Engineering Applications of Artificial Intelligence最新文献

{"title":"Green supplier evaluation based on ISO14001 standard via entropy-integrated proximity indexed value method under Linear Diophantine fuzzy sets","authors":"Sait Gül ,&nbsp;Ali Aydoğdu ,&nbsp;Umut Hulusi İnan","doi":"10.1016/j.engappai.2026.114049","DOIUrl":"10.1016/j.engappai.2026.114049","url":null,"abstract":"<div><div>The linear Diophantine fuzzy set (LDFS) incorporates two reference parameters, thereby enabling a more comprehensive representation of human judgment. This structure provides flexibility, as the decision-maker can adjust the meaning of the reference parameters to reflect changes in the decision context. Among the many applications of fuzzy sets, information measures such as distance and entropy are particularly significant. Entropy is widely employed in objective attribute-weighting procedures of Multiple Attribute Decision Making (MADM) applications, as it captures the intrinsic information content of attributes. So, the first contribution of this study is the development of a new entropy measure for LDFS. Besides, the second contribution is the extension of the Proximity Indexed Value (PIV) method into the LDFS framework, marking the first proposal of LDF-oriented PIV in the literature. PIV was selected due to its flexibility, ease of application, and the proven strength against the rank reversal phenomenon. The proposed entropy measure is integrated into the attribute-weighting procedure of this new LDF-En-PIV extension. The third contribution is an application-oriented decision model for the selection of green suppliers with respect to their performance in building and employing an ISO14001 Environmental Management System. In this case study, four green supplier alternatives were evaluated across the main components of ISO14001 by a panel of experienced industry experts, with rankings obtained through the LDF-En-PIV approach. The robustness of the proposed approach was presented through comparative analyses with crisp PIV and LDF-ARAS. All comparisons yield consistent rankings, demonstrating the reliability of the proposed approach.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114049"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175220","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":"Deep learning-based coke dry quenching material location prediction using physical information reconstruction features","authors":"Xinyang Meng ,&nbsp;Keliang Pang ,&nbsp;Zhiyuan Gu ,&nbsp;Youzhi Zheng ,&nbsp;Fujun Liu ,&nbsp;Chaoran Wan ,&nbsp;Haotian Wu ,&nbsp;Minmin Sun ,&nbsp;Hua Zhao","doi":"10.1016/j.engappai.2026.114117","DOIUrl":"10.1016/j.engappai.2026.114117","url":null,"abstract":"<div><div>Coke dry quenching (CDQ) is a common, environmentally friendly technology applied in iron and steel production and plays an important role in improving coke quality as well as in emission reduction and pollution reduction. Material location prediction is crucial for ensuring the stable operation of dry quenching systems. In this paper, we propose a novel artificial intelligence approach for predicting the location of coke materials in CDQ furnaces by incorporating a method known as physical information feature reconstruction (PIFR). This method integrates physical <em>a priori</em> knowledge (such as the law of mass conservation and furnace structural characteristics) into the feature engineering process, effectively improving the accuracy and stability of time-series predictions in both single-step and multistep forecasting tasks. The experimental results demonstrate that PIFR significantly enhances the performance of various deep learning models. Specifically, for the long short-term memory model, the mean squared error and mean absolute error decreased by 51.25% and 37.63%, respectively, whereas the coefficient of determination increased to 0.941. Moreover, PIFR effectively mitigates issues commonly encountered in multi-step prediction, such as cumulative error and prediction curve flattening. The application of PIFR not only improves the accuracy of the model but also significantly enhances its generalization capability.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114117"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175344","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":"System for diagnosis and optimization of combustion in pulverized coal boilers based on artificial intelligence methods","authors":"S.S. Abdurakipov,&nbsp;E.B. Butakov,&nbsp;E.P. Kopyev,&nbsp;D.M. Markovich","doi":"10.1016/j.engappai.2026.114103","DOIUrl":"10.1016/j.engappai.2026.114103","url":null,"abstract":"<div><div>Efficient and reliable monitoring of processes in coal-fired boilers requires advanced combustion diagnostic and optimization methods. This paper introduces an integrated system based on machine learning and deep learning technologies to diagnose and optimize combustion regimes in pulverized coal-fired boilers, aiming to improve efficiency and safety. An artificial neural network accurately simulated thermogravimetric mass loss curves, achieving an average coefficient of determination of 99%. Deep learning methods were employed to detect combustion regimes by monitoring the coal flame and identifying anomalies in flame images. For datasets lacking precise measurements, an unsupervised autoencoder was developed. It achieved an average precision of 77% and a recall of 66%. For datasets with measurements, a supervised convolutional neural network provided a higher average recall of 89%. Various machine learning algorithms were employed to predict deviations from stable combustion modes, and a long short-term memory network with an attention mechanism performed best. It had a mean absolute percentage error of up to 8% and an average coefficient of determination of 91%.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114103"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175352","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 nitrogen-oxides prediction in biomass combustion via a dual-channel neural network with flame imaging and residual attention","authors":"Runfang Hao ,&nbsp;Mingyu Wang ,&nbsp;Shengjun Chang ,&nbsp;Li Qin ,&nbsp;Yongqiang Cheng ,&nbsp;Gang Lu","doi":"10.1016/j.engappai.2026.114090","DOIUrl":"10.1016/j.engappai.2026.114090","url":null,"abstract":"<div><div>Reliable and accurate monitoring of nitrogen oxides (NOx) in flue gas is crucial for emission control in electrical power generation plants due to environmental concerns. Traditional data-driven methods for NOx prediction are often based on single-channel strategies and use multiple variables from the combustion process, which show insufficient feature correlation, making reliable and accurate NOx prediction very difficult. To tackle these limitations, this study proposes a Dual-Channel Deep Neural Network incorporating a Residual Attention Mechanism (DCDNN-RAM) which integrates flame visual features with a residual attention mechanism. An Enhanced Laplacian of Gaussian (ELG) filtering algorithm is employed to optimize the preprocessing of flame images and reduce significantly feature discrimination. An innovative heterogeneous dual-channel parallel architecture is developed, where the primary channel utilizes small convolutional kernels to extract local detail features while the secondary channel employs large kernels to capture global contextual information, coupled with a spatial-frequency collaborative feature extraction module for effective fusion of deep local and shallow global features. Notably, the incorporated dual residual attention mechanism (RAM) effectively enhances key feature representation via channel-spatial adaptive weight allocation. Experimental validation under oxy-biomass combustion conditions demonstrates that the proposed model achieves a coefficient of determination (R²) of 0.946 with a mean absolute error (MAE) of 4.26, outperforming four benchmark single-channel models with MAE reductions of 63.71%, 51.2%, 38.44%, and 24.33%, respectively. This study provides a promising solution for the reliable and accurate prediction of NOx emissions, and thus offers important practical value for promoting cleaner production and supporting the carbon neutrality goal of the power generation industry.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114090"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174783","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":"Scene graph-driven reasoning for action planning of humanoid robot","authors":"Dmitry Yudin ,&nbsp;Alexander Lazarev ,&nbsp;Eva Bakaeva ,&nbsp;Angelika Kochetkova ,&nbsp;Alexey Kovalev ,&nbsp;Aleksandr Panov","doi":"10.1016/j.engappai.2026.114150","DOIUrl":"10.1016/j.engappai.2026.114150","url":null,"abstract":"<div><div>Recent progress in visual data analysis has significantly improved the ability of autonomous robots to understand their surroundings and perform complex tasks. This paper presents a modular method named Scene Graph-driven Reasoning for Action Planning (SG-RAPL) designed for high-level planning in dynamic environments, enabling adaptive control of humanoid robots. The method employs a three-dimensional (3D) scene graph to represent the environment and detect abnormal situations, while a large language model (LLM) translates natural-language commands into consecutive low-level actions. An original perceptual segmentation and tracking module constructs the scene graph in real time by providing instance segmentation, obstacle detection, and object pose estimation using data fusion with Augmented Reality University of Cordoba (ArUco) markers. The Planner module decomposes high-level tasks into subtasks such as navigation and object manipulation. Extensive experiments conducted on a manually collected and annotated dataset demonstrate that the proposed artificial intelligence-based approach efficiently plans complex actions in both virtual and real-world warehouse environments. The code and dataset of the proposed approach will be made publicly available.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114150"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174802","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 Local-Global Fusion Vision Mamba UNet Framework for medical image segmentation","authors":"Yanbo Li ,&nbsp;Zihan Mao ,&nbsp;Feiwei Qin ,&nbsp;Yong Peng ,&nbsp;Guodao Zhang ,&nbsp;Xugang Xi ,&nbsp;Xiaoqin Ma ,&nbsp;Huanhuan Yu ,&nbsp;Yu Zhou ,&nbsp;Zhu Zhu","doi":"10.1016/j.engappai.2026.113987","DOIUrl":"10.1016/j.engappai.2026.113987","url":null,"abstract":"<div><div>As a State Space Model (SSM) that achieves long-range dependency modeling with linear computational complexity, Mamba demonstrates significant efficiency advantages in medical image segmentation. However, while Mamba-based methods enable long-range modeling with linear complexity, their global dependency mechanisms often lead to local feature attenuation, particularly affecting the processing of complex anatomical structures. Existing multi-scale fusion methods also exhibit limited compatibility with State Space Models. To address these challenges, this paper proposes the Local-Global Fusion Vision Mamba UNet (LGFVM-UNet) framework. Its core innovation lies in the Dynamic Gating-enhanced Local-Global Fusion Visual State Space (LGF-VSS) block, which enables the synergistic modeling of global context and local details. Additionally, we designed a Multi-level Cross-scale Feature Fusion Block (MCFB) that enhances multi-scale feature representation through bidirectional resampling and spatial-channel dual attention mechanisms. Additionally, we propose a Gradient Statistics-based Adaptive Hierarchical Loss that dynamically adjusts multi-level supervision weights to optimize the learning process. The proposed method is experimentally validated on five public medical image segmentation datasets spanning diverse imaging modalities and anatomical structures. Results demonstrate that our approach outperforms state-of-the-art methods, excelling in long-range dependency modeling, local detail capture, and multi-scale feature fusion. The source code of our work is available at <span><span>https://github.com/NicoleDyson/LGFVM-UNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 113987"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122756","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 deep learning model for photovoltaic soiling loss prediction and estimation based on Large Kernel Cross-Attention Fusion","authors":"Shaokai Zheng ,&nbsp;Peng Yan ,&nbsp;Shengsu Ni ,&nbsp;Daolei Wang","doi":"10.1016/j.engappai.2026.114097","DOIUrl":"10.1016/j.engappai.2026.114097","url":null,"abstract":"<div><div>The loss of photovoltaic (PV) power due to environmental soiling presents a significant challenge to the PV power generation industry, making accurate prediction and estimation of power loss critical. However, most existing algorithmic models rely on traditional fusion methods to integrate PV images and environmental factors (time and irradiance) across modalities, limiting their ability to effectively utilize high-quality cross-modal information for downstream tasks. This paper proposes a novel cross-modal interactive fusion mechanism, Large Kernel Cross-Attention Fusion (LKCA Fusion), and introduces a new photovoltaic soiling loss (PVSL) prediction and estimation model, Large Kernel Fusion Solar Network (LKFSolarNet). LKFSolarNet utilizes an improved image backbone architecture to efficiently extract features from PV soiling images, followed by LKCA Fusion to perform cross-modal fusion between these image features and environmental factors. LKCA Fusion incorporates lightweight large kernel convolutions to enhance the model's ability to capture global information across different PV modalities and improve cross-modal interaction. Additionally, a Gradient Flow Enhanced branch is introduced to further strengthen the training of the image backbone network, enhancing overall model performance. Experiments on open-source Solar Panel Soiling Image dataset demonstrate that LKFSolarNet reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 3.9% and 4.0%, respectively, in the prediction task and improves accuracy by 3.6% in the 16-class estimation task. Compared to the latest methods, LKFSolarNet reduces MAE and RMSE losses by 19.7% and 5.9%, respectively, and shows some improvement in estimation accuracy.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114097"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174862","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":"Structural-aware key node identification in hypergraphs via representation learning and fine-tuning","authors":"Xiaonan Ni ,&nbsp;Guangyuan Mei ,&nbsp;Su-Su Zhang ,&nbsp;Yang Chen ,&nbsp;Xin Xu ,&nbsp;Chuang Liu ,&nbsp;Xiu-Xiu Zhan","doi":"10.1016/j.engappai.2026.114108","DOIUrl":"10.1016/j.engappai.2026.114108","url":null,"abstract":"<div><div>The ability to pinpoint strategically important nodes plays a decisive role in shaping diffusion outcomes and maintaining the stability of complex systems. Yet, most existing approaches remain rooted in pairwise interaction assumptions, making them ill-suited for systems where collective participation and attribute-sharing give rise to higher-order structures. In this work, we introduce AHGA, a learning-driven framework that leverages autoencoder-based representations, hypergraph neural network pre-training, and an active learning mechanism to uncover nodes that jointly influence propagation dynamics and structural cohesion. Rather than relying on handcrafted descriptors, AHGA learns informative higher-order features and progressively refines node importance through selective supervision. Evaluations on eight empirical hypergraphs show that this strategy leads to substantially more reliable rankings, with improvements of up to 36.8% over classical baselines. Beyond ranking accuracy, nodes prioritized by AHGA exhibit pronounced structural leverage: their removal triggers an accelerated loss of network efficiency, reaching 0.6628, markedly exceeding the disruptive effect achieved by competing methods. These findings demonstrate that AHGA not only advances higher-order node identification methodology, but also offers practical guidance for intervention strategies in scenarios such as misinformation containment and infrastructure robustness.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114108"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174974","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 domain knowledge and cognitive law driven approach to anti-vibration hammer defect detection","authors":"Hang Niu,&nbsp;Xinyu Ge,&nbsp;Xiaoyu Zhao,&nbsp;Ke Yang,&nbsp;Qianming Wang,&nbsp;Yongjie Zhai,&nbsp;Zhedong Hu","doi":"10.1016/j.engappai.2026.114100","DOIUrl":"10.1016/j.engappai.2026.114100","url":null,"abstract":"<div><div>The intelligent detection of anti-vibration hammer defects in transmission lines via computer vision is confronted with challenges due to the limited number of defect samples and the high similarity between defect classes. To this end, a domain knowledge and cognitive law driven approach to anti-vibration hammer defect detection is proposed, which integrates a Structural Knowledge and Geometric Feature-driven image generation method (SKGF) with a Cognitive Law-guided Multilevel Progressive target Detection framework (CLMP-Det). The imposition of morphological and tilt angle constraints is incorporated into the SKGF, based on prior knowledge of the anti-vibration hammer’s structure and its tilt angle distribution characteristics. These constraints can guide the generation of artificial anti-vibration hammer samples semantically consistent with the real physical structure and solve the problem of insufficient defective samples. Secondly, CLMP-Det is designed to simulate the human visual cognitive law through a progressive strategy, progressing from ease to difficulty. This strategy includes two sequential phases: preliminary perception and in-depth discrimination, which enhance the model’s capacity to distinguish between the challenging normal and tilt defect categories. The results of the experiment demonstrate that the proposed method significantly improves the overall detection performance of several widely-used detectors. Compared to the baseline model, our approach achieves a 7.1% improvement in mean average precision. Thus, the method’s robust generalization capability and potential for engineering applications are fully validated.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114100"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175301","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":"Cross-domain few-shot hyperspectral classification via orthogonal feature disentanglement","authors":"Yurong Zhang,&nbsp;Jinrong He,&nbsp;Yuhang Li","doi":"10.1016/j.engappai.2026.114071","DOIUrl":"10.1016/j.engappai.2026.114071","url":null,"abstract":"<div><div>In cross-domain few-shot hyperspectral image classification, the limited availability of labeled target-domain samples renders the model highly sensitive to domain shifts. Existing feature disentanglement approaches struggle to simultaneously suppress domain-specific noise and retain cross-domain discriminative semantics. This leads to the entanglement of domain-shared and domain-specific components, increases the risk of negative transfer, and ultimately becomes the bottleneck limiting further improvements in accuracy and generalization. To address these challenges, this paper presents an Orthogonal Feature Disentanglement Network (OFD-Net). Using orthogonal subspace decomposition, OFD-Net projects features into two mutually exclusive subspaces: domain-shared and domain-specific. The domain-shared subspace focuses on extracting cross-domain invariant features, while the domain-specific subspace retains local domain discriminative information. This dual-stream architecture effectively suppresses interference from irrelevant inter-domain features. Additionally, feature orthogonality constraints enhance the model's adaptability to target domain shifts. OFD-Net also adopts a multi-task learning framework. Cross-domain alignment loss ensures the consistency of shared feature distributions between the source and target domains, while inter-class discriminative loss improves the class separability of specific features, creating a hierarchical feature optimization mechanism. On four public benchmark datasets including Indian Pines, Pavia University, Salinas, and Houston, OFD-Net achieves Overall Accuracy of 80.33%, 85.80%, 93.33%, and 80.05% respectively. Its performance outperforms existing state-of-the-art methods, demonstrating superior cross-domain transfer robustness and feature discriminative capability.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114071"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175402","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}