Engineering Applications of Artificial Intelligence最新文献_第4页

Data-driven estimation of the amount of under frequency load shedding in small power systems 小型电力系统欠频甩负荷量的数据驱动估算

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-13 DOI: 10.1016/j.engappai.2024.109617

Mohammad Rajabdorri , Matthias C.M. Troffaes , Behzad Kazemtabrizi , Miad Sarvarizadeh , Lukas Sigrist , Enrique Lobato

{"title":"Data-driven estimation of the amount of under frequency load shedding in small power systems","authors":"Mohammad Rajabdorri , Matthias C.M. Troffaes , Behzad Kazemtabrizi , Miad Sarvarizadeh , Lukas Sigrist , Enrique Lobato","doi":"10.1016/j.engappai.2024.109617","DOIUrl":"10.1016/j.engappai.2024.109617","url":null,"abstract":"<div><div>This paper presents a data-driven methodology for estimating under frequency load shedding (UFLS) in small power systems. UFLS plays a vital role in maintaining system stability by shedding load when the frequency drops below a specified threshold following loss of generation. Using a dynamic system frequency response (SFR) model we generate different values of UFLS (i.e., labels) predicated on a set of carefully selected operating conditions (i.e., features). Machine learning (ML) algorithms are then applied to learn the relationship between chosen features and the UFLS labels. A novel regression tree and the Tobit model are suggested for this purpose and we show how the resulting non-linear model can be directly incorporated into a mixed integer linear programming (MILP) problem. The trained model can be used to estimate UFLS in security-constrained operational planning problems, improving frequency response, optimizing reserve allocation, and reducing costs. The methodology is applied to the La Palma island power system, demonstrating its accuracy and effectiveness. The results confirm that the amount of UFLS can be estimated with the mean absolute error (MAE) as small as 0.179 MW for the whole process, with a model that is representable as a MILP for use in scheduling problems such as unit commitment among others.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109617"},"PeriodicalIF":7.5,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659091","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

Classification of similar electronic components by transfer learning methods 用迁移学习法对相似电子元件进行分类

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-12 DOI: 10.1016/j.engappai.2024.109658

Göksu Taş

{"title":"Classification of similar electronic components by transfer learning methods","authors":"Göksu Taş","doi":"10.1016/j.engappai.2024.109658","DOIUrl":"10.1016/j.engappai.2024.109658","url":null,"abstract":"<div><div>Proper selection of electronic components and automated component identification is critical for fast production processes in industry. In addition, for Internet of Things (IoT) systems, accurate and fast selection of similar electronic components is an important problem. In this study, a transfer learning-based method is proposed to classify electronic components that are difficult to select due to their similarity. Eight different convolutional neural network (CNN) models and a novel model developed only in this study were used to classify electronic components. In addition to the transfer learning methods, the parallel CNN method, in which hyperparameter determination is done by trial and error, was developed and used to solve the classification problem. In addition to the transfer learning method, the parameters were tried to be determined by the trial-and-error method for hyperparameter selection. The effect of batch size and learning rate hyperparameter variations on the prediction success of parallel CNN models is analyzed. The effect of two different batch sizes and learning rate values for transfer learning models is also analyzed. Metrics such as confusion matrix, accuracy, and loss were used for evaluation methods. The number of parameters and runtime metrics of the models were also evaluated. All experiments were averaged to obtain a general intuition of success. The success of the proposed method is given by the evaluation metrics. According to the accuracy metric, the Densely Connected Convolutional Networks (DenseNet-121) model was the most successful model with a value of 98.2925%.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109658"},"PeriodicalIF":7.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659144","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

Weather-aware energy management for unmannedaerial vehicles: a machine learning application with global data integration 无人驾驶飞行器的气象感知能源管理：全球数据整合的机器学习应用

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-12 DOI: 10.1016/j.engappai.2024.109596

Abhishek G. Somanagoudar, Walter Mérida

{"title":"Weather-aware energy management for unmannedaerial vehicles: a machine learning application with global data integration","authors":"Abhishek G. Somanagoudar, Walter Mérida","doi":"10.1016/j.engappai.2024.109596","DOIUrl":"10.1016/j.engappai.2024.109596","url":null,"abstract":"<div><div>This study introduces a machine learning (ML) framework to predict unmanned aerial vehicle (UAV) energy requirements under diverse environmental conditions. The framework correlates UAV flight patterns with publicly accessible weather data, to yield an energy management tool applicable to a wide range of UAV configurations. The model employs the Cross-industry standard process for data mining and advanced feature engineering, offering an in-depth analysis of meteorological factors and UAV energy demands. The study assesses several multi-regression linear and ML models, whereby ensemble models gradient boosting (GB) and eXtreme gradient boosting demonstrate superior performance and accuracy. Specifically, the GB model achieved a test mean absolute error (MAE) of 0.0395 V (V) for voltage, 0.808 A (A) for current, and 9.758 mA-hours (mAh) for discharge, with prediction accuracy of over 99.9% for voltage and discharge, and 97% for current, derived from the coefficient of determination (R<sup>2</sup>). A novel integration of real-world UAV logs and weather data underpins the development of a weather-aware ML prediction model for UAV energy consumption. Our framework is capable of concurrently predicting three components of energy and power with almost uniform accuracy, a feature not found in contemporary models. Empirical test flights show a discrepancy of only 0.005 W-hour (Wh) between total predicted and actual energy consumption. This work enhances both efficiency and safety in UAV operations. The resulting energy-predictive flight planning tool sets a new benchmark for artificial intelligence (AI) applications in intelligent automation for UAVs.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109596"},"PeriodicalIF":7.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659128","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

Three-branch neural network for No-Reference Quality assessment of Pan-Sharpened Images 用于泛锐化图像无参考质量评估的三分支神经网络

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-12 DOI: 10.1016/j.engappai.2024.109594

Igor Stępień, Mariusz Oszust

{"title":"Three-branch neural network for No-Reference Quality assessment of Pan-Sharpened Images","authors":"Igor Stępień, Mariusz Oszust","doi":"10.1016/j.engappai.2024.109594","DOIUrl":"10.1016/j.engappai.2024.109594","url":null,"abstract":"<div><div>Pan-Sharpening (PS) techniques aim to enhance the spatial resolution of low-resolution multispectral images by leveraging data from high-resolution panchromatic images. Their comparison typically relies on the quality assessment of the resulting Full-Resolution (FS) pan-sharpened images. However, in the absence of a reference image, a dedicated No-Reference (NR) method must be employed. Therefore, this paper introduces a novel approach called the Three-Branch Neural Network for No-Reference Quality Assessment of Pan-Sharpened Images (TBN-PSI). The network consists of three subnetworks designed for perceptual processing of image channels, featuring shared extraction of low-level features and high-level semantics. Extensive experimental evaluation demonstrates the superiority of the approach over the state-of-the-art NR PS image quality assessment methods, using six datasets containing diverse satellite images that span urban areas, green vegetation, and water scenarios. Specifically, TBN-PSI outperforms the compared methods by 4% to 9% in terms of Spearman’s Rank-Order Correlation Coefficient (SRCC), Pearson’s Linear Correlation Coefficient (PLCC), and Kendall’s Rank Correlation Coefficient (KRCC) between the obtained scores and those of three representative full-reference methods.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109594"},"PeriodicalIF":7.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659124","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

Practical framework for generative on-branch soybean pod detection in occlusion and class imbalance scenes 在遮挡和类不平衡场景中进行枝上大豆豆荚生成检测的实用框架

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-12 DOI: 10.1016/j.engappai.2024.109613

Kanglei Wu , Tan Wang , Yuan Rao , Xiu Jin , Xiaobo Wang , Jiajia Li , Zhe Zhang , Zhaohui Jiang , Xing Shao , Wu Zhang

{"title":"Practical framework for generative on-branch soybean pod detection in occlusion and class imbalance scenes","authors":"Kanglei Wu , Tan Wang , Yuan Rao , Xiu Jin , Xiaobo Wang , Jiajia Li , Zhe Zhang , Zhaohui Jiang , Xing Shao , Wu Zhang","doi":"10.1016/j.engappai.2024.109613","DOIUrl":"10.1016/j.engappai.2024.109613","url":null,"abstract":"<div><div>The number of pods per plant can serve as an effective indicator of soybean yield, and accurately determining this is essential for evaluating high-quality soybean varieties. However, traditional manual pod counting is time-consuming and laborious. Although deep learning-based pod detection methods have attracted much attention, there are still considerable challenges for the effective detection of pods in occlusion and class imbalance scenes. As a remedy, this study proposes a framework that leverages synthetic pod image generation and multi-stage transfer learning to generate detection model of on-branch soybean pods in complex scenes. This framework employs a novel pipeline: initially separating individual pods from non-occluded pod images in an off-branch pod training set, then using these to generate synthetic datasets with diverse pod features. Next, a multi-stage transfer learning method is employed to train an on-branch pod detection model, leveraging both real and synthetic datasets to enhance pod feature extraction in complex scenes. The detection model of proposed framework, YOLOv7-tiny (tiny version of You Only Look Once v7), integrates an angle prediction module based on Circular Smooth Label for rotated object detection, Coordinate Attention modules for enhanced feature extraction and Minimum Point Distance Intersection over Union Loss for precise bounding box perception. Experimental results show that proposed framework achieves an 81.1% mAP (mean Average Precision) for detecting on-branch pods in complex scenes, surpassing the best-performing model by 23.7%. This proposed method presents an effective solution for complex on-branch pod detection, having great potential of serving as robust pipeline for similar agricultural tasks.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109613"},"PeriodicalIF":7.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659279","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

Cross-modal Prompt-Driven Network for low-resource vision-to-language generation 用于低资源视觉语言生成的跨模态提示驱动网络

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-12 DOI: 10.1016/j.engappai.2024.109591

Yuena Jiang, Yanxun Chang

{"title":"Cross-modal Prompt-Driven Network for low-resource vision-to-language generation","authors":"Yuena Jiang, Yanxun Chang","doi":"10.1016/j.engappai.2024.109591","DOIUrl":"10.1016/j.engappai.2024.109591","url":null,"abstract":"<div><div>Image captioning is a classic vision-to-language generation task, which aims to generate a descriptive sentence to describe the input image, involving the understanding of the image and the generation of natural language. Conventional methods require a large-scale labeled dataset for training, which includes a large volume of image-caption pairs. However, for several application scenarios, <em>e.g.,</em> medicine and non-English, such plenty of image-caption pairs are usually not available. In this work, we propose the Cross-modal Prompt-Driven Network (XProDNet) to perform low-resource image captioning, which can generate accurate and comprehensive image captioning, with extremely limited data for training. We conduct experiments on (1) six benchmark datasets; (2) three application scenarios, <em>i.e.</em>, conventional image captioning, medical image captioning, and non-English image captioning; (3) four target languages, <em>i.e.</em>, English, Chinese, German, and French; (4) two experimental settings, <em>i.e.</em>, fully-supervised learning and few-shot learning. The extensive experiments prove the effectiveness of our approach, which can not only generate high-quality and comprehensive image captions but also significantly surpass previous state-of-the-art methods under both the few-shot learning and fully-supervised learning settings. The improved results suggest that our method has great potential for improving image captioning in real-world applications.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109591"},"PeriodicalIF":7.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659126","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

One test to predict them all: Rheological characterization of complex fluids via artificial neural network 一次测试，预测所有流体通过人工神经网络表征复杂流体的流变特性

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-12 DOI: 10.1016/j.engappai.2024.109598

Ases Akas Mishra , Viney Ghai , Valentina Matovic , Dragana Arlov , Roland Kádár

{"title":"One test to predict them all: Rheological characterization of complex fluids via artificial neural network","authors":"Ases Akas Mishra , Viney Ghai , Valentina Matovic , Dragana Arlov , Roland Kádár","doi":"10.1016/j.engappai.2024.109598","DOIUrl":"10.1016/j.engappai.2024.109598","url":null,"abstract":"<div><div>The rheological behavior of complex fluids, including thixotropy, viscoelasticity, and viscoplasticity, poses significant challenges in both measurement and prediction due to the transient nature of their stress responses. This study introduces an artificial neural network (ANN) designed to digitally characterize the rheology of complex fluids with unprecedented accuracy. By employing a data-driven approach, the ANN is trained using transient rheological tests with step inputs of shear rate. Once trained, the network adeptly captures the intricate dependencies of rheological properties on time and shear, enabling rapid and accurate predictions of various rheological tests. In contrast, traditional phenomenological structural kinetic constitutive models often fail to accurately describe the evolution of nonlinear rheological properties, particularly as material complexity increases. The ANN demonstrates high flexibility, reliability and robustness by accurately predicting transient rheology of varied materials with different shear histories. Our findings illustrate that ANNs can not only complement and validate traditional rheological characterization methods but also potentially replace them, thereby paving the way for more efficient material development and testing.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109598"},"PeriodicalIF":7.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659097","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

Designing deep neural networks for driver intention recognition 设计用于识别驾驶员意图的深度神经网络

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-12 DOI: 10.1016/j.engappai.2024.109574

Koen Vellenga , H. Joe Steinhauer , Alexander Karlsson , Göran Falkman , Asli Rhodin , Ashok Koppisetty

{"title":"Designing deep neural networks for driver intention recognition","authors":"Koen Vellenga , H. Joe Steinhauer , Alexander Karlsson , Göran Falkman , Asli Rhodin , Ashok Koppisetty","doi":"10.1016/j.engappai.2024.109574","DOIUrl":"10.1016/j.engappai.2024.109574","url":null,"abstract":"<div><div>Driver intention recognition (DIR) studies increasingly rely on deep neural networks. Deep neural networks have achieved top performance for many different tasks. However, apart from image classifications and semantic segmentation for mobile phones, it is not a common practice for components of advanced driver assistance systems to explicitly analyze the complexity and performance of the network’s architecture. Therefore, this paper applies neural architecture search to investigate the effects of the deep neural network architecture on a real-world safety critical application with limited computational capabilities. We explore a pre-defined search space for three deep neural network layer types that are capable to handle sequential data (a long-short term memory, temporal convolution, and a time-series transformer layer), and the influence of different data fusion strategies on the driver intention recognition performance. A set of eight search strategies are evaluated for two driver intention recognition datasets. For the two datasets, we observed that there is no search strategy clearly sampling better deep neural network architectures. However, performing an architecture search improves the model performance compared to the original manually designed networks. Furthermore, we observe no relation between increased model complexity and better driver intention recognition performance. The result indicate that multiple architectures can yield similar performance, regardless of the deep neural network layer type or fusion strategy. However, the optimal complexity, layer type and fusion remain unknown upfront.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109574"},"PeriodicalIF":7.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659142","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

Korean football in-game conversation state tracking dataset for dialogue and turn level evaluation 用于对话和回合水平评估的韩国足球游戏内对话状态跟踪数据集

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-12 DOI: 10.1016/j.engappai.2024.109572

Sangmin Song, Juhyoung Park, Juhwan Choi, Junho Lee, Kyohoon Jin, YoungBin Kim

{"title":"Korean football in-game conversation state tracking dataset for dialogue and turn level evaluation","authors":"Sangmin Song, Juhyoung Park, Juhwan Choi, Junho Lee, Kyohoon Jin, YoungBin Kim","doi":"10.1016/j.engappai.2024.109572","DOIUrl":"10.1016/j.engappai.2024.109572","url":null,"abstract":"<div><div>Recent research in dialogue state tracking has made significant progress in tracking user goals through dialogue-level and turn-level approaches, but existing research primarily focused on predicting dialogue-level belief states. In this study, we present the <strong>KICK</strong>: <strong>K</strong>orean football <strong>I</strong>n-game <strong>C</strong>onversation state trac<strong>K</strong>ing dataset, which introduces a conversation-based approach. This approach leverages the roles of casters and commentators within the self-contained context of sports broadcasting to examine how utterances impact the belief state at both the dialogue-level and turn-level. Towards this end, we propose a task that aims to track the states of a specific time turn and understand conversations during the entire game. The proposed dataset comprises 228 games and 2463 events over one season, with a larger number of tokens per dialogue and turn, making it more challenging than existing datasets. Experiments revealed that the roles and interactions of casters and commentators are important for improving the zero-shot state tracking performance. By better understanding role-based utterances, we identify distinct approaches to the overall game process and events at specific turns.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109572"},"PeriodicalIF":7.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659355","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 flow rate estimation method for gas–liquid two-phase flow based on filter-enhanced convolutional neural network 基于滤波增强卷积神经网络的气液两相流流速估算方法

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-11 DOI: 10.1016/j.engappai.2024.109593

Yuxiao Jiang , Yinyan Liu , Lihui Peng , Yi Li

{"title":"A flow rate estimation method for gas–liquid two-phase flow based on filter-enhanced convolutional neural network","authors":"Yuxiao Jiang , Yinyan Liu , Lihui Peng , Yi Li","doi":"10.1016/j.engappai.2024.109593","DOIUrl":"10.1016/j.engappai.2024.109593","url":null,"abstract":"<div><div>Accurate estimation of flow rate in gas–liquid two-phase flow is crucial for various industrial processes. How to accurately estimate flow rate remains a challenging problem. Previously, deep learning-based methods focused on a few human-set points with single task learning. In addition, the data were not denoised. In this study, a flow rate estimation method based on a filter-enhanced convolutional neural network (FECNN) is proposed for gas–liquid two-phase flow. The method leverages multimodal data from a Venturi tube and an electrical capacitance tomography (ECT) sensor as input, utilizing multilayer perceptron (MLP) to fuse data. Subsequently, a learnable filter module is employed to attenuate noise adaptively, followed by multiscale convolutional neural network (MSCNN) extraction of flow rate features at different scales. Finally, the method enables estimate each single-phase flow rate simultaneously through multi-task learning (MTL). The adaptive noise attenuation capabilities of the learnable filter module are demonstrated, and the ability of the proposed MSCNN to capture multiscale flow rate features through multiple comparative experiments is shown. Additionally, a qualitative comparison with recent flow rate estimation methods is provided. Overall, this study demonstrates the effectiveness and superiority of the proposed FECNN in flow rate estimation.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109593"},"PeriodicalIF":7.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659280","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