arXiv subjects

George Panoutsos

Publications and source records attributed to George Panoutsos.

4 recordsLinked to original sources

arXiv2026

Disturbance observer-based tracking control for roll-to-roll slot die coating systems under gap and pump rate disturbances

Roll-to-roll slot die coating is a widely used industrial manufacturing technique applied in a diverse range of applications such as the production of lithium-ion batteries, solar cells and optical films. The efficiency of roll-to-roll slot die coating depends on the precise control of various input parameters such as pump rate, substrate velocity and coating gap. However, these inputs are sensitive to disturbances in process conditions, leading to inconsistencies in the various characteristics of the produced film. To address this challenge, a \gls{DO} is utilized for detecting disturbances, which may occur in the same or different channels as the control signal within the system. A generalized compensator is then implemented to mitigate the impact of these disturbances on the output, thereby enhancing uncertainty suppression. Additionally, integrating the disturbance rejection system with an output tracking controller enables the coating system to maintain the desired thickness under varying input conditions and disturbances. The effectiveness of this approach is then validated using a test rig equipped with a camera system, which facilitates the development of a data-driven model of the dynamic process, represented by state-space equations. The simulation results were demonstrated to showcase the effectiveness of the DOBOTC system, which provides a resilient solution for the output tracking issue in a data-driven model with generalized disturbances.

eess.SY↗

arXiv2025

TaylorPODA: A Taylor Expansion-Based Method to Improve Post-Hoc Attributions for Opaque Models

Post-hoc model-agnostic local attribution (LA) methods have been widely adopted to explain opaque AI models by quantifying feature-wise contributions. However, many existing methods rely on heuristic or only partially justified attribution mechanisms, while the quality of attribution itself is often shaped by downstream objectives without universally accepted standards. In this work, we propose Taylor exPansion-Originated aDaptive Attribution (TaylorPODA), a new post-hoc model-agnostic LA method grounded in the Taylor expansion framework. We first introduce a set of postulates, which formalize principled requirements for explicitly and exhaustively attributing Taylor terms to the corresponding features. Based on these postulates, we analyze existing post-hoc model-agnostic LA methods and identify a fundamental tension between principled attribution and adaptation toward user-defined utilities. To address this challenge, TaylorPODA introduces a controllable allocation mechanism for Taylor interaction effects, enabling attribution results to adapt to downstream objectives while preserving the proposed postulates. Furthermore, although developed from a Taylor-expansion perspective, TaylorPODA also admits a Harsanyi-dividend interpretation, allowing the attribution mechanism to extend beyond model differentiability. Theoretical analysis demonstrates that TaylorPODA satisfies all the proposed postulates together with an additional adaptation property. Empirical results across multiple datasets and both differentiable and non-differentiable models further show that TaylorPODA achieves consistently improved alignment with user-defined utilities while maintaining the communicability of the resulting explanations. Overall, this work provides a starting point toward more trustworthy XAI systems for the deployment of increasingly powerful yet opaque task models.

stat.ML↗

arXiv2024

Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach

The optimisation of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialisation. Nowadays, the utilisation of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of convolutional neural networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The you only look once algorithmic framework is employed with traditional rectangular bounding boxes for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation.

cs.CV↗

arXiv2023

A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy

We developed a transparent computational large-scale imaging-based framework that can distinguish between normal and metastasizing human cells. The method relies on fluorescence microscopy images showing the spatial organization of actin and vimentin filaments in normal and metastasizing single cells, using a combination of multi-attention channels network and global explainable techniques. We test a classification between normal cells (Bj primary fibroblast), and their isogenically matched, transformed and invasive counterpart (BjTertSV40TRasV12). Manual annotation is not trivial to automate due to the intricacy of the biologically relevant features. In this research, we utilized established deep learning networks and our new multi-attention channel architecture. To increase the interpretability of the network - crucial for this application area - we developed an interpretable global explainable approach correlating the weighted geometric mean of the total cell images and their local GradCam scores. The significant results from our analysis unprecedently allowed a more detailed, and biologically relevant understanding of the cytoskeletal changes that accompany oncogenic transformation of normal to invasive and metastasizing cells. We also paved the way for a possible spatial micrometre-level biomarker for future development of diagnostic tools against metastasis (spatial distribution of vimentin).

eess.IV↗