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33rd ieee Intelligent Vehicles symposium (ieee IV)

作者： Faris, Muhammad Falcone, Paolo Sjoberg, Jonas Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden Univ Modena & Reggio Emilia DIEF Modena Italy

ISBN: (纸本)9781665488211

This paper considers a coordination problem for Connected and Automated Vehicles (CAVs) in mixed traffic at unsignalized intersections. In such a setting, the behavior of the Human-Driven Vehicles (HDVs) is difficult to predict, thus challenging the formulation and the solution of the coordination problem. To solve this problem, we propose a coordination strategy, where CAVs are used as both sensors and actuators in mixed platoons. A timeslot-based approach is used to coordinate the occupancy of the intersection and to compensate for the HDVs behavior. The proposed approach has a bi-level optimization structure built upon the Model Predictive control (MPC) framework that decides the crossing order and computes the vehicles' commands. In simulations, we show that the choice of the HDV prediction model heavily affects the coordination by evaluating the performance of two different HDV models: car-following and constant velocity, where the latter demonstrates more consistent results in the presence of deviation of the HDVs' behavior from a nominal model.

关键词： Uncertainty Intelligent vehicles computational modeling Optimal control Predictive models Behavioral sciences Sensors

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Cloud Based Smart Medical Recommendation System Using Pattern Recognition 1

Cloud Based Smart Medical Recommendation System Using Patter...

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1st ieee International Conference on Networking and Communications, ICNWC 2023

作者： Sujatha, E. Shanthasheela, S. Gopirajan, P.V. Department of Computer Science and Engineering Thandalam Chennai602105 India Velammal Institute of Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai603203 India

ISBN: (纸本)9798350336009

To build smart medical recommendation system the data needs to be collected from laboratories. The system is intended to provide services as control and manage data samples, industry standards, real test results, reports, human resources in the laboratories, machines, tiny instruments, and automation of the workflow. Combining the data from the laboratories and medical institutions enable ease of communication without time delay. The challenging task is that identifying appropriate hardware and software components which provides compatibility in collecting data and their processing steps, repository storage, secured distribution, and the policies used for information representation in the results. In order to automate the entire process of medical recommendation system the metric parameters are used for determining the efficiency and productivity of the lab to enhance the quality of control systems. The vulnerability lies in maintaining traditional way of records in paper and error in the data. The purpose of this project is to develop the process of daily tasks will be computed through the database using pattern recognition techniques. © 2023 ieee.

关键词： Recommender systems

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Long and Short-Term Memory Mechanism Hybrid Model for Speed Trajectory Prediction of Heavy Haul Trains*

Long and Short-Term Memory Mechanism Hybrid Model for Speed ...

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ieee symposium Series on computational intelligence (SSCI)

作者： Kexuan Xu Qiang Liu State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

The trajectory prediction of heavy heavy trains (HHTs) is crucial for ensuring the safe and automatic operation. It is inevitable to design a train model for predicting train operation trajectory of the HHTs. However, large capacity and the existence of unmodeled dynamics in the operation process caused by air resistance, working condition switching, and external environment make it difficult to establish accurate speed trajectory prediction models (STPMs) using traditional mechanism-driven methods. Most recent research considers using data-driven model to learn information from data, but they have no information about the physical characteristic of the STMP model. This makes it difficult to accurately describe the relationship between the control force and the running speed during the train operation. To overcome these issues, this study combines the mechanism-driven model with the deep learning model to establish a new long and short-term memory mechanism hybrid (LSTMMH) model. Specifically, the mechanism model describes the change of control force, while the LSTM captures the unmodeled dynamics of long time series of running process. The effectiveness of the proposed method is demonstrated while the performance is compared with the traditional LSTM and mechanism models using the real data.

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Joint Optimization of Compression, Transmission and Computation for Cooperative Perception Aided Intelligent Vehicular Networks

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ieee Transactions on Vehicular Technology 2025年第5期74卷 8201-8214页

作者： Lu, Binbin Huang, Xumin Wu, Yuan Qian, Liping Zhou, Sheng Niyato, Dusit University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 999078 China Guangdong University of Technology School of Automation Guangzhou510006 China University of Macau State Key Lab of Internet of Things for Smart City 999078 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science 999078 China Zhuhai UM Science and Technology AQ2 Research Institute Zhuhai519301 China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Nanyang Technological University School of Computer Science and Engineering 639815 Singapore

Cooperative perception is a promising paradigm to tackle the perception limitations of a single intelligent vehicle (IV) to enhance the driving safety and efficiency in intelligent vehicular networks. However, the real-time transmission and computation-intensive fusion of raw sensing data raise new challenges for satisfying the stringent delay requirement of delay-sensitive applications. In this paper, we formulate a joint optimization problem of the cooperative IV selection, compression ratio selection, transmit power control, task offloading decision and computation allocation to minimize the end-to-end delay consisting of compression, transmission and computation. In particular, to meet the perception probability requirement, the quality and quantity-aware matching algorithm is proposed to optimize the cooperative IV selection. To guarantee the queue stability of offloading tasks, we develop the Lyapunov optimization algorithm to determine the upper bound of the offloading tasks and the corresponding optimal computation allocation. The Lyapunov aided deep reinforcement learning algorithm is further proposed to dynamically adjust the task offloading decision and compression ratio selection to minimize the end-to-end delay with the transmit power control being optimized by the successive convex approximation algorithm. Simulation results demonstrate that, compared to several benchmark algorithms, our proposed algorithm achieves the lowest end-to-end delay while guaranteeing the requirements on perception probability and queue stability effectively. © 1967-2012 ieee.

关键词： Deep reinforcement learning

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Automatic System for Learning the Process of Solving Rubik's Cube

Automatic System for Learning the Process of Solving Rubik's...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Lucian Șoșdean Loredana Stanciu Adriana Albu Automation and Applied Informatics Department Politehnica University Timisoara Timisoara Romania

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

The Rubik's cube is a very successful puzzle toy, having many methods to solve. Still, the process of learning to solve the cube is not simple and it involves a lot of attention and patience, so a beginner may give up very quickly. The paper presents an automatic solution to solve a real Rubik's cube based on an Arduino MEGA 2560 R3 development board and six stepper motors, one for each face of the cube. The algorithms that control the motors were implemented in Arduino IDE and the sequential algorithm to solve the puzzle was implemented in C#. The user interface allows the user to learn how to solve the cube and, also, to manually apply what they learned. The solution can be seen virtually on the interface and also on a real cube. The purpose of the system is to ease the learning process and not to obtain the fastest solution.

关键词： Visualization Toy manufacturing industry Software algorithms Process control User interfaces Motors Software

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Using Behavior Trees for Coordination of Skills in Modular Reconfigurable CPPMs 27

Using Behavior Trees for Coordination of Skills in Modular R...

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ieee 27th International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Sidorenko, Aleksandr Hermann, Jesko Ruskowski, Martin SmartFactory KL eV Technol Initiat Kaiserslautern Germany German Res Ctr Artificial Intelligence Innovat Factory Syst Kaiserslautern Germany

ISBN: (数字)9781665499965

ISBN: (纸本)9781665499965

A skill-based engineering approach uses the concept of skill to abstract machine-specific functionality with a generic interface and common behavior. A skill is treated as a "control level service" and is used to build service-oriented architectures. Though the question of robust and flexible coordination of skills is still open. Current approaches to skills' composition result in tightly-coupled control structures. To allow frequent and rapid system's reconfiguration a framework to flexibly build complex behaviors from reusable components is needed. Behavior Trees (BTs) have proven to be a powerful tool for specifying characters' behaviors in the gaming industry and recently is getting a lot of attention in the robotics community as well as academia. An approach of using BTs for coordination of industrial skills is proposed in this paper. Combining behavior trees with the existing skill models enhances separation of concerns and modularity of control systems in the factory automation domain. Firstly, we define the requirements for a skills coordination layer and discuss some key properties of BTs that allow to satisfy these requirements. Secondly, we introduce a skill model, which utilizes BTs and has several advantages over the current skill models. Thirdly, we introduce a concept of distributed BTs to effectively distribute functionality over different computational platforms. Finally, we present a small proof of concept example to test feasibility of the proposed approach.

关键词： behavior trees skill-based engineering reactive coordination of skills

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Meat Factory Cell: Assisting meat processors address sustainability in meat production 21

Meat Factory Cell: Assisting meat processors address sustain...

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21st ieee International symposium on computational intelligence and Informatics

作者： Mason, A. Korostynska, O. Cordova-Lopez, L. E. Esper, I Romanov, D. Ross, S. Takacs, K. Haidegger, T. Norwegian Univ Life Sci NMBU N-1430 As Norway Animalia AS N-0585 Oslo Norway OsloMet Univ N-0301 Oslo Norway Obuda Univ EKIK Budapest Hungary

ISBN: (纸本)9781665426848

This paper provides a brief overview of the novel Meat Factory Cell and discusses its concept in the context of increasing sustainability in the meat sector. Job quality, environment, health risks, industrial development and education are discussed as sustainability goals that can be mapped against some of the United Nations Sustainable Development Goals (SDG). Technology can arguably help to improve related processes on a societal level, and to achieve the SDGs.

关键词： automation meat pork robotic butchering meat factory cell sustainability

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Comparison of Sensor Data Fusion Algorithms for Automotive Perception System 20

Comparison of Sensor Data Fusion Algorithms for Automotive P...

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20th ieee Jubilee World symposium on Applied Machine intelligence and Informatics, SAMI 2022

作者： Lindenmaier, Laszlo Aradi, Szilard Becsi, Tamas Toro, Oliver Budapest University of Technology and Economics Department of Control for Transportation and Vehicle Systems Budapest Hungary

ISBN: (纸本)9781665497046

Reliable environment perception has an increased role in the spread of advanced driver assistance and autonomous systems. However, at present, neither automotive sensor can reliably meet the requirements for environmental sensing. The goal of sensor fusion is to construct a more accurate and reliable environment model using the available sensor data than either sensor is applied alone. The architecture of multi-object tracking algorithms is suitable for tracking and fusing object detections from multiple sensors. However, the parameters of widely used object-tracking algorithms such as PDA and JPDA can significantly affect their computational capacity demand and the reliability of the fused environment model. The present study aims to present the parameter sensitivity of these algorithms and their effects. © 2022 ieee.

关键词： Sensitivity Runtime computational modeling Object detection Sensor fusion Real-time systems Sensors

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Multimodel Predictive control for Wastewater Treatment Process with Multirate Sampling Intervals 13

Multimodel Predictive Control for Wastewater Treatment Proce...

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13th ieee International Conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2023

作者： Fu, Shijia Sun, Haoyuan Liu, Zheng Han, Honggui Zhang, Yan Engineering Research Center of Digital Community Ministry of Education Beijing University of Technology Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China China Academy of Information and Communications Technology Beijing100124 China

ISBN: (纸本)9798350315196

Influenced by the biochemical reactions and sensor characteristics, the wastewater treatment process (WWTP) has been regarded as a multirate sampling system. To deal with the asynchronous control problem caused by multirate sampling, a multimodel predictive control (MMPC) scheme is proposed to improve the operation performance of WWTPs. First, a multimodel predictive control structure is constructed such that the controlled variables with multirate sampling intervals of WWTPs are able to be tracked synchronously at each sampling instant. Second, the fuzzy neural networks (FNNs) are adopted as the carrier for multimodel to compute the synchronous prediction outputs of all controlled variables at each sampling instant. Moreover, an adaptive cost function is excogitated to obtain the optimal control action in which the adaptive parameter is to minimize the impact of forecast errors on the tracking effect. Ultimately, the experiment results on the benchmark simulation model 1 (BSM1) have verified the efficiency of the proposed MMPC. © 2023 ieee.

关键词： Fuzzy neural networks

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Reinforcement Learning-Based Adaptive Search Strategy Combination Algorithm 22

Reinforcement Learning-Based Adaptive Search Strategy Combin...

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22nd ieee International symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Liu, Xiaotong Zeng, Zhiqiang Wan, Sicheng Tian, Jiajie Wang, Tianlei Hong, Zhiyong Wuyi University School of Mechanical and Automation Engineering Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China Wuyi University School of Electronic and Information Engineering Jiangmen China Guangdong Kejie Technology Co. Ltd. Jiangmen China

ISBN: (纸本)9798331509712

Integrating multiple search operators to leverage the characteristics of different search operators is one of the common methods to enhance the performance of evolutionary algorithms. Most of these combination algorithms adopt a fixed framework, which makes it difficult to adaptively select the appropriate search operator during the algorithm iteration process, resulting in poor performance on certain problems. To overcome the aforementioned shortcomings of combination algorithms, an adaptive search strategy combination algorithm based on reinforcement learning and neighborhood search (RLASCA) is proposed. During the algorithm iteration process, a reinforcement learning-based adaptive search operator selection method (RLAS) is designed, enabling the algorithm to adaptively select the appropriate search operator based on the state of the individual. Furthermore, to address the issue of premature convergence, a neighborhood search strategy based on differential evolution (NSDE) is designed to increase the diversity of the population. To verify the effectiveness of the proposed algorithm, a comprehensive testing was conducted using the CEC2017 test suite and two constrained engineering design problems. The algorithm was compared with four basic algorithms that compose RLASCA and four classical combination algorithms. The experimental results indicate that RLASCA outperforms the other eight algorithms in terms of convergence speed and accuracy. © 2024 ieee.

关键词： Optimization algorithms

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