Zhuoxuan Li;Iakov Korovin;Xinli Shi;Sergey Gorbachev;Nadezhda Gorbacheva;Wei Huang;Jinde Cao
Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements' rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from 19 asphalt pavements with different crude oil sources on a 2.038 km long full-scale field accelerated pavement test track (Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition, this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction (RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error (MSE), average mean absolute error (MAE), and average mean absolute percentage error (MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.
沥青路面车辙是各种路面设计指南中的一项重要设计标准。良好的公路运输基础可以为公路运输中的油气运输提供保障。本研究试图开发一个鲁棒的人工智能模型来估计不同沥青路面的车辙深度、夹痕、温度和荷载轴作为主要特征。试验数据在北京通州2.038 km的全尺寸现场加速路面试验轨道(Road track Institute, RIOHTrack)上，取自19条不同原油源的沥青路面。此外，本文还提出通过复杂网络方法和Louvain算法进行小区检测，构建不同路面车辙深度的复杂网络。可以从不同的沥青路面车辙数据中选择最关键的结构要素，并找到相似的结构要素。设计了一种带残差校正的极限学习机算法，并采用独立的自适应粒子群算法对其进行了优化。将该方法与几种经典机器学习算法的实验结果进行比较，对19条沥青路面的平均均方根误差(MSE)、平均绝对误差(MAE)和平均绝对百分比误差(MAPE)的预测结果分别达到1.742、1.363和1.94%。实验表明，在处理道路工程中的非线性问题时，RELM算法比经典的机器学习方法具有优势。值得注意的是，该方法通过对生产环境参数的认知分析，确保了模拟环境对不同抽象层次的适应。这是一种很有前途的替代方法，有助于快速评估路面状况，并可在未来的石油和天然气行业的生产过程中应用。
{"title":"A Data-Driven Rutting Depth Short-Time Prediction Model with Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack","authors":"Zhuoxuan Li;Iakov Korovin;Xinli Shi;Sergey Gorbachev;Nadezhda Gorbacheva;Wei Huang;Jinde Cao","doi":"10.1109/JAS.2023.123192","DOIUrl":"10.1109/JAS.2023.123192","url":null,"abstract":"Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements' rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from 19 asphalt pavements with different crude oil sources on a 2.038 km long full-scale field accelerated pavement test track (Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition, this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction (RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error (MSE), average mean absolute error (MAE), and average mean absolute percentage error (MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"10 10","pages":"1918-1932"},"PeriodicalIF":11.8,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43581414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuanqi Qin;Wen Hua;Junchen Jin;Jun Ge;Xingyuan Dai;Lingxi Li;Xiao Wang;Fei-Yue Wang
Online traffic simulation that feeds from online information to simulate vehicle movement in real-time has recently seen substantial advancement in the development of intelligent transportation systems and urban traffic management. It has been a challenging problem due to three aspects: 1) The diversity of traffic patterns due to heterogeneous layouts of urban intersections; 2) The nature of complex spatiotemporal correlations; 3) The requirement of dynamically adjusting the parameters of traffic models in a real-time system. To cater to these challenges, this paper proposes an online traffic simulation framework called automated urban traffic operation simulation via meta-learning (AUTOSIM). In particular, simulation models with various intersection layouts are automatically generated using an open-source simulation tool based on static traffic geometry attributes. Through a meta-learning technique, AUTOSIM enables an automated learning process for dynamic model settings of traffic scenarios featured with different spatiotemporal correlations. Besides, AUTOSIM is capable of adapting traffic model parameters according to dynamic traffic information in real-time by using a meta-learner. Through computational experiments, we demonstrate the effectiveness of the meta-learning-based framework that is capable of providing reliable supports to real-time traffic simulation and dynamic traffic operations.
{"title":"AUTOSIM: Automated Urban Traffic Operation Simulation via Meta-Learning","authors":"Yuanqi Qin;Wen Hua;Junchen Jin;Jun Ge;Xingyuan Dai;Lingxi Li;Xiao Wang;Fei-Yue Wang","doi":"10.1109/JAS.2023.123264","DOIUrl":"10.1109/JAS.2023.123264","url":null,"abstract":"Online traffic simulation that feeds from online information to simulate vehicle movement in real-time has recently seen substantial advancement in the development of intelligent transportation systems and urban traffic management. It has been a challenging problem due to three aspects: 1) The diversity of traffic patterns due to heterogeneous layouts of urban intersections; 2) The nature of complex spatiotemporal correlations; 3) The requirement of dynamically adjusting the parameters of traffic models in a real-time system. To cater to these challenges, this paper proposes an online traffic simulation framework called automated urban traffic operation simulation via meta-learning (AUTOSIM). In particular, simulation models with various intersection layouts are automatically generated using an open-source simulation tool based on static traffic geometry attributes. Through a meta-learning technique, AUTOSIM enables an automated learning process for dynamic model settings of traffic scenarios featured with different spatiotemporal correlations. Besides, AUTOSIM is capable of adapting traffic model parameters according to dynamic traffic information in real-time by using a meta-learner. Through computational experiments, we demonstrate the effectiveness of the meta-learning-based framework that is capable of providing reliable supports to real-time traffic simulation and dynamic traffic operations.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"10 9","pages":"1871-1881"},"PeriodicalIF":11.8,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45850103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work presents a trajectory tracking control method for snake robots. This method eliminates the influence of time-varying interferences on the body and reduces the offset error of a robot with a predetermined trajectory. The optimized line-of-sight (LOS) guidance strategy drives the robot's steering angle to maintain its anti-sideslip ability by predicting position errors and interferences. Then, the predictions of system parameters and viscous friction coefficients can compensate for the joint torque control input. The compensation is adopted to enhance the compatibility of a robot within ever-changing environments. Simulation and experimental outcomes show that our work can decrease the fluctuation peak of the tracking errors, reduce adjustment time, and improve accuracy.
{"title":"Position Errors and Interference Prediction-Based Trajectory Tracking for Snake Robots","authors":"Dongfang Li;Yilong Zhang;Ping Li;Rob Law;Zhengrong Xiang;Xin Xu;Limin Zhu;Edmond Q. Wu","doi":"10.1109/JAS.2023.123612","DOIUrl":"10.1109/JAS.2023.123612","url":null,"abstract":"This work presents a trajectory tracking control method for snake robots. This method eliminates the influence of time-varying interferences on the body and reduces the offset error of a robot with a predetermined trajectory. The optimized line-of-sight (LOS) guidance strategy drives the robot's steering angle to maintain its anti-sideslip ability by predicting position errors and interferences. Then, the predictions of system parameters and viscous friction coefficients can compensate for the joint torque control input. The compensation is adopted to enhance the compatibility of a robot within ever-changing environments. Simulation and experimental outcomes show that our work can decrease the fluctuation peak of the tracking errors, reduce adjustment time, and improve accuracy.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"10 9","pages":"1810-1821"},"PeriodicalIF":11.8,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44395827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article addresses the circular formation control problem of a multi-agent system moving on a circle in the presence of limited communication ranges and communication delays. To minimize the number of communication links, a novel distributed controller based on a cyclic pursuit strategy is developed in which each agent needs only its leading neighbour's information. In contrast to existing works, we propose a set of new potential functions to deal with heterogeneous communication ranges and communication delays simultaneously. A new framework based on the admissible upper bound of the formation error is established so that both connectivity maintenance and order preservation can be achieved at the same time. It is shown that the multi-agent system can be driven to the desired circular formation as time goes to infinity under the proposed controller. Finally, the effectiveness of the proposed method is illustrated by some simulation examples.
{"title":"Cyclic-Pursuit-Based Circular Formation Control of Mobile Agents with Limited Communication Ranges and Communication Delays","authors":"Boyin Zheng;Cheng Song;Lu Liu","doi":"10.1109/JAS.2023.123576","DOIUrl":"10.1109/JAS.2023.123576","url":null,"abstract":"This article addresses the circular formation control problem of a multi-agent system moving on a circle in the presence of limited communication ranges and communication delays. To minimize the number of communication links, a novel distributed controller based on a cyclic pursuit strategy is developed in which each agent needs only its leading neighbour's information. In contrast to existing works, we propose a set of new potential functions to deal with heterogeneous communication ranges and communication delays simultaneously. A new framework based on the admissible upper bound of the formation error is established so that both connectivity maintenance and order preservation can be achieved at the same time. It is shown that the multi-agent system can be driven to the desired circular formation as time goes to infinity under the proposed controller. Finally, the effectiveness of the proposed method is illustrated by some simulation examples.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"10 9","pages":"1860-1870"},"PeriodicalIF":11.8,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42528463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper is concerned with a novel integrated multi-step heuristic dynamic programming (MsHDP) algorithm for solving optimal control problems. It is shown that, initialized by the zero cost function, MsHDP can converge to the optimal solution of the Hamilton-Jacobi-Bellman (HJB) equation. Then, the stability of the system is analyzed using control policies generated by MsHDP.Also, a general stability criterion is designed to determine the admissibility of the current control policy. That is, the criterion is applicable not only to traditional value iteration and policy iteration but also to MsHDP. Further, based on the convergence and the stability criterion, the integrated MsHDP algorithm using immature control policies is developed to accelerate learning efficiency greatly. Besides, actor-critic is utilized to implement the integrated MsHDP scheme, where neural networks are used to evaluate and improve the iterative policy as the parameter architecture. Finally, two simulation examples are given to demonstrate that the learning effectiveness of the integrated MsHDP scheme surpasses those of other fixed or integrated methods.
{"title":"Adaptive Multi-Step Evaluation Design With Stability Guarantee for Discrete-Time Optimal Learning Control","authors":"Ding Wang;Jiangyu Wang;Mingming Zhao;Peng Xin;Junfei Qiao","doi":"10.1109/JAS.2023.123684","DOIUrl":"10.1109/JAS.2023.123684","url":null,"abstract":"This paper is concerned with a novel integrated multi-step heuristic dynamic programming (MsHDP) algorithm for solving optimal control problems. It is shown that, initialized by the zero cost function, MsHDP can converge to the optimal solution of the Hamilton-Jacobi-Bellman (HJB) equation. Then, the stability of the system is analyzed using control policies generated by MsHDP.Also, a general stability criterion is designed to determine the admissibility of the current control policy. That is, the criterion is applicable not only to traditional value iteration and policy iteration but also to MsHDP. Further, based on the convergence and the stability criterion, the integrated MsHDP algorithm using immature control policies is developed to accelerate learning efficiency greatly. Besides, actor-critic is utilized to implement the integrated MsHDP scheme, where neural networks are used to evaluate and improve the iterative policy as the parameter architecture. Finally, two simulation examples are given to demonstrate that the learning effectiveness of the integrated MsHDP scheme surpasses those of other fixed or integrated methods.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"10 9","pages":"1797-1809"},"PeriodicalIF":11.8,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47212745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wei Xu;Chen Zhao;Jie Cheng;Yin Wang;Yiqing Tang;Tao Zhang;Zhiming Yuan;Yisheng Lv;Fei-Yue Wang
Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway system. In such cases, train timetables need to be rescheduled. However, timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization efficiency. This paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based decision-making. Firstly, the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer, providing the better understanding of overall operation in the high-speed railway system. Then, a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast convergence. Extensive experiments on various delay scenarios are conducted. The results demonstrate the effectiveness of the proposed method in comparison to other popular methods.
{"title":"Transformer-Based Macroscopic Regulation for High-Speed Railway Timetable Rescheduling","authors":"Wei Xu;Chen Zhao;Jie Cheng;Yin Wang;Yiqing Tang;Tao Zhang;Zhiming Yuan;Yisheng Lv;Fei-Yue Wang","doi":"10.1109/JAS.2023.123501","DOIUrl":"10.1109/JAS.2023.123501","url":null,"abstract":"Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway system. In such cases, train timetables need to be rescheduled. However, timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization efficiency. This paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based decision-making. Firstly, the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer, providing the better understanding of overall operation in the high-speed railway system. Then, a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast convergence. Extensive experiments on various delay scenarios are conducted. The results demonstrate the effectiveness of the proposed method in comparison to other popular methods.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"10 9","pages":"1822-1833"},"PeriodicalIF":11.8,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46314486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yangyang Li;Chaoqun Fei;Chuanqing Wang;Hongming Shan;Ruqian Lu
Deep metric learning (DML) has achieved great results on visual understanding tasks by seamlessly integrating conventional metric learning with deep neural networks. Existing deep metric learning methods focus on designing pair-based distance loss to decrease intra-class distance while increasing interclass distance. However, these methods fail to preserve the geometric structure of data in the embedding space, which leads to the spatial structure shift across mini-batches and may slow down the convergence of embedding learning. To alleviate these issues, by assuming that the input data is embedded in a lower-dimensional sub-manifold, we propose a novel deep Riemannian metric learning (DRML) framework that exploits the non-Euclidean geometric structural information. Considering that the curvature information of data measures how much the Riemannian (non-Euclidean) metric deviates from the Euclidean metric, we leverage geometry flow, which is called a geometric evolution equation, to characterize the relation between the Riemannian metric and its curvature. Our DRML not only regularizes the local neigh-borhoods connection of the embeddings at the hidden layer but also adapts the embeddings to preserve the geometric structure of the data. On several benchmark datasets, the proposed DRML outperforms all existing methods and these results demonstrate its effectiveness.
{"title":"Geometry Flow-Based Deep Riemannian Metric Learning","authors":"Yangyang Li;Chaoqun Fei;Chuanqing Wang;Hongming Shan;Ruqian Lu","doi":"10.1109/JAS.2023.123399","DOIUrl":"10.1109/JAS.2023.123399","url":null,"abstract":"Deep metric learning (DML) has achieved great results on visual understanding tasks by seamlessly integrating conventional metric learning with deep neural networks. Existing deep metric learning methods focus on designing pair-based distance loss to decrease intra-class distance while increasing interclass distance. However, these methods fail to preserve the geometric structure of data in the embedding space, which leads to the spatial structure shift across mini-batches and may slow down the convergence of embedding learning. To alleviate these issues, by assuming that the input data is embedded in a lower-dimensional sub-manifold, we propose a novel deep Riemannian metric learning (DRML) framework that exploits the non-Euclidean geometric structural information. Considering that the curvature information of data measures how much the Riemannian (non-Euclidean) metric deviates from the Euclidean metric, we leverage geometry flow, which is called a geometric evolution equation, to characterize the relation between the Riemannian metric and its curvature. Our DRML not only regularizes the local neigh-borhoods connection of the embeddings at the hidden layer but also adapts the embeddings to preserve the geometric structure of the data. On several benchmark datasets, the proposed DRML outperforms all existing methods and these results demonstrate its effectiveness.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"10 9","pages":"1882-1892"},"PeriodicalIF":11.8,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47465595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a crucial data preprocessing method in data mining, feature selection (FS) can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected features. Evolutionary computing (EC) is promising for FS owing to its powerful search capability. However, in traditional EC-based methods, feature subsets are represented via a length-fixed individual encoding. It is ineffective for high-dimensional data, because it results in a huge search space and prohibitive training time. This work proposes a length-adaptive non-dominated sorting genetic algorithm (LA-NSGA) with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective high-dimensional FS. In LA-NSGA, an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths, and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space adaptively. Moreover, a dominance-based local search method is employed for further improvement. The experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.
{"title":"A Length-Adaptive Non-Dominated Sorting Genetic Algorithm for Bi-Objective High-Dimensional Feature Selection","authors":"Yanlu Gong;Junhai Zhou;Quanwang Wu;MengChu Zhou;Junhao Wen","doi":"10.1109/JAS.2023.123648","DOIUrl":"10.1109/JAS.2023.123648","url":null,"abstract":"As a crucial data preprocessing method in data mining, feature selection (FS) can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected features. Evolutionary computing (EC) is promising for FS owing to its powerful search capability. However, in traditional EC-based methods, feature subsets are represented via a length-fixed individual encoding. It is ineffective for high-dimensional data, because it results in a huge search space and prohibitive training time. This work proposes a length-adaptive non-dominated sorting genetic algorithm (LA-NSGA) with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective high-dimensional FS. In LA-NSGA, an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths, and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space adaptively. Moreover, a dominance-based local search method is employed for further improvement. The experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"10 9","pages":"1834-1844"},"PeriodicalIF":11.8,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49375744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
About 60% of emissions into the earth's atmosphere are produced by the transport sector, caused by exhaust gases from conventional internal combustion engines. An effective solution to this problem is electric mobility, which significantly reduces the rate of urban pollution. The use of electric vehicles (EVs) has to be encouraged and facilitated by new information and communication technology (ICT) tools. To help achieve this goal, this paper proposes innovative services for electric vehicle users aimed at improving travel and charging experience. The goal is to provide a smart service to allow drivers to find the most appropriate charging solutions during a trip based on information such as the vehicle's current position, battery type, state of charge, nearby charge point availability, and compatibility. In particular, the drivers are supported so that they can find and book the preferred charge option according to time availability and the final cost of the charge points (CPs). To this purpose, two virtual sensors (VSs) are designed, modeled and simulated in order to provide the users with an innovative service for smart CP searching and booking. In particular, the first VS is devoted to locate and find available CPs in a preferred area, whereas the second VS calculates the charging cost for the EV and supports the driver in the booking phase. A UML activity diagram describes VSs operations and cooperation, while a UML sequence diagram highlights data exchange between the VSs and other electromobility ecosystem actors (CP operator, EV manufacturer, etc.). Furthermore, two timed Petri Nets (TPNs) are designed to model the proposed VSs, functioning and interactions as discrete event systems. The Petri Nets are synchronized by a single larger TPN that is simulated in different use cases and scenarios to demonstrate the effectiveness of the proposed VSs.
{"title":"Innovative Services for Electric Mobility Based on Virtual Sensors and Petri Nets","authors":"Agostino Marcello Mangini;Michele Roccotelli","doi":"10.1109/JAS.2023.123699","DOIUrl":"10.1109/JAS.2023.123699","url":null,"abstract":"About 60% of emissions into the earth's atmosphere are produced by the transport sector, caused by exhaust gases from conventional internal combustion engines. An effective solution to this problem is electric mobility, which significantly reduces the rate of urban pollution. The use of electric vehicles (EVs) has to be encouraged and facilitated by new information and communication technology (ICT) tools. To help achieve this goal, this paper proposes innovative services for electric vehicle users aimed at improving travel and charging experience. The goal is to provide a smart service to allow drivers to find the most appropriate charging solutions during a trip based on information such as the vehicle's current position, battery type, state of charge, nearby charge point availability, and compatibility. In particular, the drivers are supported so that they can find and book the preferred charge option according to time availability and the final cost of the charge points (CPs). To this purpose, two virtual sensors (VSs) are designed, modeled and simulated in order to provide the users with an innovative service for smart CP searching and booking. In particular, the first VS is devoted to locate and find available CPs in a preferred area, whereas the second VS calculates the charging cost for the EV and supports the driver in the booking phase. A UML activity diagram describes VSs operations and cooperation, while a UML sequence diagram highlights data exchange between the VSs and other electromobility ecosystem actors (CP operator, EV manufacturer, etc.). Furthermore, two timed Petri Nets (TPNs) are designed to model the proposed VSs, functioning and interactions as discrete event systems. The Petri Nets are synchronized by a single larger TPN that is simulated in different use cases and scenarios to demonstrate the effectiveness of the proposed VSs.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"10 9","pages":"1845-1859"},"PeriodicalIF":11.8,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42362682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dear Editor, This letter presents a multi-automated guided vehicles (AGV) routing planning method based on deep reinforcement learning (DRL) and recurrent neural network (RNN), specifically utilizing proximal policy optimization (PPO) and long short-term memory (LSTM). Compared to traditional AGV pathing planning methods using genetic algorithm, ant colony optimization algorithm, etc., our proposed method has a higher degree of adaptability to deal with temporary changes of tasks or sudden failures of AGVs. Furthermore, our novel routing method, which uses LSTM to take into account temporal step information, provides a more optimized performance in terms of rewards and convergence speed as compared to existing PPO-based routing methods for AGVs.
{"title":"A Multi-AGV Routing Planning Method Based on Deep Reinforcement Learning and Recurrent Neural Network","authors":"Yishuai Lin;Gang Hue;Liang Wang;Qingshan Li;Jiawei Zhu","doi":"10.1109/JAS.2023.123300","DOIUrl":"10.1109/JAS.2023.123300","url":null,"abstract":"Dear Editor, This letter presents a multi-automated guided vehicles (AGV) routing planning method based on deep reinforcement learning (DRL) and recurrent neural network (RNN), specifically utilizing proximal policy optimization (PPO) and long short-term memory (LSTM). Compared to traditional AGV pathing planning methods using genetic algorithm, ant colony optimization algorithm, etc., our proposed method has a higher degree of adaptability to deal with temporary changes of tasks or sudden failures of AGVs. Furthermore, our novel routing method, which uses LSTM to take into account temporal step information, provides a more optimized performance in terms of rewards and convergence speed as compared to existing PPO-based routing methods for AGVs.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 7","pages":"1720-1722"},"PeriodicalIF":11.8,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10198708","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62324869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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