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Stability Region Patterns in Dominant Pole Placement based PID Controller Design for SOPTD Systems

Chemical Engineering science 2025年

作者： Kaushik Halder Saptarshi Das School of Computing & Electrical Engineering Indian Institute of Technology Mandi Mandi Himachal Pradesh Kamand 175005 India Faculty of Environment Science and Economy Centre for Environmental Mathematics University of Exeter Penryn Campus Cornwall TR10 9FE United Kingdom Institute for Data Science and Artificial Intelligence University of Exeter North Park Road Exeter Devon EX4 4QE United Kingdom

This paper proposes a new analytical formulation to design dominant pole placement based proportional-integral-derivative (PID) controllers for handling second order retarded type delay differential equations (DDEs) or in the control systems community, commonly known as the second order plus time delay (SOPTD) systems. This proposed method does not need any finite term approximation like different orders of Pade approach to derive the quasi-polynomial characteristic equation for analytical stability analysis. Rather the proposed method uses a mapping by which the transcendental exponential delay terms in the characteristic polynomial is transformed into finite number of discrete time poles by a suitable choice of the sampling time. Here, we explore a specific case, where the choice of sampling time makes the order of characteristic polynomial in the discrete time or z -domain being an odd number. The PID controllers are discretized by both Euler’s and Tustin’s method for a comparison between these two approaches. The corresponding PID controller gains are obtained using the dominant pole placement criterion. Effectiveness of the proposed methodology are demonstrated on several SOPTD test-bench plants with different lag to delay ratios and open loop damping levels, using credible numerical simulations. Here, the novel finding is that we obtain irregular, discontinuous, multi-modal stability regions for the odd number of dominant pole placement problem through optimization whereas similar previous works have reported continuous stability regions when attempted for even number of non-dominant pole placement problems.

关键词： dominant pole placement PID controller SOPTD plant pole-zero matching Euler’s and Tustin’s discretization optimization-based control

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Anomaly detection on attributed networks via contrastive self-supervised learning

arXiv

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arXiv 2021年

作者： Liu, Yixin Li, Zhao Pan, Shirui Gong, Chen Zhou, Chuan Karypis, George The Department of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia The Alibaba Group Hangzhou310000 China The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing The Hong Kong Polytechnic University Hong Kong The Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100093 China The Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States

Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this paper, we present a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA for abbreviation). Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multi-round predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the graph neural network module is batches of instance pairs instead of the full network, our framework can adapt to large networks flexibly. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all seven benchmark datasets. © 2021, CC BY.

关键词： Anomaly detection

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Retraction Note: Deep learning for predicting the onset of type 2 diabetes: enhanced ensemble classifier using modified t-SNE

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Multimedia Tools and Applications 2024年第40期83卷 88553-88553页

作者： Pokharel, Monima Alsadoon, Abeer Nguyen, Tran Quoc Vinh Al-Dala’in, Thair Pham, Duong Thu Hang Prasad, P. W. C. Mai, Ha Thi School of Computing Mathematics and Engineering Charles Sturt University (CSU) Sydney Australia School of Computer Data and Mathematical Sciences Western Sydney University (WSU) Sydney Australia Kent Institute Australia Sydney Australia Asia Pacific International College (APIC) Sydney Australia The University of Da Nang – University of Science and Education Faculty of Information Technology Da Nang Vietnam

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A Parallel and Improved Quadrivalent Quantum-Inspired Gravitational Search Algorithm in Optimal Design of WSNs 2nd

A Parallel and Improved Quadrivalent Quantum-Inspired Gravit...

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2nd International Congress on High-Performance computing and Big data Analysis, TopHPC 2019

作者： Mirhosseini, Mina Fazlali, Mahmood Gaydadjiev, Georgi Department of Data and Computer Science Faculty of Mathematical Sciences GC Shahid Beheshti University Tehran Iran Department of Computing Imperial College London London United Kingdom Maxeler Technologies Ltd London United Kingdom

ISBN: (纸本)9783030334949

Wireless Sensor Networks (WSNs) are recently used in monitoring applications. One of the most important challenges in WSNs is determining the operational mode of sensors, decreasing the energy consumption while the connectivity requirements and the special properties are satisfied. This problem is an NP-hard one and is a time–consuming progress. In this study, an improved version of quadrivalent quantum-inspired gravitational search algorithm as a new metaheuristic, well suitable for quadrivalent problems is proposed using Not Q-Gate to optimize the performance of the WSN. Beside, to enhance the speed and the accuracy of the algorithm more, we used a parallelizing technique using Open-MP. Parallelizing this algorithm on mentioned problem is useful from four aspects;1 - accelerate the speed of the algorithm, 2 - improving the quality of solutions by letting the increasing the population size, 3 - The possibility of using the algorithm, in larger-scale WSNs and 4 - power affectivity of the base station using multicore processors. To validate the performance of our proposed approach, a comparison between this approach and the previous methods is performed. Our experiments verified that the proposed method can effectively improve the performance more than 2.25 times and the speedup faster than 4 times on an 8-core CPU. © 2019, Springer Nature Switzerland AG.

关键词： Wireless sensor networks

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Design and analysis of a 2D discrete memristive map

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Physica Scripta 2025年第7期100卷 075219-075219页

作者： Haiwei Sang Qiao Wang Yuling Chen Xiong Yu Feifei Wu Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing College of Mathematics and Big Data Guizhou Education University Guiyang 550018 People’s Republic of China School of artificial intelligence Guangzhou University 510006 People’s Republic of China State Key Laboratory of Public Big Data Guizhou University Guiyang 550025 People’s Republic of China Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor 43600 Malaysia

This study proposes a novel 2D memristive hyperchaotic map (2DMHM) with hyperbolic tangent and absolute value functions. The 2DMHM exhibits an infinite of fixed points in a set of lines on the y-axis, with stability characteristics partitioned across memristor parameters and initial condition planes. System dynamics are systematically investigated through bifurcation analysis, Lyapunov exponent spectra, and basin of attraction, trajectory plots, revealing remarkable multistability and initial-sensitive chaotic behavior. The map demonstrates superior spectral entropy (SE) complexity across critical parameter ranges, significantly outperforming conventional chaotic systems. A microcontroller-based digital implementation validates the physical realizability of 2DMHM, while the NIST test success rate demonstrates its exceptional performance in pseudorandom number generation. These results establish 2DMHM as a promising candidate for secure communication systems and cryptographic applications.

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Better, Not Just More: data-Centric Machine Learning for Earth Observation

arXiv

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arXiv 2023年

作者： Roscher, Ribana Rußwurm, Marc Gevaert, Caroline Kampffmeyer, Michael dos Santos, Jefersson A. Vakalopoulou, Maria Hänsch, Ronny Hansen, Stine Nogueira, Keiller Prexl, Jonathan Tuia, Devis Data Science for Crop Systems Group Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße Jülich52428 Germany Remote Sensing Group University of Bonn Niebuhrstr. 1a Bonn53113 Germany Laboratory of Geo-information Science and Remote Sensing Wageningen University Droevendaalsesteeg 3 Wageningen Gelderland6708 PB Netherlands Department of Earth Observation Science Faculty ITC University of Twente Drienerlolaan 5 Enschede Overijssel7522 NB Netherlands Department of Physics and Technology UiT The Arctic University of Norway Klokkargårdsbakken 35 Tromsø9019 Norway Department of Computer Science University of Sheffield Sheffield City Centre 211 Portobello SheffieldS1 4DP United Kingdom Münchner Str. 20 Oberpfaffenhofen-Wessling82234 Germany Computing Science and Mathematics University of Stirling StirlingFK9 4LA United Kingdom Department of Aerospace Engineering University of the Bundeswehr Munich Werner-Heisenberg-Weg 39 Neubiberg Bavaria85579 Germany Ronquos 86 Sion1951 Switzerland

Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models have been proposed, the majority of them have been solely developed on benchmark datasets that lack strong real-world relevance. Furthermore, the performance of many methods has already saturated on these datasets. We argue that a shift from a model-centric view to a complementary data-centric perspective is necessary for further improvements in accuracy, generalization ability, and real impact on end-user applications. Furthermore, considering the entire machine learning cycle—from problem definition to model deployment with feedback—is crucial for enhancing machine learning models that can be reliable in unforeseen situations. This work presents a definition as well as a precise categorization and overview of automated data-centric learning approaches for geospatial data. It highlights the complementary role of data-centric learning with respect to model-centric in the larger machine learning deployment cycle. We review papers across the entire geospatial field and categorize them into different groups. A set of representative experiments shows concrete implementation examples. These examples provide concrete steps to act on geospatial data with data-centric machine learning approaches. © 2023, CC BY.

关键词： Adversarial machine learning

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Assessing responsible innovation training

引用

Journal of Responsible Technology 2023年 16卷

作者： Stahl, Bernd Carsten Aicardi, Christine Brooks, Laurence Craigon, Peter J. Cunden, Mayen Burton, Saheli Datta Heaver, Martin De Saille, Stevienna De Dolby, Serena Dowthwaite, Liz Eke, Damian Hughes, Stephen Keene, Paul Kuh, Vivienne Portillo, Virginia Shanley, Danielle Smallman, Melanie Smith, Michael Stilgoe, Jack Ulnicane, Inga Wagner, Christian Webb, Helena School of Computer Science University of Nottingham United Kingdom Dept. of Informatics and School of Biomedical Engineering & Imaging Sciences King's College London United Kingdom Information School University of Sheffield Sheffield United Kingdom Horizon Digital Economy Research School of Computer Science University of Nottingham United Kingdom Independent Researcher Dept of Science and Technology Studies University College London United Kingdom ORBIT RRI limited Leicester United Kingdom Dept of Sociological Studies University of Sheffield Sheffield United Kingdom Centre for Computing and Social Responsibility De Montfort University Leicester United Kingdom School of Chemistry University of Bristol United Kingdom Department of Philosophy Faculty of Arts and Social Sciences Maastricht University Netherlands Biodiversity and Conservation Science Department of Biodiversity Conservation and Attractions Wildlife Research Centre Western Australia De Montfort University United Kingdom Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham United Kingdom

There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be observed concerning the question of what this training should consist of, how it should be delivered and how it could be assessed. The increasing institutional embedding of RI leads to calls for the alignment of RI training with training in other subjects. One can therefore observe a push towards the official assessment of RI training, for example in the recent call for proposals for centres for doctoral training by UK Research and Innovation. This editorial article takes its point of departure from the recognition that the RI community will need to react to the call for assessment of RI training. It provides an overview of the background and open questions around RI training and assessment as a background of examples of RI training assessment at doctoral level. There is unlikely to be one right way of assessing RI training across institutions and disciplines, but we expect that the examples provided in this article can help RI scholars and practitioners orient their training and its assessment in ways that are academically viable as well as supportive of the overall aims of RI. © 2023

关键词： Assessment Doctoral training Education Responsible innovation

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2023年 648 LNNS卷 v-vi页

作者： Abraham, Ajith Siarry, Patrick Hanne, Thomas Jesus, Isabel Zaki, Nazar Faculty of Computing and Data Science FLAME University Maharashtra Pune India Scientific Network for Innovation and Research Excellence Machine Intelligence Research Labs AuburnAL United States University of Applied Sciences and Arts Northwestern Switzerland Olten Switzerland Université Paris-Est Créteil Créteil France

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CFAD: Coarse-to-fine action detector for spatiotemporal action localization

arXiv

引用

arXiv 2020年

作者： Li, Yuxi Lin, Weiyao See, John Xu, Ning Xu, Shugong Yan, Ke Yang, Cong Department of Electronic Engineering Shanghai Jiao Tong University China Institute for Advanced Communication and Data Science Shanghai University China Faculty of Computing and Informatics Multimedia University Malaysia Adobe Research United States Clobotics China

Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is exploited and the efficiency is hindered by dense per-frame localization. In this paper, we propose Coarse-to-Fine Action Detector (CFAD), an original end-to-end trainable framework for efficient spatio-temporal action localization. The CFAD introduces a new paradigm that first estimates coarse spatio-temporal action tubes from video streams, and then refines the tubes’ location based on key timestamps. This concept is implemented by two key components, the Coarse and Refine Modules in our framework. The parameterized modeling of long temporal information in the Coarse Module helps obtain accurate initial tube estimation, while the Refine Module selectively adjusts the tube location under the guidance of key timestamps. Against other methods, the proposed CFAD achieves competitive results on action detection benchmarks of UCF101-24, UCFSports and JHMDB-21 with inference speed that is 3.3× faster than the nearest competitor. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Machine learning for modelling unstructured grid data in computational physics: a review

arXiv

引用

arXiv 2025年

作者： Cheng, Sibo Bocquet, Marc Ding, Weiping Finn, Tobias Sebastian Fu, Rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, Dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, Rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China Centre for Health Informatics Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Department of Community Health Sciences Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1E 6BT United Kingdom Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G 1M8 Canada School of Mechanical Medical and Process Engineering Faculty of Engineering Queensland University of Technology BrisbaneQLD Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025, CC BY-NC-SA.

关键词： Mesh generation

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