Engineering Applications of Artificial Intelligence最新文献

{"title":"Reinforced semantic information acquiring for contrastive clustering","authors":"Pengfei Cui ,&nbsp;Haowei Wu ,&nbsp;Jun Yin","doi":"10.1016/j.engappai.2026.114115","DOIUrl":"10.1016/j.engappai.2026.114115","url":null,"abstract":"<div><div>The goal of deep image clustering is to assign images to their corresponding clusters in an unsupervised manner. Contrastive Clustering with Effective Sample pairs construction (CCES) (Yin et al., 2023) has demonstrated the effectiveness of combining ContrastiveCrop (Peng et al., 2022) with nearest-neighbor mining for clustering. However, a key limitation of CCES is that it treats augmentations of different samples from the same class as negative pairs, which introduces false negatives and conflicts with the goal of clustering. To address this issue, we propose a novel contrastive clustering framework termed Reinforced Semantic Information Acquiring for Contrastive Clustering (RASCC). We extend CCES by introducing a pseudo-label-guided dual-level fine-tuning strategy that mitigates the adverse impact of false negatives and improves clustering quality. Specifically, RASCC consists of two stages. In Stage 1, we follow CCES to learn discriminative features and generate high-confidence pseudo labels based on clustering predictions. In Stage 2, we leverage these pseudo labels to perform dual-level fine-tuning. At the instance level, we mitigate false negatives by excluding same-pseudo-label pairs from the negative set. At the cluster level, we improve assignment quality and balance by refining cluster predictions with a class-balanced self-labeling loss that each sample is weighted inversely proportional to the frequency of its high-confidence pseudo-label. We conduct experiments on four challenging datasets. The proposed method surpasses many state-of-the-art deep clustering algorithms, demonstrating the effectiveness of sample pair construction and fine-tuning strategy.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114115"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175021","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":"Multi-granularity alignment and cross-modal reasoning for fake news video explanation","authors":"Chao Cheng ,&nbsp;Weiwei Jiang","doi":"10.1016/j.engappai.2026.114153","DOIUrl":"10.1016/j.engappai.2026.114153","url":null,"abstract":"<div><div>Fake news video explanation generation aims to provide accurate and insightful explanations through in-depth analysis of news video content. However, existing methods typically align video context with overall descriptions and generate explanations via multi-modal fusion, often neglecting the rich details of key semantic elements such as nouns and verbs. To address this limitation, this paper proposes a unified Artificial Intelligence (AI) framework named Multi-Granularity Alignment and Reasoning (MGAR). MGAR not only focuses on the semantic alignment of overall descriptions but also delves into the semantic elements in language, particularly nouns and verbs, and aligns them with frame-level and motion-level features of fake news videos for multi-granularity reasoning. Additionally, we design a unified residual-structured multi-granularity language module that employs a context exchange mechanism (e.g., word-level and sentence-level) to adapt to semantic understanding at different granularity. Extensive experiments on the FakeVE dataset demonstrate the superiority of MGAR, achieving improvements of +10.1% BLEU-1 and +11.1% ROUGE-L over state-of-the-art baselines, showcasing the potential of AI applications in combating false information.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114153"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175303","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":"Compact axial attention with detail enhancement for visual object tracking","authors":"Yuanyun Wang ,&nbsp;Geng Gu ,&nbsp;Chao Tang ,&nbsp;Jun Wang","doi":"10.1016/j.engappai.2026.114121","DOIUrl":"10.1016/j.engappai.2026.114121","url":null,"abstract":"<div><div>The current Transformer-based trackers are mainly divided into two types, Convolutional Neural Networks-Transformer-based and fully Transformer-based. Due to the local receptive field of Convolutional Neural Networks, Convolutional Neural Networks-Transformer-based trackers still struggle to capture long-range dependencies and global context, which can degrade tracking performance in complex scenarios such as occlusion and large viewpoint change. The fully Transformer-based uses Transformer for feature extraction and relationship modeling. This type of tracker is often a single-stream single-stage tracking framework. This not only brings huge computational costs, but also loses the template features cached in the test phase of the two-stage trackers. In response to this, we design a compact axial attention with a detail enhancement module, which compresses the features onto a single axis to reduce computational complexity without compromising global information. Based on this, a novel Transformer-based feature extraction backbone is designed. Further, we propose a new tracker, compact axial attention with detail enhancement for visual object tracking, which achieves the state-of-the-art performance on six challenging tracking benchmarks. In particular, the proposed tracker achieves an area under curve (AUC) score of 65.8% on large-scale single object tracking (LaSOT), a normalized precision score of 86.8% on the large-scale dataset and benchmark for object tracking in the wild (TrackingNet), and a precision score of 90.0% on unmanned aerial vehicle dataset (UAV123).</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114121"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175405","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":"Semi-supervised vessel trajectory analysis for unregulated fishing activity detection","authors":"Agam Sanghera ,&nbsp;Paramveer Singh ,&nbsp;Elaine Chu ,&nbsp;Sumin Leem ,&nbsp;Ruizhi Li ,&nbsp;Sogol Ghattan ,&nbsp;Andy Man Yeung Tai","doi":"10.1016/j.engappai.2026.113933","DOIUrl":"10.1016/j.engappai.2026.113933","url":null,"abstract":"<div><h3>Background/Problem</h3><div>Maritime crimes such as Illegal, Unreported, and Unregulated (IUU) fishing, piracy, and smuggling pose significant threats to marine ecosystems, trade, and coastal security, especially in developing regions. Automatic Identification System (AIS) data offers a scalable solution for vessel monitoring, but the use of fully supervised machine learning models is constrained by the substantial manual effort and expert input required to label training data.</div></div><div><h3>Methods</h3><div>To address this challenge, authors propose a semi-supervised machine learning pipeline that classifies vessel activities from AIS data without relying on pre-labeled datasets. Our approach leverages scaled geospatial and temporal features, including latitude, longitude, speed, and time difference, to train multiple Hidden Markov Models (HMMs) on trajectory segments. These segments are then grouped using similarity-based K-means clustering and subsequently classified with supervised models, including Random Forest and Long Short-Term Memory (LSTM) networks. The pipeline effectively identifies and labels maritime activities such as sailing, fishing, idling, and other activities.</div></div><div><h3>Results/conclusions</h3><div>Experiments were conducted on a dataset comprising 156,379 AIS points, partitioned into training and test sets. The LSTM-based supervised model achieved an F1 score of 0.86 on the local test set, while the end-to-end pipeline achieved an F1 score of 0.5 on a global evaluation set. These results demonstrate the feasibility of automating maritime activity classification through artificial intelligence and hybrid learning, offering a scalable solution for real-world maritime surveillance.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 113933"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175351","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 new distance approach to measure the evidence conflict in orderable set and its applications","authors":"Xiaoyan Su,&nbsp;Yanying Yu","doi":"10.1016/j.engappai.2026.114112","DOIUrl":"10.1016/j.engappai.2026.114112","url":null,"abstract":"<div><div>Dempster–Shafer (D–S) evidence theory can effectively represent and handle uncertainty, and has been widely applied across various domains. In D–S evidence theory, distance is a key tool for quantifying the conflict between basic probability assignments (BPAs). Such measurement is crucial in artificial intelligence (AI) tasks such as clustering and pattern recognition under uncertainty, and plays an important role in ensuring the reliability of multi-source information fusion. In this paper, a new distance metric is proposed to account for the ordered frame of discernment (FOD). A relative position coefficient is first defined to capture the intrinsic ordinal relationships among focal elements. Based on this coefficient, a relative position matrix is then constructed to characterize the overall distributional differences between two pieces of evidence. The proposed distance satisfies the axioms of non-negativity, symmetry, and triangle inequality. It avoids counter-intuitive results and improves conflict quantification. For engineering applications, the method is validated through two case studies: the reliability assessment of vehicle roll angle sensors and the expert group decision-making process. The results indicate that the proposed distance not only advances the modeling of ordered uncertainty, but also provides practical benefits for improving system reliability in real-world engineering scenarios. Overall, the research offers a theoretically sound and practically effective framework for handling ordered uncertainty, enabling more reliable conflict analysis.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114112"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175350","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":"Enhanced steel alloy identification using hybrid attention mechanism with femtosecond laser ablation-spark-induced breakdown spectroscopy","authors":"Zhenman Gao ,&nbsp;Xudong Chen ,&nbsp;Jianyong Zhuang ,&nbsp;Chunhui Huang ,&nbsp;Zuxiang Xie ,&nbsp;Xiaoyong He","doi":"10.1016/j.engappai.2026.114002","DOIUrl":"10.1016/j.engappai.2026.114002","url":null,"abstract":"<div><div>This paper proposes a deep learning architecture integrating a hybrid attention mechanism with femtosecond laser ablation-spark-induced breakdown spectroscopy technology to overcome efficiency and accuracy limitations in steel alloy classification associated with traditional laser-induced breakdown spectroscopy techniques. Traditional methods often struggle with feature redundancy and noise interference when processing high-dimensional, low signal-to-noise ratio spectral data. This paper introduces a dynamic feature optimization strategy through heterogeneous fusion of efficient channel attention network and Transformer self-attention modules, enabling adaptive multi-level feature selection and enhancement. The model utilizes channel attention in the shallow layers to identify critical spectral response regions, while deeper layers use self-attention to model global spectral sequence relationships, establishing a “local-to-global” bimodal feature extraction approach. This design reduces data dimensions from 2048 spectral points to a 6-dimensional feature vector (0.3% of the original features) while improving temporal spectral analysis through synergistic feature selection and noise suppression. Focusing on iron element responses in steel matrices, experiments achieved 100% test-set classification accuracy, demonstrating the superiority of the hybrid attention mechanism. Theoretical and empirical analysis reveal that Convolutional Neural Networks-Transformer integration dynamically balances local detail sensitivity and global context awareness, yielding 40% faster inference speeds. The developed dual-stage architecture (shallow efficient channel attention + deep Transformer) offers significant advantages for rapid metal material detection, providing an innovative approach for industrial-grade online steel alloy identification. This work establishes a novel “convolution-attention” co-optimization paradigm for complex spectral signal analysis.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114002"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122553","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 double deep Q network-powered approach to discovering symbolic solutions for nonlinear integral equations","authors":"Mahdi Movahedian Moghaddam ,&nbsp;Hassan Dana Mazraeh ,&nbsp;Kourosh Parand","doi":"10.1016/j.engappai.2026.114113","DOIUrl":"10.1016/j.engappai.2026.114113","url":null,"abstract":"<div><div>This study introduces a reinforcement learning framework using Double Deep Q-Networks (DDQN) to discover interpretable, symbolic solutions to integral and integro-differential equations. The method leverages Context-Free Grammars to guide expression generation and Physics-Informed Neural Networks (PINN) to optimize coefficients. Evaluated on diverse equations (Fredholm, Volterra, and fractional types), DDQN outperforms standard DQN in stability, convergence, and interpretability, and succeeds in complex cases like population models where DQN fails. This approach is highly valuable for engineering domains such as control theory, electromagnetics, and fluid mechanics, where such equations are prevalent. It provides engineers with compact, analytical expressions that offer immediate physical insight, unlike opaque numerical solutions. These symbolic models enable faster system analysis, real-time control, and robust design optimization, bridging the gap between high-fidelity simulation and practical, interpretable models.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114113"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174973","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 novel rotating machinery fault diagnosis method based on multi-channel correlation strategy image information enhancement","authors":"Jiayao Hu ,&nbsp;Wennian Yu ,&nbsp;Zixu Chen ,&nbsp;Quanyi Luo ,&nbsp;Qiang Zeng ,&nbsp;Xiaoxi Ding","doi":"10.1016/j.engappai.2026.114102","DOIUrl":"10.1016/j.engappai.2026.114102","url":null,"abstract":"<div><div>To address the problem of blurred feature information caused by color mapping during the conversion of one-dimensional signals of industrial rotating machinery to pseudo-color image matrices, an image enhancement method based on matrix information expansion via a multi-channel correlation strategy is proposed. Building on the traditional Markov transition field (MTF) pseudo-color image generation method, the study develops a multi-channel correlation Markov transition field (MCMTF) image coding method. Without introducing additional variables, the multi-channel correlation strategy enhances the information representation capability of images through correlation calculations based on the inherent matrix information, while alleviating feature blurring from pseudo-color conversion. In addition, a max pooling and average pooling parallel convolutional neural network (MAPCNN) is developed to strengthen the network model's global feature extraction capability. The proposed method attains 99.51% and 99.88% accuracy on the self-collected sliding bearing and gear datasets, respectively. Comparisons with state-of-the-art image generation methods and classical models prove its superior performance. The study also explains the mechanism behind the improved performance from the perspective of image information entropy. Scalability experiments of the multi-channel correlation strategy applied to various image generation methods demonstrate the universality of this concept. Experiments on the rolling bearing dataset further confirm the performance of the proposed method. For industrial scenarios involving wind turbines, machine tools, and gas turbines, the proposed method provides both an effective diagnostic approach and new insights for the diagnosis of rotating machinery.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114102"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175306","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":"Dual-attention semi-cycled generative adversarial network data augmentation structure for gearbox fault diagnosis using infrared thermal images","authors":"Zhenlong Chen ,&nbsp;Xiao Zhuang ,&nbsp;Di Zhou ,&nbsp;Weifang Sun ,&nbsp;Jing Cai ,&nbsp;Jiawei Xiang","doi":"10.1016/j.engappai.2026.114118","DOIUrl":"10.1016/j.engappai.2026.114118","url":null,"abstract":"<div><div>In the real world, the data imbalance problem is an everlasting challenge in the field of knowledge-based fault diagnosis. The cycle-consistent generative adversarial network (CycleGAN) has been widely used in sample generation tasks with good results, but still faces the discrepancy between generator-generated fault samples and synthesized fault samples, which leads to the joint participation of the same degenerate branch affecting the final performance, resulting in the tendency to produce artifacts in difficult scenarios. To better solve the data imbalanced problem in infrared image-based fault diagnosis, in this paper, a novel Dual-attention Semi-Cycled Data Augmentation Structure using generative adversarial network (DASCGAN) is proposed to generate high-quality infrared thermal images to solve the fault diagnosis of gearbox under data imbalance. Firstly, the Dual-attention Semi-Cycled Data Augmentation Structure is constructed, which includes the forward and backward semi-cycle sub-network to generate diverse infrared images. The two semi-cycle sub-networks consist of two independent fault-free generation branches and a shared fault generation branch. Secondly, Convolutional Block Attention Module (CBAM) is embedded in the fault-free generation branch to increase the attention of the fault part by suppressing irrelevant features. Thirdly, self-attention module is embedded in the fault generation branch to increase the attention of the fault generation branch on the global hot pixels. Finally, comparison experiments are conducted. Experimental results show that the proposed DASCGAN method outperforms other benchmark generative adversarial networks. The proposed DASCGAN can effectively solve the data imbalance so as to improve the reliability and accuracy of gearbox fault diagnosis.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114118"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175211","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":"Time-optimal path planning for robots via Deep Lagrangian Networks","authors":"Pu Yang ,&nbsp;Jiaheng Zhang ,&nbsp;Zhiman Duan ,&nbsp;Donghao Shi ,&nbsp;Shaoping Bai ,&nbsp;Qinchuan Li","doi":"10.1016/j.engappai.2026.114174","DOIUrl":"10.1016/j.engappai.2026.114174","url":null,"abstract":"<div><div>This paper presents a time-optimal path planning (TOPP) method based on Deep Lagrangian Networks (DeLaN) for robot systems, overcoming the complexities of dynamic modeling. In artificial intelligence, we extend the DeLaN model by incorporating Coulomb friction, significantly enhancing dynamic parameter identification, and propose an excitation trajectory optimization method for training. In engineering, we introduce the data-driven DeLaN-TOPP algorithm, which generates time-optimal paths without the need for explicit dynamic modeling. Experimental validation on a 6-prismatic-spherical-spherical (6-PSS) parallel robot and a Franka serial manipulator demonstrates that the DeLaN-TOPP algorithm achieves time-optimal paths comparable to traditional physics-based methods, while substantially reducing modeling complexity and improving scalability across various platforms, showcasing its efficiency and flexibility for real-world robotic applications. Notably, for high-degree-of-freedom systems, generalization may be constrained by sample efficiency in sparse data regimes.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114174"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175225","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}