自主智能系统(英文)最新文献_第2页

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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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

Prediction for nonlinear time series by improved deep echo state network based on reservoir states reconstruction 基于储层状态重构的改进型深度回波态网络的非线性时间序列预测

自主智能系统(英文) Pub Date : 2024-02-21 DOI: 10.1007/s43684-023-00057-3

Qiufeng Yu, Hui Zhao, Li Teng, Li Li, Ansar Yasar, Stéphane Galland

{"title":"Prediction for nonlinear time series by improved deep echo state network based on reservoir states reconstruction","authors":"Qiufeng Yu, Hui Zhao, Li Teng, Li Li, Ansar Yasar, Stéphane Galland","doi":"10.1007/s43684-023-00057-3","DOIUrl":"10.1007/s43684-023-00057-3","url":null,"abstract":"<div><p>With the aim to enhance prediction accuracy for nonlinear time series, this paper put forward an improved deep Echo State Network based on reservoir states reconstruction driven by a Self-Normalizing Activation (SNA) function as the replacement for the traditional Hyperbolic tangent activation function to reduce the model’s sensitivity to hyper-parameters. The Strategy was implemented in a two-state reconstruction process by first inputting the time series data to the model separately. Once, the time data passes through the reservoirs and is activated by the SNA activation function, the new state for the reservoirs is created. The state is input to the next layer, and the concatenate states module saves. Pairs of states are selected from the activated multi-layer reservoirs and input into the state reconstruction module. Multiple input states are transformed through the state reconstruction module and finally saved to the concatenate state module. Two evaluation metrics were used to benchmark against three other ESNs with SNA activation functions to achieve better prediction accuracy.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-023-00057-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140443110","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

Shapley value: from cooperative game to explainable artificial intelligence 沙普利值：从合作博弈到可解释的人工智能

自主智能系统(英文) Pub Date : 2024-02-09 DOI: 10.1007/s43684-023-00060-8

Meng Li, Hengyang Sun, Yanjun Huang, Hong Chen

{"title":"Shapley value: from cooperative game to explainable artificial intelligence","authors":"Meng Li, Hengyang Sun, Yanjun Huang, Hong Chen","doi":"10.1007/s43684-023-00060-8","DOIUrl":"10.1007/s43684-023-00060-8","url":null,"abstract":"<div><p>With the tremendous success of machine learning (ML), concerns about their black-box nature have grown. The issue of interpretability affects trust in ML systems and raises ethical concerns such as algorithmic bias. In recent years, the feature attribution explanation method based on Shapley value has become the mainstream explainable artificial intelligence approach for explaining ML models. This paper provides a comprehensive overview of Shapley value-based attribution methods. We begin by outlining the foundational theory of Shapley value rooted in cooperative game theory and discussing its desirable properties. To enhance comprehension and aid in identifying relevant algorithms, we propose a comprehensive classification framework for existing Shapley value-based feature attribution methods from three dimensions: Shapley value type, feature replacement method, and approximation method. Furthermore, we emphasize the practical application of the Shapley value at different stages of ML model development, encompassing pre-modeling, modeling, and post-modeling phases. Finally, this work summarizes the limitations associated with the Shapley value and discusses potential directions for future research.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-023-00060-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139850285","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

Driving into the future: a cross-cutting analysis of distributed artificial intelligence, CCAM and the platform economy 驶向未来：对分布式人工智能、CCAM 和平台经济的横向分析

自主智能系统(英文) Pub Date : 2024-01-03 DOI: 10.1007/s43684-023-00059-1

Marc Guerreiro Augusto, Benjamin Acar, Andrea Carolina Soto, Fikret Sivrikaya, Sahin Albayrak

{"title":"Driving into the future: a cross-cutting analysis of distributed artificial intelligence, CCAM and the platform economy","authors":"Marc Guerreiro Augusto, Benjamin Acar, Andrea Carolina Soto, Fikret Sivrikaya, Sahin Albayrak","doi":"10.1007/s43684-023-00059-1","DOIUrl":"10.1007/s43684-023-00059-1","url":null,"abstract":"<div><p>The future of driving is autonomous. It requires a comprehensive stack of embedded software components, enabled by open-source and proprietary platforms at different abstraction layers, and then operating within a larger ecosystem. Autonomous driving demands connectivity, cooperation and automation to form the cornerstone of autonomous mobility solutions. Platform economy principles have revolutionized the way we produce, deliver and consume products and services worldwide. More and more businesses in the field of mobility and transport appear to implement transaction, innovation, and integration platforms as core enablers for Mobility-as-a-Service and transport applications. Artificial intelligence approaches, especially those dealing with distributed systems, enable new mobility solutions, such as autonomous driving. This paper contributes to understanding the intertwining role between distributed artificial intelligence, autonomous mobility and the resulting platform ecosystem. A systematic literature review is applied, in order to identify the intersection between those aspects. Furthermore, the research project BeIntelli is considered as a hands-on application of our findings. Taking into account our analysis and the aforementioned research project, we pose a blueprint architecture for autonomous mobility. This architecture is the subject of further research. Our conclusions facilitate the development and implementation of future urban transportation systems and resulting mobility ecosystems in practice.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-023-00059-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139387631","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