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H observer-based control for fuzzy Hadamard fractional-order systems

Computational and Applied Mathematics 2025年第4期44卷 1-14页

作者： Rhaima, Mohamed Gassara, Hamdi Mchiri, Lassaad El Hajjaji, Ahmed Ben Makhlouf, Abdellatif Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Laboratory of Sciences Techniques of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Sfax Tunisia Panthéon-Assas University Paris II Paris France Modeling Information and Systems Laboratory University of Picardie Jules Verne UFR of Sciences Amiens France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

While the $$H_\infty $$ observer-based control has found widespread application in the literature for integer-order systems, researchers have shown less interest in addressing the same issue within the fractional-order framework. In this context, this study delves into the application of $$H_\infty $$ observer-based control for the Hadamard fractional-order system (HFOS) described by the Takagi–Sugeno fuzzy models (TSFM). Using Lyapunov approach and by employing a matrix decoupling technique, LMI based conditions ensuring the existence of an observer and controller, are proposed. To minimize the impact of disturbances on the controlled output, $$H_\infty $$ optimization technique is used. The validity of our approach is substantiated through an example, underscoring the robustness and reliability of our proposed findings.

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A generic optimization and learning framework for Parkinson disease via speech and handwritten records

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10673-10693页

作者： Yousif, Nada R. Balaha, Hossam Magdy Haikal, Amira Y. El-Gendy, Eman M. Computer and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Parkinson’s disease (PD) is a neurodegenerative disorder with slow progression whose symptoms can be identified at late stages. Early diagnosis and treatment of PD can help to relieve the symptoms and delay progression. However, this is very challenging due to the similarities between the symptoms of PD and other diseases. The current study proposes a generic framework for the diagnosis of PD using handwritten images and (or) speech signals. For the handwriting images, 8 pre-trained convolutional neural networks (CNN) via transfer learning tuned by Aquila Optimizer were trained on the NewHandPD dataset to diagnose PD. For the speech signals, features from the MDVR-KCL dataset are extracted numerically using 16 feature extraction algorithms and fed to 4 different machine learning algorithms tuned by Grid Search algorithm, and graphically using 5 different techniques and fed to the 8 pretrained CNN structures. The authors propose a new technique in extracting the features from the voice dataset based on the segmentation of variable speech-signal-segment-durations, i.e., the use of different durations in the segmentation phase. Using the proposed technique, 5 datasets with 281 numerical features are generated. Results from different experiments are collected and recorded. For the NewHandPD dataset, the best-reported metric is 99.75% using the VGG19 structure. For the MDVR-KCL dataset, the best-reported metrics are 99.94% using the KNN and SVM ML algorithms and the combined numerical features;and 100% using the combined the mel-specgram graphical features and VGG19 structure. These results are better than other state-of-the-art researches. © 2022, The Author(s).

关键词： Feature extraction

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SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 PP卷 PP页

作者： Jun Chen Liling Yang Wei Yu Wenping Gong Zhanchuan Cai Jiayi Ma School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Faculty of Engineering China University of Geosciences Wuhan China School of Computer Science and Engineering Macau University of Science and Technology Taipa Macau China Electronic Information School Wuhan University Wuhan China

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. The code will be posted at: https://***/Liling-yang/SDSFusion.

关键词： Degradation Image fusion Semantics Training Visualization Meteorology Feature extraction Lighting Image segmentation Brightness

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on control of Network systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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A concept for an intelligent and fault-tolerant robot system

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Journal of Intelligent and Robotic systems 1988年第3期1卷 259-286页

作者： Hörmann, A. Hugel, Th. Meier, W. Institute for Real-Time Computer Control Systems and Robotics Faculty of Informatics University of Karlsruhe Karlsruhe Germany

A concept for the intelligent control of subsystems of a flexible assembly cell is presented. Unknown or uncertain data about the real world may lead towards failure during an assembly task. Therefore, a fault tolerant system must be capable of reacting immediately to error situations. Thus, the major topic of this paper is the dynamic handling of unforeseen situations during realtime activities. This will be achieved by combining sensor guided actions with an advanced autonomous supervision system. Experimental results will be derived from the mobile two-arm robot system KAMRO of the Institute for Real-Time computer control systems and Robotics, University of Karlsruhe, Federal Republic of Germany. © 1988 Kluwer Academic Publishers.

关键词： computer control systems

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A review of artificial intelligence based malware detection using deep learning

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Materials Today: Proceedings 2023年 80卷 2678-2683页

作者： Mustafa Majid, Al-Ani Alshaibi, Ahmed Jamal Kostyuchenko, Evgeny Shelupanov, Alexander Department of Complex Information Security of Computer Systems Faculty of Security Tomsk State University of Control Systems and Radioelectronics Tomsk634050 Russia

Malware propagation by adversaries has witnessed many issues across the globe. Often it is found that malware is released in different countries for monetary gains. With the proliferation of malware spreading activities, it is made possible that now we have malware patterns that are used for training machine learning models. Thus machine learning became indispensable for malware detection. The traditional machine learning models have limitations in performance as the training depth is limited. The emergence of deep learning models paved way for more training possibilities and improvement in detection accuracy with least false positives. This paper reviews literature on deep learning techniques that are used for malware detection. The deep learning methods used for malware detection include CNN, RNN, LSTM and auto encoders. LSTM is found to have memory in the cell to have better possibilities. Auto encoders are found to have better unsupervised approach with encoding and decoding to arrive at abnormalities (malware) detection. There are many contributions found using machine learning and deep learning towards Android malware detection. This paper provides knowledge that leads to further research in deep learning which is essential to improve the state of the art. © 2021

关键词： Malware

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LEARNING OF MODULAR STRUCTURED NETWORKS

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ARTIFICIAL INTELLIGENCE 1995年第1期75卷 51-62页

作者： ISHIKAWA, M Department of Control Engineering and Science Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka 820 Japan

Learning of large-scale neural networks suffers from computational cost and the local minima problem. One solution to these difficulties is the use of modular structured networks. Proposed here is the learning of modular networks using structural learning with forgetting. It enables the formation of modules. It also enables automatic utilization of appropriate modules from among the previously learned ones. This not only achieves efficient learning, but also makes the resulting network understandable due to its modular character. In the learning of a Boolean function, the present module acquires information from its subtask module without any supervision. In the parity problem, a previously learned lower-order parity problem is automatically used. The geometrical transformation of figures can be realized by a sequence of elementary transformations. This sequence can also be discovered by the learning of multi-layer modular networks. These examples well demonstrate the effectiveness of modular structured networks constructed by structural learning with forgetting.

关键词： Learning systems

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AN ACTION-BASED PROCESS MODEL FOR DEXTEROUS MULTIFINGER HANDS

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JOURNAL OF INTELLIGENT & ROBOTIC systems 1991年第3期4卷 201-219页

作者： WOHLKE, G BRAUN, T 1. Institute for Real-Time Computer Control Systems and Robotics Faculty for Informatics University of Karlsruhe 7500 Karlsruhe Germany

In this article, the development of an action-based process model is described which was especially tailored to the needs of modelling and simulating multifinger hands. The research has been concerned with a description technique using constraints for modelling the working environment and with the design of an appropriate process model for simulating complex movements of manipulation objects with multifinger hands. The paper presents the results of an object-oriented approach to the proposed model and consists of two parts: a constraint net that describes the connections and relations between cell components (robots, grippers, objects) in the working environment and a propagation algorithm that computes the new locations regarding the modifications and logical dependencies.

关键词： SYSTEM SIMULATION PROCESS MODEL CONSTRAINT NET PROPAGATION ALGORITHM OBJECT-ORIENTED PROGRAMMING

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Data imputation methods for intermittent renewable energy sources: Implications for energy system modeling

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Energy Conversion and Management 2025年 339卷

作者： Mantuano, Claudio Omoyele, Olalekan Hoffmann, Maximilian Weinand, Jann Michael Panella, Massimo Stolten, Detlef Forschungszentrum Jülich GmbH Institute of Climate and Energy Systems – Jülich Systems Analysis (ICE-2) Jülich 52425 Germany RWTH Aachen University Chair for Fuel Cells Faculty of Mechanical Engineering Aachen 52062 Germany Sapienza University of Rome Department of Computer Control and Management Engineering (DIAG) Rome 00185 Italy Sapienza University of Rome Department of Information Engineering Electronics and Telecommunications (DIET) Rome 00184 Italy

To incorporate a high share of intermittent renewable sources in energy systems, energy system optimization models rely on weather and climate time series data. However, data for renewable energy sources often contains missing values due to sensor or transmission faults. This study evaluates various data imputation methods for minutely-resolved global horizontal irradiance, direct normal irradiance, and wind speed time series, with missingness ranging from two to ninety percent. Alongside standard statistical tests, a novel validation criterion is introduced by directly evaluating the impact of imputation methods on energy system modeling. While certain imputation methods demonstrate strong point-wise statistical accuracy, they do not necessarily preserve the underlying data distribution. The performance of these methods is strongly influenced by the type of time series and the missingness mechanism, either continuous gaps or randomly missing data points. In energy system optimization, multiple imputation by chained equations, k-nearest neighbors, linear interpolation, and simple moving average yield the best results, outperforming more sophisticated deep learning-based methods. Overall, k-nearest neighbors consistently outperformed the other approaches across all validation criteria. By comprehensively evaluating the statistical performance of imputation methods and their impact on energy system modeling, this study offers valuable insights for researchers and practitioners addressing missing data in energy system applications. © 2025 The Authors

关键词： Data imputation Energy system optimization Energy time series Machine learning Renewable energy systems Self-sufficiency

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Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms: Challenges and a Roadmap

arXiv

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arXiv 2025年

作者： Xu, Jianye Alrifaee, Bassam Betz, Johannes Mokhtarian, Armin Mittal, Archak Cai, Mengchi Mangharam, Rahul Shehata, Omar M. Elias, Catherine M. Zaech, Jan-Nico Scheffe, Patrick Jahncke, Felix Ulhas, Sangeet Sankaramangalam Arfvidsson, Kaj Munhoz Department of Computer Science RWTH Aachen University Germany Department of Aerospace Engineering University of the Bundeswehr Munich Germany Professorship of Autonomous Vehicle Systems Technical University of Munich Germany Department of Civil Engineering Indian Institute of Technology Bombay India School of Vehicle and Mobility Tsinghua University China Department of Electrical and Systems Engineering University of Pennsylvania United States Mechatronics Engineering Department Faculty of Engineering and Materials Science The German University in Cairo Egypt Department of Computer Science Engineering The German University in Cairo Egypt INSAIT Sofia University "St. Kliment Ohridski" Bulgaria School for Engineering of Matter Transport and Energy Arizona State University United States Division of Decision and Control Systems KTH Royal Institute of Technology Sweden

This article proposes a roadmap to address the current challenges in small-scale testbeds for Connected and Automated Vehicles (CAVs) and robot swarms. The roadmap is a joint effort of participants in the workshop "1st Workshop on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms," held on June 2 at the IEEE Intelligent Vehicles Symposium (IV) 2024 in Jeju, South Korea. The roadmap contains three parts: 1) enhancing accessibility and diversity, especially for underrepresented communities, 2) sharing best practices for the development and maintenance of testbeds, and 3) connecting testbeds through an abstraction layer to support collaboration. The workshop features eight invited speakers, four contributed papers [1]–[4], and a presentation of a survey paper on testbeds [5]. The survey paper provides an online comparative table of more than 25 testbeds, available at https://***/testbeds-survey. The workshop’s own website is available at https://***/iv24-workshop. Copyright © 2025, The Authors. All rights reserved.

关键词： Testbeds

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