自主智能系统(英文) Pub Date : 2025-04-28 DOI: 10.1007/s43684-025-00094-0

Fengnian Liu, Ding Wang, Junya Tang, Lei Wang

引用次数: 0

Nonlinear optimal control for the five-axle and three-steering coupled-vehicle system 五轴三转向耦合车辆系统的非线性最优控制

自主智能系统(英文) Pub Date : 2025-04-23 DOI: 10.1007/s43684-025-00097-x

G. Rigatos, M. Abbaszadeh, K. Busawon, P. Siano, M. Al Numay, G. Cuccurullo, F. Zouari

{"title":"Nonlinear optimal control for the five-axle and three-steering coupled-vehicle system","authors":"G. Rigatos, M. Abbaszadeh, K. Busawon, P. Siano, M. Al Numay, G. Cuccurullo, F. Zouari","doi":"10.1007/s43684-025-00097-x","DOIUrl":"10.1007/s43684-025-00097-x","url":null,"abstract":"<div><p>Transportation of heavy loads is often performed by multi-axle multi-steered heavy duty vehicles In this article a novel nonlinear optimal control method is applied to the kinematic model of the five-axle and three-steering coupled vehicle system. First, it is proven that the dynamic model of this articulated multi-vehicle system is differentially flat. Next. the state-space model of the five-axle and three-steering vehicle system undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization is based on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the five-axle and three-steering vehicle system a stabilizing optimal (H-infinity) feedback controller is designed. This controller stands for the solution of the nonlinear optimal control problem under model uncertainty and external perturbations. To compute the controller’s feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed nonlinear optimal control approach achieves fast and accurate tracking of setpoints under moderate variations of the control inputs and minimal dispersion of energy by the propulsion and steering system of the five-axle and three-steering vehicle system.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00097-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861351","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

Intelligent hierarchical federated learning system based on semi-asynchronous and scheduled synchronous control strategies in satellite network 卫星网络中基于半异步和定时同步控制策略的智能分层联邦学习系统

自主智能系统(英文) Pub Date : 2025-03-20 DOI: 10.1007/s43684-025-00095-z

Qiang Mei, Rui Huang, Duo Li, Jingyi Li, Nan Shi, Mei Du, Yingkang Zhong, Chunqi Tian

{"title":"Intelligent hierarchical federated learning system based on semi-asynchronous and scheduled synchronous control strategies in satellite network","authors":"Qiang Mei, Rui Huang, Duo Li, Jingyi Li, Nan Shi, Mei Du, Yingkang Zhong, Chunqi Tian","doi":"10.1007/s43684-025-00095-z","DOIUrl":"10.1007/s43684-025-00095-z","url":null,"abstract":"<div><p>Federated learning (FL) is a technology that allows multiple devices to collaboratively train a global model without sharing original data, which is a hot topic in distributed intelligent systems. Combined with satellite network, FL can overcome the geographical limitation and achieve broader applications. However, it also faces the issues such as straggler effect, unreliable network environments and non-independent and identically distributed (Non-IID) samples. To address these problems, we propose an intelligent hierarchical FL system based on semi-asynchronous and scheduled synchronous control strategies in cloud-edge-client structure for satellite network. Our intelligent system effectively handles multiple client requests by distributing the communication load of the central cloud to various edge clouds. Additionally, the cloud server selection algorithm and the edge-client semi-asynchronous control strategy minimize clients’ waiting time, improving the overall efficiency of the FL process. Furthermore, the center-edge scheduled synchronous control strategy ensures the timeliness of partial models. Based on the experiment results, our proposed intelligent hierarchical FL system demonstrates a distinct advantage in global accuracy over traditional FedAvg, achieving 2% higher global accuracy within the same time frame and reducing 52% training time to achieve the target accuracy.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00095-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655326","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

A glance over the past decade: road scene parsing towards safe and comfortable autonomous driving 回顾过去十年：路况解析走向安全舒适的自动驾驶

自主智能系统(英文) Pub Date : 2025-03-13 DOI: 10.1007/s43684-025-00096-y

Rui Fan, Jiahang Li, Jiaqi Li, Jiale Wang, Ziwei Long, Ning Jia, Yanan Liu, Wenshuo Wang, Mohammud J. Bocus, Sergey Vityazev, Xieyuanli Chen, Junhao Xiao, Stepan Andreev, Huimin Lu, Alexander Dvorkovich

引用次数: 0

自主智能系统(英文) Pub Date : 2025-03-05 DOI: 10.1007/s43684-025-00089-x

Kezhou Chen, Tao Wang, Huimin Zhuo, Lianglun Cheng

{"title":"WGO: a similarly encoded whale-goshawk optimization algorithm for uncertain cloud manufacturing service composition","authors":"Kezhou Chen, Tao Wang, Huimin Zhuo, Lianglun Cheng","doi":"10.1007/s43684-025-00089-x","DOIUrl":"10.1007/s43684-025-00089-x","url":null,"abstract":"<div><p>Service Composition and Optimization Selection (SCOS) is crucial in Cloud Manufacturing (CMfg), but the uncertainties in service states and working environments pose challenges for existing QoS-based methods. Recently, digital twins have gained prominence in CMfg due to their predictive capabilities, enhancing the reliability of service composition. Heuristic algorithms are widely used in this field for their flexibility and compatibility with uncertain environments. This paper proposes the Whale-Goshawk Optimization Algorithm (WGO), which combines the Whale Optimization Algorithm (WOA) and Northern Goshawk Optimization Algorithm (NGO). A novel similar integer coding method, incorporating spatial feature information, addresses the limitations of traditional integer coding, while a whale-optimized prey generation strategy improves NGO’s global optimization efficiency. Additionally, a local search method based on similar integer coding enhances WGO’s local search ability. Experimental results demonstrate the effectiveness of the proposed approach.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00089-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553998","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

Explanation framework for industrial recommendation systems based on the generative adversarial network with embedding constraints 基于嵌入约束的生成对抗网络的工业推荐系统解释框架

自主智能系统(英文) Pub Date : 2025-03-03 DOI: 10.1007/s43684-025-00092-2

Binchuan Qi, Wei Gong, Li Li

{"title":"Explanation framework for industrial recommendation systems based on the generative adversarial network with embedding constraints","authors":"Binchuan Qi, Wei Gong, Li Li","doi":"10.1007/s43684-025-00092-2","DOIUrl":"10.1007/s43684-025-00092-2","url":null,"abstract":"<div><p>The explainability of recommendation systems refers to the ability to explain the logic that guides the system’s decision to endorse or exclude an item. In industrial-grade recommendation systems, the high complexity of features, the presence of embedding layers, the existence of adversarial samples and the requirements for explanation accuracy and efficiency pose significant challenges to current explanation methods. This paper proposes a novel framework AdvLIME (Adversarial Local Interpretable Model-agnostic Explanation) that leverages Generative Adversarial Networks (GANs) with Embedding Constraints to enhance explainability. This method utilizes adversarial samples as references to explain recommendation decisions, generating these samples in accordance with realistic distributions and ensuring they meet the structural constraints of the embedding module. AdvLIME requires no modifications to the existing model architecture and needs only a single training session for global explanation, making it ideal for industrial applications. This work contributes two significant advancements. First, it develops a model-independent global explanation method via adversarial generation. Second, it introduces a model discrimination method to guarantee that the generated samples adhere to the embedding constraints. We evaluate the AdvLIME framework on the Behavior Sequence Transformer (BST) model using the MovieLens 20 M dataset. The experimental results show that AdvLIME outperforms traditional methods such as LIME and DLIME, reducing the approximation error of real samples by 50% and demonstrating improved stability and accuracy.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00092-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529968","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

Adaptive control of bilateral teleoperation systems under denial-of-service attacks 拒绝服务攻击下双边远程操作系统的自适应控制

自主智能系统(英文) Pub Date : 2025-02-26 DOI: 10.1007/s43684-025-00093-1

Lanyan Wei, Yuling Li

引用次数: 0

Efficient and accurate road crack detection technology based on YOLOv8-ES 基于YOLOv8-ES的高效、准确的道路裂缝检测技术

自主智能系统(英文) Pub Date : 2025-02-10 DOI: 10.1007/s43684-025-00091-3

Kaili Zeng, Rui Fan, Xiaoyu Tang

{"title":"Efficient and accurate road crack detection technology based on YOLOv8-ES","authors":"Kaili Zeng, Rui Fan, Xiaoyu Tang","doi":"10.1007/s43684-025-00091-3","DOIUrl":"10.1007/s43684-025-00091-3","url":null,"abstract":"<div><p>Road damage detection is an important aspect of road maintenance. Traditional manual inspections are laborious and imprecise. With the rise of deep learning technology, pavement detection methods employing deep neural networks give an efficient and accurate solution. However, due to background diversity, limited resolution, and fracture similarity, it is tough to detect road cracks with high accuracy. In this study, we offer a unique, efficient and accurate road crack damage detection, namely YOLOv8-ES. We present a novel dynamic convolutional layer(EDCM) that successfully increases the feature extraction capabilities for small fractures. At the same time, we also present a new attention mechanism (SGAM). It can effectively retain crucial information and increase the network feature extraction capacity. The Wise-IoU technique contains a dynamic, non-monotonic focusing mechanism designed to return to the goal-bounding box more precisely, especially for low-quality samples. We validate our method on both RDD2022 and VOC2007 datasets. The experimental results suggest that YOLOv8-ES performs well. This unique approach provides great support for the development of intelligent road maintenance systems and is projected to achieve further advances in future applications.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00091-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143373272","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

A cooperative jamming decision-making method based on multi-agent reinforcement learning 基于多智能体强化学习的协同干扰决策方法

自主智能系统(英文) Pub Date : 2025-02-05 DOI: 10.1007/s43684-025-00090-4

Bingchen Cai, Haoran Li, Naimin Zhang, Mingyu Cao, Han Yu

{"title":"A cooperative jamming decision-making method based on multi-agent reinforcement learning","authors":"Bingchen Cai, Haoran Li, Naimin Zhang, Mingyu Cao, Han Yu","doi":"10.1007/s43684-025-00090-4","DOIUrl":"10.1007/s43684-025-00090-4","url":null,"abstract":"<div><p>Electromagnetic jamming is a critical countermeasure in defense interception scenarios. This paper addresses the complex electromagnetic game involving multiple active jammers and radar systems by proposing a multi-agent reinforcement learning-based cooperative jamming decision-making method (MA-CJD). The proposed approach achieves high-quality and efficient target allocation, jamming mode selection, and power control. Mathematical models for radar systems and active jamming are developed to represent a multi-jammer and multi-radar electromagnetic confrontation scenario. The cooperative jamming decision-making process is then modeled as a Markov game, where the QMix multi-agent reinforcement learning algorithm is innovatively applied to handle inter-jammer cooperation. To tackle the challenges of a parameterized action space, the MP-DQN network structure is adopted, forming the basis of the MA-CJD algorithm. Simulation experiments validate the effectiveness of the proposed MA-CJD algorithm. Results show that MA-CJD significantly reduces the time defense units are detected while minimizing jamming resource consumption. Compared with existing algorithms, MA-CJD achieves better solutions, demonstrating its superiority in cooperative jamming scenarios.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00090-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184710","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

Enhanced bearing RUL prediction based on dynamic temporal attention and mixed MLP 基于动态时间关注和混合MLP的增强轴承RUL预测

自主智能系统(英文) Pub Date : 2025-01-10 DOI: 10.1007/s43684-024-00088-4

Zhongtian Jin, Chong Chen, Aris Syntetos, Ying Liu

{"title":"Enhanced bearing RUL prediction based on dynamic temporal attention and mixed MLP","authors":"Zhongtian Jin, Chong Chen, Aris Syntetos, Ying Liu","doi":"10.1007/s43684-024-00088-4","DOIUrl":"10.1007/s43684-024-00088-4","url":null,"abstract":"<div><p>Bearings are critical components in machinery, and accurately predicting their remaining useful life (RUL) is essential for effective predictive maintenance. Traditional RUL prediction methods often rely on manual feature extraction and expert knowledge, which face specific challenges such as handling non-stationary data and avoiding overfitting due to the inclusion of numerous irrelevant features. This paper presents an approach that leverages Continuous Wavelet Transform (CWT) for feature extraction, a Channel-Temporal Mixed MLP (CT-MLP) layer for capturing intricate dependencies, and a dynamic attention mechanism to adjust its focus based on the temporal importance of features within the time series. The dynamic attention mechanism integrates multi-head attention with innovative enhancements, making it particularly effective for datasets exhibiting non-stationary behaviour. An experimental study using the XJTU-SY rolling bearings dataset and the PRONOSTIA bearing dataset revealed that the proposed deep learning algorithm significantly outperforms other state-of-the-art algorithms in terms of RMSE and MAE, demonstrating its robustness and accuracy.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00088-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939427","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

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