自主智能系统(英文) Pub Date : 2025-09-22 DOI: 10.1007/s43684-025-00112-1

Dongyuan Lu, Haoyang Du, Zhengfei Wu, Shuo Yang

{"title":"Risk assessment in autonomous driving: a comprehensive survey of risk sources, methodologies, and system architectures","authors":"Dongyuan Lu, Haoyang Du, Zhengfei Wu, Shuo Yang","doi":"10.1007/s43684-025-00112-1","DOIUrl":"10.1007/s43684-025-00112-1","url":null,"abstract":"<div><p>As autonomous driving technology advances from assisted to higher levels of autonomy, the complexity of operational environments and the uncertainty of driving tasks continue to increase, posing significant challenges to system safety. The key to ensuring safety lies in conducting comprehensive and rational risk assessments to identify potential hazards and inform policy optimization. Consequently, risk assessment has emerged as a critical component for ensuring the safe operation of higher-level autonomous driving systems. This review focuses on research into risk assessment for autonomous driving. It systematically surveys the state-of-the-art literature from three key perspectives: risk sources, assessment methodologies, data foundations, and system architectures. For each perspective, the paper provides an in-depth analysis of representative technical approaches, modeling principles, and typical application scenarios, while summarizing their research characteristics and applicable boundaries. Finally, this paper synthesizes the three fundamental challenges that persist in current research and further explores future directions and development opportunities. It provides a theoretical foundation and methodological references for the development of autonomous driving systems that exhibit high safety and reliability.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00112-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100773","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

Correction to: An intelligent surface roughness prediction method based on automatic feature extraction and adaptive data fusion 一种基于自动特征提取和自适应数据融合的表面粗糙度智能预测方法

自主智能系统(英文) Pub Date : 2025-09-10 DOI: 10.1007/s43684-025-00107-y

Xun Zhang, Sibao Wang, Fangrui Gao, Hao Wang, Haoyu Wu, Ying Liu

引用次数: 0

Correction to: Output-based adaptive distributed observer for general linear leader systems over periodic switching digraphs 修正：周期切换有向图上一般线性先导系统的基于输出的自适应分布式观测器

自主智能系统(英文) Pub Date : 2025-09-10 DOI: 10.1007/s43684-025-00109-w

Changran He, Jie Huang

引用次数: 0

Correction to: Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction 修正：基于多域融合的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 : 2025-09-10 DOI: 10.1007/s43684-025-00106-z

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

引用次数: 0

Correction to: A novel method for measuring center-axis velocity of unmanned aerial vehicles through synthetic motion blur images 一种利用合成运动模糊图像测量无人机中心轴速度的新方法

自主智能系统(英文) Pub Date : 2025-09-10 DOI: 10.1007/s43684-025-00108-x

Quanxi Zhan, Yanmin Zhou, Junrui Zhang, Chenyang Sun, Runjie Shen, Bin He

引用次数: 0

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

自主智能系统(英文) Pub Date : 2025-09-10 DOI: 10.1007/s43684-025-00110-3

Binchuan Qi, Wei Gong, Li Li

引用次数: 0

Large language models for PHM: a review of optimization techniques and applications PHM的大型语言模型：优化技术和应用综述

自主智能系统(英文) Pub Date : 2025-08-19 DOI: 10.1007/s43684-025-00100-5

Tingyi Yu, Junya Tang, Qingyun Yu, Li Li, Ying Liu, Raul Poler

{"title":"Large language models for PHM: a review of optimization techniques and applications","authors":"Tingyi Yu, Junya Tang, Qingyun Yu, Li Li, Ying Liu, Raul Poler","doi":"10.1007/s43684-025-00100-5","DOIUrl":"10.1007/s43684-025-00100-5","url":null,"abstract":"<div><p>The rapid advancement of Large Language Models (LLMs) has created unprecedented opportunities for industrial automation, process optimization, and decision support systems. As industries seek to leverage LLMs for industrial tasks, understanding their architecture, deployment strategies, and fine-tuning methods becomes critical. In this review, we aim to summarize the challenges, key technologies, current status, and future directions of LLM in Prognostics and Health Management(PHM). First, this review introduces deep learning for PHM. We begin by analyzing the architectural considerations and deployment strategies for industrial environments, including acceleration techniques and quantization methods that enable efficient operation on resource-constrained industrial hardware. Second, we investigate Parameter Efficient Fine-Tuning (PEFT) techniques that allow industry-specific adaptation without prohibitive computational costs. Multi-modal capabilities extending LLMs beyond text to process sensor data, images, and time-series information are also discussed. Finally, we explore emerging PHM including anomaly detection systems that identify equipment malfunctions, fault diagnosis frameworks that determine root causes, and specialized question-answering systems that empower workers with instant domain expertise. We conclude by identifying key challenges and future research directions for LLM deployment in PHM. This review provides a timely resource for researchers, engineers, and decision-makers navigating the transformative potential of language models in industry 4.0 environments.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00100-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868630","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

Optimizing predictive maintenance and mission assignment to enhance fleet readiness under uncertainty 优化预测性维护和任务分配，增强不确定条件下的机队战备状态

自主智能系统(英文) Pub Date : 2025-08-15 DOI: 10.1007/s43684-025-00104-1

Ryan O’Neil, Abdelhakim Khatab, Claver Diallo

{"title":"Optimizing predictive maintenance and mission assignment to enhance fleet readiness under uncertainty","authors":"Ryan O’Neil, Abdelhakim Khatab, Claver Diallo","doi":"10.1007/s43684-025-00104-1","DOIUrl":"10.1007/s43684-025-00104-1","url":null,"abstract":"<div><p>In many industrial settings, fleets of assets are required to operate through alternating missions and breaks. Fleet Selective Maintenance (FSM) is widely used in such contexts to improve the fleet performance. However, existing FSM models assume that upcoming missions are identical and require only a single system configuration for completion. Additionally, these models typically assume that all missions must be completed, overlooking resource constraints that may prevent readying all systems within the available break duration. This makes mission prioritization and assignment a necessary consideration for the decision-maker. This work proposes a novel FSM model that jointly optimizes system to mission assignment, component and maintenance level selection, and repair task allocation. The proposed framework integrates analytical models for standard components and Deep Neural Networks (DNNs) for sensor-monitored ones, enabling a hybrid reliability assessment approach that better reflects real-world multi-component systems. To account for uncertainties in maintenance and break durations, a chance-constrained optimization model is developed to ensure that maintenance is completed within the available break duration with a specified confidence level. The optimization model is reformulated using two well-known techniques: Sample Average Approximation (SAA) and Conditional Value-at-Risk (CVaR) approximation. A case study of military aircraft fleet maintenance is investigated to demonstrate the accuracy and added value 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":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00104-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843250","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

Learning to trade autonomously in stocks and shares: integrating uncertainty into trading strategies 学习自主交易股票：将不确定性纳入交易策略

自主智能系统(英文) Pub Date : 2025-08-11 DOI: 10.1007/s43684-025-00101-4

Yuyang Li, Minghui Liwang, Li Li

{"title":"Learning to trade autonomously in stocks and shares: integrating uncertainty into trading strategies","authors":"Yuyang Li, Minghui Liwang, Li Li","doi":"10.1007/s43684-025-00101-4","DOIUrl":"10.1007/s43684-025-00101-4","url":null,"abstract":"<div><p>Machine learning, a revolutionary and advanced technology, has been widely applied in the field of stock trading. However, training an autonomous trading strategy which can effectively balance risk and Return On Investment without human supervision in the stock market with high uncertainty is still a bottleneck. This paper constructs a Bayesian-inferenced Gated Recurrent Unit architecture to support long-term stock price prediction based on characteristics of the stock information learned from historical data, augmented with memory of recent up- and-down fluctuations occur in the data of short-term stock movement. The Gated Recurrent Unit architecture incorporates uncertainty estimation into the prediction process, which take care of decision-making in an ever-changing dynamic environment. Three trading strategies were implemented in this model; namely, a Price Model Strategy, a Probabilistic Model Strategy, and a Bayesian Gated Recurrent Unit Strategy, each leveraging the respective model’s outputs to optimize trading decisions. The experimental results show that, compared with the standard Gated Recurrent Unit models, the modified model exhibits a huge tremendous/dramatic advantage in managing volatility and improving return on investment Return On Investment. The results and findings underscore the significant potential of combining Bayesian inference with machine learning to operate effectively in chaotic decision-making environments.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00101-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810783","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

Automated reinforcement learning for sequential ordering problem using hyperparameter optimization and metalearning 基于超参数优化和元学习的序列排序问题自动强化学习

自主智能系统(英文) Pub Date : 2025-07-29 DOI: 10.1007/s43684-025-00103-2

André Luiz Carvalho Ottoni

{"title":"Automated reinforcement learning for sequential ordering problem using hyperparameter optimization and metalearning","authors":"André Luiz Carvalho Ottoni","doi":"10.1007/s43684-025-00103-2","DOIUrl":"10.1007/s43684-025-00103-2","url":null,"abstract":"<div><p>AutoML systems seek to assist Artificial Intelligence users in finding the best configurations for machine learning models. Following this line, recently the area of Automated Reinforcement Learning (AutoRL) has become increasingly relevant, given the growing increase in applications for reinforcement learning algorithms. However, the literature still lacks specific AutoRL systems for combinatorial optimization, especially for the Sequential Ordering Problem (SOP). Therefore, this paper aims to present a new AutoRL approach for SOP. For this, two new methods are proposed using hyperparameter optimization and metalearning: AutoRL-SOP and AutoRL-SOP-MtL. The proposed AutoRL techniques enable the combined tuning of three SARSA hyperparameters, being <i>ϵ</i>-greedy policy, learning rate, and discount factor. Furthermore, the new metalearning approach enables the transfer of hyperparameters between two combinatorial optimization domains: TSP (source) and SOP (target). The results show that the application of metalearning generates a reduction in computational cost in hyperparameter optimization. Furthermore, the proposed AutoRL methods achieved the best solutions in 23 out of 28 simulated TSPLIB instances compared to recent literature studies.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00103-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145171555","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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