arXiv subjects

Xiaoning Jin

Publications and source records attributed to Xiaoning Jin.

5 recordsLinked to original sources

arXiv2025

A Model-Free Terminal Iterative Learning Control Scheme for Multi-Layer Printing Alignment Control Problems

Roll-to-roll (R2R) printing technologies are promising for high-volume continuous production of substrate-based electronic products. One of the major challenges in R2R flexible electronics printing is achieving tight alignment tolerances, as specified by the device resolution (usually at the micro-meter level), for multi-layer printed electronics. The alignment of the printed patterns in different layers is known as registration. Conventional registration control methods rely on real-time feedback controllers, such as PID control, to regulate the web tension and the web speed. However, those methods may lose effectiveness in compensating for recurring disturbances and supporting effective mitigation of registration errors. In this paper, we propose a Spatial-Terminal Iterative Learning Control (STILC) method integrated with PID control to iteratively learn and reduce registration error cycle-by-cycle, converging it to zero. This approach enables unprecedented precision in the creation, integration, and manipulation of multi-layer microstructures in R2R processes. We theoretically prove the convergence of the proposed STILC-PID hybrid approach and validate its effectiveness through a simulated registration error scenario caused by axis mismatch between roller and motor, a common issue in R2R systems. The results demonstrate that the STILC-PID hybrid control method can fully eliminate the registration error after a feasible number of iterations. Additionally, we analyze the impact of different learning gains on the convergence performance of STILC.

eess.SY↗

arXiv2025

OffLight: An Offline Multi-Agent Reinforcement Learning Framework for Traffic Signal Control

Efficient traffic control (TSC) is essential for urban mobility, but traditional systems struggle to handle the complexity of real-world traffic. Multi-agent Reinforcement Learning (MARL) offers adaptive solutions, but online MARL requires extensive interactions with the environment, making it costly and impractical. Offline MARL mitigates these challenges by using historical traffic data for training but faces significant difficulties with heterogeneous behavior policies in real-world datasets, where mixed-quality data complicates learning. We introduce OffLight, a novel offline MARL framework designed to handle heterogeneous behavior policies in TSC datasets. To improve learning efficiency, OffLight incorporates Importance Sampling (IS) to correct for distributional shifts and Return-Based Prioritized Sampling (RBPS) to focus on high-quality experiences. OffLight utilizes a Gaussian Mixture Variational Graph Autoencoder (GMM-VGAE) to capture the diverse distribution of behavior policies from local observations. Extensive experiments across real-world urban traffic scenarios show that OffLight outperforms existing offline RL methods, achieving up to a 7.8% reduction in average travel time and 11.2% decrease in queue length. Ablation studies confirm the effectiveness of OffLight's components in handling heterogeneous data and improving policy performance. These results highlight OffLight's scalability and potential to improve urban traffic management without the risks of online learning.

cs.AI↗

arXiv2024

PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control

Multi-Agent Reinforcement Learning (MARL) presents a promising approach for addressing the complexity of Traffic Signal Control (TSC) in urban environments. However, existing platforms for MARL-based TSC research face challenges such as slow simulation speeds and convoluted, difficult-to-maintain codebases. To address these limitations, we introduce PyTSC, a robust and flexible simulation environment that facilitates the training and evaluation of MARL algorithms for TSC. PyTSC integrates multiple simulators, such as SUMO and CityFlow, and offers a streamlined API, empowering researchers to explore a broad spectrum of MARL approaches efficiently. PyTSC accelerates experimentation and provides new opportunities for advancing intelligent traffic management systems in real-world applications.

cs.MA↗

arXiv2023

Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control

Traffic signal control (TSC) is a challenging problem within intelligent transportation systems and has been tackled using multi-agent reinforcement learning (MARL). While centralized approaches are often infeasible for large-scale TSC problems, decentralized approaches provide scalability but introduce new challenges, such as partial observability. Communication plays a critical role in decentralized MARL, as agents must learn to exchange information using messages to better understand the system and achieve effective coordination. Deep MARL has been used to enable inter-agent communication by learning communication protocols in a differentiable manner. However, many deep MARL communication frameworks proposed for TSC allow agents to communicate with all other agents at all times, which can add to the existing noise in the system and degrade overall performance. In this study, we propose a communication-based MARL framework for large-scale TSC. Our framework allows each agent to learn a communication policy that dictates "which" part of the message is sent "to whom". In essence, our framework enables agents to selectively choose the recipients of their messages and exchange variable length messages with them. This results in a decentralized and flexible communication mechanism in which agents can effectively use the communication channel only when necessary. We designed two networks, a synthetic $4 \times 4$ grid network and a real-world network based on the Pasubio neighborhood in Bologna. Our framework achieved the lowest network congestion compared to related methods, with agents utilizing $\sim 47-65 \%$ of the communication channel. Ablation studies further demonstrated the effectiveness of the communication policies learned within our framework.

cs.MA↗

arXiv2018

MMDF2018 Workshop Report

Driven by the recent advances in smart, miniaturized, and mass produced sensors, networked systems, and high-speed data communication and computing, the ability to collect and process larger volumes of higher veracity real-time data from a variety of modalities is expanding. However, despite research thrusts explored since the late 1990's, to date no standard, generalizable solutions have emerged for effectively integrating and processing multimodal data, and consequently practitioners across a wide variety of disciplines must still follow a trial-and-error process to identify the optimum procedure for each individual application and data sources. A deeper understanding of the utility and capabilities (as well as the shortcomings and challenges) of existing multimodal data fusion methods as a function of data and challenge characteristics has the potential to deliver better data analysis tools across all sectors, therein enabling more efficient and effective automated manufacturing, patient care, infrastructure maintenance, environmental understanding, transportation networks, energy systems, etc. There is therefore an urgent need to identify the underlying patterns that can be used to determine a priori which techniques will be most useful for any specific dataset or application. This next stage of understanding and discovery (i.e., the development of generalized solutions) can only be achieved via a high level cross-disciplinary aggregation of learnings, and this workshop was proposed at an opportune time as many domains have already started exploring use of multimodal data fusion techniques in a wide range of application-specific contexts.

cs.OH↗