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3rd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Adaptive neural finite-time self-triggered control for nonstrict-feedback nonlinear systems with sensor faults

Robotic Intelligence and Automation

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Robotic Intelligence and Automation 2025年

作者： Zhang, Wenxin Xu, Ning Zhao, Ning Al-Barakati, Abdullah A. College of Control Science and Engineering Bohai University Jinzhou China Department of Information Systems Faculty of Computing and Information Technology Communication Systems and Networks Research Group King Abdulaziz University Jeddah Saudi Arabia

Purpose: This paper aims to investigate the problem of adaptive neural finite-time self-triggered tracking control for interconnected large scale nonlinear systems in nonstrict-feedback forms with sensor faults. Design/methodology/approach: To begin with, by combining backstepping techniques and neural networks (NNs), an adaptive NN controller is designed to compensate for sensor faults. Then, command filters are introduced to deal with the complexity explosion problem in backstepping design processes. Moreover, to reduce unnecessary data transmissions, a self-triggered control strategy is presented. Findings: Based on self-triggered strategy, an adaptive neural finite-time control scheme for interconnected large-scale systems with sensor faults is proposed. Originality/value: This article considers sensor faults in interconnected large-scale nonlinear systems with nonstrict-feedback forms. Moreover, the introduction of command filters not only effectively avoids the complexity explosion problem arising from the repetitive differentiation of virtual control inputs, but also simplifies the controller design process. Besides, this paper proposes a self-triggered mechanism that calculates the next trigger point based on current system data, overcoming the need for continuous monitoring of measurement errors in event-triggered mechanisms. Furthermore, the controller guarantees the finite-time stability of interconnected large-scale systems, with the tracking error converging to a small neighborhood of the origin within a finite time frame. © 2025, Emerald Publishing Limited.

关键词： Large scale systems

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ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian Sensors

arXiv

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arXiv 2025年

作者： Rap, Jake Das, Amritam Eindhoven University of Technology Department of Electrical Engineering Control Systems group P.O. Box 513 Eindhoven5600 MB Netherlands

Accurate traffic flow estimation and prediction are critical for the efficient management of transportation systems, particularly under increasing urbanization. Traditional methods relying on static sensors often suffer from limited spatial coverage, while probe vehicles provide richer, albeit sparse and irregular data. This work introduces ON-Traffic, a novel deep operator Network and a receding horizon learning-based framework tailored for online estimation of spatio-temporal traffic state along with quantified uncertainty by using measurements from moving probe vehicles and downstream boundary inputs. Our framework is evaluated in both numerical and simulation datasets, showcasing its ability to handle irregular, sparse input data, adapt to time-shifted scenarios, and provide well-calibrated uncertainty estimates. The results demonstrate that the model captures complex traffic phenomena, including shockwaves and congestion propagation, while maintaining robustness to noise and sensor dropout. These advancements present a significant step toward online, adaptive traffic management systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Information management

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Estimation of Metro Passenger Capacity Based on Passenger-Train Matching Model 3rd

Estimation of Metro Passenger Capacity Based on Passenger-Tr...

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3rd International Conference on Information control, Electrical engineering, and Rail Transit, ICEERT 2023

作者： Mei, Zhenkun Chen, Beijia Liu, Jiajun Shao, Jiayu Zhang, Ning Suzhou Rail Transit Group Co. Ltd. Jiangsu Suzhou215004 China School of Automation Southeast University Jiangsu Nanjing210096 China Key Laboratory of Measurement and Control of Complex Engineering Systems Ministry of Education Southeast University Jiangsu Nanjing210096 China ITS Engineering Research Center Ministry of Education Southeast University Jiangsu Nanjing211189 China

ISBN: (纸本)9789819788903

In the present scenario of the ‘one-tapin-one-tapout’ operation mode in urban rail transportation, determining passenger travel trajectories is challenging, thereby decreasing the effective counting of passengers on trains. In order to address this issue, a method for calculating passenger capacity on trains was developed by analyzing passenger travel information extracted from AFC data and train timetables. First, this research estimated the components of journey time to infer and reconstruct the passenger's specific spatiotemporal travel trajectory. The passenger-train matching model was then used to assign passengers to specific trains and calculate the passenger load of different trains at each station. At last, it took Suzhou Metro Line 3 as an example and analyzed the spatiotemporal distribution of urban rail transit passenger flow on a single line based on the proposed passenger-train model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Urban transportation

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Cerebral ischemia detection using deep learning techniques

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Health Information Science and systems 2025年第1期13卷 1-19页

作者： Pastor-Vargas, Rafael Antón-Munárriz, Cristina Haut, Juan M. Robles-Gómez, Antonio Paoletti, Mercedes E. Benítez-Andrades, José Alberto Communications and Control Systems Computer Engineering Science Faculty UNED Madrid Spain Radiology area Hospital Universitario de Navarra Navarra Spain School of Technology University of Extremadura Cáceres Spain ALBA Research Group Department of Electric Systems and Automatics Engineering University of Leon León Spain

Cerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to contemporary mortality and morbidity rates, often leading to lasting disabilities. Early identification is crucial in mitigating its impact and reducing mortality. Non-contrast computed tomography (NCCT) remains the primary diagnostic tool in stroke emergencies due to its speed, accessibility, and cost-effectiveness. NCCT enables the exclusion of hemorrhage and directs attention to ischemic causes resulting from arterial flow obstruction. Quantification of NCCT findings employs the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), which evaluates affected brain structures. This study seeks to identify early alterations in NCCT density in patients with stroke symptoms using a binary classifier distinguishing NCCT scans with and without stroke. To achieve this, various well-known deep learning architectures, namely VGG3D, ResNet3D, and DenseNet3D, validated in the ImageNet challenges, are implemented with 3D images covering the entire brain volume. The training results of these networks are presented, wherein diverse parameters are examined for optimal performance. The DenseNet3D network emerges as the most effective model, attaining a training set accuracy of 98% and a test set accuracy of 95%. The aim is to alert medical professionals to potential stroke cases in their early stages based on NCCT findings displaying altered density patterns.

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Discontinuous Galerkin Finite Element Methods for Linear Port-Hamiltonian Dynamical systems

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Journal of Scientific Computing 2025年第1期104卷 1-47页

作者： Cheng, Xiaoyu van der Vegt, J. J. W. Xu, Yan Zwart, H. J. School of Mathematics University of Science and Technology of China Anhui Hefei China Department of Applied Mathematics Mathematics of Computational Science Group University of Twente Enschede The Netherlands Department of Applied Mathematics Mathematics of Systems Theory Group University of Twente Enschede The Netherlands Department of Mechanical Engineering Dynamics and Control Group Eindhoven University of Technology Eindhoven The Netherlands

In this paper, we present discontinuous Galerkin (DG) finite element discretizations for a class of linear hyperbolic port-Hamiltonian dynamical systems. The key point in constructing a port-Hamiltonian system is a Stokes-Dirac structure. Instead of following the traditional approach of defining the strong form of the Dirac structure, we define a Dirac structure in weak form, specifically in the input-state-output form. This is implemented within broken Sobolev spaces on a tessellation with polyhedral elements. After that, we state the weak port-Hamiltonian formulation and prove that it relates to a Poisson bracket. In our work, a crucial aspect of constructing the above-mentioned Dirac structure is that we provide a conservative relation between the boundary ports. Next, we state DG discretizations of the port-Hamiltonian system by using the weak form of the Dirac structure and broken polynomial spaces of differential forms, and we provide a priori error estimates for the structure-preserving port-Hamiltonian discontinuous Galerkin (PHDG) discretizations. The accuracy and capability of the methods developed in this paper are demonstrated by presenting several numerical experiments.

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Dynamic Collision Avoidance Using Velocity Obstacle-Based control Barrier Functions

arXiv

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arXiv 2025年

作者： Huang, Jihao Zeng, Jun Chi, Xuemin Sreenath, Koushil Liu, Zhitao Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Institute of Intelligence Science and Engineering Shenzhen Polytechnic University Shenzhen China Hybrid Robotics Group The Department of Mechanical Engineering UC Berkeley United States

Designing safety-critical controllers for acceleration-controlled unicycle robots is challenging, as control inputs may not appear in the constraints of control Lyapunov functions (CLFs) and control barrier functions (CBFs), leading to invalid controllers. Existing methods often rely on state-feedback-based CLFs and high-order CBFs (HOCBFs), which are computationally expensive to construct and fail to maintain effectiveness in dynamic environments with fast-moving, nearby obstacles. To address these challenges, we propose constructing velocity obstacle-based CBFs (VOCBFs) in the velocity space to enhance dynamic collision avoidance capabilities, instead of relying on distance-based CBFs that require the introduction of HOCBFs. Additionally, by extending VOCBFs using variants of VO, we enable reactive collision avoidance between robots. We formulate a safety-critical controller for acceleration-controlled unicycle robots as a mixed-integer quadratic programming (MIQP), integrating state-feedback-based CLFs for navigation and VOCBFs for collision avoidance. To enhance the efficiency of solving the MIQP, we split the MIQP into multiple sub-optimization problems and employ a decision network to reduce computational costs. Numerical simulations demonstrate that our approach effectively guides the robot to its target while avoiding collisions. Compared to HOCBFs, VOCBFs exhibit significantly improved dynamic obstacle avoidance performance, especially when obstacles are fast-moving and close to the robot. Furthermore, we extend our method to distributed multi-robot systems. Copyright © 2025, The Authors. All rights reserved.

关键词： State feedback

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Model-based Optimization for Laboratory-Scale Upstream Processing in Monoclonal Antibody Production

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IFAC-PapersOnLine 2025年第1期59卷 403-408页

作者： Guilherme A. Pimentel Camilo Garcia-Tenorio Radhouane Fekih-Salem Cédric Toussaint Ahmed Kanfoud Thomas Cornet Thibault Helleputte Adrien Boes Riccardo Marega Alain Vande Wouwer Laurent Dewasme Systems EstimationControl and Optimization Group University of Mons Belgium DNAlytics Ottignies-Louvain-la-Neuve Belgium Immunobiology Laboratory CER Groupe Belgium University of Tunis El Manar National Engineering School of Tunis LAMSIN Tunisia

The production of monoclonal antibodies (mAbs) is a complex pharmacological process involving upstream and downstream chains, focusing on mAbs production and purification, respectively. This paper presents a process optimization based on an end-to-end mechanistic model for the upstream process chain to enhance the production of mAbs. Upstream processing considers cell expansion, cell production, and primary harvesting steps by filtration. The optimization results offer users the optimal initial concentrations of the medium in the bioreactors, the duration of the cultures, the feed flow rate profiles, and the permeate flow profile in the filtration operation. Simulations incorporating interconnected reactors and process constraints led to optimized initial media concentrations and feed flow profiles, resulting in a 25% increase in mAb production compared to baseline conditions.

关键词： process optimization monoclonal antibodies upstream processing mechanistic modeling

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Ground Testing of Spacecraft Attitude Dynamics during In-Space Assembly and Manufacturing

Ground Testing of Spacecraft Attitude Dynamics during In-Spa...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Bhundiya, Harsh G. Cordero, Zachary C. Marshall, Michael A. Sternberg, David Lo, Kevin Mohan, Swati Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA United States Guidance and Control Analyst Space Exploration Sector The Johns Hopkins University Applied Physics Laboratory LaurelMD United States Payload/Instrument Pointing Control Systems Engineering Group Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States GNC Hardware and Testbed Development Group Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The on-orbit construction of structures, i.e., in-space assembly and manufacturing (ISAM), is a promising approach for increasing mission flexibility and achieving structures optimized for the space environment. Although many construction processes have been proposed, a critical aspect that needs further investigation is the spacecraft attitude dynamics during ISAM given the large changes in mass properties of the spacecraft and the long timescales of fabrication. This work investigates the ground testing of spacecraft attitude dynamics during Bend-Forming, a candidate process for the on-orbit construction of truss structures via plastic deformation of metallic feedstock. Specifically, we conduct experiments on a planar air bearing platform and develop rigid body dynamics models to understand the platform motion during Bend-Forming. The experimental results reveal 1) the effect of metal spring back on the attitude dynamics, and 2) the possibility of control-structure interaction during fabrication with closed-loop, thruster-based position and attitude control. Overall, this study highlights how artifacts of the manufacturing process like spring back affect attitude dynamics and results in important guidelines for the ground testing of spacecraft attitude dynamics during ISAM. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Trusses

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Hybrid adaptive Sine Cosine Algorithm with Finite-Time Prescribed Performance PID control for pneumatic servo systems

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IFAC Journal of systems and control 2025年 32卷

作者： Irawan, Addie Suid, Mohd Helmi Ismail, R.M.T. Raja Jusof, Mohd Falfazli Mat Azahar, Mohd Iskandar Putra Nasir, Ahmad Nor Kasruddin Robotics Intelligent Systems and Control Engineering (RiSC) research group Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia

This paper addresses the challenge of enhancing pressure regulation in pneumatic servo systems, specifically for proportional valve-controlled double-acting pneumatic cylinders (PPVDC). A Hybrid Nonlinear Sine Cosine Algorithm (HNSCA) is proposed to optimize a Finite-Time Prescribed Performance control (FT-PPC) integrated with a PID controller. The HNSCA combines the Nonlinear Sine Cosine Algorithm (NSCA) with Adaptive Safe Experimentation Dynamics (ASED) to fine-tune FT-PPC-PID parameters, achieving rapid transient response and system stability. Simulation results demonstrate significant improvements over other optimization variants like ESCA and ASCA, including a 96% faster rise time, 61.9% reduction in settling time, and 6.4% lower overshoot. Additionally, HNSCA reduced pressure oscillations by 25%–30%, lowered power consumption by 20%–30%, and achieved up to a 50% reduction in energy consumption under a 10 kg load. It also enhanced subsonic flow stability by 10%–15% under choked flow conditions. These advancements offer practical benefits for industries utilizing pneumatic systems, such as manufacturing and robotics, by providing more precise control, reducing energy costs, and extending equipment lifespan. The findings highlight the effectiveness of the proposed approach in error minimization and long-term stability for pneumatic servo systems. © 2025 Elsevier Ltd

关键词： Metaheuristic Nonlinear sine cosine algorithm Pneumatic actuator Prescribed performance control Safe experimentation dynamics

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