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8th International Conference on Speckle Metrology, Speckle 2023

作者： Lin, Angyi Chen, Rui Lin, Huixiang You, Jiangfeng Zhang, Fucai The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China School of Control and Computer Engineering North China Electric Power University Baoding071003 China

ISBN: (数字)9781510674592

ISBN: (纸本)9781510674585

Currently, the dominant encoding method for computer-generated holograms is phase-only holography. The Gerchberg-Saxton (GS) algorithm is a commonly used approach for hologram generation. However, it suffers from issues such as poor convergence and low efficiency. In this paper, we introduce an iterative hologram algorithm that addresses these challenges. Our proposed algorithm divides the target object into segments with a certain degree of data redundancy. It computes the hologram iteratively, giving extra weight to each iteration. We also incorporate overlap constraints to enhance hologram quality and accelerate convergence by leveraging data redundancy. What sets our algorithm apart from others is that it varies the order in which segments are processed during iterations, resulting in different final holograms. By randomly selecting the order in each iteration, we can rapidly generate multiple diverse holograms. Importantly, objects reconstructed from these holograms exhibit consistency, allowing for the quick generation of multiple phase-only holograms that help mitigate the speckle effect. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Holograms

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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Multi-Agent Coordination for a Partially Observable and Dynamic Robot Soccer Environment with Limited Communication 10

Multi-Agent Coordination for a Partially Observable and Dyna...

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10th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2023

作者： Affinita, Daniele Volpi, Flavio Spagnoli, Valerio Suriani, Vincenzo Nardi, Daniele Bloisi, Domenico D. Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Italy Faculty of Political Science and Sociopsychological Dynamics UNINT University Rome Italy

RoboCup represents an International testbed for advancing research in AI and robotics, focusing on a definite goal: developing a robot team that can win against the human world soccer champion team by the year 2050. To achieve this goal, autonomous humanoid robots' coordination is crucial. This paper explores novel solutions within the RoboCup Standard Platform League (SPL), where a reduction in WiFi communication is imperative, leading to the development of new coordination paradigms. The SPL has experienced a substantial decrease in network packet rate, compelling the need for advanced coordination architectures to maintain optimal team functionality in dynamic environments. Inspired by market-based task assignment, we introduce a novel distributed coordination system to orchestrate autonomous robots' actions efficiently in low communication scenarios. This approach has been tested with NAO robots during official RoboCup competitions and in the SimRobot simulator, demonstrating a notable reduction in task overlaps in limited communication settings. © 2024 Copyright for this paper by its authors.

关键词： Anthropomorphic robots

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Human Mobility, AI assistants, and urban emissions: an insidious triangle

Human Mobility, AI assistants, and urban emissions: an insid...

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2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Pappalardo, Luca Böhm, Matteo Cornacchia, Giuliano Mauro, Giovanni Pedreschi, Dino Nanni, Mirco Pisa Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Department of Computer Science University of Pisa Pisa Italy IMT Lucca Italy

Transportation remains a significant contributor to greenhouse gas emissions, with a substantial proportion originating from road transport and passenger travel in particular. Today, the relationship between transportation and urban emissions is even more complex, given the increasingly prevalent role and the pervasiveness of AI-based GPS navigation systems such as Google Maps and TomTom. While these services offer benefits to individual drivers, they can also exacerbate congestion and increase pollution if too many drivers are directed onto the same route. In this article, we provide two examples from our research group that explore the impact of vehicular transportation and mobility-AI-based applications on urban emissions. By conducting realistic simulations and studying the impact of GPS navigation systems on emissions, we provide insights into the potential for mitigating transportation emissions and developing policies that promote sustainable urban mobility. Our examples demonstrate how vehicle-generated emissions can be reduced and how studying the impact of GPS navigation systems on emissions can lead to unexpected findings. Overall, our analysis suggests that it is crucial to consider the impact of emerging technologies on transportation and emissions, and to develop strategies that promote sustainable mobility while ensuring the optimal use of these tools. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Climate change

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Microservice Deployment for Satellite Edge AI Inference via Deep Reinforcement Learning 35

Microservice Deployment for Satellite Edge AI Inference via ...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Yu, Zhiyong Cheng, Hei Victor Yang, Zhanpeng Liu, Xin Jiang, Yuning Zhou, Yong Shi, Yuanming ShanghaiTech University School of Information Science and Technology Shanghai201210 China Aarhus University Department of Electrical and Computer Engineering Denmark Automatic Control Laboratory Switzerland

ISBN: (纸本)9798350362244

Artificial intelligence (AI) is critical in evolving 5G and developing 6G networks, running on edge devices, and solving resource management challenges. The burgeoning number of edge devices draws attention to the potential of low-earth orbit (LEO) satellite networks with their onboard computing capabilities for edge inference. This paper explores LEO scenarios where multiple remote sensing edge AI inference tasks concurrently process data from a single source. However, due to there being parts with the same functions between different AI applications, traditional monolithic edge AI architecture must be deployed repeatedly and falls short in efficiently harnessing the heterogeneous resources of LEO satellite networks. To solve this problem, we utilize the microservice architecture to decouple a single AI application into several independent microservices to reuse these same functions. However, due to the high latency caused by multiple microservices' communication, we need to design a deployment strategy to fully utilize resources to reduce the service latency. We present a microservice deployment model to minimize the total service latency across all AI applications and meet resource constraints with the constraints of hardware, energy, and memory limitations. This latency optimization problem is rewritten as a Markov decision process (MDP) to effectively deal with the challenge posed by the time-varying transmission rate caused by satellite mobility. To increase the training data utilization, we employ a Proximal Policy Optimization (PPO) based reinforcement learning algorithm to meet the dynamic environment challenge. Finally, we obtain a sub-optimal solution with minimal accuracy loss and an acceptable solution time. © 2024 IEEE.

关键词： Markov processes

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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

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computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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An adaptive observer-based SOC estimation method for lithium-ion batteries against sensor measurement uncertainties 38

An adaptive observer-based SOC estimation method for lithium...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Xu, Xiaoyi Luo, Hao Chow, Mo-Yuen Huang, Cong-Sheng Jiang, Yuchen Harbin Institute of Technology Department of Control Science and Engineering Harbin China North Carolina State University Department of Electrical and Computer Engineering RaleighNC United States

ISBN: (纸本)9798350303636

Accurate estimation of battery state of charge (SOC) is critical for efficient and safe battery applications. The measurement uncertainties of sensors, including measurement noises and sensor bias will affect the estimation accuracy inevitably. Therefore, quantifying the relationship between the sensor measurement uncertainties and the SOC estimation errors and seeking better SOC estimation methods has been a hot topic of research. In this paper, model errors and SOC estimation errors under measurement noise and sensor bias are derived. The resulting analyses can be used to assess the robustness of the SOC estimates. In addition, an adaptive observer-based SOC estimation method for lithium-ion batteries is proposed to cope with the measurement uncertainty. Simulation experiments demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： adaptive observer lithium-ion battery sensor bias sensor noises State-of-Charge (SOC) estimation

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Enhanced cyber-physical system with semantic technologies and machine learning to support smart farming 24

Enhanced cyber-physical system with semantic technologies an...

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24th International Conference on control Systems and computer science, CSCS 2023

作者： Ciolofan, Sorin N. Dragoicea, Monica Popeanga, Daniel-Catalin Politehnica University of Bucharest Department of Computer Science Bucharest Romania Politehnica University of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9798350313390

An important aspect related to the effects of agricultural activities on the environment is represented by the nutrient loss in water and air (specifically nitrogen). The interactions between catchments hydrological processes, management of farm activities, climate changes and nitrogen losses constitute a complex phenomenon yet not well understood, being an important concern from the sustainable agriculture perspective. Nitrogen can be lost with water as leaching or runoff, or as gas as ammonia volatilization. Nitrous oxide (N2O) is particularly problematic because it is also a powerful greenhouse gas. The goal of the current article is to present innovative digital techniques to advance in understanding of this phenomena through an Information System that integrates Artificial Intelligence techniques such as Semantic Technologies and Machine Learning (ML) into Cyber-Physical Systems (CPS) to support smart farming and sustainable agriculture. © 2023 IEEE.

关键词： Semantics

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Behavioural Swarm Optimisation for Stable Slung-Load Aerial Transportation

Behavioural Swarm Optimisation for Stable Slung-Load Aerial ...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Chen, Jingyu Oyekan, John The University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom University of York Department of Computer Science York United Kingdom

ISBN: (纸本)9798350314588

Collaboration of agents in a natural swarm enables the accomplishment of tasks that would be difficult or impossible for a single agent to complete alone. For example, a swarm of autonomous Unmanned Aerial Vehicles (UAVs) enables the collaborative sensing of bulky loads for transportation over impassable terrains when the load weighs several times more than each UAV. In this work, we propose a hierarchical algorithmic architecture that supports the search and coverage of various unknown payload profiles for subsequent transportation. The grasping formation of UAVs over the payload emerges from the synthetic behaviours in the architecture without any path planning. Experiments show that our proposed design can be successfully applied in searching and coverage of various loads and has been validated in the real world through the use of Crazyflie micro-UAVs. Furthermore, the proposed grasping formation satisfies static equilibrium thereby reducing orientation changes in the load-swarm system during transportation. © 2023 IEEE.

关键词： Motion planning

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Hamiltonian learning using machine-learning models trained with continuous measurements

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Physical Review Applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

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