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GSLB: The Graph Structure learning Benchmark 37

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GSLB: The Graph Structure Learning Benchmark

37th Conference on Neural Information Processing systems, NeurIPS 2023

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9781713899921

Graph Structure learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Graphic methods

Change-Point Detection in Industrial Data Streams based on Online Dynamic Mode Decomposition with Control

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arXiv 2024年

作者： Wadinger, Marek Kvasnica, Michal Kawahara, Yoshinobu Institute of Information Engineering Automation and Mathematics Slovak University of Technology in Bratislava Radlinskeho 9 Bratislava812 37 Slovakia Machine Learning & Systems Laboratory Osaka University 1–5 Yamadaoka Osaka Suita565–0871 Japan

We propose a novel change-point detection method based on online Dynamic Mode Decomposition with control (ODMDwC). Leveraging ODMDwC’s ability to find and track linear approximation of a non-linear system while incorporating control effects, the proposed method dynamically adapts to its changing behavior due to aging and seasonality. This approach enables the detection of changes in spatial, temporal, and spectral patterns, providing a robust solution that preserves correspondence between the score and the extent of change in the system dynamics. We formulate a truncated version of ODMDwC and utilize higher-order time-delay embeddings to mitigate noise and extract broad-band features. Our method addresses the challenges faced in industrial settings where safety-critical systems generate non-uniform data streams while requiring timely and accurate change-point detection to protect profit and life. Our results demonstrate that this method yields intuitive and improved detection results compared to the Singular-Value-Decomposition-based method. We validate our approach using synthetic and real-world data, showing its competitiveness to other approaches on complex systems’ benchmark datasets. Provided guidelines for hyperparameters selection enhance our method’s practical applicability. © 2024, CC BY.

关键词： Dynamic mode decomposition

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach

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arXiv 2024年

作者： Ilic, Sasa Karaman, Abdulkerim Pöppelbaum, Johannes Reimann, Jan Niclas Marré, Michael Schwung, Andreas Labor für Massivumformung South Westphalia University of Applied Sciences Frauenstuhlweg 31 Iserlohn58644 Germany Department for Automation Technology and Learning Systems South Westphalia University of Applied Sciences Lübecker Ring 2 Soest59494 Germany

This study presents a novel approach for predicting wall thickness changes in tubes during the nosing process. Specifically, we first provide a thorough analysis of nosing processes and the influencing parameters. We further set-up a Finite Element Method (FEM) simulation to better analyse the effects of varying process parameters. As however traditional FEM simulations, while accurate, are time-consuming and computationally intensive, which renders them inapplicable for real-time application, we present a novel modeling framework based on specifically designed graph neural networks as surrogate models. To this end, we extend the neural network architecture by directly incorporating information about the nosing process by adding different types of edges and their corresponding encoders to model object interactions. This augmentation enhances model accuracy and opens the possibility for employing precise surrogate models within closed-loop production processes. The proposed approach is evaluated using a new evaluation metric termed area between thickness curves (ABTC). The results demonstrate promising performance and highlight the potential of neural networks as surrogate models in predicting wall thickness changes during nosing forging processes. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

Synthetic Image Generation for Visual Particle Size Distribution Estimation based on U-NET Convolutional Networks

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Synthetic Image Generation for Visual Particle Size Distribu...

Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Akshay Chavan Antonius Schmidt Hendrik Klopries Dominik Aufderheide Industrial Measurement Section (iMe) Faculty of Electrical Engineering South Westphalia University of Applied Sciences Soest Germany Automation Technology and Learning Systems Faculty of Electrical Engineering South Westphalia University of Applied Sciences Soest Germany

Many applications in the field of bulk material handling require the determination of the Particle Size Distribution (PSD) of the handled materials. Typically, this can only be achieved by manual sampling and a subsequent laboratory analysis. However, the availability of novel image segmentation algorithms based on Convolutional Neural Networks (CNNs) leads to the possibility for an implementation of visual PSD estimation techniques. One prominent example of such approaches is the U-Net image segmentation architecture. However, one fundamental problem for the training of the underlying CNN is the difficulty to gather ground truth data, since the exact correspondence between a set of images and the underlying particle sizes cannot be achieved in practice. This paper introduces a novel synthetic image generation procedure for the visual PSD estimation by using the U-NET structure. The overall approach is validated and verified by using a prototypic implementation of a U-NET segmentation pipeline. It is shown, that the proposed synthetic image dataset generation can be applied for all visual PSD estimation based on machine learning algorithms.

关键词： Training Visualization Image segmentation Machine learning algorithms Image synthesis Pipelines Estimation

Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg Games

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arXiv 2024年

作者： Yuwono, Steve Hussain, Ahmar Kamal Schwung, Dorothea Schwung, Andreas Department of Automation Technology and Learning Systems South Westphalia University of Applied Sciences Lübecker Ring 2 Soest59494 Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Universitätsplatz 2 Magdeburg39106 Germany Department of Artificial Intelligence and Data Science in Automation Technology Hochschule Düsseldorf University of Applied Sciences Münsterstraße 156 Düsseldorf40476 Germany

In this study, we introduce Modular State-based Stackelberg Games (Mod-SbSG), a novel game structure developed for distributed self-learning in modular manufacturing systems. Mod-SbSG enhances cooperative decision-making among self-learning agents within production systems by integrating State-based Potential Games (SbPG) with Stackelberg games. This hierarchical structure assigns more important modules of the manufacturing system a first-mover advantage, while less important modules respond optimally to the leaders' decisions. This decision-making process differs from typical multi-agent learning algorithms in manufacturing systems, where decisions are made simultaneously. We provide convergence guarantees for the novel game structure and design learning algorithms to account for the hierarchical game structure. We further analyse the effects of single-leader/multiple-follower and multiple-leader/multiple-follower scenarios within a Mod-SbSG. To assess its effectiveness, we implement and test Mod-SbSG in an industrial control setting using two laboratory-scale testbeds featuring sequential and serial-parallel processes. The proposed approach delivers promising results compared to the vanilla SbPG, which reduces overflow by 97.1%, and in some cases, prevents overflow entirely. Additionally, it decreases power consumption by 5-13% while satisfying the production demand, which significantly improves potential (global objective) values. © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

Performance benchmarking of multimodal data-driven approaches in industrial settings

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Machine Learning with Applications

Machine learning with Applications 2025年 21卷

作者： Diyar Altinses Andreas Schwung Department of Automation Technology and Learning Systems South Westphalia University of Applied Sciences Soest 59494 NRW Germany

Data-driven solutions are increasingly transforming the industrial sector, yet collecting large-scale, multimodal datasets remains costly and challenging. This paper presents three synthetic multimodal datasets that replicate real-world industrial conditions across varying levels of complexity, designed to benchmark multimodal machine learning models. We validate their utility through a series of experiments. Cross-modal prediction and domain adaptation demonstrate that the datasets effectively capture strong multimodal correlations. Multimodal reconstruction experiments confirm the internal consistency and richness of the fused representations, indicating that the modalities complement each other in capturing underlying structure. Additionally, multimodal regression significantly outperforms unimodal baselines, underscoring the predictive strength gained through multimodal integration. Together, these results demonstrate the utility of our datasets, establishing a solid baseline for future research and encouraging further advancements in industrial data-driven solutions.

关键词： Benchmarking Industrial applications Machine learning Multimodal datasets

GSLB: the graph structure learning benchmark 23

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GSLB: the graph structure learning benchmark

Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

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Stability Analysis of Soft Sensor-Based Control systems–A Luré-Postnikov Approach Using a Novel Gated Recurrent Neural Network

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arXiv 2025年

作者： Hilgert, Eric Schwung, Andreas Department of Technology and Systems Graduate School for Applied Research in North Rhine-Westphalia Bochum Germany Department of Automation Technology and Learning Systems South Westphalia University of Applied Sciences Soest Germany

This article addresses certification of closed-loop stability when a soft-sensor based on a gated recurrent neural network operates in the feedback path of a nonlinear control system. The Hadamard gating used in standard GRU/LSTM cells is shown to violate the Luré–Postnikov Lyapunov conditions of absolute-stability theory, leading to conservative analysis. To overcome this limitation, a modified architecture—termed the Luré–Postnikov gated recurrent neural network (LP-GRNN)—is proposed;its affine update law is compatible with the Luré–Postnikov framework while matching the prediction accuracy of vanilla GRU/LSTM models on the NASA CMAPSS benchmark. Embedding the LP-GRNN, the plant, and a saturated PI controller in a unified standard nonlinear operator form (SNOF) reduces the stability problem to a compact set of tractable linear matrix inequalities (LMIs) whose feasibility certifies global asymptotic stability. A linearized boiler case study illustrates the workflow and validates the closed-loop performance, thereby bridging modern soft-sensor design with formal stability guarantees. Copyright © 2025, The Authors. All rights reserved.

关键词： Lyapunov methods