Engineering Applications of Artificial Intelligence最新文献

{"title":"Multi-scale information sharing and selection network with boundary attention for polyp segmentation","authors":"Xiaolu Kang,&nbsp;Zhuoqi Ma,&nbsp;Kang Liu,&nbsp;Yunan Li,&nbsp;Qiguang Miao","doi":"10.1016/j.engappai.2024.109467","DOIUrl":"10.1016/j.engappai.2024.109467","url":null,"abstract":"<div><div>Polyp segmentation in colonoscopy images is essential in clinical practice, offering valuable information for the diagnosis of colorectal cancer and subsequent surgical procedures. Despite the relatively good performance of existing methods, polyp segmentation still faces the following challenges: (1) Varying lighting conditions in colonoscopy and differences in polyp locations, sizes, and morphologies. (2) The indistinct boundary between polyps and surrounding tissue. To tackle these challenges, we propose a Multi-scale Information Sharing and Selection Network (MISNet) for the polyp segmentation task. We have designed a Selectively Shared Fusion Module (SSFM) to facilitate information sharing and the active selection between low-level and high-level features, thus enhancing the model’s ability to capture comprehensive information. Subsequently, we have developed a Parallel Attention Module (PAM) to improve the model’s attention on boundaries, and a Balancing Weight Module (BWM) to support the continuous refinement of boundary segmentation through the bottom-up process. Extensive experiments on five benchmark datasets show competitive results compared to existing representative methods. Specifically, our method has reached the mean Dice coefficient of 0.903 and 0.918 on the Kvasir and CVC-ClinicDB datasets, 0.762 and 0.764 on the challenging CVC-ColonDB and ETIS datasets. These innovative modules in our proposed MISNet effectively address key challenges, providing a robust solution for accurate polyp segmentation in clinical diagnosis and treatment. The proposed model is available at <span><span>https://github.com/q1216355254/MISNet.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109467"},"PeriodicalIF":7.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659087","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":"Machine learning based state observer for discrete time systems evolving on Lie groups","authors":"Soham Shanbhag,&nbsp;Dong Eui Chang","doi":"10.1016/j.engappai.2024.109576","DOIUrl":"10.1016/j.engappai.2024.109576","url":null,"abstract":"<div><div>In this paper, a machine learning based observer for systems evolving on manifolds is designed such that the state of the observer is restricted to the Lie group on which the system evolves. Designing machine learning based observers for systems evolving on Lie groups using charts would require training a machine learning based observer for each chart of the Lie group, and switching between the trained models based on the state of the system. We propose a novel deep learning based technique whose predictions are restricted to certain measure 0 subsets of the Euclidean space without using charts. Using this network, we design an observer ensuring that the state of the observer is restricted to the Lie group, and predicting the state using only one trained algorithm. The deep learning network predicts an error term on the Lie algebra of the Lie group, uses the map from the Lie algebra to the group, the group operation, and the present state to estimate the state at the next epoch. This approach, being purely data driven, does not require a model of the system. The proposed algorithm provides a novel framework for constraining the output of machine learning networks to certain measure 0 subsets of a Euclidean space without training on each specific chart and without requiring switching. We show the validity of this method using Monte Carlo simulations performed of the rigid body rotation and translation system.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109576"},"PeriodicalIF":7.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659274","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":"An image-to-image adversarial network to generate high resolution wind data over complex terrains from weather predictions","authors":"Jaime Milla-Val ,&nbsp;Carlos Montañés ,&nbsp;Norberto Fueyo","doi":"10.1016/j.engappai.2024.109533","DOIUrl":"10.1016/j.engappai.2024.109533","url":null,"abstract":"<div><div>In this work, we propose a Machine Learning method to predict detailed wind fields over extensive, complex terrains. The ability to predict local wind fields is becoming increasingly important for a range of applications, including sports in Nature, large outdoors events, light-aircraft flying, or the management of natural disasters. The intricate nature of wind dynamics, particularly in regions with complex orography such as a mountain range, presents a major challenge to traditional forecasting models. This work presents an efficient way to predict local wind conditions with a high resolution, similar to that of Computational Fluid Dynamics (CFD), in large geographical areas with complex terrain, using the results from relatively coarse (and therefore economical) data from Numerical Weather Prediction (NWP). To achieve this goal, we developed a conditional Generative Adversarial Neural network model (cGAN) to convert NWP data into CFD-like simulations. We apply the method to a rugged region in the Pyrenees mountain range in Spain. The results show that the proposed model outperforms traditional Machine Learning methods, such as Support Vector Machines (SVM), in terms of accuracy and computational efficiency. The method is four orders of magnitude <em>faster than</em> traditional CFD. <em>Mean Average Errors of</em> <span><math><mrow><mn>1</mn><mo>.</mo><mn>36</mn><mspace></mspace><mi>m/s</mi></mrow></math></span> <em>for wind speed and 18.73°for wind direction are obtained with the proposed approach.</em></div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109533"},"PeriodicalIF":7.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659272","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}

{"title":"Federated Reinforcement Learning for smart and privacy-preserving energy management of residential microgrids clusters","authors":"Mao Tan ,&nbsp;Jie Zhao ,&nbsp;Xiao Liu ,&nbsp;Yongxin Su ,&nbsp;Ling Wang ,&nbsp;Rui Wang ,&nbsp;Zhuocen Dai","doi":"10.1016/j.engappai.2024.109579","DOIUrl":"10.1016/j.engappai.2024.109579","url":null,"abstract":"<div><div>Real-time energy management optimizes energy utilization and manages electrical loads, which is crucial for improving the operational efficiency of residential microgrids. However, existing management methods suffer from model complexity and slow training speed. To solve this problem, we introduce Federated Reinforcement Learning to manage residential microgrids by training a control strategy in a decentralized and privacy-preserving manner. Specifically, a residential microgrid energy optimization management model is first established based on the Proximal Policy Optimization (PPO) method. Then, we propose a cooperative training strategy for multiple Residential microgrids based on Federated Reinforcement Learning (RFRL). The proposed method improves the training speed of residential microgrid models by sharing parameter information, such as network weights, while protects users’ usage data. Finally, clustering analysis is introduced in the case of heterogeneous residential microgrid data. Extensive experimental evaluation shows that our method outperforms the alternative residential microgrid management methods in terms of cost efficiency.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109579"},"PeriodicalIF":7.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659043","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":"Integration of in-wheel motor sensorless systems and hierarchical direct yaw moment control for distributed drive electric vehicles","authors":"Xiaodong Wang ,&nbsp;Maoping Ran ,&nbsp;Xinglin Zhou","doi":"10.1016/j.engappai.2024.109600","DOIUrl":"10.1016/j.engappai.2024.109600","url":null,"abstract":"<div><div>Ensuring robust and reliable control of distributed vehicles powered by in-wheel motor systems poses a significant challenge due to the harsh operating environments and high costs of such motor systems. Poor motor control, parameter variations, and sensor malfunction under these conditions can compromise the vehicle yaw stability. Integrating permanent magnet synchronous motor (PMSM) sensorless systems with vehicle yaw moment control offers a cost-effective solution for this issue without wheel angular speed sensors while enhancing yaw stability. In this paper, a composite nonlinear feedback sliding mode controller that can enhance the PMSM speed response is proposed. The proposed scheme exhibits a rotor speed overshoot and transient time of only 0.64% and 0.07s, respectively, which are smaller and shorter compared with other methods under motor parameter changes. Subsequently, the key states and tire-road friction coefficients required for vehicle control were estimated using sensorless rotor speeds and unscented Kalman filters, enabling the integration of the PMSM sensorless system with the vehicle yaw moment control. Additionally, a fuzzy adaptive hybrid sliding mode method is presented for yaw moment control enhancement. This method maintained the smallest sideslip angle root mean square error during double lane changes (0.4192 deg) compared with other methods. Analysis results show that different motor controllers and parameter changes significantly affect the vehicle dynamics performance. The proposed integrated scheme is feasible and effectively enhances the yaw moment control via high-performance sensorless PMSM systems.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109600"},"PeriodicalIF":7.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659140","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":"Dynamic flame feature-driven prediction model for basic oxygen furnace steelmaking endpoint carbon content based on three-dimensional multi-layer complex networks","authors":"JianXun Liu ,&nbsp;Hui Liu ,&nbsp;FuGang Chen ,&nbsp;YunKe Su ,&nbsp;Heng Li ,&nbsp;XiaoJun Xue","doi":"10.1016/j.engappai.2024.109564","DOIUrl":"10.1016/j.engappai.2024.109564","url":null,"abstract":"<div><div>Accurate prediction of carbon content at the endpoint is crucial for the endpoint management of Basic Oxygen Furnace (BOF) steelmaking. The carbon content in the molten pool is closely related to the dynamic and static characteristics of the flame at the furnace’s mouth. However, the flame’s texture change exhibits multidirectional and multiscale properties, posing challenges for existing algorithms to effectively extract dynamic color texture features. To address this issue, this paper proposes a dynamic texture feature extraction model based on a three-dimensional multi-layer complex network (3D-MLCN). The model constructs an unbounded complex network for a single-frame flame picture by integrating spatiotemporal position information of the image region’s centroid with color information, thereby quantizing the single-frame image into a complex network with spatiotemporal properties. Subsequently, a multi-scale multi-direction weighted dynamic color texture complex network is built for the flame video at the furnace mouth, utilizing the temporal index of the video frames in combination with vertex color values to capture the time-varying features of the flame video. The proposed method quantifies network characteristics through vertex degree distribution features to obtain dynamic color texture feature descriptors. These descriptors are then combined with static color texture features and color features to construct dynamic and static feature descriptors for the flame video, enabling the prediction of the endpoint carbon content using a regression model. By analyzing the actual production data of BOF steelmaking, the prediction accuracy of carbon content within the error range of ±0.02% is 87.91%, the <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> value is 0.8547, and the RMSE value is 2.0959, which verifies the effectiveness of the proposed method.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109564"},"PeriodicalIF":7.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659273","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":"Explained fire resistance machine learning models for compressed steel members of trusses and bracing systems","authors":"Luca Possidente ,&nbsp;Carlos Couto","doi":"10.1016/j.engappai.2024.109571","DOIUrl":"10.1016/j.engappai.2024.109571","url":null,"abstract":"<div><div>Trusses and bracing systems are usually constructed from monosymmetric and built-up cross-sections, which under compression stresses may buckle in torsional or flexural–torsional modes. In fire, this phenomenon is utterly important as failure in bracing systems or trusses may cause the collapse of buildings and result in loss of lives or severe economic impacts. Machine learning models, including neural networks, random forests and support vector machines, are developed considering a dataset with 21879 samples and are further assessed in this study as an alternative with greater accuracy and ease of application over existing design methods, namely the Eurocode 3 Part 1-2 and a recent proposal for its improvement. The machine learning models are explained using a combination of domain knowledge inference, partial dependence plots and SHapley Additive exPlanations. The accuracy versus safety trade-off is discussed for a better-informed model selection. The proposed approach and discussion create an additional confidence layer for applying these techniques for the fire design.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109571"},"PeriodicalIF":7.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658987","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}

{"title":"Residual-based attention Physics-informed Neural Networks for spatio-temporal ageing assessment of transformers operated in renewable power plants","authors":"Ibai Ramirez ,&nbsp;Joel Pino ,&nbsp;David Pardo ,&nbsp;Mikel Sanz ,&nbsp;Luis del Rio ,&nbsp;Alvaro Ortiz ,&nbsp;Kateryna Morozovska ,&nbsp;Jose I. Aizpurua","doi":"10.1016/j.engappai.2024.109556","DOIUrl":"10.1016/j.engappai.2024.109556","url":null,"abstract":"<div><div>Transformers are crucial for reliable and efficient power system operations, particularly in supporting the integration of renewable energy. Effective monitoring of transformer health is critical to maintain grid stability and performance. Thermal insulation ageing is a key transformer failure mode, which is generally tracked by monitoring the hotspot temperature (HST). However, HST measurement is complex, costly, and often estimated from indirect measurements. Existing HST models focus on space-agnostic thermal models, providing worst-case HST estimates. This article introduces a spatio-temporal model for transformer winding temperature and ageing estimation, which leverages physics-based partial differential equations (PDEs) with data-driven Neural Networks (NN) in a Physics Informed Neural Networks (PINNs) configuration to improve prediction accuracy and acquire spatio-temporal resolution. The computational accuracy of the PINN model is improved through the implementation of the Residual-Based Attention (PINN-RBA) scheme that accelerates the PINN model convergence. The PINN-RBA model is benchmarked against self-adaptive attention schemes and classical vanilla PINN configurations. For the first time, PINN based oil temperature predictions are used to estimate spatio-temporal transformer winding temperature values, validated through PDE numerical solution and fiber optic sensor measurements. Furthermore, the spatio-temporal transformer ageing model is inferred, which supports transformer health management decision-making. Results are validated with a distribution transformer operating on a floating photovoltaic power plant.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109556"},"PeriodicalIF":7.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659044","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":"WIGNN: An adaptive graph-structured reasoning model for credit default prediction","authors":"Zhipeng Yan ,&nbsp;Hanwen Qu ,&nbsp;Chen Chen ,&nbsp;Xiaoyi Lv ,&nbsp;Enguang Zuo ,&nbsp;Kui Wang ,&nbsp;Xulun Cai","doi":"10.1016/j.engappai.2024.109597","DOIUrl":"10.1016/j.engappai.2024.109597","url":null,"abstract":"<div><div>In credit default prediction, the main challenge is handling complex data structures and addressing data class imbalance. Given class imbalance and multi-dimensional data, general models find it difficult to fully explore the deep interdependencies within the data and the interaction effects between local and global. To overcome these challenges, this study proposes a Weighted Imbalanced Graph Neural Network (WIGNN) model that integrates adaptive graph structure inference with differential weight connectivity strategy, and the model solves the existing problems from the perspective of differential weight connectivity and graph balancing. Here, the weight connection uses the Gaussian kernel function to refine calculations and an adaptive percentile method to adjust sparsity, improving the understanding and efficiency of mining data connections. The weighted graph generated by this method can reflect the interaction between nodes and improve the model’s ability to analyse complex data structures. Based on this weighted graph, the graph imbalance module adopts a reinforcement learning-driven neighbour sampling strategy to adjust the sampling threshold automatically, optimizes the node embedding through message aggregation, and combines with a cost-sensitive matrix to improve classification accuracy and cost-effectiveness of the model on diverse credit datasets. We applied the WIGNN model to six real and class-imbalanced credit datasets, comparing it with 11 mainstream credit default prediction models. Evaluated using metrics Area Under the Curve (AUC), Geometric Mean (G-mean), and Accuracy. The results show that WIGNN significantly outperforms other models in handling class imbalance and graph sparsity, demonstrating its potential in financial credit applications.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109597"},"PeriodicalIF":7.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659275","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":"Guided deep reinforcement learning framework using automated curriculum scheme for accurate motion planning","authors":"Deun-Sol Cho ,&nbsp;Jae-Min Cho ,&nbsp;Won-Tae Kim","doi":"10.1016/j.engappai.2024.109541","DOIUrl":"10.1016/j.engappai.2024.109541","url":null,"abstract":"<div><div>Collaborative robotic arms in smart factories should ensure the safety and interactivity during their operation such as reaching and grasping objects. Especially, the advanced motion planner including the path planning and the motion control functions is essential for human-machine co-working. Since the traditional physics-based motion planning approaches require extreme computational resources to obtain near-optimal solutions, deep reinforcement learning algorithms have been actively adopted and have effectively solved the limitation. They, however, have the easy task preference problem, primarily taking the simpler ways for the more rewards, due to randomly training the agents how to reach the target points in the large-scale search spaces. Therefore, we propose a novel curriculum-based deep reinforcement learning framework that makes the agents learn the motion planning tasks in unbiased ways from the ones with the low complexities to the others with the high complexities. It uses the unsupervised learning algorithms to cluster the target points with the similar task complexities for generating the effective curriculum. In addition, the review and buffer flushing mechanisms are integrated into the framework to mitigate the catastrophic forgetting problem where the agent abruptly lose the previous learned knowledge upon learning new one in the curriculum. The evaluation results of the proposed framework show that the curriculum significantly enhances the success rate on the task with the highest complexity from 12% to 56% and the mechanisms improve the success rate on the tasks with the easier complexities from an average of 66% to 76.5%, despite requiring less training time.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109541"},"PeriodicalIF":7.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659270","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}