自主智能系统(英文)最新文献_第6页

Multi-agent reinforcement learning for autonomous vehicles: a survey 自动驾驶汽车的多智能体强化学习研究

自主智能系统(英文) Pub Date : 2022-11-16 DOI: 10.1007/s43684-022-00045-z

Joris Dinneweth, Abderrahmane Boubezoul, René Mandiau, Stéphane Espié

{"title":"Multi-agent reinforcement learning for autonomous vehicles: a survey","authors":"Joris Dinneweth, Abderrahmane Boubezoul, René Mandiau, Stéphane Espié","doi":"10.1007/s43684-022-00045-z","DOIUrl":"10.1007/s43684-022-00045-z","url":null,"abstract":"<div><p>In the near future, autonomous vehicles (AVs) may cohabit with human drivers in mixed traffic. This cohabitation raises serious challenges, both in terms of traffic flow and individual mobility, as well as from the road safety point of view. Mixed traffic may fail to fulfill expected security requirements due to the heterogeneity and unpredictability of human drivers, and autonomous cars could then monopolize the traffic. Using multi-agent reinforcement learning (MARL) algorithms, researchers have attempted to design autonomous vehicles for both scenarios, and this paper investigates their recent advances. We focus on articles tackling decision-making problems and identify four paradigms. While some authors address mixed traffic problems with or without social-desirable AVs, others tackle the case of fully-autonomous traffic. While the latter case is essentially a communication problem, most authors addressing the mixed traffic admit some limitations. The current human driver models found in the literature are too simplistic since they do not cover the heterogeneity of the drivers’ behaviors. As a result, they fail to generalize over the wide range of possible behaviors. For each paper investigated, we analyze how the authors formulated the MARL problem in terms of observation, action, and rewards to match the paradigm they apply.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00045-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47231282","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

Deep learning prediction of amplitude death 深度学习预测振幅死亡

自主智能系统(英文) Pub Date : 2022-11-15 DOI: 10.1007/s43684-022-00044-0

Pengcheng Ji, Tingyi Yu, Yaxuan Zhang, Wei Gong, Qingyun Yu, Li Li

引用次数: 0

Urban rail transit FAO system: technological development and trends 城市轨道交通FAO系统：技术发展和趋势

自主智能系统(英文) Pub Date : 2022-10-28 DOI: 10.1007/s43684-022-00043-1

Tao Tang, Wentao Liu, Shukui Ding, Chunhai Gao, Shuai Su

引用次数: 0

Forecasting the yield of wafer by using improved genetic algorithm, high dimensional alternating feature selection and SVM with uneven distribution and high-dimensional data 采用改进的遗传算法、高维交替特征选择和支持向量机对分布不均匀的高维数据进行晶圆产量预测

自主智能系统(英文) Pub Date : 2022-09-26 DOI: 10.1007/s43684-022-00041-3

Qiuhao Xu, Chuqiao Xu, Junliang Wang

{"title":"Forecasting the yield of wafer by using improved genetic algorithm, high dimensional alternating feature selection and SVM with uneven distribution and high-dimensional data","authors":"Qiuhao Xu, Chuqiao Xu, Junliang Wang","doi":"10.1007/s43684-022-00041-3","DOIUrl":"10.1007/s43684-022-00041-3","url":null,"abstract":"<div><p>Wafer yield prediction, as the basis of quality control, is dedicated to predicting quality indices of the wafer manufacturing process. In recent years, data-driven machine learning methods have received a lot of attention due to their accuracy, robustness, and convenience for the prediction of quality indices. However, the existing studies mainly focus on the model level to improve the accuracy of yield prediction does not consider the impact of data characteristics on yield prediction. To tackle the above issues, a novel wafer yield prediction method is proposed, in which the improved genetic algorithm (IGA) is an under-sampling method, which is used to solve the problem of data overlap between finished products and defective products caused by the similarity of manufacturing processes between finished products and defective products in the wafer manufacturing process, and the problem of data imbalance caused by too few defective samples, that is, the problem of uneven distribution of data. In addition, the high-dimensional alternating feature selection method (HAFS) is used to select key influencing processes, that is, key parameters to avoid overfitting in the prediction model caused by many input parameters. Finally, SVM is used to predict the yield. Furthermore, experiments are conducted on a public wafer yield prediction dataset collected from an actual wafer manufacturing system. IGA-HAFS-SVM achieves state-of-art results on this dataset, which confirms the effectiveness of IGA-HAFS-SVM. Additionally, on this dataset, the proposed method improves the AUC score, G-Mean and F1-score by 21.6%, 34.6% and 0.6% respectively compared with the conventional method. Moreover, the experimental results prove the influence of data characteristics on wafer yield prediction.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00041-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46477594","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

New aggregation functions for spherical fuzzy sets and the spherical fuzzy distance within the MULTIMOORA method with applications MULTIMOORA方法中球面模糊集和球面模糊距离的新聚合函数及其应用

自主智能系统(英文) Pub Date : 2022-09-20 DOI: 10.1007/s43684-022-00042-2

Iman Mohamad Sharaf

{"title":"New aggregation functions for spherical fuzzy sets and the spherical fuzzy distance within the MULTIMOORA method with applications","authors":"Iman Mohamad Sharaf","doi":"10.1007/s43684-022-00042-2","DOIUrl":"10.1007/s43684-022-00042-2","url":null,"abstract":"<div><p>This article develops a novel approach for multi-objective optimization on the basis of ratio analysis plus the full multiplicative form (MULTIMOORA) using spherical fuzzy sets (SFSs) to obtain proper evaluations. SFSs surpass Pythagorean and intuitionistic fuzzy sets in modeling human cognition since the degree of hesitation is expressed explicitly in a three-dimensional space. In the spherical fuzzy environment, the implementation of the MULTIMOORA encounters two major problems in the aggregation operators and the distance measures that might lead to erroneous results. The extant aggregation operators in some cases can result in a biased evaluation. Therefore, two aggregation functions for SFSs are proposed. These functions guarantee balanced evaluation and avoid false ranking. In the reference point technique, when comparing SFSs, being closer to the ideal solution does not necessarily imply an SFS with a better score. To make up for this drawback, two reference points are employed instead of one, and the distance is not expressed as a crisp value but as an SFS instead. To overcome the disadvantages of the dominance theory in large-scale applications, the results of the three techniques are aggregated to get the overall utility on which the ranking is based. The illustration and validation of the proposed spherical fuzzy MULTIMOORA are examined through two applications, personnel selection, and energy storage technologies selection. The results are compared with the results of other methods to explicate the adequacy of the proposed method and validate the results.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00042-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48565853","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 pothole detection system using YOLOX algorithm 使用 YOLOX 算法的增强型坑洞检测系统

自主智能系统(英文) Pub Date : 2022-08-31 DOI: 10.1007/s43684-022-00037-z

Mohan Prakash B, Sriharipriya K.C

{"title":"Enhanced pothole detection system using YOLOX algorithm","authors":"Mohan Prakash B, Sriharipriya K.C","doi":"10.1007/s43684-022-00037-z","DOIUrl":"10.1007/s43684-022-00037-z","url":null,"abstract":"<div><p>The road is the most commonly used means of transportation and serves as a country’s arteries, so it is extremely important to keep the roads in good condition. Potholes that happen to appear in the road must be repaired to keep the road in good condition. Spotting potholes on the road is difficult, especially in a country like India where roads stretch millions of kilometres across the country. Therefore, there is a need to automate the identification of potholes with high speed and real-time precision. YOLOX is an object detection algorithm and our main goal of this article is to train and analyse the YOLOX model for pothole detection. The YOLOX model is trained with a pothole dataset and the results obtained are analysed by calculating the accuracy, recall and size of the model which is then compared to other YOLO algorithms. The experimental results in this article show that the YOLOX-Nano model predicts potholes with higher accuracy compared to other models while having low computational costs. We were able to achieve an Average Precision (AP) value of 85.6% from training the model and the total size of the model is 7.22 MB. The pothole detection capabilities of the newly developed YOLOX algorithm have never been tested before and this paper is one of the first to detect potholes using the YOLOX object detection algorithm. The research conducted in this paper will help reduce costs and increase the speed of pothole identification and will be of great help in road maintenance.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00037-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"52856349","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

Nonlinear optimal control for the 4-DOF underactuated robotic tower crane 四自由度欠驱动塔机非线性最优控制

自主智能系统(英文) Pub Date : 2022-08-30 DOI: 10.1007/s43684-022-00040-4

G. Rigatos, M. Abbaszadeh, J. Pomares

{"title":"Nonlinear optimal control for the 4-DOF underactuated robotic tower crane","authors":"G. Rigatos, M. Abbaszadeh, J. Pomares","doi":"10.1007/s43684-022-00040-4","DOIUrl":"10.1007/s43684-022-00040-4","url":null,"abstract":"<div><p>Tower cranes find wide use in construction works, in ports and in several loading and unloading procedures met in industry. A nonlinear optimal control approach is proposed for the dynamic model of the 4-DOF underactuated tower crane. The dynamic model of the robotic crane undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the system a stabilizing optimal (H-infinity) feedback controller is designed. 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 control approach is advantageous because: (i) unlike the popular computed torque method for robotic manipulators, the new control approach is characterized by optimality and is also applicable when the number of control inputs is not equal to the robot’s number of DOFs, (ii) it achieves fast and accurate tracking of reference setpoints under minimal energy consumption by the robot’s actuators, (iii) unlike the popular Nonlinear Model Predictive Control method, the article’s nonlinear optimal control scheme is of proven global stability and convergence to the optimum.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00040-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44027859","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

Collaboration effectiveness-based complex operations allocation strategy towards to human–robot interaction 基于协作有效性的面向人机交互的复杂作业分配策略

自主智能系统(英文) Pub Date : 2022-08-25 DOI: 10.1007/s43684-022-00039-x

Fuqiang Zhang, Yanrui Zhang, Shilin Xu

{"title":"Collaboration effectiveness-based complex operations allocation strategy towards to human–robot interaction","authors":"Fuqiang Zhang, Yanrui Zhang, Shilin Xu","doi":"10.1007/s43684-022-00039-x","DOIUrl":"10.1007/s43684-022-00039-x","url":null,"abstract":"<div><p>Under the background of the fourth industrial revolution driven by the new generation information technology and artificial intelligence, human–robot collaboration has become an important part of smart manufacturing. The new “human–robot–environment” relationship conducts industrial robots to collaborate with workers to adapt to environmental changes harmoniously. How to determine a reasonable human–robot interaction operations allocation strategy is the primary problem, by comprehensively considering the workers’ flexibility and industrial robots’ automation. In this paper, a human–robot collaborative operation framework based on CNC (Computer Number Control) machine tool was proposed, which divided into three stages: pre-machining, machining and post-machining. Then, an action-based granularity decomposition method was used to construct the human–robot interaction hierarchical model. Further, a collaboration effectiveness-based operations allocation function was established through normalizing the time, cost, efficiency, accuracy and complexity of human–robot interaction. Finally, a simulated annealing algorithm was adopted to solve preferable collaboration scheme; a case was used to verify the feasibility and effectiveness of the proposed method. It is expected that this study can provide useful guidance for human–robot interaction operations allocation on CNC machine tools.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00039-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44802557","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

Recent advances of AI for engineering service and maintenance 人工智能在工程服务和维护中的最新进展

自主智能系统(英文) Pub Date : 2022-08-23 DOI: 10.1007/s43684-022-00038-y

Chong Chen, Dazhong Wu, Ying Liu

引用次数: 0

A service-oriented energy assessment system based on BPMN and machine learning 基于BPMN和机器学习的面向服务的能源评估系统

自主智能系统(英文) Pub Date : 2022-08-11 DOI: 10.1007/s43684-022-00036-0

Wei Yan, Xinyi Wang, Qingshan Gong, Xumei Zhang, Hua Zhang, Zhigang Jiang

{"title":"A service-oriented energy assessment system based on BPMN and machine learning","authors":"Wei Yan, Xinyi Wang, Qingshan Gong, Xumei Zhang, Hua Zhang, Zhigang Jiang","doi":"10.1007/s43684-022-00036-0","DOIUrl":"10.1007/s43684-022-00036-0","url":null,"abstract":"<div><p>Increasing energy cost and environmental problems push forward research on energy saving and emission reduction strategy in the manufacturing industry. Energy assessment of machining, as the basis for energy saving and emission reduction, plays an irreplaceable role in engineering service and maintenance for manufacturing enterprises. Due to the complex energy nature and relationships between machine tools, machining parts, and machining processes, there is still a lack of practical energy evaluation methods and tools for manufacturing enterprises. To fill this gap, a serviced-oriented energy assessment system is designed and developed to assist managers in clarifying the energy consumption of machining in this paper. Firstly, the operational requirements of the serviced-oriented energy assessment system are analyzed from the perspective of enterprises. Then, based on the establishment of system architecture, three key technologies, namely data integration, process integration, and energy evaluation, are studied in this paper. In this section, the energy characteristics of machine tools and the energy relationships are studied through the working states of machine tools, machining features of parts and process activities of processes, and the relational database, BPMN 2.0 specification, and machine learning approach are employed to implement the above function respectively. Finally, a case study of machine tool center stand base machining in a manufacturing enterprise was applied to verify the effectiveness and practicality of the proposed approach and system.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00036-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43424453","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