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Assessing quality of decision reducts

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Procedia computer Science 2020年 176卷 3273-3282页

作者： Urszula Stańczyk Beata Zielosko 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 Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 41-200 Sosnowiec Poland

The paper presents research focused on decision reducts, a feature reduction mechanism inherent to rough sets theory. As a reduct enables to protect the discriminative properties of attributes with respect to described concepts, from the point of data representation, a reduct length is considered to be the most important measure of its quality. However, such approach is insufficient while taking into account the performance of a reduct-based rule classifier applied to test samples. When many reducts of the same length are available, they can lead to vastly different predictions. The paper provides a description for the proposed procedure for iterative reduct generation, which results in decrease of diversity in the observed levels of accuracy, supporting reduct selection. The procedure was applied for binary classification with balanced classes, for the stylometric task of authorship attribution.

关键词： reduct rough sets decision rules classification stylometry feature reduction

A market-based approach for enabling inter-area reserve exchange

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arXiv 2021年

作者： Karaca, Orcun Delikaraoglou, Stefanos Kamgarpour, Maryam Automatic Control Laboratory D-ITET ETH Zürich ETL K12 Physikstrasse 3 Zürich8092 Switzerland Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA United States Electrical and Computer Engineering Department University of British Columbia Vancouver Canada

Considering the sequential clearing of energy and reserves in Europe, enabling inter-area reserve exchange requires optimally allocating inter-area transmission capacities between these two markets. To achieve this, we provide a market-based allocation framework and derive payments with desirable properties. The proposed min-max least core selecting payments achieve individual rationality, budget balance, and approximate incentive compatibility and coalitional stability. The results extend the works on private discrete items to a network of continuous public choices. Copyright © 2021, The Authors. All rights reserved.

关键词： Budget control

A spectral small-gain condition for input-to-state stability of infinite networks

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IFAC-PapersOnLine 2020年第2期53卷 5303-5308页

作者： Christoph Kawan Andrii Mironchenko Abdalla Swikir Navid Noroozi Majid Zamani Institute of Informatics Ludwig Maximilian University Munich Germany Faculty of Computer Science and Mathematics University of Passau Germany Department of Electrical and Computer Engineering Technical University of Munich Germany Otto-von-Guericke University Magdeburg Laboratory for Systems Theory and Automatic Control Germany Computer Science Department University of Colorado Boulder USA

This paper presents a tight small-gain theorem for networks composed of infinitely many finite-dimensional subsystems. Assuming that each subsystem is exponentially input-to-state stable, we show that if the gain operator, collecting all the information about the internal Lyapunov gains, has a spectral radius less than one, the overall infinite network is exponentially input-to-state stable. We illustrate the effectiveness of our result by applying it to traffic networks.

关键词： Nonlinear systems small-gain theorems infinite-dimensional systems input-to-state stability Lyapunov methods large-scale systems

Decentralized PID control with inverted decoupling and superheating reference generation for efficient operation: Application to the Benchmark PID 2018

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IFAC-PapersOnLine 2018年第4期51卷 710-715页

作者： Garrido, J. Lara, M. Ruz, M.L. Vázquez, F. Alfaya, J.A. Morilla, F. Computer Science and Numerical Analysis Department University of Cordoba Cordoba14071 Spain Systems Engineering and Automatic Control Department University of Sevilla Sevilla41092 Spain Computer Science and Automatic Control Department UNED Madrid Spain

This paper deals with the control problem of a refrigeration vapor compression system proposed as a benchmark for the IFAC Conference on Advances in PID controllers (PID'18). This refrigeration system is a multivariable nonlinear process that shows interactions and is subjected to input constraints. In this work, a decentralized PID control with inverted decoupling is proposed as control structure. The inverted decoupling is designed from an approximated multivariable linear model. Then, the PID controllers are tuned by genetic algorithms to minimize a performance index. In addition, a superheat reference generation is developed to achieve stationary operation points with maximum coefficient of performance which is a widespread efficiency measurement in these systems. Simulations considered in the benchmark show that the proposed design achieves better performance than the reference case. © 2018

关键词： Three term control systems Benchmarking controllers Electric control equipment Genetic algorithms Proportional control systems Vapor compression refrigeration Coefficient of Performance Decentralized PID control Decoupling controls Efficiency measurement Optimal operation Stationary operations Vapor compression refrigeration cycle Vapor compression systems

Dynamic sequential neighbor processing: A liquid neural network-inspired framework for enhanced graph neural networks

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Information Sciences 2025年 719卷

作者： Kuijie Zhang Shanchen Pang Yuanyuan Zhang Yun Bai Luqi Wang Jerry Chun-Wei Lin State Key Laboratory of Chemical Safety Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software the Qingdao Institute of Software China University of Petroleum Qingdao 266580 China School of information and control engineering Qingdao University of Technology Shandong266520 China Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and IT Devices Silesian University of Technology 44-100 Gliwice Poland

Integrating information from multi-order neighborhoods is a fundamental strategy in Graph Neural Networks (GNNs) for capturing higher-order structural patterns and enhancing the expressive power of node representations. However, most existing GNNs treat neighbors from different orders as unordered sets and integrate them using static or parallel strategies, thus overlooking the sequential and evolving nature of neighborhood expansion. To address this limitation, we propose a novel GNN framework, SL, which integrates Serialized Neighbor Features with Liquid Neural Networks (LNNs) to enable order-aware, dynamic adaptation of neighbor influence. By modeling neighbor features as ordered sequences and leveraging LNNs' internal feedback dynamics, SL adapts feature extraction in real time based on local context and propagation history. This design offers fine-grained control over hierarchical dependencies and allows dynamic modulation of contributions from different neighborhood layers. SL is model-agnostic and can be seamlessly integrated with both classical and state-of-the-art GNNs. Extensive experiments across ten benchmark datasets show that SL consistently improves node classification accuracy and significantly alleviates over-smoothing in deep GNNs. These results highlight that order-aware and dynamically regulated propagation represents a powerful, flexible alternative to traditional multi-order aggregation, enhancing the adaptability and expressiveness of GNNs for complex graph learning tasks.

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Combining Trajectory Data with Analytical Lyapunov Functions for Improved Region of Attraction Estimation

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arXiv 2021年

作者： Lugnani, Lucas Jones, Morgan Alberto, Luís F.C. Peet, Matthew Dotta, Daniel Faculty of Electrical and Computer Engineering University of Campinas Brazil The Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom The São Carlos Engineering School University of São Paulo São Carlos Brazil The School for the Engineering of Matter Transport and Energy Arizona State University Tempe United States

The increasing uptake of inverter based resources (IBRs) has resulted in many new challenges for power system operators around the world. The high level of complexity of IBR generators makes accurate classical model-based stability analysis a difficult task. This paper proposes a novel methodology for solving the problem of estimating the Region of Attraction (ROA) of a nonlinear system by combining classical model based methods with modern data driven methods. Our method yields certifiable inner approximations of the ROA, typical to that of model based methods, but also harnesses trajectory data to yield an improved accurate ROA estimation. The method is carried out by using analytical Lyapunov functions, such as energy functions, in combination with data that is used to fit a converse Lyapunov function. Our methodology is independent of the function fitting method used. In this work, for implementation purposes, we use Bernstein polynomials to function fit. Several numerical examples of ROA estimation are provided, including the Single Machine Infinite Bus (SMIB) system, a three machine system and the Van-der-Pol system. Copyright © 2021, The Authors. All rights reserved.

关键词： Lyapunov functions

A global to local double embedding method for multi-person pose estimation

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arXiv 2021年

作者： Xu, Yiming Li, Jiaxin Peng, Yiheng Ding, Yan Wei, Hualiang Yingcai Honors College University of Electronic Science and Technology of China Chengdu China Key Laboratory of Dynamics and Control of Flight Vehicle Ministry of Education School of Aerospace Engineering Beijing Institute of Technology Beijing100081 China School of Computer Science and Technology Donghua University Shanghai China Department of Automatic Control and Systems Engineering University of Shefield ShefieldS1 3JD United Kingdom

Multi-person pose estimation is a fundamental and challenging problem to many computer vision tasks. Most existing methods can be broadly categorized into two classes: top-down and bottom-up methods. Both of the two types of methods involve two stages, namely, person detection and joints detection. Conventionally, the two stages are implemented separately without considering their interactions between them, and this may inevitably cause some issue intrinsically. In this paper, we present a novel method to simplify the pipeline by implementing person detection and joints detection simultaneously. We propose a Double Embedding (DE) method to complete the multi-person pose estimation task in a global-to-local way. DE consists of Global Embedding (GE) and Local Embedding (LE). GE encodes different person instances and processes information covering the whole image and LE encodes the local limbs information. GE functions for the person detection in top-down strategy while LE connects the rest joints sequentially which functions for joint grouping and information processing in A bottom-up strategy. Based on LE, we design the Mutual Refine Machine (MRM) to reduce the prediction difficulty in complex scenarios. MRM can effectively realize the information communicating between keypoints and further improve the accuracy. We achieve the competitive results on benchmarks MSCOCO, MPII and CrowdPose, demonstrating the effectiveness and generalization ability of our method. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

Asymptotic Learning Requirements for Stealth Attacks on Linearized State Estimation

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arXiv 2021年

作者： Sun, Ke Esnaola, Iñaki Tulino, Antonia M. Poor, H. Vincent The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The DIETI Università degli Studi di Napoli Federico II Naples80138 Italy The ECE Department New York University Tandon School of Engineering BrooklynNY10003 United States

Information-theoretic stealth attacks are data injection attacks that minimize the amount of information acquired by the operator about the state variables, while simultaneously limiting the Kullback-Leibler divergence between the distribution of the measurements under attack and the distribution under normal operation with the aim of controling the probability of attack detection. For Gaussian distributed state variables, attack construction requires knowledge of the second order statistics of the state variables, which is estimated from a finite number of past realizations using a sample covariance matrix. Within this framework, the attack performance is studied for the attack construction with the sample covariance matrix. This results in an analysis of the amount of data required to learn the covariance matrix of the state variables used on the attack construction. The ergodic attack performance is characterized using asymptotic random matrix theory tools, and the variance of the attack performance is bounded. The ergodic performance and the variance bounds are assessed with simulations on IEEE test systems. © 2021, CC BY-NC-ND.

关键词： Covariance matrix

Lecture Notes in Networks and Systems

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Preface

Lecture Notes in Networks and systems 2024年 1076 LNNS卷 v-vii页

作者： Biele, Cezary Kacprzyk, Janusz Kopeć, Wieslaw Możaryn, Jakub Owsiński, Jan W. Romanowski, Andrzej Sikorski, Marcin National Information Processing Institute Warszaw Poland Polish Academy of Sciences Systems Research Institute Warsaw Poland Polish-Japanese Academy of Information Technology Warsaw Poland Institute of Automatic Control and Robotics Faculty of Mechatronics Warsaw University of Technology Warsaw Poland Institute of Applied Computer Science Faculty of Electrical Electronic Computer and Control Engineering Lódż University of Technology LODZ Poland Department of Applied Informatics in Management Faculty of Management and Economics Gdańsk University of Technology Gdańsk Poland