CAAI Transactions on Intelligence Technology最新文献

{"title":"Credit-Driven Adaptive Grouping for Refined Cooperative Multi-Agent Reinforcement Learning","authors":"Yirui Liu,&nbsp;Xueqiang Gu,&nbsp;Caiming Zheng,&nbsp;Xin Xu,&nbsp;Zhangling Wang,&nbsp;Wanpeng Zhang,&nbsp;Yixing Lan","doi":"10.1049/cit2.70127","DOIUrl":"https://doi.org/10.1049/cit2.70127","url":null,"abstract":"<p>Policy heterogeneity is crucial for achieving sophisticated coordination in complex collaborative tasks, which has emerged as one of the key challenges in multi-agent reinforcement learning (MARL) in recent years. Notably, the grouping paradigm has made remarkable progress in addressing policy heterogeneity. However, most existing grouping methods require predefining the number of groups or the composition and quantity of members within each group, which need to be individually configured for each scenario and are difficult to set without sufficient expert knowledge. By contrast, we propose a novel MARL grouping algorithm named Credit-driven adaptive Grouping (CreateG) which divides the entire training process into multiple phases and reallocates poorly adapted (low-credit) individuals at each training stage. With the help of the mechanism we designed, an environment-adaptive grouping is ultimately formed. Furthermore, we design a hierarchical hypernetwork architecture to accommodate this adaptive grouping mechanism. Experiments conducted on StarCraft II micromanagement hard and superhard tasks, Google Research Football and TAG scenarios show CreateG achives state-of-the-art MARL performance. Moreover, extensive ablation studies elucidate the operational mechanism of the grouping strategy and other components demonstrate how they enhance overall performance.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 5","pages":"1496-1510"},"PeriodicalIF":5.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149311753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Single-Encounter-Emphasise-Attention Neural Network for Predictive Maintenance in Urban Infrastructure","authors":"Tawfeeq Shawly,&nbsp;Ahmed A. Alsheikhy","doi":"10.1049/cit2.70171","DOIUrl":"https://doi.org/10.1049/cit2.70171","url":null,"abstract":"<p>The Riyadh Metro represents a significant project in Saudi Arabia, designed to transform urban transportation and reduce traffic congestion within the city. With six metro lines and 85 stations, the network is expected to serve millions of passengers daily, necessitating innovative digitally driven maintenance approaches to ensure reliable efficient operation. We hypothesise that integrating Internet of things (IoT) sensors, digital twin technology, and a temporal graph neural network (TempGNN) enables real-time health monitoring. This paper introduces a real-time digital health monitoring framework that incorporates IoT sensors, digital twin technology, a proposed TempGNN and a new module single encounter-emphasise attention neural network (SEEA-NN). TempGNN exploits both temporal patterns and spatial relationships in these sensor data to feed SEEA-NN with its required data. SEEA-NN is designed to detect and assess the importance of rare single-occurrence failure precursors, often overlooked by conventional models, alongside recurring patterns. The proposed framework contains an input gating module to filter out noisy sensor data, a multihead encounter attention module that employs a specific attention head to isolate and enhance the signal from anomalous single-encounter events, and a temporal synthesis layer that combines these emphasised features with sequential data for thorough analysis. This enables the framework to predict potential failures with high accuracy of approximately 98%, allowing for proactive maintenance scheduling and decreased unplanned downtime by more than 30% and maintenance expenses by around 25%, whilst also enhancing energy efficiency and passenger satisfaction in simulations. This approach demonstrates a scalable model for the digital transformation of large-scale transport infrastructure and for implementing intelligent predictive maintenance in metro systems worldwide and in logistics fleets, container handling systems and freight rail networks.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 5","pages":"1444-1462"},"PeriodicalIF":5.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149312034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing Long-Tailed Drug–Drug Interactions Through Minimisation of Predictive Uncertainty and Loss Sharpness","authors":"Chao Liu,&nbsp;Xizhao Wang,&nbsp;Farhad Pourpanah,&nbsp;Sam Kwong","doi":"10.1049/cit2.70166","DOIUrl":"https://doi.org/10.1049/cit2.70166","url":null,"abstract":"<p>Drug–Drug Interaction (DDI) prediction is critical for ensuring patient safety, particularly under long-tailed distributions, where frequent (head) interactions dominate whereas rare (tail) interactions remain underrepresented. Conventional loss functions such as cross-entropy often tend to overfit head classes while they underperform on rare but clinically important classes. Recent studies have shown that sharpness-aware minimisation (SAM) improves generalisation by encouraging solutions that lie in flatter regions of the loss landscape. Given that the loss function plays a fundamental role in model training and that uncertainty estimation is crucial for handling ambiguous or difficult samples, we propose the uncertainty and SAM (USAM) loss for improving DDI prediction under long-tail scenarios. The USAM loss integrates predictive uncertainty with SAM via two key components: (i) a dynamic reweighting strategy based on uncertainty, which leverages prediction entropy to emphasise difficult samples and (ii) a class-balanced SAM regularisation term, which encourages flatter minima and enhances generalisation across imbalanced classes. Extensive experiments on four long-tailed DDI benchmarks demonstrate that the USAM loss consistently outperforms existing methods in terms of both F1 score and recall. These results highlight the effectiveness of incorporating uncertainty modelling with sharpness-aware optimisation in addressing long-tailed problems.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 5","pages":"1613-1625"},"PeriodicalIF":5.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70166","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149311732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FPGA-Based Real-Time Implementation of Closed-Loop Brain-Controlled Eye Movement System","authors":"Guodao Zhang,&nbsp;Yupeng Li,&nbsp;Yanjie Lu,&nbsp;Genfu Yang,&nbsp;Xiaotian Pan,&nbsp;Jing Wang","doi":"10.1049/cit2.70172","DOIUrl":"https://doi.org/10.1049/cit2.70172","url":null,"abstract":"<p>This paper presents a comprehensive closed-loop brain-eye movement framework, spanning parametric modelling, dynamic analysis and real-time FPGA implementation. A simplified first-order oculomotor model was derived from the classical Robinson formulation to preserve the essential neural-mechanical feedback structure while enabling efficient hardware realization. Through systematic parametric variations of neural and mechanical properties and multiple classes of brain-generated inputs (step, ramp, sinusoidal and noisy), the system exhibited stable closed-loop behaviour with fast convergence and low steady-state errors. Time-domain and phase-plane analyses confirmed that the eye accurately tracked diverse target trajectories across wide parameter ranges, with all equilibrium points classified as asymptotically stable. The discrete-time model was implemented on a Xilinx Virtex-4 FPGA using only adders, subtractors and shifters, completely avoiding multipliers. The design achieved a maximum clock frequency of 118 MHz, operated at 100 MHz with a closed-loop latency of nine cycles (90 ns), and consumed approximately 280 mW total power while utilising less than 4% of the device resources. To validate the system in a practical setting, a real-time hardware platform with a 3 × 3 target grid (nine fixation points) was developed. Experimental results demonstrated that the closed-loop brain-eye architecture reliably steered the eye towards each target with minimal tracking error and smooth dynamic response. Overall, this work bridges physiological oculomotor models and neuromorphic hardware, offering a scalable resource-efficient platform for real-time brain-eye closed-loop emulation and control.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 5","pages":"1626-1643"},"PeriodicalIF":5.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149311711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Multi-Source Batch Normalisation for Test-Time Batch Adaptation","authors":"Xinlin Xiao,&nbsp;Xiangtao Zheng,&nbsp;Jianhua Tao,&nbsp;Xiaoqiang Lu","doi":"10.1049/cit2.70159","DOIUrl":"https://doi.org/10.1049/cit2.70159","url":null,"abstract":"<p>Test-Time Batch Adaptation (TTBA) aims to adapt a pre-trained source model to small, unlabelled target batches at test time. The TTBA methods focus on adapting the pre-trained model or the target data in a one-to-one alignment paradigm. However, these one-to-one alignment paradigms assume that the source domain may share the same knowledge with the target domain. It becomes ineffective when the target domain is different from the source domain. In this paper, a multi-source batch normalisation method is introduced. Specifically, spectral similarity clustering selects a small set of representative source domains, spectral entropy-guided weighting combines source statistics based on their relevance to the target domain, and batch-wise re-initialisation with unsupervised refinement stabilises updates. Extensive experiments on multiple benchmark datasets verify the effectiveness of the proposed method, achieving superior performance over state-of-the-art TTBA methods, especially in scenarios with small target batches.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 5","pages":"1567-1584"},"PeriodicalIF":5.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149311324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Auto-ISC: Inter-Sample Contrastive Learning for Automated Curve Angle Calculation From Ultrasound","authors":"Chen Zhang,&nbsp;Anh Hoang Phuc Nguyen,&nbsp;Yongping Zheng,&nbsp;Sai Ho Ling","doi":"10.1049/cit2.70157","DOIUrl":"https://doi.org/10.1049/cit2.70157","url":null,"abstract":"<p>Scoliosis assessment has been increasingly supported by AI-assisted tools, enabling more efficient and standardised clinical workflows. However, existing ultrasound-based pipelines often suffer from limited generalisation and unstable performance. These limitations primarily arise from two challenges: (1) suboptimal segmentation under low-contrast and noisy imaging conditions, and (2) unreliable anatomical landmark identification due to missing or ambiguous features. To address these issues, we propose Auto-ISC, a novel two-stage automated ultrasound scoliosis assessment system. The first stage employs the ISC-Swin segmentation model, enhanced by an Inter-Sample Contrastive Bank (ISCB) to improve cross-sample feature consistency and class-level discrimination. The second stage introduces a dynamic pairing logic algorithm for accurate matching of thoracic vertebrae, enabling reliable downstream angle estimation. Experimental results demonstrate strong segmentation performance (Dice: 86.10%, IoU: 76.08%) and high agreement with manual measurements. The proposed method achieves low average absolute differences (ABS) (thoracic: 2.83° <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>±</mo>\u0000 </mrow>\u0000 <annotation> $pm $</annotation>\u0000 </semantics></math> 2.16°; lumbar: 2.89° <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>±</mo>\u0000 </mrow>\u0000 <annotation> $pm $</annotation>\u0000 </semantics></math> 2.55°) and strong correlations (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>R</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${R}^{2}$</annotation>\u0000 </semantics></math> = 0.91 for thoracic and 0.81 for lumbar). Overall, Auto-ISC provides a reliable and radiation-free solution for automated scoliosis assessment, with improved clinical applicability.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 5","pages":"1321-1332"},"PeriodicalIF":5.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70157","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149311449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DualSS: A Dual-Diffusion Synthetic Sampling Framework for Screening Fatty Liver Severity With Class-Imbalanced Tongue Images","authors":"Tao Chen,&nbsp;Jie Gao,&nbsp;Yong Xu,&nbsp;Weihong Qiu,&nbsp;Kunhong Liu,&nbsp;Yijie Wu,&nbsp;Weimin Ye","doi":"10.1049/cit2.70174","DOIUrl":"https://doi.org/10.1049/cit2.70174","url":null,"abstract":"<p>Fatty liver disease (FLD) is a prevalent chronic condition that can progress to clinically significant liver injury if left untreated. Given its high prevalence, FLD poses a substantial public health burden. Traditional Chinese medicine suggests that the appearance of tongue reflects hepatic and metabolic status, enabling artificial intelligence-based tongue diagnosis to provide rapid low-cost prescreening for FLD severity. In practice, the development of high-quality models is hindered by costly disease labelling, scarcity of positive cases, and severe class imbalance. In this work, we propose DualSS, a dual-diffusion synthetic sampling framework to mitigate these limitations. DualSS first trains a disease-agnostic dual-diffusion generator (DDG) on tongue-image data, leveraging abundant negative samples to learn unified tongue representations. Building on DDG, we design a latent space synthetic sampling pipeline that synthesises realistic positive-class images to construct a more balanced augmented training dataset. We then couple this pipeline with a noise-robust training strategy on the augmented dataset to further enhance the learning of downstream diagnostic models. Extensive experiments on FLD severity classification using tongue images and physiological indicators show that DualSS consistently outperforms existing rebalancing baselines, offering an effective and generalisable solution to alleviate positive sample scarcity in tongue image-based medical diagnosis.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 5","pages":"1511-1528"},"PeriodicalIF":5.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149311721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fault-Tolerant Fuzzy Boundary Control for Nonlinear Distributed Parameter Systems Under Limited Measurements and Markovian Failures","authors":"Yanlin Li,&nbsp;Kaibo Shi,&nbsp;Yue Yu,&nbsp;Xiangkun Wang,&nbsp;Ming Zhu,&nbsp;Xiao Cai","doi":"10.1049/cit2.70139","DOIUrl":"https://doi.org/10.1049/cit2.70139","url":null,"abstract":"<p>This paper proposes a boundary control method for nonlinear distributed parameter systems (DPSs) with limited boundary measurements (BMs), as typically encountered in networked cyber-physical processes with spatially distributed dynamics such as thermal and biomedical diffusion systems. The method aims to address cyber-physical threats and hybrid attacks whilst improving state estimation accuracy and system performance. First, a partial differential equation (PDE)-based state observer is established, with an embedded adaptive event-triggered mechanism (AMET) that optimises communication through dynamic sampling adjustment. Second, a Takagi–Sugeno (T–S) fuzzy noncollocated boundary sampled-data controller (NBSDC) is designed. It adopts a spatially separated architecture to mitigate observation-actuation mismatch under partial observability. Furthermore, the controller is designed to handle hybrid attacks with inconsistent modes, including denial-of-service (DoS) interruptions and deceptive signal alterations. Their impacts are bounded by energy constraints to ensure practical feasibility. In addition, a Markov jump process is employed to characterise stochastic controller failures, capturing abrupt mode transitions and enhancing fault tolerance under adverse conditions. Third, stability analysis based on Lyapunov–Krasovskii functionals (LKFs) provides theoretical guarantees for system stability and estimation error convergence. Finally, the effectiveness and applicability of the proposed method are validated through simulations on a tumour cell diffusion model.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 5","pages":"1529-1545"},"PeriodicalIF":5.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149312310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RES-AD: An AutoML-Driven Robust Ensemble Framework for Real-Time Anomaly Detection and Energy Forecasting in Smart Grids","authors":"Murad Ali Khan,&nbsp;Naeem Iqbal,&nbsp;Syed Shehryar Ali Naqvi,&nbsp;Muhammad Faseeh,&nbsp;Do-Hyeun Kim","doi":"10.1049/cit2.70155","DOIUrl":"https://doi.org/10.1049/cit2.70155","url":null,"abstract":"<p>Accurate load forecasting and reliable anomaly detection are critical for the stable operation of modern smart grids (SGs), which increasingly rely on cyber-connected infrastructures. However, the integration of smart metres and two-way communication exposes SGs to data integrity attacks that can manipulate consumption measurements, degrade forecast accuracy and compromise grid stability. This study proposes RES-AD, a robust AutoML-driven ensemble framework designed for real-time detection of abnormal consumption patterns and accurate short-term energy forecasting. RES-AD consists of an Optimal Ensemble Classifier (OEC) for anomaly detection and an Optimal Ensemble Regressor (OER) for forecasting, both selected and optimised automatically using H2O AutoML. Five types of synthetic data integrity attacks are injected to simulate realistic adversarial conditions, whereas SMOTE and correlation-based feature selection ensure balanced and informative training data. The revised framework further incorporates stealthy adversarial attack simulation evaluated using FGSM and demonstrates strong robustness with only a 1.7% degradation under perturbation, further mitigated through adversarial augmentation. Additionally, real-time readiness is validated through latency and memory profiling, where the OEC ensemble requires only 23.36 ms per inference with a footprint below 0.05 MB, making it suitable for edge or SCADA-integrated deployment. Experimental results using public residential smart metre data show that OEC achieves 99.7% (hourly) and 99.9% (daily) detection accuracy, whereas OER improves energy forecasting with a MAPE reduction of 11.11% and 11.72%, respectively. These outcomes demonstrate that RES-AD provides a scalable, attack-aware and deployment-ready solution that enhances both the reliability and resilience of modern smart grid operations.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 5","pages":"1375-1395"},"PeriodicalIF":5.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149312062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRPose: Multi-Robot Relative 6D Pose Estimation for Unseen Objects From RGB Images","authors":"Jiale Ren,&nbsp;Mingxin Tan,&nbsp;Mengyuan Liu,&nbsp;Yingxuan You,&nbsp;Hong Liu","doi":"10.1049/cit2.70176","DOIUrl":"https://doi.org/10.1049/cit2.70176","url":null,"abstract":"<p>Relative pose estimation under the query–reference paradigm has emerged as a practical alternative for estimating the 6D pose of unseen objects without relying on CAD models or extensive annotations. However, many existing approaches remain difficult to deploy in practice, as their reliance on geometric matching makes robustness highly sensitive to matching quality. In this paper, we present MRPose, a framework for relative 6D object pose estimation under a multi-robot setting, where query and reference views are independently captured by different robots. Without requiring CAD models, shared calibration across robots or pose annotations, MRPose directly estimates the full relative transformation between unseen objects from RGB images. Built upon the power of vision foundation models, MRPose introduces a multiplex feature learning strategy that jointly exploits patch-level appearance correspondence and sparse geometric cues through attention-based cross-view feature aggregation. Instead of relying on traditional geometric solvers such as PnP or essential matrix estimation, the proposed framework directly regresses the full relative 6D pose in an end-to-end manner. Extensive experiments on the LINEMOD and YCB-Video benchmarks demonstrate that MRPose compares favourably with existing RGB-based relative pose estimation methods. Real-world experiments further validate its applicability in practical scenarios.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 5","pages":"1430-1443"},"PeriodicalIF":5.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149311709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}