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Event-Triggered Mechanism-Based MPC for Path-Tracking Control of Four-Wheel Steering Vehicles

Event-Triggered Mechanism-Based MPC for Path-Tracking Contro...

引用

IEEE international conference on Industrial Informatics (IndIN)

作者： Xiangyu Zhang Guoyan Xu Han Li Peng Chen Qi Xia Han Cai State Key Lab of Intelligent Transportation System School of Transportation Science and Engineering The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In this study, we tackle the path-tracking problem of a nonlinear four-wheel steering vehicle dynamics model subject to model mismatches and propose a model predictive control (MPC) algorithm based on an event-triggered mechanism (ET -MPC). The goal is to maintain closed-loop control performance while reducing the computational and communication burdens of traditional MPC. We introduce an ET -MPC framework utilizing a model-free reinforcement learning agent with proximal policy optimization (PPO). This agent interacts with the MPC system, progressively learning to determine the optimal event-triggered mechanism. To enhance exploration and training efficiency, we incorporate the Long Short-Term Memory (LSTM) technique into PPO. Experimental results show that the proposed ET -MPC framework, combined with reinforcement learning for reward optimization, demonstrates superior overall performance in path-tracking control of four-wheel steering vehicles.

关键词： Training Event detection Computational modeling Heuristic algorithms Predictive models Prediction algorithms Vehicle dynamics Long short term memory Frequency control Predictive control

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Reinforcement learning-Based Stream Reservation for Distributed Time Sensitive Networks 22

Reinforcement Learning-Based Stream Reservation for Distribu...

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22nd IEEE international conference on Communication Technology, ICCT 2022

作者： Xu, Fengyou Qiu, Danfeng Lv, Bo College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Beijing Institute of Astronautical Systems Engineering Beijing China

ISBN: (纸本)9781665470674

In order to achieve the pursuit of deterministic delay and unified network transmission in the industrial automation field, the time-sensitive networking group has developed a stream reservation mechanism to reserve the required bandwidth for time-sensitive traffic. In the distributed network configuration model, how to configure per-hop delay guarantee properly has become a key problem to restrict network performance, the reason is that an excessive loose or tight guarantee will reduce the streams that can be added to the network. Therefore, this paper proposes a stream reservation method based on reinforcement learning which can dynamically adjust the per-hop delay guarantee to improve the acceptance of streams. For this, it firstly defines the optimization problem of network configuration in the reservation process and give the per-hop delay model. Then the operation of the algorithm on bridge's data plane and control plane is simulated. We evaluate the performance of the algorithm with acceptance rate and bandwidth utilization in different scenarios, and the results demonstrate the effectiveness of the proposed algorithm. © 2022 IEEE.

关键词： Reinforcement learning

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SpeciServe. a gRPC Infrastructure Concept

SpeciServe. a gRPC Infrastructure Concept

引用

international conference on Software engineering Research, Management and Applications (SERA)

作者： Chase Carthen Araam Zaremehrjardi Zachary Estreito Alireza Tavakkoli Frederick C. Harris Sergiu M. Dascalu Computer Science and Engineering University of Nevada Reno Reno Nevada

ISBN: (数字)9798350391343

ISBN: (纸本)9798350391350

Smart city projects require data to be transferred from one destination to the next using a number of different network protocols. The data pipelines involved in these smart city projects often have limited bandwidth or compute resources due to the low power nature of most embedded hardware. The data transferred between devices in these types of embedded systems are often structured in non-standard data schemata. Remote procedure calls (RPC) are implemented to transfer data between devices and switching between RPC implementations can be tricky due to the lack of standardization. There is no guarantee that an existing data schema will work with a different RPC implementation. This makes it difficult for a researcher or system developer to benchmark and compare different RPC im-plementations. In this paper, a conceptual infrastructure named SpeciServe is introduced where gRPC is used as a communication backbone due its support for flatbuffers and multiple server modes. Multiple software services are described to allow for dissimilar RPC implementations to be run in parallel. This system is intended to allow for researchers in machine learning, smart cities, and Internet of Things (loT) to be able test different versions of RPCs and provide support for system developers to define the functions of an edge service.

关键词： Protocols Smart cities Pipelines Switches Standardization Machine learning Benchmark testing

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Wind Turbine Modelling Based on Neural Networks: A First Approach 22nd

Wind Turbine Modelling Based on Neural Networks: A First App...

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22nd international conference on intelligent data engineering and automated learning, IDEAL 2021

作者： Sierra-García, J. Enrique Santos, Matilde Department of Electromechanical Engineering University of Burgos Burgos09006 Spain Institute of Knowledge Technology Complutense University of Madrid Madrid28040 Spain

ISBN: (纸本)9783030916077

Wind turbine modelling is a complex task due to the rotation of the spinning rotor, the aerodynamic of the blades, and the fluid viscosity. They are normally studied by advanced flow dynamics simulators, complex aerodynamics theory, and blade element methods. There are some wind turbine modelling software such as FAST that consider all these phenomena. However, the complexity of these models makes it impossible to integrate them into program logic controllers. Neural networks can be powerful tools to help simplify these complex models and embedded them into the wind turbine controllers. In this work recurrent neural networks have been successfully used to obtain a low-complexity control-oriented computational models of a wind turbine. Graphical and numerical results prove the validity of the approach. © 2021, Springer Nature Switzerland AG.

关键词： Wind turbines

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A Comparative Study of learning Outcomes for Online learning Platforms 22nd

A Comparative Study of Learning Outcomes for Online Learning...

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22nd international conference on Artificial Intelligence in Education (AIED) - Mind the Gap - AIED for Equity and Inclusion

作者： St-Hilaire, Francois Burns, Nathan Belfer, Robert Shayan, Muhammad Smofsky, Ariella Vu, Dung Do Frau, Antoine Potochny, Joseph Faraji, Farid Pavero, Vincent Ko, Neroli Ching, Ansona Onyi Elkins, Sabina Stepanyan, Anush Matajova, Adela Charlin, Laurent Bengio, Yoshua Serban, Iulian Vlad Kochmar, Ekaterina Korbit Technol Inc Montreal PQ Canada Quebec Artificial Intelligence Inst Mila Montreal PQ Canada Univ Bath Bath Avon England

ISBN: (纸本)9783030782702;9783030782696

Personalization and active learning help educational systems to close the gap between students with varying abilities. We run a comparative head-to-head study of learning outcomes for two popular online platforms: Platform A, which delivers content over lecture videos and multiple-choice quizzes, and Platform B, which provides interactive problem-solving exercises and personalized feedback. We observe a statistically significant increase in the learning outcomes on Platform B. Further, the results of the self-assessment questionnaire suggest that participants using Platform B improve their metacognition.

关键词： Online and distance learning Models of teaching and learning intelligent and interactive technologies data science

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COVID-19 Prediction Using Time Series Models 4

COVID-19 Prediction Using Time Series Models

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Deepthi, A.R. Indiramma, M. B.M.S. College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665474368

Real-time data has evolved to become an integral part of understanding events across different timelines. Machine learning uses different varieties of algorithms to determine the relationship between sets of data spread across timelines, visualize the current situation, and forecast the future, which is the most important aspect. Due to the breakout of COVID-19, a novel coronavirus, the entire planet is currently experiencing a disastrous crisis. At this time, the SARS-CoV-2 virus has proven to be a possible hazard to human life. The ARIMA Model i.e., Autoregressive Integrated Moving Average is compared with Facebook's Prophet and VARMAX model to foretell the future. The dataset is divided into the training and testing set. The size of the COVID-19 dataset is relatively small as it is a pandemic that occurred recently, due to which much of the data is used for training purposes and the last twelve days have been used for testing and validating the model. The model is trained and fits on the training data set. The algorithms are now ready to anticipate future forecasts after it has been tested and trained. The models also record the predicted and actual values, allowing them to improve their accuracy in the future. In this paper, the results of the ARIMA model are compared against Prophet and VARMAX which are other popular machine learning time series models. For the ease of visualization of covid trends, a dashboard is built using Python's Plotly and Dash and has been deployed using Voila. © 2022 IEEE.

关键词： COVID-19

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Machine learning Operations: A Mapping Study 22nd

Machine Learning Operations: A Mapping Study

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22nd international conference on Software engineering Research and Practice, SERP 2024, and 23rd international conference on e-learning, e-Business, Enterprise Information Systems, and e-Government, EEE 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Chakraborty, Abhijit Das, Suddhasvatta Gary, Kevin Arizona State University TempeAZ85287 United States

ISBN: (纸本)9783031866432

Machine learning and AI have been recently embraced by many companies. Machine learning Operations, (MLOps), refers to the use of continuous software engineering processes, such as DevOps, in the deployment of machine learning models to production. Nevertheless, not all machine learning initiatives successfully transition to the production stage owing to the multitude of intricate factors involved. This article discusses the issues that exist in several components of the MLOps pipeline, namely the data manipulation pipeline, model building pipeline, and deployment pipeline. A systematic mapping study is performed to identify the challenges that arise in the MLOps system categorized by different focus areas. Using this data, realistic and applicable recommendations are offered for tools or solutions that can be used for their implementation. The main value of this work is it maps distinctive challenges in MLOps along with the recommended solutions outlined in our study. These guidelines are not specific to any particular tool and are applicable to both research and industrial settings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： DevOps

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An Evolvable Knowledge Graph Supporting a Hybrid Intelligence Autonomous Driving System

An Evolvable Knowledge Graph Supporting a Hybrid Intelligenc...

引用

IEEE international conference on Software Architecture Workshops (ICSAW)

作者： Anna Teern Nada Elgendy Pertti Seppänen Tero Päivärinta Empirical Software Engineering in Software Systems and Services University of Oulu Oulu Finland

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Route planning is influenced by various factors, with speed and distance often prioritized in map services. However, dynamic conditions such as road maintenance, weather, or accidents can significantly impact these routes. By leveraging hybrid intelligence (HI), the collaboration between human intuition and machine efficiency, we focus on supporting decision-making in autonomous and semi-autonomous driving contexts. This study explores integrating knowledge of the dynamic conditions into HI Autonomous Driving Systems (HI-ADS) within the 6G Visible Project. The findings demonstrate the potential of knowledge graphs (KGs) to enhance decision-making by integrating evolving data and ensuring adaptability to real-world driving conditions. Based on the design objectives of learning for evolvable KGs, concrete requirements for the HI-ADS KG are established.

关键词： Technological innovation Software architecture Smart cities Roads Decision making Knowledge graphs Ontologies Planning Autonomous vehicles Open data

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Machine learning Models for Predicting 30-Day Readmission of Elderly Patients Using Custom Target Encoding Approach 22nd

Machine Learning Models for Predicting 30-Day Readmission of...

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22nd Annual international conference on Computational Science (ICCS)

作者： Nazyrova, Nodira Chaussalet, Thierry J. Chahed, Salma Univ Westminster Sch Comp Sci & Engn London W1W 6UW England

ISBN: (纸本)9783031087578;9783031087561

The readmission rate is an important indicator of the hospital quality of care. With the upsetting increase in readmission rates worldwide, especially in geriatric patients, predicting unplanned readmissions becomes a very important task, that can help to improve the patient's well-being and reduce healthcare costs. With the aim of reducing hospital readmission, more attention is to be paid to home healthcare services, since home healthcare patients on average have more compromised health conditions. Machine learning and Artificial intelligence algorithms were used to develop predictive models using MIMIC-IV repository. Developed predictive models account for various patient details, including demographical, administrative, disease-related and prescription-related data. Categorical features were encoded with a novel customized target encoding approach to improve the model performance avoiding data leakage and overfitting. This new risk-score based target encoding approach demonstrated similar performance to existing target encoding and Bayesian encoding approaches, with reduced data leakage, when assessed using Gini-importance. Developed models demonstrated good discriminative performance, AUC 0.75, TPR 0.69 TNR 0.67 for the best model. These encouraging results, as well as an effective feature engineering approach, can be used in further studies to develop more reliable 30-day readmission predictive models.

关键词： 30-day readmission Home care patient readmission Machine learning model Categorical feature encoding Customized target encoding

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Adaptive Anomaly Detection and Congestion Avoidance for Large Scale 5G-IIoT: U-Plane Unsupervised Online learning in RIC

Adaptive Anomaly Detection and Congestion Avoidance for Larg...

引用

Consumer Communications and Networking conference, CCNC IEEE

作者： Yuxuan Shi Qianqian Pan Akihiro Nakao Graduate School of Engineering The University of Tokyo Tokyo Japan

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

The large-scale deployment of Internet of Things (IoT) devices and the increasing traffic volume bring congestion-related challenges to IoT systems. These challenges are particularly significant in large-scale Industrial IoT (IIoT) environments, where congestion caused by attacks or malfunctions threatens reliability. However, existing methods suffer from the following issues: 1) Control plane (C-Plane) key performance indicators (KPI)-based congestion avoidance methods cannot accurately classify devices based solely on C-Plane features. 2) Existing User Plane (U-Plane) empowered anomaly detection methods are mainly based on supervised learning and rely heavily on labeled datasets. To solve the problems, we propose an anomaly detection and congestion solution system based on U-Plane traffic data and Radio access network intelligent controller (RIC). First, we propose an unsupervised online learning-based anomaly traffic detection and congestion avoidance framework, where U-plane data is utilized to provide accurate classification. Second, we propose an anomaly device isolation mechanism from the main network by steering traffic using a near real-time RIC application (xAPP). Finally, we establish the simulation and hardware experimental platform to evaluate the effectiveness of our proposed system. In the experiment, the proposed method achieves a 94% classification accuracy, while the accuracy of the traditional method is 63%.

关键词： Accuracy Scalability Supervised learning Production Real-time systems Reliability Anomaly detection Industrial Internet of Things Radio access networks Overfitting

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