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arXiv 2023年

作者： Liu, Yuhan Wang, Pengyu Lee, Chang-Hun Tóth, Roland The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task. To address this issue, a recursive online sparse Gaussian Process (GP)-based learning strategy for attitude takeover control of noncooperative targets with maneuverability is proposed, where the unknown dynamics are online compensated based on the learnt GP model in a semi-feedforward manner. The method enables the continuous use of on-orbit data to successively improve the learnt model during online operation and has reduced computational load compared to standard GP regression. Next to the GP-based feedforward, a feedback controller is proposed that varies its gains based on the predicted model confidence, ensuring robustness of the overall scheme. Moreover, rigorous theoretical proofs of Lyapunov stability and boundedness guarantees of the proposed method-driven closed-loop system are provided in the probabilistic sense. A simulation study based on a high-fidelity simulator is used to show the effectiveness of the proposed strategy and demonstrate its high performance. © 2023, CC BY-NC-SA.

关键词： Maneuverability

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Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models

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IFAC-PapersOnLine 2023年第2期56卷 5152-5157页

作者： Sarvin Moradi Nick Jaensson Roland Tóth Maarten Schoukens Control System group Eindhoven University of Technology Eindhoven the Netherlands Eindhoven Artificial Intelligence Systems Institute Eindhoven the Netherlands Processing and Performance of Materials Group Eindhoven University of Technology Eindhoven the Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In order to make data-driven models of physical systems interpretable and reliable, it is essential to include prior physical knowledge in the modeling framework. Hamiltonian Neural Networks (HNNs) implement Hamiltonian theory in deep learning and form a comprehensive framework for modeling autonomous energy-conservative systems. Despite being suitable to estimate a wide range of physical system behavior from data, classical HNNs are restricted to systems without inputs and require noiseless state measurements and information on the derivative of the state to be available. To address these challenges, this paper introduces an Output Error Hamiltonian Neural Network (OE-HNN) modeling approach to address the modeling of physical systems with inputs and noisy state measurements. Furthermore, it does not require the state derivatives to be known. Instead, the OE-HNN utilizes an ODE-solver embedded in the training process, which enables the OE-HNN to learn the dynamics from noisy state measurements. In addition, extending HNNs based on the generalized Hamiltonian theory enables to include external inputs into the framework which are important for engineering applications. We demonstrate via simulation examples that the proposed OE-HNNs results in superior modeling performance compared to classical HNNs.

关键词： Hamiltonian Neural Networks Nonlinear system identification Machine learning

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Multi-gated perimeter flow control for monocentric cities: Efficiency and equity

arXiv

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arXiv 2024年

作者： Jusoh, Ruzanna Mat Ampountolas, Konstantinos Department of Defence Science National Defence University of Malaysia Kuala Lumpur57000 Malaysia Automatic Control & Autonomous Systems Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece

A control scheme for the multi-gated perimeter traffic flow control problem of monocentric cities is presented. The proposed scheme determines feasible and optimally distributed input flows for various gates located at the periphery of a protected network area. A parsimonious model is employed to describe the traffic dynamics of the protected network. To describe traffic dynamics outside of the protected area, the basic state-space model is augmented with additional state variables to account for vehicle queues at store-and-forward origin links at the periphery. The multi-gated perimeter flow control problem is formulated as a convex optimisation problem with finite horizon, and constrained control and state variables. This scheme aims to equalise the relative queues at origin links and to maintain the vehicle accumulation in the protected network around a desired set point, while the system’s throughput is maximised. For real-time control, the optimal control problem is embedded in a rolling-horizon scheme using the current state of the whole system as the initial state as well as predicted demand flows at origin/entrance links. Furthermore, practical flow allocation policies for single-region perimeter control strategies without explicitly considering entrance link dynamics are presented. These policies allocate a global perimeter-ordered flow to a number of candidate gates at the periphery of a protected network area by taking into account the different geometric characteristics of origin links. The proposed flow allocation policies are then benchmarked against the multi-gated perimeter flow control. A meticulous study is carried out for a 2.5 square mile protected network area of San Francisco, CA, including fifteen gates of different geometric characteristics. The results showed that the proposed approach is able to manage excessive queues outside of the protected network area and to optimally distribute the input flows, which confirms its efficiency and equity

关键词： Efficiency

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Evaluation of importance for condition attributes based on quality of decision reducts

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Procedia computer science 2022年 207卷 2144-2153页

作者： Urszula Stańczyk Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

Relative or decision reducts belong with mechanisms dedicated to feature selection, and they are embedded in rough set approach to data processing. Algorithms for reduct construction typically aim at dimensionality reduction aspect, searching for smallest reducts, which are considered as the most advantageous from the point of view of knowledge representation. However, classifiers build on reduced data models, based on reducts, can significantly vary in performance. Therefore, to ensure quality of predictions, other characteristics of reducts, apart from their cardinalities, need to be taken into account. The paper presents research in which estimation of reduct quality through their characteristics was reflected in calculation of the proposed weighting factors leading to attribute rankings. These rankings were next employed in the process of filtering decision rules, inferred by classic rough set approach. Constructed rule-based classifiers were applied in the stylometric domain to solve a task of authorship attribution.

关键词： Decision reduct Rough set theory Ranking Weighting Feature selection Rule filtering

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On heterogeneity or sub-classes aspect in construction of stylometric input datasets

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Procedia computer science 2022年 207卷 2526-2535页

作者： Urszula Stańczyk Grzegorz Baron Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

Stylometric analysis of texts relies on learning characteristic traits of writing styles for authors. Once these patterns are discovered, they can be compared to the ones present in other text samples, to recognise their authorship. This recognition can be compromised if input datasets are prepared without taking into consideration possible stratification of the input space, leading to specific grouping of datapoints, or sub-classes within distinguished classes. The paper shows research dedicated to construction of various structures of input datasets, and combinations of such structures between train and test sets. In the research the influence of different stratification forms on the performance of selected popular classification systems was observed. To minimise the number of influencing factors, a task of authorship attribution was performed as binary classification with balanced classes. Stylometric descriptors exploited belonged to lexical and syntactic group, giving frequencies of occurrence for chosen style-markers. It resulted in real-valued attributes and these values were explored without applying discretisation, in order to avoid the possible bias of this procedure on observations.

关键词： Stylometry Dataset construction Heterogeneity Sub-classes Stratification Authorship attribution

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Consistent and Asymptotically Statistically-Efficient Solution to Camera Motion Estimation

arXiv

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arXiv 2024年

作者： Zeng, Guangyang Zeng, Qingcheng Li, Xinghan Mu, Biqiang Chen, Jiming Shi, Ling Wu, Junfeng The School of Data Science Chinese University of Hong Kong Shenzhen Shenzhen518172 China The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Given 2D point correspondences between an image pair, inferring the camera motion is a fundamental issue in the computer vision community. The existing works generally set out from the epipolar constraint and estimate the essential matrix, which is not optimal in the maximum likelihood (ML) sense. In this paper, we dive into the original measurement model with respect to the rotation matrix and normalized translation vector and formulate the ML problem. We then propose a two-step algorithm to solve it: In the first step, we estimate the variance of measurement noises and devise a consistent estimator based on bias elimination;In the second step, we execute a one-step Gauss-Newton iteration on manifold to refine the consistent estimate. We prove that the proposed estimate owns the same asymptotic statistical properties as the ML estimate: The first is consistency, i.e., the estimate converges to the ground truth as the point number increases;The second is asymptotic efficiency, i.e., the mean squared error of the estimate converges to the theoretical lower bound - Cramer-Rao bound. In addition, we show that our algorithm has linear time complexity. These appealing characteristics endow our estimator with a great advantage in the case of dense point correspondences. Experiments on both synthetic data and real images demonstrate that when the point number reaches the order of hundreds, our estimator outperforms the state-of-the-art ones in terms of estimation accuracy and CPU time. Copyright © 2024, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Traditional Chinese Medicine Health Status Identification with Graph Attention Network 1

引用

4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Fu, Amin Ma, Jishun Wang, Chuansheng Zhou, Changen Li, Zuoyong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona08034 Spain Research Base of Traditional Chinese Medicine Syndrome Fujian University of Traditional Chinese Medicine Fuzhou350122 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The Traditional Chinese Medicine Health Status Identification plays an important role in TCM diagnosis and prescription recommendation. In this paper, we propose a method of Status Identification via Graph Attention Network, named SIGAT, which captures the complex medical correlation in the symptom-syndrome graph. More specifically, we construct a symptom-syndrome graph in that symptoms are taken as nodes and the edges are connected by syndromes. And we realize automatic induction of symptom to state element classification by using the attention mechanism and perceptron classifier. Finally, we conduct experiments by using hamming loss, coverage, 0/1 error, ranking loss, average precision, macro-F1 score, and micro-F1 score as evaluation metrics. The results demonstrate that the SIGAT model outperforms comparison algorithms on Traditional Chinese Medicine Prescription Dictionary dataset. The case study results suggest that the proposed method is a valuable way to identify the state element. The application of the graph attention network classification algorithm in TCM health status identification is of high precision and methodological feasibility. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Scientific Research Transformation Needed for Comprehensive Artificial Intelligence Implementation

Scientific Research Transformation Needed for Comprehensive ...

引用

International Conference on Management of Large-Scale System Development (MLSD)

作者： Viktor Medennikov Larisa Bogatyreva Information and Computing Systems Department Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russia Laboratory of modular data processing and control systems V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Moscow Russia

ISBN: (数字)9798350375718

ISBN: (纸本)9798350375725

The paper deals with complementary changes in scientific research and real economy (exemplified by the farming industry) taking place when using the holistic approach to the industry”s digital transformation resulting in the appearance of precise agrarian technologies that require a comprehensive., system-based combination of research and production resources that are able to ensure increased productivity in the industry. We demonstrate that among these new technologies, the artificial intelligence methods have a special significance. They have to, however, go through integration transformations to become standards for the proposed common digital farming management platform.

关键词： Productivity Digital transformation Large-scale systems Artificial intelligence Standards Farming

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