Applied Intelligence最新文献

{"title":"Emotion recognition by global and local feature fusion for people with facial defects","authors":"Qianqian Niu,&nbsp;Dongsheng Wu,&nbsp;Yifan Chen,&nbsp;Ke Li","doi":"10.1007/s10489-025-06903-6","DOIUrl":"10.1007/s10489-025-06903-6","url":null,"abstract":"<div><p>Aiming at the problem of improving network performance by ignoring imperfections and performing recognition based on localization, ignoring the correlation between features and thus encountering challenges in the face recognition task for individuals with facial defects, a method combining facial texture reconstruction with a two-channel emotion recognition system is proposed. First, a defect removal module is added in the feature processing stage to smooth the damaged facial region and refine the texture. An adaptive module is introduced to deal with the fuzzy boundary between normal skin and damaged regions. In addition, a local fine-grained feature extraction module is introduced to capture multi-location information subspace features. Finally, a dual-channel mechanism combining local and global features is adopted to focus on detailed local features of the undamaged region, supplemented by reconstructed global features for emotion recognition. Extensive experiments show that the method’s performance in this paper is 89.57% on RAF-DB, 89.93% on FERPlus, 64.6% on AffectNet-7, and 60.73% on AffectNet-8.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 15","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic segmentation in power grid scenarios using scale-transforming transformer","authors":"Wenjie Pan,&nbsp;Linhan Huang,&nbsp;Yutao Chen,&nbsp;Yuqing Fu,&nbsp;Jianqing Zhu,&nbsp;Yibing Zhan","doi":"10.1007/s10489-025-06883-7","DOIUrl":"10.1007/s10489-025-06883-7","url":null,"abstract":"<div><p>Semantic segmentation of power grids is challenging due to size variations and intricate deformations caused by different shooting distances and angles. Traditional hierarchical architectures and pyramidal methods can learn multi-scale features to address size variations but struggle with deformations due to fixed aspect ratios of features. To address this issue, we propose a scale-transforming transformer (STT) approach. Our approach’s novelty lies in a scale-transforming module (STM), which implements cost-effective aspect ratio adjustments, patch splitting, and patch combining. This process generates local patches comprising various versions of the original patch, characterized by distinct aspect ratios and scales. In particular, this approach ensures that the output and input feature maintain uniform dimensions. We also control computational loads through a channel grouping strategy, which deploys different STMs in distinct feature groups. Consequently, our STM seamlessly integrates into existing transformer models to build STT models. Experiments show that our STT models achieve state-of-the-art performance. </p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 15","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning-based optimal bipartite formation tracking for uncertain networked agents via enhanced adaptive policy iteration","authors":"Zhai Peiyu,&nbsp;Qin Kaiyu,&nbsp;Yue Jiangfeng,&nbsp;Lin Boxian,&nbsp;Li Weihao,&nbsp;Shi Mengji","doi":"10.1007/s10489-025-06913-4","DOIUrl":"10.1007/s10489-025-06913-4","url":null,"abstract":"<div><p>Optimal bipartite formation tracking of uncertain networked agent systems (NASs) is a hotspot with extensive applications in many fields, and there is an urgent demand for strategies that optimize system performance while ensuring efficiency and stability. With this in mind, this paper proposes a reinforcement learning-based optimal control scheme using an Enhanced Adaptive Policy Iteration (EAPI) algorithm with an adaptive termination mechanism. This scheme enables follower agents to achieve bipartite formation tracking of the leader while optimizing the performance index. Firstly, the definition of the optimal bipartite formation tracking control problem is presented, and the Bellman form of the optimal value function and control law is derived based on the coupled Hamilton-Jacobi-Bellman (HJB) equations. Then, an EAPI algorithm with an adaptive termination mechanism is introduced, which could avoid repeated iterations by setting a termination threshold, thus reducing the computational cost and decreasing the running time without reducing the control performance. Furthermore, the stability, convergence, and optimality of EAPI algorithm are analyzed. Moreover, the optimal control law is approximated and solved through the reinforcement learning framework. Finally, numerical results are conducted to verify the effectiveness of the proposed EAPI-based optimal bipartite formation tracking scheme for NASs.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 15","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GSV-Pose: Pose estimation based on geometric similarity voting","authors":"Xi Zhao,&nbsp;Yuekun Zhang,&nbsp;Jinji Wu","doi":"10.1007/s10489-025-06853-z","DOIUrl":"10.1007/s10489-025-06853-z","url":null,"abstract":"<div><p>Object pose estimation is a fundamental problem in 3D computer vision and has gained significant attention with the rapid advancements in autonomous driving, robotics, and augmented reality. Traditional voting-based approaches often suffer from reduced accuracy when dealing with partially observed objects. To overcome this limitation, our method incorporates a superpoint matching network to compute local geometric similarities, which effectively guides the voting process and enhances pose estimation robustness. Experimental results demonstrate that our approach achieves comparable performance to the current state-of-the-art (SOTA) method, GPV-Pose, under standard conditions. More importantly, in robustness tests with incomplete objects, our method significantly surpasses GPV-Pose. For instance, under a 20% incompleteness ratio, the accuracy of GPV-Pose drops by 61.6% under the <span>(5^{circ }2,text {cm})</span> criterion, whereas our method experiences only a 21.8% reduction.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 15","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A manufacturing knowledge graph completion method based on a lightweight dual encoding model","authors":"Xing Qi,&nbsp;Xiaoyu Shen,&nbsp;Yucheng Zhang,&nbsp;Bo Yang,&nbsp;Keqiang Xie,&nbsp;Nan Dong","doi":"10.1007/s10489-025-06909-0","DOIUrl":"10.1007/s10489-025-06909-0","url":null,"abstract":"<div><p>Ensuring stable equipment operation is crucial for manufacturing. Intelligent maintenance decisions powered by manufacturing knowledge graphs can reduce reliance on manual maintenance and enhance efficiency. However, existing knowledge graphs face challenges such as sparse information and complex relationship modeling. Knowledge graph completion can predict missing relationships and entities to enrich the graph. Current completion methods neglect semantic information in entity descriptions, leading to incomplete data, while encoding triples and descriptions increases computational costs. Therefore, this paper proposes a Lightweight Dual Encoding Model (LDEM) for manufacturing knowledge graph completion. LDEM uses ALBERT to encode entity descriptions and captures rich semantics through precomputed embeddings. The graph attention module aggregates neighborhood information, and ConvKB decodes embeddings into predictions. The dataset used in this study comes from a vehicle welding workshop in Chongqing, China. Experiments show that LDEM outperforms state-of-the-art models in all metrics, achieving 80.1 points in Hits@10 and demonstrating superior ability to capture entity relationships and semantic information, thereby enhancing the completion of the manufacturing knowledge graph.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 15","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classifier enhancement based on credible sample selection for partial multi-label learning","authors":"Jiaguo Mu,&nbsp;Yu Chen,&nbsp;Weijun Sun,&nbsp;Zhenyu Wan,&nbsp;Shengwei Wang,&nbsp;Tao Tao","doi":"10.1007/s10489-025-06769-8","DOIUrl":"10.1007/s10489-025-06769-8","url":null,"abstract":"<div><p>Partial multi-label learning (PML) is a weakly supervised framework where each training sample is associated with several candidate labels, which include noisy labels. The main goal is to overcome the noise interference and achieve a well-trained classifier. Given that the sample features contain redundancy and the sample labels include noise, these factors can introduce interference during classifier training. Therefore, we aim to construct the sample set that prioritizes those with less noise, higher representativeness and confidence to improve the effectiveness of the model. To achieve this, we propose a new PML approach with classifier enhancement based on credible sample selection, called PML-CECS. Specifically, this paper first projects the feature space and label space into the subset space, enhancing the consistency of representation within the subset space by sharing projection information during this process. Then orthogonalization is applied to the subset space to reduce noise and redundant correlations, thereby improving the representativeness and reliability of the data. Next, the manifold structure reinforces the instance-level consistency between features and labels within the subset space. And leveraging the subset samples as new learning information further enhances the classifier’s performance. Finally, to mitigate erroneous correlations arising from noise interference, pseudo-labels are introduced and integrated into the model training. Extensive experiments have validated the feasibility of this approach.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 15","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GPSP-CLIP: learning generic pseudo-state prompts for flexible zero-shot anomaly detection","authors":"Weiyu Hu,&nbsp;Shubo Zhou,&nbsp;Yongbin Gao,&nbsp;Xue-Qin Jiang","doi":"10.1007/s10489-025-06843-1","DOIUrl":"10.1007/s10489-025-06843-1","url":null,"abstract":"<div><p>Large-scale foundation models such as Contrastive Language-Image Pre-training (CLIP) have shown great potential in zero-shot anomaly detection (ZSAD) task, allowing a single model to generalize to unseen categories without fine-tuning on specific classes. However, existing ZSAD methods often rely on rigid prompt designs, which makes them difficult to adapt to the diverse characteristics of industrial products. Additionally, the need to manually define category-specific and state-specific prompts limits their scalability and generalization. This paper proposes a generic pseudo-state prompting model based on CLIP (<i>GPSP-CLIP</i>) to address these challenges. The motivation behind <i>GPSP-CLIP</i> is to develop a flexible prompting method capable of representing normal and anomalous conditions across various applications without relying on predefined text prompts. Technically, <i>GPSP-CLIP</i> employs fully learnable parameters to generate broad, pseudo-state text features, enabling generalization across different industrial contexts. By employing distinct prompt learning strategies for anomaly classification and segmentation, <i>GPSP-CLIP</i> optimizes each task independently. This enables the model to effectively capture high-level semantics through global prompts while identifying fine-grained defect patterns via local prompts. Experimental results on the well-known MVTec and VisA datasets demonstrate improved performance, with a 1.8% improvement in AP for anomaly classification and a 1.3% gain in AUPRO for anomaly segmentation compared to state-of-the-art methods.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 15","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time task scheduling strategy for 3D printing cloud platforms in health scenes","authors":"Jianjia He,&nbsp;Jian Wu,&nbsp;Jingran Ni,&nbsp;Yuning Zhang,&nbsp;Keng Leng Siau","doi":"10.1007/s10489-025-06907-2","DOIUrl":"10.1007/s10489-025-06907-2","url":null,"abstract":"<div><p>In health scenes, 3D Printing Cloud Platform (3DPCP) needs to cope with unpredictable fluctuations in tasks and resources, but traditional scheduling methods have problems such as incomplete consideration of factors, poor optimization, and weak dynamic adaptability, which make it difficult to meet real-time scheduling requirements. To this end, the real-time task scheduling problem of 3DPCP for health scenes is defined, a real-time task scheduling model is established, the design time of user personalized services is considered, a rescheduling scheme is designed in combination with task variations and device variations, and a scheduling strategy that incorporates dynamic mechanisms and improved multi-objective greywolf optimization algorithms is proposed in order to minimize the integrated scheduling cost and the average delivery time of the product. The findings of simulation experiments show that when equipment changes are not considered, compared with the optimal heuristic algorithm in this field, the average cost of the proposed algorithm is reduced by 2014.1 yuan, and the average delivery time is shortened by 1.52 h. When equipment changes are considered, compared with the multi-objective Genetic Algorithm Dynamic Strategies (GADS), the average cost of the proposed algorithm is reduced by 2984.57 yuan, and the average delivery time is shortened by 0.39 h, which validates the effectiveness of the proposed method.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 15","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on full lifecycle health management of permanent magnet synchronous electric drum driven by digital twin with dynamic update","authors":"Wei Chen,&nbsp;Weimin Wu,&nbsp;Mei Zhang,&nbsp;Huashun Li,&nbsp;Qing Shi","doi":"10.1007/s10489-025-06910-7","DOIUrl":"10.1007/s10489-025-06910-7","url":null,"abstract":"<div><p>Currently, research on fault Prognostics and Health Management (PHM) based on Digital Twin mainly focuses on integrating real-time data from various sources to facilitate comprehensive product inspection and health management. However, existing DT research faces three main theoretical bottlenecks: the lack of dynamic evolution mechanisms in multi-physics coupled modeling, static models’ difficulty in adapting to the drift of equipment degradation characteristics, and health status assessment’s reliance on prior fault samples. To address these issues, this paper proposes a comprehensive lifecycle dynamic management method for the Tubular Permanent Magnet Synchronous Electric Drum (TPMSED), by constructing a Dual-service Lifecycle Management Digital Twin Model (DSL-DT) that achieves deep integration of physical entities and virtual spaces. Firstly, a multi-physics coupled dynamic model is established, integrating the nonlinear interactions of electromagnetic fields, temperature fields, and dynamic fields. This is achieved through a combination of finite element simulation and data-driven approaches, addressing the challenge of characterizing equipment performance degradation under complex operating conditions. Secondly, an innovative dual dynamic adjustment mechanism for compensator updates and parameter updates is designed, utilizing ridge regression algorithms and adaptive gradient algorithms to achieve online optimization of model parameters, effectively suppressing model mismatch during the degradation process. Lastly, a health index (HI)-based state assessment method is proposed, which triggers model updates by comparing characteristic deviations with thresholds, significantly enhancing the accuracy of Remaining Useful Life (RUL) predictions. Experimental validation on an intelligent conveying system development platform demonstrates that this method can accurately track the performance evolution of equipment throughout its lifecycle, providing a new theoretical paradigm and technical pathway for health management of complex electromechanical systems.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 15","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 3D efficient and essentialized swin transformer network for alzheimer’s disease diagnosis","authors":"Shengchao Huang,&nbsp;Qun Dai","doi":"10.1007/s10489-025-06884-6","DOIUrl":"10.1007/s10489-025-06884-6","url":null,"abstract":"<div><p>Deep learning methods (e.g., convolutional neural networks, CNNs) have been widely applied to Alzheimer’s disease diagnosis based on structural magnetic resonance imaging (sMRI) data. However, CNN-based methods face significant Limitations in capturing the global feature distribution of the whole brain. Transformer-based models have shown promise in addressing this issue, but they often sacrifice local feature sensitivity. Moreover, the large number of parameters in Transformer-based models results in a strong dependence on large-scale datasets, which is difficult to satisfy in real-world 3D medical imaging scenarios. Through comprehensive consideration, we propose a 3D Efficient and Essentialized Swin Transformer Network (E2STN) to strike a balance between being lightweight and comprehensive feature extraction, thereby boosting Alzheimer’s disease diagnosis performance in 3D dataset scenarios. Specifically, E2STN includes four modules: an Efficient Swin Transformer (EST) module for identifying global structural information and being lightweight to reduce reliance on large-scale datasets, which is a novel task-oriented Transformer architecture; a Focused Feature Enhancement Convolution Unit (FFE-CU) for enhancing lesion details, thereby compensating for the limited perception of fine-grained pathological information by the Transformer; a Disease Risk Map generator (DRMg) for visualizing pathological regions; and an ROI-based classifier for precise categorization. Our proposed method has been validated by two diagnosis tasks (i.e., Alzheimer’s disease diagnosis and mild cognitive impairment conversion prediction) on the ADNI dataset. Compared to several state-of-the-art methods, our model demonstrates superior performance.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 15","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}