Engineering Applications of Artificial Intelligence最新文献

Chimney detection and size estimation from high-resolution optical satellite imagery using deep learning models 利用深度学习模型从高分辨率光学卫星图像中检测和估算烟囱大小

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-17 DOI: 10.1016/j.engappai.2024.109686

Che-Won Park , Hyung-Sup Jung , Won-Jin Lee , Kwang-Jae Lee , Kwan-Young Oh , Joong-Sun Won

{"title":"Chimney detection and size estimation from high-resolution optical satellite imagery using deep learning models","authors":"Che-Won Park , Hyung-Sup Jung , Won-Jin Lee , Kwang-Jae Lee , Kwan-Young Oh , Joong-Sun Won","doi":"10.1016/j.engappai.2024.109686","DOIUrl":"10.1016/j.engappai.2024.109686","url":null,"abstract":"<div><div>This study shows an efficient method to estimate the location and size of chimneys from high-resolution satellite optical images using deep learning models. Korean multi-purpose satellite (KOMPSAT) −3 and -3A satellite images with spatial resolutions of 0.7 m and 0.55 m were used for model performance estimation, and the You Only Look Once version 8 (YOLOv8) and Residual Network (ResNet) regression models were integrated for the detection and size estimation of the chimneys. In the chimney detection and size estimation, we compared the model performances between 1) imbalanced and balanced data, 2) South Korea and Thailand data, and 3) KOMPSAT-3 and -3A data. We also analyzed the model performance according to the ResNet convolutional layers in chimney size estimation. In chimney detection, the model performances between the imbalanced and balanced data, South Korea and Thailand data, and KOMPSAT-3 and -3A data were about 0.723 and 0.739, 0.674 and 0.805, and 0.702 and 0.786 in the average precision (AP) 50–95 measure, respectively. The model performance between the South Korea and Thailand data showed a significant difference, likely because the chimneys in South Korea are very diverse, making it harder to generalize the YOLOv8 model. Furthermore, the model root mean square errors (RMSE) between the imbalanced and balanced data, South Korea and Thailand data, and KOMPSAT-3 and -3A data were about 2.917 and 2.788, 2.690 and 2.951, and 2.913 and 2.580 in chimney height, respectively, and about 1.285 and 1.190, 1.228 and 1.120, and 1.291 and 1.013 in chimney diameter, respectively. Keywords: Chimneys; deep learning; You Only Look Once version 8; Residual Network; Korean Multi-purpose Satellite-3/3A; object detection; regression model.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109686"},"PeriodicalIF":7.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658986","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}

引用次数: 0

Fault diagnosis in electric machines and propellers for electrical propulsion aircraft: A review 用于电力推进飞机的电机和螺旋桨的故障诊断：综述

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-16 DOI: 10.1016/j.engappai.2024.109577

Leonardo Duarte Milfont , Gabriela Torllone de Carvalho Ferreira , Mateus Giesbrecht

{"title":"Fault diagnosis in electric machines and propellers for electrical propulsion aircraft: A review","authors":"Leonardo Duarte Milfont , Gabriela Torllone de Carvalho Ferreira , Mateus Giesbrecht","doi":"10.1016/j.engappai.2024.109577","DOIUrl":"10.1016/j.engappai.2024.109577","url":null,"abstract":"<div><div>The present work aims to conduct an extensive literature review on the fault diagnosis and classification in electric machines, especially those with permanent magnets, for aeronautical propulsion applications. The main contribution of this research is to assess how intelligent systems focused on fault detection and diagnosis in electric propulsion systems have evolved over the past five years, what are the main types of algorithms used, and how the rapid advancement of machine learning techniques has impacted this research area. Initially, an introduction to the main diagnostic methods is provided, including techniques based on mathematical models, signal analysis, as well as the use of machine learning and deep learning. Subsequently, a detailed study of the main references found in recent years for each type of fault, whether electrical, magnetic, or mechanical, is undertaken. Regarding aeronautical applications, a study of faults in rotating blades and on coupling systems between an electric motor and a set of propellers is conducted. Throughout the text, some of the main datasets found during the research are presented. These datasets include characteristics of healthy operation and fault of windings, bearings, as well as other mechanical components that can be connected to the machine’s shaft, such as gearboxes. Finally, some statistics from this research are presented showing results regarding the annual distribution of publication of all reviewed references, the proportion of faults addressed in all articles, as well as a detailed analysis of the proportion in which each type of algorithm appears in the cited references.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109577"},"PeriodicalIF":7.5,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659145","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}

引用次数: 0

Cooperative task assignment of heterogeneous unmanned aerial vehicles for simultaneous multi-directional attack on a moving target 异构无人飞行器合作分配任务，同时多方位攻击移动目标

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-16 DOI: 10.1016/j.engappai.2024.109595

Sami Shahid , Ziyang Zhen , Umair Javaid

{"title":"Cooperative task assignment of heterogeneous unmanned aerial vehicles for simultaneous multi-directional attack on a moving target","authors":"Sami Shahid , Ziyang Zhen , Umair Javaid","doi":"10.1016/j.engappai.2024.109595","DOIUrl":"10.1016/j.engappai.2024.109595","url":null,"abstract":"<div><div>Multiple unmanned aerial vehicle (UAV) attacks, on moving targets with directional priorities, need careful task allocation especially when the UAVs have variable attacking power. In addition, the constraints of simultaneous attack from different directions multi-folds the complexity of the problem. In this work, an extended contract net protocol (ECNP) based autonomous and cooperative task assignment method is proposed to deal with the time-sensitive position assignment for multi-directional attack and uniform resource allocation. Initially, an optimization problem is formulated using distances between each UAV and expected attack positions (APs), arrival time, and attack power. In addition, for uniform resource allocation, a variable is introduced to monitor available resources at a given time. An agent-based model is built with UAV information, such as position and speed, along with the direction of the high-value moving target (HVMT). Each UAV identifies the possible arrival points based on its speed constraints, and current position, and the position and velocity of HVMT. Moreover, distance and expected arrival time to all APs are computed. Finally, the agents make attack point allocations using the proposed ECNP after making a consensus about simultaneous attack time. The proposed method ensures uniform resource allocation. The simulation results show the superiority of the proposed method (ECNP) in comparison with classical contract net protocol (CNP) and Genetic Algorithms (GA) in terms of uniform resource allocation and mission accomplishment.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109595"},"PeriodicalIF":7.5,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659103","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}

引用次数: 0

Predicting rapid impact compaction of soil using a parallel transformer and long short-term memory architecture for sequential soil profile encoding 利用并行变压器和长短期存储器架构进行土壤剖面顺序编码，预测土壤的快速冲击压实效果

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-16 DOI: 10.1016/j.engappai.2024.109664

Sompote Youwai, Sirasak Detcheewa

{"title":"Predicting rapid impact compaction of soil using a parallel transformer and long short-term memory architecture for sequential soil profile encoding","authors":"Sompote Youwai, Sirasak Detcheewa","doi":"10.1016/j.engappai.2024.109664","DOIUrl":"10.1016/j.engappai.2024.109664","url":null,"abstract":"<div><div>This study presents an advanced deep learning approach for predicting the effectiveness of Rapid Impact Compaction (RIC). The model integrates the focused attention mechanisms of transformer architectures with the sequential data processing capabilities of Long Short-Term Memory (LSTM) networks. Input parameters include the initial soil profile and feature vectors representing the soil's initial state, applied compaction effort, and compaction hammer energy. Utilizing an encoder-decoder framework, the model encodes soil profile information at various depths into tokens, which are subsequently decoded to predict the resulting ground improvement. An ablation study was conducted to assess the significance of each model component. The model's predictive accuracy was validated using field test data, demonstrating a strong correlation with observed outcomes (mean absolute error of 0.42 for test data). Shapley value analysis of the trained model revealed that compaction effort exerted the highest influence on predictions, followed by fine content and fill thickness. The model architecture also demonstrated successful application to alternative RIC case studies, indicating potential generalizability. Furthermore, the model's capability to simulate hypothetical scenarios with varying compaction efforts provides valuable insights for strategic planning and optimization of RIC project designs.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109664"},"PeriodicalIF":7.5,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659099","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}

引用次数: 0

Learning discriminative representations by a Canonical Correlation Analysis-based Siamese Network for offline signature verification 通过基于佳能相关分析的连体网络学习鉴别表征，用于离线签名验证

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-16 DOI: 10.1016/j.engappai.2024.109640

Lidong Zheng , Xingbiao Zhao , Shengjie Xu, Yuanyuan Ren, Yuchen Zheng

{"title":"Learning discriminative representations by a Canonical Correlation Analysis-based Siamese Network for offline signature verification","authors":"Lidong Zheng , Xingbiao Zhao , Shengjie Xu, Yuanyuan Ren, Yuchen Zheng","doi":"10.1016/j.engappai.2024.109640","DOIUrl":"10.1016/j.engappai.2024.109640","url":null,"abstract":"<div><div>In offline signature verification tasks, capturing different writing behaviors between genuine and forged signatures is a crucial and challenging step. In this paper, a novel writer independent Canonical Correlation Analysis-based Siamese Network (CCASigNet) is proposed to learn discriminative representations between different signature pairs. Specifically, we first construct signature pairs with three types: genuine-genuine, genuine-forged, and forged-forged. Then, different signature pairs are fed into CCASigNet for training with the Canonical Correlation Analysis (CCA) and classification-based losses. After network training, we extract the feature of signatures by CCASigNet and use writer-dependent classifiers to construct a comprehensive verification system. Extensive experiments on four benchmark signature datasets demonstrate that the proposed CCASigNet learns discriminative representations between different signature pairs and achieves state-of-the-art or competitive performance compared with advanced verification systems. In addition, the proposed CCASigNet has good generalization ability and is easy to transfer to different datasets with different language scripts within the realm of offline signature verification tasks.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109640"},"PeriodicalIF":7.5,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659100","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}

引用次数: 0

A novel hybrid data-driven domain generalization approach with dual-perspective feature fusion for intelligent fault diagnosis 用于智能故障诊断的双视角特征融合的新型混合数据驱动领域泛化方法

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-16 DOI: 10.1016/j.engappai.2024.109614

Lanjun Wan , Jian Zhou , Jiaen Ning , Yuanyuan Li , Changyun Li

{"title":"A novel hybrid data-driven domain generalization approach with dual-perspective feature fusion for intelligent fault diagnosis","authors":"Lanjun Wan , Jian Zhou , Jiaen Ning , Yuanyuan Li , Changyun Li","doi":"10.1016/j.engappai.2024.109614","DOIUrl":"10.1016/j.engappai.2024.109614","url":null,"abstract":"<div><div>Domain generalization-based fault diagnosis (DGFD) approaches do not require access to the target domain during model training, but they usually rely on numerous labeled source domain data. However, only few labeled source domain data can be obtained in actual diagnosis scenarios. Therefore, a novel hybrid data-driven domain generalization (DG) approach with dual-perspective feature fusion for intelligent fault diagnosis (FD) is proposed. Firstly, to solve the problem of scarce training samples in the source domains, the rolling bearing (RB) and the gear simulated vibration models are established to generate numerous labeled simulated vibration data, and the improved auxiliary classifier generative adversarial network (ACGAN) is used to effectively balance the simulated and real data. Secondly, a simulated and real data-driven DG network that fuses intra-domain invariant features and mutually-invariant features between domains (SRDGN-IM) is proposed, where the intra-domain invariant features are learned through distillation idea and the mutually-invariant features are learned through adversarial training, which can make the diagnosis model better learn the key generalization features from source domains to obtain more accurate diagnosis results. Finally, a series of DG experiments are conducted on the gearbox and bearing datasets, and the average FD accuracies of the proposed approach reach 87.45% and 89.10% respectively under different DG tasks.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109614"},"PeriodicalIF":7.5,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659102","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}

引用次数: 0

Decoding text from electroencephalography signals: A novel Hierarchical Gated Recurrent Unit with Masked Residual Attention Mechanism 从脑电图信号解码文本：具有屏蔽残留注意力机制的新型分层门控递归单元

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-16 DOI: 10.1016/j.engappai.2024.109615

Qiupu Chen , Yimou Wang , Fenmei Wang , Duolin Sun , Qiankun Li

{"title":"Decoding text from electroencephalography signals: A novel Hierarchical Gated Recurrent Unit with Masked Residual Attention Mechanism","authors":"Qiupu Chen , Yimou Wang , Fenmei Wang , Duolin Sun , Qiankun Li","doi":"10.1016/j.engappai.2024.109615","DOIUrl":"10.1016/j.engappai.2024.109615","url":null,"abstract":"<div><div>Progress in both neuroscience and natural language processing has opened doors for investigating brain to text techniques to reconstruct what individuals see, perceive, or focus on from human brain activity patterns. Non-invasive decoding, utilizing electroencephalography (EEG) signals, is preferred due to its comfort, cost-effectiveness, and portability. In brain-to-text applications, a pressing need has arisen to develop effective models that can accurately capture the intricate details of EEG signals, such as global and local contextual information and long-term dependencies. In response to this need, we propose the Hierarchical Gated Recurrent Unit with Masked Residual Attention Mechanism (HGRU-MRAM) model, which ingeniously combines the hierarchical structure and the masked residual attention mechanism to deliver a robust brain-to-text decoding system. Our experimental results on the ZuCo dataset demonstrate that this model significantly outperforms existing baselines, achieving state-of-the-art performance with Bilingual Evaluation Understudy Score (BLEU), Recall-Oriented Understudy for Gisting Evaluation (ROUGE), US National Institute of Standards and Technology Metric (NIST), Metric for Evaluation of Translation with Explicit Ordering (METEOR), Translation Edit Rate (TER), and BiLingual Evaluation Understudy with Representations from Transformers (BLEURT) scores of 48.29, 34.84, 4.07, 34.57, 21.98, and 40.45, respectively. The code is available at <span><span>https://github.com/qpuchen/EEG-To-Sentence</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109615"},"PeriodicalIF":7.5,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659101","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}

引用次数: 0

Chin electromyography-based motor unit decomposition for alternative screening of obstructive sleep apnea events: A comprehensive analysis 基于下巴肌电图的运动单元分解，用于阻塞性睡眠呼吸暂停事件的替代筛查：综合分析

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-16 DOI: 10.1016/j.engappai.2024.109534

Adil Rehman , Mostafa Moussa , Hani Saleh , Ali Khraibi , Ahsan H. Khandoker

{"title":"Chin electromyography-based motor unit decomposition for alternative screening of obstructive sleep apnea events: A comprehensive analysis","authors":"Adil Rehman , Mostafa Moussa , Hani Saleh , Ali Khraibi , Ahsan H. Khandoker","doi":"10.1016/j.engappai.2024.109534","DOIUrl":"10.1016/j.engappai.2024.109534","url":null,"abstract":"<div><div>Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a prevalent sleep disorder characterized by recurrent episodes of obstructed breathing due to the relaxation of muscles in the upper airway during sleep, often linked with neuromuscular and cardiovascular disorders. This study introduces a novel method using traditional machine learning classifiers and surface electromyography (SEMG) features extracted from motor units (MUs) decomposed from chin electromyography (EMG) signals to screen for OSA events in OSAHS subjects. SEMG features were extracted from individual MUs decomposed from chin EMG segments using a novel dataset. An apnea detection algorithm was designed to label these events for OSAHS subjects across sleep stages. Analysis of motor neuron firing patterns in OSAHS subjects revealed lower activation during OSA events and higher activation during non-OSA segments. Additionally, we evaluated the proposed system on a publicly available dataset, achieving a maximum accuracy of 72% for OSAHS subjects in the midlife phase age group (40–59 years) and 72.5% for subjects in the severe phase of OSAHS using Support Vector Machines (SVM). The random forest (RF) classifier demonstrated robust performance, achieving 97% accuracy, 93.2% sensitivity, 100% specificity, 100% precision, a 96.48% F1-score, and an area under the curve (AUC) of 0.996. This system facilitates early differentiation between OSA and non-OSA events, enabling timely intervention in the mild apnea phase to prevent progression to severe OSAHS. Moreover, it offers a convenient alternative to conventional polysomnography (PSG), enhancing diagnostic accessibility and clinical management.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109534"},"PeriodicalIF":7.5,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659141","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}

引用次数: 0

Hybrid multi-attention transformer for robust video object detection 用于稳健视频对象检测的混合多注意变换器

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-15 DOI: 10.1016/j.engappai.2024.109606

Sathishkumar Moorthy , Sachin Sakthi K.S. , Sathiyamoorthi Arthanari , Jae Hoon Jeong , Young Hoon Joo

{"title":"Hybrid multi-attention transformer for robust video object detection","authors":"Sathishkumar Moorthy , Sachin Sakthi K.S. , Sathiyamoorthi Arthanari , Jae Hoon Jeong , Young Hoon Joo","doi":"10.1016/j.engappai.2024.109606","DOIUrl":"10.1016/j.engappai.2024.109606","url":null,"abstract":"<div><div>Video object detection (VOD) is the task of detecting objects in videos, a challenge due to the changing appearance of objects over time, leading to potential detection errors. Recent research has addressed this by aggregating features from neighboring frames and incorporating information from distant frames to mitigate appearance deterioration. However, relying solely on object candidate regions in distant frames, independent of object position, has limitations, as it depends heavily on the performance of these regions and struggles with deteriorated appearances. To overcome these challenges, we propose a novel Hybrid Multi-Attention Transformer (HyMAT) module as our main contribution. HyMAT enhances relevant correlations while suppressing flawed information by searching for an agreement between whole correlation vectors. This module is designed for flexibility and can be integrated into both self- and cross-attention blocks to significantly improve detection accuracy. Additionally, we introduce a simplified Transformer-based object detection framework, named Hybrid Multi-Attention Object Detection (HyMATOD), which leverages competent feature reprocessing and target-background embeddings to more effectively utilize temporal references. Our approach demonstrates state-of-the-art performance, as evaluated on the ImageNet video object detection benchmark (ImageNet VID) and the University at Albany DEtection and TRACking (UA-DETRAC) benchmarks. Specifically, our HyMATOD model achieves an impressive 86.7% mean Average Precision (mAP) on the ImageNet VID dataset, establishing its superiority and practicality for video object detection tasks. These results underscore the significance of our contributions to advancing the field of VOD.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109606"},"PeriodicalIF":7.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659096","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}

引用次数: 0

Artificial intelligent pancreas for type 1 diabetic patients using adaptive type 3 fuzzy fault tolerant predictive control 利用自适应 3 型模糊容错预测控制为 1 型糖尿病患者提供人工智能胰腺

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-15 DOI: 10.1016/j.engappai.2024.109627

Arman Khani , Peyman Bagheri , Mahdi Baradarannia , Ardashir Mohammadzadeh

{"title":"Artificial intelligent pancreas for type 1 diabetic patients using adaptive type 3 fuzzy fault tolerant predictive control","authors":"Arman Khani , Peyman Bagheri , Mahdi Baradarannia , Ardashir Mohammadzadeh","doi":"10.1016/j.engappai.2024.109627","DOIUrl":"10.1016/j.engappai.2024.109627","url":null,"abstract":"<div><div>In this paper, the design methodology of artificial intelligent pancreas is presented. Accurate regulation of blood glucose levels in type 1 diabetic patients is of great importance in the presence of possible faults caused by sensor measurements. Regulation of blood glucose levels using a type 3 fuzzy predictive controller in type 1 diabetic patients in the presence of sensor faults is considered. The proposed structure includes a main control structure and a virtual dynamic, in which the main structure includes a fuzzy identifier, predictive controller, and an adaptive compensator, and the virtual structure is used to identify the sensor faults. Glucose is unknown in the dynamics of type 1 diabetes and is estimated on-line using a type 3 fuzzy system. Also, Lyapunov stability analysis is used to design the adaptive compensator to ensure the stability of the closed-loop system. The proposed methodology is evaluated based on Bergman’s minimum model for different patients under various parametric uncertainties and disturbances.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109627"},"PeriodicalIF":7.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659143","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}

引用次数: 0