(1) a concise and efficient feature extraction module is designed, employing linear and convolutional layers with a minimal number of parameters and incorporating a feature fusion mechanism to achieve both lightweight deployment and strong discriminative capability in feature extraction; (2) a data augmentation module is introduced to generate CSI data for unknown points, thereby expanding the training sample space and significantly enhancing the model’s recognition ability for untrained locations. Experiments were conducted in two typical indoor scenarios. The results show that compared to the most advanced benchmark models, the proposed localization model has reduced the average RMSE by 12.5% in two indoor scenarios, and the inference time for a single localization is only 0.08 seconds. The proposed localization model not only effectively improves positioning accuracy but also significantly reduces the required positioning time.关键词：smartphone channel state information;indoor localization;deep learning;lightweight model57|38|0更新时间：2026-03-03
- 摘要：In low-earth-orbit (LEO) satellite communication systems, addressing the poor performance of traditional channel estimation algorithms under dynamic delay variations and low signal-to-noise ratio (SNR) caused by high-speed satellite-terrestrial motion, this paper proposes a deep neural network (DNN)-based channel estimation algorithm combined with sub-block merging of channel responses under an orthogonal frequency-division multiplexing (OFDM) framework. This method enhances the reliability and stability of data transmission in non-terrestrial networks (NTN). The algorithm divides the frequency-domain pilot channel response matrix into sub-blocks for averaging and employs a DNN to adaptively determine the optimal sub-block merging number under different channel conditions with varying delay and noise. The bit error rate (BER) performance of this algorithm is compared through simulations against existing channel estimation methods under dynamically randomized delays. Results demonstrate that the proposed algorithm achieves lower BER at identical SNR levels than traditional methods, exhibiting stronger robustness. Compared to existing neural network-based channel estimation algorithms, it adapts better to larger delays and dynamic delay variations, showing superior generalization capability.关键词：low Earth orbit satellite;non-terrestrial network;channel estimation;deep neural network;orthogonal frequency-division multiplexing63|37|0更新时间：2026-03-03
- 摘要：IoT-based environmental monitoring systems are crucial infrastructure for early forest fire warnings, playing a decisive role in preventing disaster spread. However, field-deployed terminals face limitations in communication bandwidth, computational power, and energy supply, hindering both real-time transmission of high-resolution images and on-device deployment of complex neural networks. This paper proposes an edge-cloud collaborative forest fire recognition method. Lightweight recognition and offloading models are deployed on terminals. Most images are processed locally at the edge; only images challenging for the lightweight model are offloaded via the IoT network to a cloud center for recognition. This balances recognition accuracy with the core IoT requirements of low latency, low bandwidth, and energy efficiency. The designed lightweight fire recognition model integrates Ghost Module and ShuffleNetv2, enhanced with an Efficient Channel Attention (ECA) module, significantly reducing computational and storage demands. To improve offloading decision effectiveness in dynamic IoT environments, a Noisy Double Proximal Policy Optimization (NDPPO) algorithm is proposed to train the offloading model. Comparative experiments validate the effectiveness of this lightweight collaborative recognition approach for IoT-based forest fire monitoring.关键词：forest fire identification;lightweight model;collaborative identification;strategy optimization algorithm;IoT monitoring terminal67|37|0更新时间：2026-03-03
- 摘要：Accurate segmentation of pulmonary nodules is a crucial prerequisite for early lung cancer screening and clinical diagnosis. However, pulmonary nodules in CT images exhibit challenges such as irregular shapes, blurred boundaries, and significant size variations (ranging from a few millimeters to tens of millimeters). Additionally, they are easily interfered with by surrounding tissues like blood vessels and pleura, which leads to limitations in traditional deep learning segmentation methods (e.g., U-Net, baseline V-Net), including large boundary localization errors, high missed diagnosis rates of small lesions, and poor segmentation consistency. Furthermore, the fixed setting of the region of interest (ROI) tends to cause redundancy of target information or loss of key details, further restricting segmentation accuracy.To address the above challenges, this study proposes a two-stage pulmonary nodule segmentation method that combines an adaptive ROI algorithm with a multi-view 3D segmentation strategy. In the first stage, the V-Net architecture is employed to perform initial segmentation along the axial axis. An innovative adaptive ROI (A-ROI) algorithm dynamically adjusts the position and size of the ROI, maintaining the area ratio of the nodule to the ROI below a threshold RT (experimentally determined as 0.6) to reduce interference from irrelevant tissues. In the second stage, patch-based analysis is conducted along the coronal and sagittal axes, and finally, a consensus module integrates the multi-plane prediction results (with a consistency ratio set to 50%) to enhance segmentation stability.Experiments on the public LUNA16 and LNDb datasets show that this method achieves Dice coefficients of 92.6% and 92.3%, respectively, representing improvements of 6.2 and 6.1 percentage points compared to the baseline V-Net, while the Hausdorff distance is reduced to 2.92±1.89 mm. Compared with the traditional U-Net, the segmentation accuracy is also significantly improved. Ablation experiments verify that the adaptive ROI reduces boundary errors by 37.5%, and multi-plane collaborative analysis improves shape similarity by 29.8%. This method effectively addresses the core challenges in pulmonary nodule segmentation in CT images, providing reliable technical support for clinical accurate diagnosis of early lung cancer and efficacy evaluation.关键词：Lung Nodules;Adaptive ROI algorithm;Multi-view segmentation;V-Net architecture;segmentation stability41|29|0更新时间：2026-03-03
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