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arXiv 2024年

作者： Hoekstra, Jan H. Verhoek, Chris Tóth, Roland Schoukens, Maarten Control Systems Group Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Nonlinear system identification (NL-SI) has proven to be effective in obtaining accurate models for highly complex systems. Especially, recent encoder-based methods for artificial neural networks state-space (ANN-SS) models have achieved state-of-the-art performance on various benchmarks, while offering consistency and computational efficiency. The inclusion of prior knowledge of the system can be exploited to increase (i) estimation speed, (ii) accuracy, and (iii) interpretability of the resulting models. This paper proposes an encoder based model augmentation method incorporating prior knowledge from first-principles (FP) models. We introduce a novel linear-fractional-representation (LFR) model structure that allows for the unified representation of various augmentation structures including the ones that are commonly used in the literature, and an identification algorithm for estimating the proposed structure together with appropriate initialization methods. The performance and generalization capabilities of the proposed method are demonstrated on a hardening mass-spring-damper simulation. © 2024, CC BY-NC-SA.

关键词： Nonlinear systems

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Immoral post detection using a one-dimensional convolutional neural network-based LSTM network

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Multimedia Tools and Applications 2025年 1-20页

作者： Saqia, Bibi Khan, Khairullah Rahman, Atta Ur Khan, Aurangzeb Jain, Deepak Kumar Department of Computer Science University of Science and Technology Bannu28100 Pakistan Riphah Institute of Systems Engineering Riphah International University Islamabad46000 Pakistan Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China Symbiosis Institute of Technology Symbiosis International University Pune India

In text mining and Natural Language Processing (NLP), extracting emotions from textual data is gaining rapid attraction. The proliferation of online content and the freedom of expression on social media platforms has compelled the advancement of more sophisticated methods for the automatic identification of unwanted content. The One-Dimensional Convolutional Neural Network (1D-CNN) performs best for feature extraction, while the Long Short-Term Memory Network (LSTM) is well-known for extracting sequential features and classification of textual data. This study proposes a novel solution by combining 1D-CNN with LSTM. Utilizing a context-aware model, this study aims to enhance the detection of immoral posts, including hate speech and other forms of immoral content on social media platforms. Various posts are extracted from social media, which are then processed using Glove word embedding models. It depicts the word representations in the text, which yields the form of a real-valued vector determining the words' meaning. The 1D convolution utilizes a filter window over the input time-series data to extract features. Relying on the acquired parameters of the filters, they act as feature extractors. This feature extraction technique allows the model to distinguish detailed patterns and nuances from the textual contents of posts. The sequential processing capabilities of the LSTM network enable an in-depth examination of the text, capturing temporal relationships and context, which is critical for successful content categorization. To validate the proposed model, we utilized a benchmark dataset, called the Twitter dataset. The proposed model achieved the highest accuracy of 95.37% and outperforms state-of-the-art works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Heuristic Algorithm for Solving Travelling Salesman Problem: Adding the Endpoints of the Longest Edges 5

Heuristic Algorithm for Solving Travelling Salesman Problem:...

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5th International Conference on Problems of Cybernetics and Informatics, PCI 2023

作者： Nuriyeva, Fidan Dokuz Eylul University Department of Computer Science Izmir Turkey Institute of Control Systems Laboratory of Recognition Identification and Methods of Optimal Solutions Baku Azerbaijan

ISBN: (纸本)9798350319064

This study proposes, a new heuristic algorithm based on insertion heuristic to solve the symmetric Travelling Salesman Problem. In the proposed algorithm, firstly the two longest edges are selected and the starting tour passing through their endpoints is constructed, then the endpoints of the next longest edges are added to this tour and the solution passing through all the vertices is found. This problem is known to be NP-Hard. The proposed method was successfully tested on different-sized problems. The experimental results show that the proposed algorithm outperforms many existing methods. © 2023 IEEE.

关键词： Traveling salesman problem

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Secure Synchronization of Heterogeneous Pulse-Coupled Oscillators

arXiv

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arXiv 2024年

作者： Yan, Jiaqi Ishii, Hideaki The Automatic Control Laboratory ETH Zurich Switzerland The Department of Computer Science Tokyo Institute of Technology Japan

In this paper, we consider the synchronization of heterogeneous pulse-coupled oscillators (PCOs), where some of the oscillators might be faulty or malicious. The oscillators interact through identical pulses at discrete instants and evolve continuously with different frequencies otherwise. Despite the presence of misbehaviors, benign oscillators aim to reach synchronization. To achieve this objective, two resilient synchronization protocols are developed in this paper by adapting the real-valued mean-subsequence reduced (MSR) algorithm to pulse-based interactions. The first protocol relies on packet-based communication to transmit absolute frequencies, while the second protocol operates purely with pulses to calculate relative frequencies. In both protocols, each normal oscillator periodically counts the received pulses to detect possible malicious behaviors. By disregarding suspicious pulses from its neighbors, the oscillator updates both its phases and frequencies. The paper establishes sufficient conditions on the initial states and graph structure under which resilient synchronization is achieved in the PCO network. Specifically, the normal oscillators can either detect the presence of malicious nodes or synchronize in both phases and frequencies. Additionally, a comparison between the two algorithms reveals a trade-off between relaxed initial conditions and reduced communication burden. © 2024, CC BY.

关键词： Synchronization

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Conservative-Progressive Collaborative Learning for Semi-Supervised Semantic Segmentation

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IEEE Transactions on Image Processing 2023年 32卷 6183-6194页

作者： Fan, Siqi Zhu, Fenghua Feng, Zunlei Lv, Yisheng Song, Mingli Wang, Fei-Yue Institute of Automation State Key Laboratory of Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China Tsinghua University Beijing100190 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China

Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels. Addressing that, we propose a novel learning approach, called Conservative-Progressive Collaborative Learning (CPCL), among which two predictive networks are trained in parallel, and the pseudo supervision is implemented based on both the agreement and disagreement of the two predictions. One network seeks common ground via intersection supervision and is supervised by the high-quality labels to ensure a more reliable supervision, while the other network reserves differences via union supervision and is supervised by all the pseudo labels to keep exploring with curiosity. Thus, the collaboration of conservative evolution and progressive exploration can be achieved. To reduce the influences of the suspicious pseudo labels, the loss is dynamic re-weighted according to the prediction confidence. Extensive experiments demonstrate that CPCL achieves state-of-the-art performance for semi-supervised semantic segmentation. © 1992-2012 IEEE.

关键词： Semantic Segmentation

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Parameters Extraction of Photovoltaic Cell Using Particle Swarm Optimization 4th

Parameters Extraction of Photovoltaic Cell Using Particle Sw...

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4th International Conference on Electronic Engineering and Renewable Energy systems, ICEERE 2024

作者： Chermite, Charaf Douiri, Moulay Rachid Elamri, Oumaymah Control and Computer Science for Intelligent Systems and Green Energy Department of Applied Physics Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco LAMIGEP EMSI of Marrakech Systems and Applications Engineering Laboratory National School of Applied Sciences Cadi Ayyad University Marrakech Marrakesh Morocco

ISBN: (纸本)9789819799749

Extracting parameters accurately and effectively from solar photovoltaic (PV) models is crucial for detailed simulation, evaluation, and management of PV systems. Although there has been an increase in the development of analytical, numerical, and metaheuristic methods targeting this task, accurately determining these parameters continues to be a significant challenge. This study focuses on applying Particle Swarm Optimization (PSO) for the extraction of key parameters of RTC France solar cells, a critical step towards enhancing PV system efficiency and reliability. Additionally, the PSO's performance is evaluated against other metaheuristic algorithms including Enhanced Chaotic JAYA (CJAYA), Performance-Guided JAYA (PGJAYA), and Enhanced Gradient-Based Optimizer (EGBO), underscoring the effectiveness of the proposed PSO-based approach. Utilizing MATLAB-SIMULINK, comprehensive analysis and results are presented, shedding light on the efficacy of PSO in addressing the intricacies of parameter extraction in PV systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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Management of Scientific Agricultural Digital Ecosystems Based on Ecosystem Classification 16

Management of Scientific Agricultural Digital Ecosystems Bas...

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Kulba, Vladimir Medennikov, Viktor V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Laboratory of Modular Data Processing and Control Systems Moscow Russia Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Information and Computing Systems Department Moscow Russia

ISBN: (纸本)9798350337907

The paper considers a mathematical model for managing a scientific digital ecosystem exemplified by the agriculture case;the model enables assessing the impact science exerts on the regions' socioeconomic position in order to build an adequate response to the advanced economies' regime of severe sanctions against Russia. For these purposes, a systemic analysis of the subject identification and classification of ecosystems was conducted to address the critical issue of determining the basic concepts and terms of the country's digitalization. © 2023 IEEE.

关键词： Agriculture

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Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning 32

Uncertainty-Informed Model Selection Method for Nonlinear Sy...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Gu, Yuanlin Wei, Hua-Liang University of Stirling Division of Computer Science and Maths StirlingFK9 4LA United Kingdom University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom

ISBN: (纸本)9798350395440

Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks. © 2024 IEEE.

关键词： Machine learning

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On Convergence in Distribution of Stein's Unbiased Risk Hyper-parameter Estimator for Regularized System Identification 41

On Convergence in Distribution of Stein's Unbiased Risk Hype...

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第41届中国控制会议

作者： Yue Ju Tianshi Chen Biqiang Mu Lennart Ljung School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of Sciences Division of Automatic Control Department of Electrical EngineeringLink?ping University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Asymptotic theory for the regularized system identification has received increasing interests in recent *** this paper,for the finite impulse response(FIR) model and filtered white noise inputs,we show the convergence in distribution of the Stein's unbiased risk estimator(SURE) based hyper-parameter estimator and find factors that influence its convergence *** particular,we consider the ridge regression case to obtain closed-form expressions of the limit of the regression matrix and the variance of the limiting distribution of the SURE based hyper-parameter estimator,and then demonstrate their relation numerically.

关键词： Asymptotic theory Stein's unbiased risk estimator Hyper-parameter estimator Regularized system identification

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The Model of Managing a Group of UAVs by a Single Operator for Tasks Requiring Increased Attention

The Model of Managing a Group of UAVs by a Single Operator f...

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2023 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2023

作者： Khachumov, Mikhail Khachumov, Vyacheslav Intelligent Control Laboratory Ailamazyan Program Systems Institute of Ras Pereslavl-Zalessky Russia Rudn University Moscow Russia Federal Research Center Computer Science and Control of Ras Moscow Russia

ISBN: (纸本)9781665475952

The problem of optimizing the load on an operator of unmanned aerial vehicles (UAVs), which performs real-time tasks of researching and monitoring territories in an unstable environment is considered. Working load depends on the intensity of the information exchange between onboard and ground control stations and is determined by the agreed limit that leads to a limitation in the number of managed vehicles. As a mathematical model of the multi-functional control system, we use a closed-loop queuing system, which allows to establish coordination among its major elements (onboard computing complex, ground control station and operator) and reduce the loss of service requests. The latter is one of the main requirements to functioning of the system during emergencies. Of considerable interest are optimization statements for the problems of distribution of functions between elements and choosing the load modes. We gave formulations of some optimization problems in a general form and made appropriate conclusions. It is assumed that as an applied task, the problem of monitoring fire sites in forest areas can be considered, which requires increased attention from the operator to process information coming to the ground control station. © 2023 IEEE.

关键词： Distribution functions

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