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2024 International conference on Intelligent Robots and systems

作者： Ranne, Alex Kuan, Liming Velikova, Yordanka Navab, Nassir Rodriguez Y Baena, Ferdinando Imperial Coll London Hamlyn Ctr Robot Surg London SW7 2AZ England Tech Univ Munich Chair Comp Aided Med Procedures & Augmented Real Garching Germany Munich Ctr Machine Learning Munich Germany

ISBN: (纸本)9798350377712;9798350377705

In minimally invasive endovascular procedures, contrast-enhanced angiography remains the most robust imaging technique, but exposes patients and surgeons to prolonged radiation. Alternatives such as ultrasound are difficult to interpret, are highly prone to artifacts and noise, and vary in quality, depending on the experience of the interventional radiologist and machine settings. In this work, we seek to address both problems by introducing a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images, without demanding any labeled data. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism, and is capable of learning feature changes across time and space. To facilitate training, we used synthetic ultrasound data based on physics-driven catheter insertion simulations, and translated the data into a unique CT-Ultrasound common domain, CACTUSS, to improve the segmentation performance. We generated ground truth segmentation masks by computing the optical flow between adjacent frames using FlowNet2, and performed thresholding to obtain a binary mask estimate. Finally, we validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms, thus demonstrating its potential for applications to clinical data in the future.

关键词： Catheters

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Adaptive data-driven Modeling and Tracking control of Autonomous Vehicles using Deep Bilinear Koopman Method 8

Adaptive Data-Driven Modeling and Tracking Control of Autono...

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8th CAA International conference on Vehicular control and Intelligence, CVCI 2024

作者： Yi, Peng Liao, Junyu Zhao, Di Bai, Wenyan Tongji University Faculty of Electronic and Information Engineering Shanghai China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (纸本)9798331504892

This study addresses the problem of data-driven modeling and tracking control for autonomous vehicles with unknown parameters. We use Koopman theory and deep neural networks to approximate vehicle dynamics, learning a finitedimensional bilinear Koopman operator from offline data. An adaptive method is proposed to continuously adjust the Koopman model based on online deviations. The bilinear model is then used to design a model predictive controller for trajectory tracking. Numerical simulations validate the method's effectiveness in adapting to uncertainties and tracking predefined trajectories. © 2024 ieee.

关键词： Predictive control systems

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Cooperative adaptive optimal output regulation of multi-agent systems with an application to connected and autonomous vehicles 7

Cooperative adaptive optimal output regulation of multi-agen...

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ieee 7th International conference on Industrial Cyber-Physical systems (ICPS)

作者： Dong, Yuchen Gao, Weinan Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China

ISBN: (纸本)9798350363029;9798350363012

This paper proposes a novel control approach based on gradient descent methods to solve the problem of cooperative adaptive optimal output regulation of continuous-time linear multi-agent systems. This proposed approach calculates gradients through online data rather than model information, such that a data-driven distributed adaptive controller is developed by adaptive dynamic programming, which ensures that each follower can achieve asymptotic tracking and disturbance rejection. Finally, the effectiveness of the proposed control algorithm is verified by simulation of connected and autonomous vehicles.

关键词： Cooperative output regulation adaptive dynamic programming gradient descent method connected and autonomous vehicles

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Transfer learning for Transient Stability Predictions in Modern Power systems Under Enduring Topological Changes

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ieee TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第3期21卷 3274-3288页

作者： Hijazi, Maeshal Dehghanian, Payman Wang, Shiyuan George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA Quanta Technol LLC Raleigh NC 27607 USA

The topological configuration of a bulk power grid is often altered by network investment upgrades, forecasted disasters and random faults, as well as planned operator-triggered transmission line maintenance and controlled switching actions. Such topological variations can drastically change the measurement data distribution from phasor measurement units (PMUs), which may in turn compromise the accuracy of the artificial intelligence (AI)-aided monitoring and control applications using the measurements. For instance, data-driven transient stability assessment (TSA) models that were trained with static network topologies may no longer be accurate for monitoring power grid stability as the network topology changes. Not only would the number of possible topology changes be too vast to train all possible scenarios, but also the training process will render computationally intensive. This paper proposes a model-based transfer learning (TL) approach that integrates a convolutional neural network and a long short-term memory network (ConvLSTM), to efficiently train a new stability prediction model that predicts the system operating states (SOSs) and identify critical generators (CGs) in case of instability when the system undergoes enduring topological changes. Numerical analyses on three test cases including the ieee 39-bus test system, the ieee 118-bus test system, and the large-scale 2000-bus synthetic power grid in the state of Texas verify the efficiency of the proposed approach and highlight benefits in training time and accuracy, when compared to the state-of-the-art *** to Practitioners-As the national power grid goes through transitions towards digitalization and modernization with emerging technologies, maintaining its reliability and resiliency against environmental stressors and cyber attacks remains an urgent need. The power system topology is expected to change more frequently, sometimes to accommodate the proliferation of heterogeneous distribu

关键词： Power system stability Transient analysis Numerical stability Stability criteria Training Topology Network topology Convolutional neural network (CNN) long short-term memory (LSTM) topology change transfer learning (TL) transient stability prediction

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data Augmentation for Supervised Code Translation learning 21

Data Augmentation for Supervised Code Translation Learning

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ieee/ACM 21st International conference on Mining Software Repositories (MSR)

作者： Chen, Binger Golebiowski, Jacek Abedjan, Ziawasch TU Berlin Berlin Germany Amazon AWS Berlin Germany Leibniz Univ Hannover Hannover Germany L3S Res Ctr Hannover Germany

ISBN: (纸本)9798350363982;9798400705878

data-driven program translation has been recently the focus of several lines of research. A common and robust strategy is supervised learning. However, there is typically a lack of parallel training data, i.e., pairs of code snippets in the source and target language. While many data augmentation techniques exist in the domain of natural language processing, they cannot be easily adapted to tackle code translation due to the unique restrictions of programming languages. In this paper, we develop a novel rule-based augmentation approach tailored for code translation data, and a novel retrieval-based approach that combines code samples from unorganized big code repositories to obtain new training data. Both approaches are language-independent. We perform an extensive empirical evaluation on existing Java-C#-benchmarks showing that our method improves the accuracy of state-of-the-art supervised translation techniques by up to 35%.

关键词： Natural language processing systems

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Compensation of Pressure Dependent Disturbance: Poppet Position control in a Pneumatic Valve

Compensation of Pressure Dependent Disturbance: Poppet Posit...

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ieee/ASME International conference on Advanced Intelligent Mechatronics (AIM)

作者： Hattori, Koki Ohnishi, Wataru Koseki, Takafumi Univ Tokyo 7-3-1 HongoBunkyo Ku Tokyo 1138656 Japan

ISBN: (纸本)9798350355376;9798350355369

Pneumatic valves are key components for controlling mass flow rates in general industrial applications. However, they have several nonlinearities such as dead zone and airflow force, making precise control of mass flow rates challenging. Since the poppet position mostly determines the mass flow rate of a valve, this study employs a new valve with an internal position sensor. The authors propose a data-driven feedforward control method to precisely control the poppet position at arbitrary pressure differences by estimating air disturbance force including airflow force. The developed approach compensates for the air disturbance force to the poppet position and enables fast movement without overshooting. The performance improvement is experimentally validated in the poppet position tracking experiments.

关键词： Pneumatic valve Mass flow rate control Poppet position control Disturbance estimation Iterative learning control

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AI-driven Integration of Sensing and Communication in the 6G Era

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ieee NETWORK 2024年第3期38卷 210-217页

作者： Liu, Xiangnan Zhang, Haijun Sun, Kai Long, Keping Karagiannidis, George K. Univ Sci & Technol Beijing Beijing Engn & Technol Res Ctr Convergence Network Beijing 100083 Peoples R China Inner Mongolia Univ Coll Elect Informat Engn Hohhot 020021 Peoples R China Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece Lebanese Amer Univ LAU Artificial Intelligence & Cyber Syst Res Ctr Beirut 11022801 Lebanon

Attributing to the rapid growth of AI, the integration of sensing and communication (ISAC) networks has embraced AI in the upcoming new-style mobile communication networks. A FedFog network architecture for ISAC networks is proposed in this article, which consists of the terminal perception layer, the edge base station processing layer, and the cloud data layer. In the context of multiple base stations (BSs), the handover between BSs and user equipment is worthy to be studied. Referring to the concept of coordinated multiples BSs, we design a handover procedures in the ISAC networks. Meanwhile, a federated reinforcement learning scheme of user control is designed. However, due to new unlicensed spectrum bands such as millimeter wave band and Terahertz band, the hybrid beamforming can reduce the expenses of hardware. A learning-based interference management utilizing the hybrid beamforming is designed. Meanwhile, we consider self-interference and mutual interference cancellation with deep neural networks. Simulation results show the performance of AI-driven ISAC networks in terms of mobility and interference management, and further prove that services are boosted for 6G networks.

关键词： Sensors Handover 6G mobile communication Network architecture Federated learning Base stations Array signal processing Artificial intelligence Communication systems

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Enhancing Sample Efficiency and Uncertainty Compensation in learning-based Model Predictive control for Aerial Robots

Enhancing Sample Efficiency and Uncertainty Compensation in ...

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ieee/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Chee, Kong Yao Silva, Thales C. Hsieh, M. Ani Pappas, George J. Univ Penn GRASP Lab Philadelphia PA 19104 USA

ISBN: (纸本)9781665491907

The recent increase in data availability and reliability has led to a surge in the development of learning-based model predictive control (MPC) frameworks for robot systems. Despite attaining substantial performance improvements over their non-learning counterparts, many of these frameworks rely on an offline learning procedure to synthesize a dynamics model. This implies that uncertainties encountered by the robot during deployment are not accounted for in the learning process. On the other hand, learning-based MPC methods that learn dynamics models online are computationally expensive and often require a significant amount of data. To alleviate these shortcomings, we propose a novel learning-enhanced MPC framework that incorporates components from L-1 adaptive control into learning-based MPC. This integration enables the accurate compensation of both matched and unmatched uncertainties in a sample-efficient way, enhancing the control performance during deployment. In our proposed framework, we present two variants and apply them to the control of a quadrotor system. Through simulations and physical experiments, we demonstrate that the proposed framework not only allows the synthesis of an accurate dynamics model on-the-fly, but also significantly improves the closed-loop control performance under a wide range of spatio-temporal uncertainties.

关键词： Model predictive control

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EMG-Based Intention Detection Using Deep learning for Shared control in Upper-Limb Assistive Exoskeletons

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ieee ROBOTICS AND AUTOMATION LETTERS 2024年第1期9卷 41-48页

作者： Sedighi, Paniz Li, Xingyu Tavakoli, Mahdi Univ Alberta Elect & Comp Engn Dept Edmonton AB T6G 1HQ Canada

In the field of human-robot interaction, surface electromyography (sEMG) provides a valuable tool for measuring active muscular effort. While numerous studies have investigated real-time control of upper extremity exoskeletons based on user intention and task-specific movements, the prediction of body joint positions based on EMG features has remained largely unexplored. In this letter, we address this gap by proposing a novel approach that leverages Convolutional Neural Networks and Long-Short-Term Memory (CNN-LSTM) models to generate exoskeleton joint trajectories. Our methodology involves collecting data from three channels of EMG and three degrees-of-freedom (DoF) joint angles and enables us to position control a pneumatic cable-driven upper-limb exoskeleton, thereby assisting users in various tasks. Through extensive experimentation, our intention-based model demonstrates robust performance across different speeds and is capable of detecting variations in payload and electrode placement. The empirical results yielded from our study underscore the efficacy of our approach, particularly in reducing the EMG levels of the user during different tasks by providing exoskeleton assistance as needed.

关键词： Electromyography Exoskeletons data models Robots Real-time systems Predictive models Load modeling CNN-LSTM EMG human-robot interaction proportional myoelectric control soft exoskeleton

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A Lightweight Meta-learning for Few-Shot Fault Diagnosis of control Valve

A Lightweight Meta-Learning for Few-Shot Fault Diagnosis of ...

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2024 International conference on Intelligent Robotics and Automatic control

作者： Fan, Ruipeng Nan, Jing Dai, Wei China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9798350389814;9798350389807

Due to the distribution of control valves across different control loops within industrial systems, posing a significant challenge in collecting adequate fault data that covers various working conditions. Furthermore, industrial equipment often faces resource constraints when deploying data-driven models. To this end, a lightweight meta-learning fault diagnosis method is proposed to address the problem of few-shot fault diagnosis under varying operating conditions. First, to effectively address the unsteady time-varying fault signals, the two-dimensional (2-D) cloud model (CM) is employed to reconstruct the fault signals, thereby characterizing the states and features of the fault signals more comprehensively. Subsequently, a lightweight meta-learning model is constructed, the initial network parameters are randomly generated, and the parameters are updated by gradient descent. Finally, the parameters of the meta-learner are improved on the new task, and a few-shot data can be used to achieve rapid adaptation to new tasks. The experimental results show that the proposed method has significant advantages compared with other traditional meta-learning methods.

关键词： Few-shot fault diagnosis various operating conditions cloud model meta-learning control valve

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