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6th Annual learning for Dynamics and control conference

作者： Shang, Xu Zheng, Yang Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

The Willems' fundamental lemma, which characterizes linear time-invariant (LTI) systems using input and output trajectories, has found many successful applications. Combining this with receding horizon control leads to a popular data-EnablEd Predictive control (DeePC) scheme. DeePC is first established for LTI systems and has been extended and applied for practical systems beyond LTI settings. However, the relationship between different DeePC variants, involving regularization and dimension reduction, remains unclear. In this paper, we first discuss a bi-level optimization formulation that combines a data pre-processing step as an inner problem (system identification) and predictive control as an outer problem (online control). We next introduce a series of convex approximations by relaxing some hard constraints in the bi-level optimization as suitable regularization terms, accounting for an implicit identification. These include some existing DeePC variants as well as two new variants, for which we establish their equivalence under appropriate settings. Notably, our analysis reveals a novel variant, called DeePC-SVD-Iter, which has remarkable empirical performance of direct methods on systems beyond deterministic LTI settings.

关键词： data-driven control Bi-level optimization Convex approximation

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Rebalancing Autonomous Electric Vehicles for Mobility-on-Demand by data-driven Model Predictive control 26

Rebalancing Autonomous Electric Vehicles for Mobility-on-Dem...

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ieee 26th International conference on Intelligent Transportation systems (ITSC)

作者： Ali, Muhammad Sajid Tangirala, Nagacharan Teja Knoll, Alois Eckhoff, David Bitwatt Syst GmbH Dusseldorf Germany Tech Univ Munich Chair Robot Artificial Intelligence & Realtime Sy D-85748 Garching Germany TUMCREATE Singapore 138602 Singapore

ISBN: (纸本)9798350399462

The main goal of this paper is to present an end-to-end, data-driven framework for the control of Autonomous Electric Vehicles (AEV) for Mobility-on-Demand (MoD). We present a data-driven Model Predictive control (MPC) algorithm that rebalances (i.e. preemptively repositions) the AEV fleet in order to meet the mobility demand in the near future. The algorithm consists of Mixed Integer Linear Programming (MILP) that leverages the short-term forecast of the mobility demand as well as the charging station availability in order to optimally rebalance the AEV fleet. The proposed algorithm is evaluated by means of simulations with the New York City (NYC) taxi data. The proposed algorithm outperforms other state-of-the-art rebalancing strategies by reducing the mean customer wait time by 82.3% and the number of rejected requests by 94.6% for a given fleet size and a number of charging stations.

关键词： Model predictive control

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SNN-LIF Model for Glaucoma Classification 6

SNN-LIF Model for Glaucoma Classification

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6th International conference on AI Circuits and systems (AICAS)

作者： Chandra, Harshiv Ghosh, Akash Singh, Rahul Srinivas, M. B. Reckitt Dubai U Arab Emirates IIT Roorkee Roorkee Uttar Pradesh India BITS Pilani Dubai Campus EEE Dept Dubai U Arab Emirates BITS Pilani Hyderabad Campus EEE Dept Hyderabad India

ISBN: (纸本)9798350383638;9798350383645

Glaucoma is a debilitating disease, that affects tens of millions of people worldwide. It causes optic nerve damage, that leads to a loss of vision. This makes its detection pertinent at an earlier stage. Several machine learning and deep learning approaches have been proposed in the past to classify the retinal fundus images into healthy and diseased. Traditional artificial neural networks (ANNs), which are used to classify images, have been known to be computationally expensive. On the other hand, spiking neural networks (SNNs) are computationally efficient given the binary spike-driven nature. However, datasets for SNN training are hard to find given the event- based data that SNN requires. Here we propose a novel hybrid ANN-SNN based glaucoma classifier, with multiple SNN-LIF layers in between the ANN layers. This does not require encoding the input pixel data into spikes which may be a bottleneck for several datasets. The proposed model has been trained and tested on the G1020 dataset, and achieves an accuracy score of 60%, recall score of 61%, precision score of 59%, and F1 score of 59.8%.

关键词： Spiking Neural Networks LIF model Glaucoma Fundus Classification Surrogate learning

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Continuum Robotic Catheter systems for Transcatheter Mitral Valve Procedures: A Technical Review

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ieee ACCESS 2025年 13卷 43275-43288页

作者： Ding, Di Yao, Tianliang Wang, Haoyu Sun, Xusen Luo, Rong Fudan Univ Qingpu Branch Dept Cardiol Zhongshan Hosp Shanghai 200433 Peoples R China Tongji Univ Coll Elect & Informat Engn Dept Control Sci & Engn Shanghai 200092 Peoples R China Chinese Univ Hong Kong Fac Engn Dept Elect Engn Hong Kong Peoples R China Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China

Transcatheter mitral valve procedures have transformed the treatment of mitral regurgitation and stenosis by providing less invasive alternatives to conventional open-heart surgery. However, they introduce stringent requirements for catheter navigation, force modulation, and real-time imaging in a highly dynamic cardiac environment. In this article, a comprehensive technical overview of continuum robotic catheter systems developed specifically for transcatheter mitral valve interventions is presented. Fundamental design principles of flexible, tendon-driven architectures are examined, highlighting their capacity to navigate tortuous vascular pathways and offer multi-degree-of-freedom control. The integration of advanced sensing technologies, real-time imaging methods, and intelligent control strategies is discussed. Clinical studies and in vivo validations are reviewed, underscoring critical performance metrics such as positional accuracy, procedural safety, and device miniaturization. Persistent challenges are also addressed, including limited high-fidelity data for machine learning, a lack of robust haptic feedback in delicate cardiac tissue manipulation, and regulatory hurdles for complex robotic platforms. Furthermore, emerging innovations in materials science, three-dimensional printing, and sensor fusion are explored, illustrating the potential for next-generation systems that enhance precision while reducing operator workload. Finally, key opportunities for future research are outlined, with an emphasis on personalized navigation algorithms, standardized evaluation protocols, and broader applicability in cardiovascular and endovascular procedures.

关键词： Robots Catheters Heart valves Navigation Valves Robot sensing systems Accuracy Real-time systems Reviews Force Continuum robots surgical robotics catheter navigation transcatheter mitral valve procedure medical robotics minimally invasive surgery

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IAQ Monitoring System Optimizing data-driven Sensor Placement 20

IAQ Monitoring System Optimizing Data-Driven Sensor Placemen...

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20th Annual International conference on Distributed Computing in Smart Sensor systems and the Internet of Things (DCOSS-IoT)

作者： Filios, Gabriel Nikoletseas, Sotiris Stivaros, Ioannis Univ Patras Dept Comp Engn & Informat Patras Greece Comp Technol Inst & Press Diophantus Patras Greece Univ Patras Dept Elect & Comp Engn Patras Greece

ISBN: (纸本)9798350369458;9798350369441

Indoor Air Quality (IAQ) significantly impacts people's health and comfort in buildings. Although IAQ research spans two decades, a comprehensive assessment of factors affecting indoor air pollution remains elusive. Recent efforts focus on real-time monitoring using virtual sensing, a computational technique in engineering and data science. This paper presents a novel IAQ monitoring system emphasizing dynamic sensor placement for enhanced efficiency. The system employs random sensor positions and calculates measurement predictability, allowing identification and removal of less useful sensors, reducing data volume, and saving energy. Multiple reduction strategies are available, depending on the target number of edge devices or the desired maximum prediction error. Importantly, the system operates locally, without relying on internet connectivity. It consists of edge devices using air quality sensors, a gateway for data gathering and algorithm initiation, by training and evaluating multiple different machine learning techniques to determine point combination predictability. Deployed in two indoor settings, one with HVAC and the other naturally ventilated, the system's effectiveness is assessed, shortcomings identified, and conclusions drawn for future work.

关键词： data driven Sensor Placement Indoor Air Quality Monitoring System Machine learning Virtual Sensing Sensor Reduction

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SCADA data-driven Spatio-Temporal Graph Convolutional Neural Network for Wind Turbine Fault Diagnosis

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ieee TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Ma, Jiachen Fu, Yang Cheng, Tianle He, Deqiang Cao, Hongrui Yu, Bin Guangxi Univ Sch Mech Engn Guangxi Key Lab Mfg Syst & Adv Mfg Technol Nanning 530004 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Beijing Goldwind Sci & Wingpower Equipment Co Ltd Beijing 100176 Peoples R China

Supervisory control and data acquisition (SCADA) systems collect vast amounts of multi-sensor monitoring data, which is widely used in the intelligent fault diagnosis of wind turbines with the fast development of deep learning technologies. However, the complex structure of wind turbines and their time-varying operating conditions result in intricate spatio-temporal correlations within SCADA data, presenting significant challenges for feature extraction and accurate fault diagnosis. Current spatio-temporal fusion methods often treat SCADA data as Euclidean data, limiting their ability to capture the complex spatio-temporal coupling characteristics, which leads to reduced diagnostic accuracy. To solve abovementioned problems, a novel deep learning-based spatio-temporal graph convolutional neural network (STGCN) is developed for intelligent fault diagnosis of wind turbines in this article. First, an adjacency matrix is constructed based on the Gaussian kernel function to graphically represent the SCADA data, so as to improve the representation capacities for spatial characteristics. Then, the spatial and temporal fault features are extracted using the graph convolutional network (GCN) and the 1-D convolutional network (1D-CNN), respectively. Finally, a spatio-temporal feature fusion module is developed as the sandwich structure to construct the proposed STGCN. The feasibility and effectiveness of the proposed method are verified by two cases of blade icing detection and main bearing wear diagnosis. The results show that the proposed method is able to accurately describe the spatio-temporal correlation of SCADA data, and improve the diagnostic accuracy.

关键词： Wind turbines Feature extraction Fault diagnosis Correlation Kernel Sensor phenomena and characterization Deep learning data mining Accuracy Wind speed graph neural network intelligent fault diagnosis spatio-temporal fusion wind turbine

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Client Selection in Fault-Tolerant Federated Reinforcement learning for IoT Networks 59

Client Selection in Fault-Tolerant Federated Reinforcement L...

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59th Annual ieee International conference on Communications (ieee ICC)

作者： Cal, Semih Sun, Xiang Yao, Jingjing Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA

ISBN: (纸本)9781728190549

In wireless Internet of Things (IoT) networks, Federated Reinforcement learning (FRL) has emerged as a decentralized strategy for data-driven decision-making, enabling devices to learn directly from real-time environmental interactions, sidestepping the need for labeled data. This method promises enhanced data privacy and finds practical applications in autonomous driving, smart grids, and industrial automation. However, the integrity of FRL can be compromised by malicious clients injecting false data, underlining the need for a fault-tolerant mechanism to sustain the robustness and accuracy of the learning phase. Moreover, the inherent client heterogeneity within IoT networks propels the demand for judicious client selection, optimizing computational and communication resources. This paper investigates client selection problem within a fault-tolerant FRL framework for wireless IoT networks. Our objective is to explore the tradeoff between maximizing client participation and minimizing energy consumption of IoT devices. We formulate our problem as a mixed-integer linear programming (MILP) model and design an efficient algorithm with low computational complexity to address it. Extensive simulations are conducted to demonstrate the superiority of our proposed algorithm.

关键词： Federated reinforcement learning internet of things client selection fault tolerance energy consumption

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Multi-agent reinforcement learning for shared resource scheduling conflict resolution

Multi-agent reinforcement learning for shared resource sched...

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2024 International conference on Artificial Intelligence, Computer, data Sciences, and Applications, ACDSA 2024

作者： Sankaranarayanasamy, Malarvizhi Vennelakanti, Ravigopal Hitachi America Ltd R&d Big Data Analytics Solution Lab Santa Clara United States

ISBN: (纸本)9798350394528

Transportation operations especially in railroad domain are time critical. Scheduling conflicts driven by disruptions and delays in any one zone significantly affect the overall network operations. In this work applicability of multi agent reinforcement learning approach to resolve scheduling conflicts and improve the railroad network operations was explored. Based on a custom 2D grid environment here we attempt to learn ideal coordinated agent actions based on simulated schedule conflict by introducing stochastic delays in train arrival. We were able to achieve converges for multi-agent simulation based setup with 30% malfunction rate. The focus of work is to presents the problem setup in mobility domain and simulation design for the multi-agent reinforcement learning. With respect to real world application this approach is promising as it reduces the requirement of a highly customized solution by experts and if a high-performance simulation-based reinforcement learning solution is reached this would provide an opportunity to build generalized interoperable control techniques for transit systems across the world. © 2024 ieee.

关键词： Multi agent systems

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Q-learning for Continuous-Time Linear systems: A data-driven Implementation of the Kleinman Algorithm

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ieee TRANSACTIONS ON systems MAN CYBERNETICS-systems 2022年第10期52卷 6487-6497页

作者： Possieri, Corrado Sassano, Mario CNR Ist Anal Sistemi & Informat A Ruberti I-00185 Rome Italy Univ Roma Tor Vergata Dipartimento Ingn Civile & Ingn Informat I-00133 Rome Italy

A data-driven strategy to estimate the optimal feedback and the value function in an infinite-horizon, continuous-time, linear-quadratic optimal control problem for an unknown system is proposed. The method permits the construction of the optimal policy without any knowledge of the model, without requiring that the time derivatives of the state are available for the design, and without even assuming that an initial stabilizing feedback policy is available. Two alternative architectures are discussed: the first scheme revolves around the periodic computation of some matrix inversions involving the Q-function, whereas the second approach relies on a purely continuous-time implementation of some dynamic systems whose trajectories are uniformly attracted by the solutions to the above algebraic equations. Interestingly, the proposed strategy essentially constitutes a (direct) data-driven implementation of the celebrated Kleinman algorithm, hence subsuming the particularly appealing features of the latter, such as quadratic monotone convergence to the optimal solution. The theory is then validated by the means of practically motivated applications.

关键词： Optimal control Trajectory Costs Convergence Symmetric matrices Riccati equations Q-learning Linear systems optimal control reinforcement learning uncertain unknown systems

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Identifying Maintenance Needs with Machine learning: a Case Study in Railways 32

Identifying Maintenance Needs with Machine Learning: a Case ...

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32nd ieee International Requirements Engineering conference (RE)

作者： Ferdous, Raihana Spagnolo, Giorgio Borselli, Alessandro Rota, Lucio Ferrari, Alessio Consiglio Nazl Ric CNR Pisa Italy Trenord Milan Italy

ISBN: (纸本)9798350395525;9798350395518

Cyber-physical systems, particularly those with extended service lives such as railways, often necessitate significant investment in maintenance activities encompassing repairs, upgrades, or inspections. These decisions are generally based on fixed schedules, or informed by the judgment of experienced maintenance staff. To improve this process, predictive maintenance (PdM) has emerged as a viable solution to anticipate maintenance needs and preempt system failures. With data-driven PdM, maintenance needs are identified through machine learning (ML) solutions that monitor the system logs and recommend interventions before a failure occurs. This paper presents preliminary findings from a case study concerning the development of a ML system for PdM in railways. We present the current maintenance process, the existing logging platform, and our strategy for leveraging log data to support PdM. Our preliminary results are promising. However, they show that, although the log dataset spans three years and three railway vehicles, in some cases the log data alone are insufficient for accurately inferring maintenance requirements. To address the problem, we discuss the necessity of employing synthetic data generation methods and rule-based, knowledge-driven strategies.

关键词： artificial intelligence requirements engineering maintenance requirements railways

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