自主智能系统(英文)最新文献_第2页

Water-saving control system based on multiple intelligent algorithms 基于多种智能算法的节水控制系统

自主智能系统(英文) Pub Date : 2024-07-04 DOI: 10.1007/s43684-024-00068-8

Fengnian Liu, Xiang Yu, Junya Tang

引用次数: 0

A nonlinear optimal control approach for 3-DOF four-cable driven parallel robots 3-DOF 四缆驱动并联机器人的非线性优化控制方法

自主智能系统(英文) Pub Date : 2024-07-01 DOI: 10.1007/s43684-024-00066-w

G. Rigatos, M. Abbaszadeh, J. Pomares

引用次数: 0

A binary-domain recurrent-like architecture-based dynamic graph neural network 基于二元域循环结构的动态图神经网络

自主智能系统(英文) Pub Date : 2024-06-25 DOI: 10.1007/s43684-024-00067-9

Zi-chao Chen, Sui Lin

{"title":"A binary-domain recurrent-like architecture-based dynamic graph neural network","authors":"Zi-chao Chen, Sui Lin","doi":"10.1007/s43684-024-00067-9","DOIUrl":"10.1007/s43684-024-00067-9","url":null,"abstract":"<div><p>The integration of Dynamic Graph Neural Networks (DGNNs) with Smart Manufacturing is crucial as it enables real-time, adaptive analysis of complex data, leading to enhanced predictive accuracy and operational efficiency in industrial environments. To address the problem of poor combination effect and low prediction accuracy of current dynamic graph neural networks in spatial and temporal domains, and over-smoothing caused by traditional graph neural networks, a dynamic graph prediction method based on spatiotemporal binary-domain recurrent-like architecture is proposed: Binary Domain Graph Neural Network (BDGNN). The proposed model begins by utilizing a modified Graph Convolutional Network (GCN) without an activation function to extract meaningful graph topology information, ensuring non-redundant embeddings. In the temporal domain, Recurrent Neural Network (RNN) and residual systems are employed to facilitate the transfer of dynamic graph node information between learner weights, aiming to mitigate the impact of noise within the graph sequence. In the spatial domain, the AdaBoost (Adaptive Boosting) algorithm is applied to replace the traditional approach of stacking layers in a graph neural network. This allows for the utilization of multiple independent graph learners, enabling the extraction of higher-order neighborhood information and alleviating the issue of over-smoothing. The efficacy of BDGNN is evaluated through a series of experiments, with performance metrics including Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) for link prediction tasks, as well as metrics for traffic speed regression tasks across diverse test sets. Compared with other models, the better experiments results demonstrate that BDGNN model can not only better integrate the connection between time and space information, but also extract higher-order neighbor information to alleviate the over-smoothing phenomenon of the original GCN.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00067-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction 多域融合货运无人机故障诊断知识图谱构建

自主智能系统(英文) Pub Date : 2024-06-21 DOI: 10.1007/s43684-024-00072-y

Ao Xiao, Wei Yan, Xumei Zhang, Ying Liu, Hua Zhang, Qi Liu

{"title":"Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction","authors":"Ao Xiao, Wei Yan, Xumei Zhang, Ying Liu, Hua Zhang, Qi Liu","doi":"10.1007/s43684-024-00072-y","DOIUrl":"10.1007/s43684-024-00072-y","url":null,"abstract":"<div><p>The fault diagnosis of cargo UAVs (Unmanned Aerial Vehicles) is crucial to ensure the safety of logistics distribution. In the context of smart logistics, the new trend of utilizing knowledge graph (KG) for fault diagnosis is gradually emerging, bringing new opportunities to improve the efficiency and accuracy of fault diagnosis in the era of Industry 4.0. The operating environment of cargo UAVs is complex, and their faults are typically closely related to it. However, the available data only considers faults and maintenance data, making it difficult to diagnose faults accurately. Moreover, the existing KG suffers from the problem of confusing entity boundaries during the extraction process, which leads to lower extraction efficiency. Therefore, a fault diagnosis knowledge graph (FDKG) for cargo UAVs constructed based on multi-domain fusion and incorporating an attention mechanism is proposed. Firstly, the multi-domain ontology modeling is realized based on the multi-domain fault diagnosis concept analysis expression model and multi-dimensional similarity calculation method for cargo UAVs. Secondly, a multi-head attention mechanism is added to the BERT-BILSTM-CRF network model for entity extraction, relationship extraction is performed through ERNIE, and the extracted triples are stored in the Neo4j graph database. Finally, the DJI cargo UAV failure is taken as an example for validation, and the results show that the new model based on multi-domain fusion data is better than the traditional model, and the precision rate, recall rate, and F1 value can reach 87.52%, 90.47%, and 88.97%, respectively.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00072-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142412924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human feedback enhanced autonomous intelligent systems: a perspective from intelligent driving 人的反馈增强型自主智能系统：智能驾驶的视角

自主智能系统(英文) Pub Date : 2024-06-13 DOI: 10.1007/s43684-024-00071-z

Kang Yuan, Yanjun Huang, Lulu Guo, Hong Chen, Jie Chen

引用次数: 0

Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives 基于变分自动编码器的技术，用于简化电气传动中的跨拓扑建模和优化工作流程

自主智能系统(英文) Pub Date : 2024-05-24 DOI: 10.1007/s43684-024-00065-x

Marius Benkert, Michael Heroth, Rainer Herrler, Magda Gregorová, Helmut C. Schmid

{"title":"Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives","authors":"Marius Benkert, Michael Heroth, Rainer Herrler, Magda Gregorová, Helmut C. Schmid","doi":"10.1007/s43684-024-00065-x","DOIUrl":"10.1007/s43684-024-00065-x","url":null,"abstract":"<div><p>The generation and optimization of simulation data for electrical machines remain challenging, largely due to the complexities of magneto-static finite element analysis. Traditional methodologies are not only resource-intensive, but also time-consuming. Deep learning models can be used to shortcut these calculations. However, challenges arise when considering the unique parameter sets specific to each machine topology. Building on two recent studies (Parekh et al. in IEEE Trans. Magn. 58(9):1–4, 2022; Parekh et al., Deep learning based meta-modeling for multi-objective technology optimization of electrical machines, 2023, arXiv:2306.09087), that utilized a variational autoencoder to cohesively map diverse topologies into a singular latent space for subsequent optimization, this paper proposes a refined architecture and optimization workflow. Our modifications aim to streamline and enhance the robustness of both the training and optimization processes, and compare the results with the variational autoencoder architecture proposed recently.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00065-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distant supervision knowledge extraction and knowledge graph construction method for supply chain management domain 供应链管理领域的远程监督知识提取和知识图谱构建方法

自主智能系统(英文) Pub Date : 2024-05-22 DOI: 10.1007/s43684-024-00064-y

Feiyue Huang, Lianglun Cheng

{"title":"Distant supervision knowledge extraction and knowledge graph construction method for supply chain management domain","authors":"Feiyue Huang, Lianglun Cheng","doi":"10.1007/s43684-024-00064-y","DOIUrl":"10.1007/s43684-024-00064-y","url":null,"abstract":"<div><p>As the core competitiveness of the national industry, large-scale equipment such as ships, high-speed rail and nuclear power equipment, their production process involves in-depth personalization. It includes complex processes and long manufacturing cycles. In addition, the equipment’s supply chain management is extremely complex. Therefore, the development of a supply chain management knowledge graph is of significant strategic significance. It not only enhances the synergistic effect of the supply chain management but also upgrades the level of intelligent management. This paper proposes a distant supervision knowledge extraction and knowledge graph construction method in the supply chain management of large equipment manufacturing, which achieves digital and structured management and efficient use of supply chain management knowledge in the industry. This paper presents an approach to extract entity-relation knowledge using limited samples. We achieve this by establishing a distant supervision model. Furthermore, we introduce a fusion gate mechanism and integrate ontology information, thereby enhancing the model’s capability to effectively discern sentence-level semantics. Subsequently, we promptly modify the weights of input features using the gate mechanism to strengthen the model’s resilience and address the issue of vector noise diffusion. Finally, an inter-bag sentence attention mechanism is introduced to integrate different sentence bag information at the sentence bag level, which achieves more accurate entity-relation knowledge extraction. The experimental results prove that compared with the latest distant supervision method, the accuracy of relation extraction is improved by 2.8%, and the AUC value is increased by 3.9%, effectively improving the quality of knowledge graph in supply chain management.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00064-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141112358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed gradient-free and projection-free algorithm for stochastic constrained optimization 随机约束优化的分布式无梯度和无投影算法

自主智能系统(英文) Pub Date : 2024-05-01 DOI: 10.1007/s43684-024-00062-0

Jie Hou, Xianlin Zeng, Chen Chen

{"title":"Distributed gradient-free and projection-free algorithm for stochastic constrained optimization","authors":"Jie Hou, Xianlin Zeng, Chen Chen","doi":"10.1007/s43684-024-00062-0","DOIUrl":"10.1007/s43684-024-00062-0","url":null,"abstract":"<div><p>Distributed stochastic zeroth-order optimization (DSZO), in which the objective function is allocated over multiple agents and the derivative of cost functions is unavailable, arises frequently in large-scale machine learning and reinforcement learning. This paper introduces a distributed stochastic algorithm for DSZO in a projection-free and gradient-free manner via the Frank-Wolfe framework and the stochastic zeroth-order oracle (SZO). Such a scheme is particularly useful in large-scale constrained optimization problems where calculating gradients or projection operators is impractical, costly, or when the objective function is not differentiable everywhere. Specifically, the proposed algorithm, enhanced by recursive momentum and gradient tracking techniques, guarantees convergence with just a single batch per iteration. This significant improvement over existing algorithms substantially lowers the computational complexity. Under mild conditions, we prove that the complexity bounds on SZO of the proposed algorithm are <span>(mathcal{O}(n/epsilon ^{2}))</span> and <span>(mathcal{O}(n(2^{frac{1}{epsilon}})))</span> for convex and nonconvex cases, respectively. The efficacy of the algorithm is verified on black-box binary classification problems against several competing alternatives.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00062-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141049164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Behavioral models of drivers in developing countries with an agent-based perspective: a literature review 基于代理视角的发展中国家驾驶员行为模型：文献综述

自主智能系统(英文) Pub Date : 2024-04-25 DOI: 10.1007/s43684-024-00061-1

Vishal A. Gracian, Stéphane Galland, Alexandre Lombard, Thomas Martinet, Nicolas Gaud, Hui Zhao, Ansar-Ul-Haque Yasar

引用次数: 0

Distributed optimization via dynamic event-triggered scheme with metric subregularity condition 通过具有度量次规则条件的动态事件触发方案进行分布式优化

自主智能系统(英文) Pub Date : 2024-04-23 DOI: 10.1007/s43684-024-00063-z

Xin Yu, Xi Chen, Yuan Fan, Songsong Cheng

引用次数: 0