CAAI Transactions on Intelligence Technology最新文献

{"title":"Design and Validation of Zeroing Neural Network With Active Noise Rejection Capability for Time-Varying Problems Solving","authors":"Yilin Shang,&nbsp;Wenbo Zhang,&nbsp;Dongsheng Guo,&nbsp;Shan Xue","doi":"10.1049/cit2.70087","DOIUrl":"10.1049/cit2.70087","url":null,"abstract":"<p>Recently, the zeroing neural network (ZNN) has demonstrated remarkable effectiveness in tackling time-varying problems, delivering robust performance across both noise-free and noisy environments. However, existing ZNN models are limited in their ability to actively suppress noise, which constrains their robustness and precision in solving time-varying problems. This paper introduces a novel active noise rejection ZNN (ANR-ZNN) design that enhances noise suppression by integrating computational error dynamics and harmonic behaviour. Through rigorous theoretical analysis, we demonstrate that the proposed ANR-ZNN maintains robust convergence in computational error performance under environmental noise. As a case study, the ANR-ZNN model is specifically applied to time-varying matrix inversion. Comprehensive computer simulations and robotic experiments further validate the ANR-ZNN's effectiveness, emphasising the proposed design's superiority and potential for solving time-varying problems.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"256-266"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637164","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":"Deviation-Guided Attention for Semi-Supervised Anomaly Detection With Contrastive Regularisation","authors":"Guanglei Xie,&nbsp;Xiaochang Hu,&nbsp;Yi Sun,&nbsp;Wenzhuo Zhang,&nbsp;Yafeng Bu,&nbsp;Hao Fu,&nbsp;Xin Xu","doi":"10.1049/cit2.70072","DOIUrl":"https://doi.org/10.1049/cit2.70072","url":null,"abstract":"<p>Anomaly detection (AD) aims to identify abnormal patterns that deviate from normal behaviour, playing a critical role in applications such as industrial inspection, medical imaging and autonomous driving. However, AD often faces a scarcity of labelled data. To address this challenge, we propose a novel semi-supervised anomaly detection method, DASAD (Deviation-Guided Attention for Semi-Supervised Anomaly Detection), which integrates deviation-guided attention with contrastive regularisation to reduce the unreliability of pseudo-labels. Specifically, a deviation-guided attention mechanism is designed to combine three types of deviations: latent embeddings, residual direction vectors and hierarchical reconstruction errors to capture anomaly specific cues effectively, thereby enhancing the credibility of pseudo-labels for unlabelled samples. Furthermore, a class-asymmetric contrastive loss is constructed to promote compact representations of normal instances while preserving the structural diversity of anomalies. Extensive experiments on 8 benchmark datasets demonstrate that DASAD consistently outperforms state-of-the-art methods and exhibits strong generalisation across 6 anomaly detection domains.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"66-82"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637085","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":"Design and Validation of Zeroing Neural Network With Active Noise Rejection Capability for Time-Varying Problems Solving","authors":"Yilin Shang,&nbsp;Wenbo Zhang,&nbsp;Dongsheng Guo,&nbsp;Shan Xue","doi":"10.1049/cit2.70087","DOIUrl":"https://doi.org/10.1049/cit2.70087","url":null,"abstract":"<p>Recently, the zeroing neural network (ZNN) has demonstrated remarkable effectiveness in tackling time-varying problems, delivering robust performance across both noise-free and noisy environments. However, existing ZNN models are limited in their ability to actively suppress noise, which constrains their robustness and precision in solving time-varying problems. This paper introduces a novel active noise rejection ZNN (ANR-ZNN) design that enhances noise suppression by integrating computational error dynamics and harmonic behaviour. Through rigorous theoretical analysis, we demonstrate that the proposed ANR-ZNN maintains robust convergence in computational error performance under environmental noise. As a case study, the ANR-ZNN model is specifically applied to time-varying matrix inversion. Comprehensive computer simulations and robotic experiments further validate the ANR-ZNN's effectiveness, emphasising the proposed design's superiority and potential for solving time-varying problems.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"256-266"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637165","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":"UNO: Unified Self-Supervised Monocular Odometry for Platform-Agnostic Deployment","authors":"Wentao Zhao,&nbsp;Yihe Niu,&nbsp;Yanbo Wang,&nbsp;Tianchen Deng,&nbsp;Shenghai Yuan,&nbsp;Zhenli Wang,&nbsp;Rui Guo,&nbsp;Jingchuan Wang","doi":"10.1049/cit2.70095","DOIUrl":"10.1049/cit2.70095","url":null,"abstract":"<p>This work presents UNO, a unified monocular visual odometry framework that enables robust and adaptable pose estimation across diverse environments, platforms and motion patterns. Unlike traditional methods that rely on deployment-specific tuning or predefined motion priors, our approach generalises effectively across a wide range of real-world scenarios, including autonomous vehicles, aerial drones, mobile robots and handheld devices. To this end, we introduce a mixture-of-experts strategy for local state estimation, with several specialised decoders that each handle a distinct class of ego-motion patterns. Moreover, we introduce a fully differentiable Gumbel-softmax module that constructs a robust inter-frame correlation graph, selects the optimal expert decoder and prunes erroneous estimates. These cues are then fed into a unified back-end that combines pretrained scale-independent depth priors with a lightweight bundling adjustment to enforce geometric consistency. We extensively evaluate our method on three major benchmark datasets: KITTI (outdoor/autonomous driving), EuRoC-MAV (indoor/aerial drones) and TUM-RGBD (indoor/handheld), demonstrating state-of-the-art performance.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"205-222"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147618106","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":"TCSR-SQL: Towards Table Content-Aware Text-to-SQL With Self-Retrieval","authors":"Wenbo Xu,&nbsp;Liang Yan,&nbsp;Chuanyi Liu,&nbsp;Peiyi Han,&nbsp;Haifeng Zhu,&nbsp;Yong Xu,&nbsp;Yingwei Liang,&nbsp;Bob Zhang","doi":"10.1049/cit2.70071","DOIUrl":"10.1049/cit2.70071","url":null,"abstract":"<p>Large language model-based (LLM-based) text-to-SQL methods have achieved important progress in generating SQL queries for real-world applications. When confronted with table content-aware questions in real-world scenarios, ambiguous data content keywords and nonexistent database schema column names within the question lead to the poor performance of existing methods. To solve this problem, we propose a novel approach towards table content-aware text-to-SQL with self-retrieval (TCSR-SQL). It leverages LLM's in-context learning capability to extract data content keywords within the question and infer possible related database schema, which is used to generate Seed SQL to fuzz search databases. The search results are further used to confirm the encoding knowledge with the designed encoding knowledge table, including column names and exact stored content values used in the SQL. The encoding knowledge is sent to obtain the final <i>Precise SQL</i> following multi-rounds of generation-execution-revision process. To validate our approach, we introduce a table-content-aware, question-related benchmark dataset, containing 2115 question-SQL pairs. Comprehensive experiments conducted on this benchmark demonstrate the remarkable performance of TCSR-SQL, achieving an improvement of at least 27.8% in execution accuracy compared to other state-of-the-art methods.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"26-40"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147618128","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":"Dynamic Neural-Model-Based Predictive Control for Autonomous Wheel-Legged Robot System","authors":"Jiehao Li,&nbsp;Junzheng Wang,&nbsp;Hongbo Gao,&nbsp;Xiwen Luo,&nbsp;C. L. Philip Chen","doi":"10.1049/cit2.70091","DOIUrl":"https://doi.org/10.1049/cit2.70091","url":null,"abstract":"<p>Mobile wheel-legged robots exhibiting mobility, stability and reliability have garnered heightened research attention in demanding real-world scenarios, especially in material transport, emergency response and space exploration. The kinematics model merely delineates the geometric relationship of the controlled objective, disregarding force feedback. This study investigates model predictive trajectory tracking control utilising the robot dynamic model (DRMPC) in the context of unpredictable interactions. The predictive tracking controller for the wheel-legged robot is introduced in the context of position tracking. A dynamic approximator is employed to address the uncertain interactions in the tracking process. Ultimately, co-simulation and empirical tests are conducted to demonstrate the efficacy of the devised control methodology, which achieves high precision and dependable robustness. This work can elucidate the technical and practical oversight of autonomous movement in complicated environments and enhance the manoeuverability and flexibility.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"83-97"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637041","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":"MVI-Depth: Multi-View Indoor Depth Estimation Based on the Fusion of Semantic Information","authors":"Ying Zhu,&nbsp;Buyun Chen,&nbsp;Hong Liu,&nbsp;Xia Li","doi":"10.1049/cit2.70061","DOIUrl":"10.1049/cit2.70061","url":null,"abstract":"<p>Compared to monocular depth estimation, multi-view depth estimation often yields more accurate results. However, traditional multi-view depth estimation methods often fail to leverage semantic information fully and struggle to effectively fuse information from multiple views, leading to suboptimal prediction performance in challenging scenarios such as texture-less regions and reflective surfaces. To address these limitations, we present MVI-Depth, a novel framework with two core innovations: (1) a Semantic Fusion Module (SFM) that establishes semantic correspondence, and (2) a Depth Updating Module (DUM) enabling iterative depth refinement. Specifically, MVI-Depth initially establishes a main view representation that integrates single-view depth, depth features, and semantic features. Subsequent feature extraction from neighbouring views enables the construction of the original cost volume. Recognising the inherent limitations of direct cost volume utilisation in complex scenes, the proposed SFM constructs an aligned semantic cost volume to utilise the complementarity between semantic and depth information, forming an improved final cost volume. The final cost volume is updated through the proposed DUM to achieve iterative depth optimisation. Comprehensive evaluations demonstrate that MVI-Depth achieves superior performance across all standard metrics on both ScanNet and KITTI benchmarks, outperforming existing methods. Additional experiments on the 7-Scenes dataset further confirm the framework's robust generalisation capabilities in diverse environments.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"98-110"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147618072","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":"An Object Detection Algorithm Based on Deep Learning and Salient Feature Fusion for Roadside Surveillance Camera","authors":"Yang He,&nbsp;Lisheng Jin,&nbsp;Huanhuan Wang,&nbsp;Xinyu Sun,&nbsp;Zhen Huo,&nbsp;Guangqi Wang","doi":"10.1049/cit2.12406","DOIUrl":"10.1049/cit2.12406","url":null,"abstract":"<p>In intelligent transportation systems, object detection for a surveillance video is one of the important functions. The performance of existing surveillance video object detection algorithms is affected by the conflict between the features of the objects, which leads to a decline in precision. Therefore, an object detection algorithm based on deep learning and salient feature fusion is proposed. The proposed method introduces a non-weight-sharing network to process the salient features of the image and fuse them with the features extracted from the red blue green branch. Different from the previous solutions, the salient feature extraction branch uses the boundary features and statistical features of the image and fuses the features of the two branches in the efficient layer aggregation networks structure. At the same time, the attention module is used in efficient layer aggregation networks with convolutional block attention module to improve the efficiency of feature utilisation. The training and evaluation are carried out in the constructed surveillance video feature conflict dataset, and eight scenes are constructed in the way of orthogonal experiments. The experimental results show that the performance of object detection can be significantly improved by using the proposed method in the object detection task of the intelligent transportation system surveillance video feature conflict scene.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"279-295"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12406","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147618174","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":"Coevolutionary Neural Dynamics With Learnable Parameters for Nonconvex Optimisation","authors":"Yipiao Chen,&nbsp;Wenbin Du,&nbsp;Huichao Cao,&nbsp;Long Jin","doi":"10.1049/cit2.70074","DOIUrl":"https://doi.org/10.1049/cit2.70074","url":null,"abstract":"<p>Nonconvex optimisation plays a crucial role in science and industry. However, existing methods often encounter local optima or provide inferior solutions when solving nonconvex optimisation problems, lacking robustness in noise scenarios. To address these limitations, we aim to develop a robust, efficient and globally convergent solver for nonconvex optimisation. This is achieved by combining the efficient local exploitation ability of a parameter-learnt neural dynamics (PLND) model with the global search capability of the coevolutionary mechanism. We combine their characteristics to design a coevolutionary neural dynamics with learnable parameters (CNDLP) model. The gradient information is used to find the optimal solution more effectively, and neural dynamics models have robustness, which ensures that the influence of noise can be effectively suppressed in the calculation process. Theoretical analyses show the global convergence and robustness of the designed CNDLP model. Numerical experiments on 9 benchmark functions and a practical engineering design example are conducted with five existing meta-heuristic algorithms. Benchmarks cover diverse problems, from classical landscapes like benchmark Shubert to high-dimensional cases such as 30-dimensional Rosenbrock. Results confirm CNDLP's excellent performance in both solution quality and convergence speed under noise.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"111-122"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637205","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":"Optimising Wave Energy Plant Location Through Neutrosophic Multi-Criteria Group Decision-Making","authors":"Hafiz Muhammad Athar Farid,&nbsp;Ayesha Razzaq,&nbsp;Muhammad Riaz,&nbsp;Tapan Senapati,&nbsp;Sarbast Moslem","doi":"10.1049/cit2.70058","DOIUrl":"https://doi.org/10.1049/cit2.70058","url":null,"abstract":"<p>The global shift towards sustainable energy has intensified research into renewable sources, particularly wave energy. Pakistan, with its long coastline, holds significant potential for wave energy development. However, identifying optimal locations for wave energy plants involves evaluating complex, multi-faceted criteria. This study employs a multi-criteria group decision-making (MCGDM) approach using single-valued neutrosophic numbers (SVNNs) to address both qualitative and quantitative uncertainties inherent in real-world scenarios. To enhance decision quality, we introduce two novel operators: the single-valued neutrosophic prioritised averaging (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mrow>\u0000 <msub>\u0000 <mtext>SVNPA</mtext>\u0000 <mi>d</mi>\u0000 </msub>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation> ${text{SVNPA}}_{d}$</annotation>\u0000 </semantics></math>) operator and the single-valued neutrosophic prioritised geometric (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mrow>\u0000 <msub>\u0000 <mtext>SVNPG</mtext>\u0000 <mi>d</mi>\u0000 </msub>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation> ${text{SVNPG}}_{d}$</annotation>\u0000 </semantics></math>) operator, both incorporating priority degrees. These tools allow decision-makers to express preferences better and handle ambiguous data. The proposed model is validated through comparative analysis with prior studies and demonstrates improved robustness in site selection. Furthermore, we analyse how variations in priority degrees influence decision outcomes, enabling a more dynamic and tailored decision-making process. Our method contributes a more holistic and adaptive framework for selecting locations for wave energy projects, ultimately supporting informed investments in renewable energy infrastructure and improving energy access in underserved coastal regions.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"167-189"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637125","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}