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H control for General Conformable Polynomial Fuzzy Models

International Journal of Fuzzy Systems 2025年 1-10页

作者： Laajimi, Malek Gassara, Hamdi Rhaima, Mohamed Mchiri, Lassaad Makhlouf, Abdellatif Ben Laboratory of Sciences and Techniques of Automatic control & Computer Engineering National School of Engineering of Sfax Sfax Tunisia Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Panthéon-Assas University Paris II Paris France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

This research focuses on exploring the exponential $$H_\infty $$ stability of general conformable nonlinear system. In order to address the nonlinearity inherent in the system, a polynomial fuzzy (PF) method is employed. Modeling a general conformable nonlinear system within the polynomial framework reduces the number of fuzzy rules compared to the classical Takagi–Sugeno fuzzy (TSF). Furthermore, controlling such a complex system, which accounts for perturbations, employs a PF model instead TSF model to describe its nonlinear dynamics, and incorporates a general conformable derivative instead of an integer-order one, is significantly more challenging, and remains unaddressed in previous studies. In this paper, a PF controller is designed in the form of sum of squares (SOS) to enhance the resilience against perturbations and ensure the exponential stability of the proposed model. The proposed SOS can be solved numerically, and partially symbolically, using the recently developed SOSTOOLS. In order to ensure the $$H_\infty $$ performance, a generalized criterion is defined for the general conformable nonlinear system. To demonstrate the effectiveness of the proposed method, a numerical example is provided.

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Enhancing Wastewater Treatment Sensor Fault Detection through Deep Learning

Enhancing Wastewater Treatment Sensor Fault Detection throug...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Liliana-Maria Ghinea Mihaela Miron Marian Barbu Department of Automatic Control and Electrical Engineering University "Dunărea de Jos" Galați Romania Department of Computer Science and Information Technology University "Dunărea de Jos" Galați Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Efficient operation and maintenance of wastewater treatment plants (WWTPs) are essential for safeguarding public health and the environment. The emergence of mechanical faults within complex systems can lead to disruptions, increased operational costs, and environmental risks. As the world moves towards a digitally connected and sustainable future, the development of Deep Learning (DL) tools for fault detection and isolation (FDI) in wastewater treatment processes is expected to become paramount. Therefore, in this study, we developed two neural models, a Feedforward Neural Network (FFNN) and a Long Short-Term Memory (LSTM), to address the detection of mechanical faults such as bias, stuck, spikes, and precision degradation of the Dissolved Oxygen (DO) sensor. The classification results showed remarkable accuracy performances during testing: for Dataset 1, FFNN achieved 96.56%, while LSTM reached 99.36%; and for Dataset 2, FFNN achieved 99.36%, and LSTM reached 99.57%.

关键词： Deep learning Degradation Accuracy Fault detection Plants (biology) Biological system modeling Data models Wastewater treatment Long short term memory Testing

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Applying Transformer-Based Neural Networks to Corpus Linguistic Data for Predictive Text Generation in Multilingual Environments 6

Applying Transformer-Based Neural Networks to Corpus Linguis...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Taufikin Orosoo, Myagmarsuren Rengarajan, Manikandan Fatma, Gulnaz Yuldashev, Dilyorjon Raj, I Infant IAIN Kudus Indonesia Mongolian National University of Education Mongolia Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Tamil Nadu Chennai India University College Al Ardah Jazan University Jazan Saudi Arabia Turin Polytechnic University in Tashkent Automatic Control and Computer Engineering Department Tashkent Uzbekistan K. Ramakrishnan College of Engineering Department of CSE Tamil Nadu Trichy India

ISBN: (纸本)9798350306613

In the subject of corpus linguistics, this study explores the use of transformer-based neural networks to the problems of predictive text production in multilingual contexts. The Transformer architecture presents a viable framework for natural language processing problems because of its reputation for capturing complex patterns and long-range dependencies in sequential data. The research employs a two-phase approach, whereby the Transformer is first pre-Trained using a variety of multilingual corpora. This helps the model learn representations that are independent of language, which makes it easier for it to adapt to various linguistic circumstances. The model is then fine-Tuned using language-specific datasets to improve its ability to produce linguistically nuanced and contextually appropriate text. The study looks into how different model architectures, training methods, and hyperparameters affect how well the suggested multilingual predictive text generation system performs. Evaluation measures are used to evaluate the model's performance in several languages, including linguistic diversity measurements, BLEU scores, and perplexity. The overall accuracy, calculated across all languages, stands at 80.3%, indicating the model's robust cross-lingual generalization and its effectiveness in capturing diverse linguistic nuances The efficacy and generalization of Transformer-based models are improved across a wide range of languages to handle linguistic diversity. © 2024 IEEE.

关键词： Corpus Linguistics Cultural Context Integration Multilingual Text Generation Neural Language Models Transformer-Based Neural Networks

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Deep reinforcement learning and its application in autonomous fitting optimization for attack areas of UCAVs

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Journal of Systems engineering and Electronics 2020年第4期31卷 734-742页

作者： LI Yue QIU Xiaohui LIU Xiaodong XIA Qunli School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Science and Technology on Electro-Optic Control Laboratory Luoyang 471000China Beijing Aerospace Automatic Control Research Institute Beijing 100854China

The ever-changing battlefield environment requires the use of robust and adaptive technologies integrated into a reliable platform. Unmanned combat aerial vehicles(UCAVs) aim to integrate such advanced technologies while increasing the tactical capabilities of combat aircraft. As a research object, common UCAV uses the neural network fitting strategy to obtain values of attack areas. However, this simple strategy cannot cope with complex environmental changes and autonomously optimize decision-making problems. To solve the problem, this paper proposes a new deep deterministic policy gradient(DDPG) strategy based on deep reinforcement learning for the attack area fitting of UCAVs in the future battlefield. Simulation results show that the autonomy and environmental adaptability of UCAVs in the future battlefield will be improved based on the new DDPG algorithm and the training process converges quickly. We can obtain the optimal values of attack areas in real time during the whole flight with the well-trained deep network.

关键词： attack area neural network deep deterministic policy gradient(DDPG) unmanned combat aerial vehicle(UCAV)

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Attack Stealthiness and Detection of Multiagent Systems: A Zero-Sum Formulation

Attack Stealthiness and Detection of Multiagent Systems: A Z...

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IEEE Conference on Decision and control

作者： Shiyong Zhu Miaomiao Wang Jie Chen Anders Rantzer Department of Electrical Engineering City University of Hong Kong Hong Kong China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China Department of Automatic Control Lund University Lund Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Cybersecurity has in recent years emerged as a paramount concern in the design and operation of industrial systems and civil infrastructures, due mainly to their susceptibility to malicious cyber attacks which take advantage of the vulnerability of communication networks and IT devices. In this paper, we investigate such an attack and counter attack scenario by considering multiagent systems, a somewhat basic prototype of cyberphysical systems. We study false data injection attacks launched on the agent sensors, and possible defense of such attacks at the agent actuators. The primary issue under consideration is the stealthiness of the attacks, while steering a multiagent system away from its consensual state. We propose a metric to quantify the stealthiness, and formulate the stealthiness problem as one of zero-sum games. We solve the problem explicitly, which gives rise to a fundamental bound on the stealthiness achievable, and as well optimal attack and defense strategies that achieve the optimal stealthiness, both of which can be obtained in terms of certain augmented controllability Gramians associated with the agents. The stealthiness bound is seen to depend on agent dynamics and network characteristics including a measure of connectivity.

关键词： Actuators Protocols Prototypes Games Sensor phenomena and characterization controllability Sensor systems Game theory Observability Multi-agent systems

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Self-feedback LSTM regression model for real-time particle source apportionment

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Journal of Environmental sciences 2022年第4期34卷 10-20页

作者： Wei Wang Weiman Xu Shuai Deng Yimeng Chai Ruoyu Ma Guoliang Shi Bo Xu Mei Li Yue Li Trusted AI System Laboratory College of Computer ScienceNankai UniversityTianjin 300350China KLMDASR Tianjin Key Laboratory of Network and Data Security TechnologyTianjin 300350China State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and EngineeringNankai UniversityTianjin 300071China Institute of Mass Spectrometry and Atmospheric Environment Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution Jinan UniversityGuangzhou 510632China Guangdong-Hongkong-Macao Joint Laboratory of Collaborative Innovation for Environmental Quality Guangzhou 510632China

Atmospheric particulate matter pollution has attracted much wider attention *** recent years,the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments *** demands are summarized,in this paper,as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances,such as the existence of secondary source and similar *** this study,we firstly analyze the possible and potential restraints in single particle source apportionment,then propose a novel three-step self-feedback long short-term memory(SF-LSTM)network for approximating the source *** proposed deep learning neural network includes three modules,as generation,scoring and refining,and regeneration *** from the scoring modules,SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment,meanwhile,the regeneration module calculates the source contribution in a non-linear *** results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators(residual sum of squares,stability,sparsity,negativity)for the ***,in short time-resolution analyzing,SF-LSTM provides better results under the restraint of stability.

关键词： Time series Regression Self-feedback LSTM network Particle source apportionment

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GITANet: Group Interactive Threshold-based Attention Network for Hyperspectral Image Classification

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IEEE Transactions on Multimedia 2025年

作者： Duan, Yule Chen, Chuang Fu, Maixia Gong, Xiuwen Niu, Yingying Luo, Fulin The University of Sydney Faculty of Engineering NSW2006 Australia Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education Zhengzhou450001 China Henan University of Technology Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China Chongqing University College of Computer Science Chongqing400044 China

Group convolution networks have shown great potential in hyperspectral image (HSI) classification because of their ability to divide total spectral bands into multiple groups and focus on fine discrimination within different spectral ranges. Most group convolution networks process parallel spectral groups independently;however, they neglect the important relevance of nearby spectral ranges. Moreover, the feature maps from different spectral groups are not considered for recalibration in the existing attention. To address these issues, we propose a novel group interactive threshold attention network (GITANet). In the network, a stratified-split-concatenation strategy, which not only splits all bands into multiple groups for intragroup convolution but also propagates the intergroup information via the stratified concatenation operation between different groups, is designed for bandwise group convolution. Relying on the high dependencies among nearby spectra, the cross-group interactive attention block is designed to encourage significant spectral features. Subsequently, from different spectral ranges, a learnable threshold generation block is built to estimate the information validity of each pixel. On the basis of this threshold, soft threshold spatial attention is developed in the bandwise encoderdecoder architecture, which emphasizes high-value spatial areas during the fusion of group convolutional features. Therefore, complementary and discriminative spectral-spatial features are obtained to improve the performance of HSI classification. The experimental results on three HSI datasets illustrate that GITANet is superior to several state-of-the-art networks. © 1999-2012 IEEE.

关键词： Convolution

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Constant modulus waveform design for radar with Doppler tolerance based on local ambiguity function template matching 5

Constant modulus waveform design for radar with Doppler tole...

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5th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2022

作者： Zhang, Qian Du, Xiaolin Cui, Guolong Zheng, Jibin Dong, Jun School of Computer and Control Engineering Yantai University Yantai China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Xidian University National Laboratory of Radar Signal Processing Xi'an China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

To tackle the problem of Doppler sensitivity of phase-coded waveform in radar systems, a method for designing transmit waveform with Doppler tolerance under constant modulus (CM) constraint is considered. In this method, the variance between the ideal local ambiguity function (LAF) and the real one is used as the objective function to establish an optimization problem. To solve the non-convex quartic problem, a template matching sequence optimization (TMSO) algorithm is proposed. By introducing an auxiliary variable and problem transformation, the solution to the original problem can finally be obtained, involving two alternately iterative subproblems with closed-form solutions. Numerical experiments show that, compared with the reference algorithms, the proposed algorithm can not only obtain a transmit waveform with good Doppler tolerance, but also has a significant advantage in the execution efficiency. © 2022 IEEE.

关键词： Template matching

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The Task Collaborative Migration Method for Marine IoT Based on Edge Computing-RESTful Architecture

The Task Collaborative Migration Method for Marine IoT Based...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Yifan Hu Fuqiang Wang School of information and control engineering Qingdao University of Technology Qingdao China Shandong Computer Science Center and Shandong Provincial Key Laboratory of Computer Networks Qilu University of Technology (Shandong Academy of Sciences) Jinan China

Marine Internet of Things (Marine IoT) has garnered increasing interest in monitoring oceanic environments. But establishing a Marine IoT system for observing and processing marine environmental data presents various challenges. These include limited underwater communication bandwidth, and uneven distribution of tasks among data-sensing devices. This paper proposes an Edge Computing-RESTful (ECR) architecture to efficiently integrate and process the substantial volume of data gathered through marine observations. 1) The ECR method is introduced for task collaborative migration, aiming to schedule and manage the tasks within the Marine IoT system. 2) Through unified scheduling and management of tasks, the edge gateway can facilitate migration, scheduling, and interaction of task loads among various edge sensing devices. The proposed dynamic and adaptive approach matches computing tasks with edge sensing devices, minimizing energy consumption and migration delay within the Marine IoT system. Simulation results validate the superiority of the proposed method for Marine IoT, showcasing improved performance metrics such as reduced time delays and lower energy consumption during task collaborative migration.

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Application of Hybrid Variable Admittance Force Tracking and Fixed-time Position control for Robot Interaction Tasks

Application of Hybrid Variable Admittance Force Tracking and...

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International Conference on control, Mechatronics and Automation (ICCMA)

作者： Hong Zhan Chao Zeng Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

This paper applies the proposed hybrid force and position control method to the physical robot system with interaction tasks to further improve our previous study. In the control scheme, the variable stiffness based on proportional integral derivative(PID) admittance control is adopted for interaction force tracking and the radial basis function neural network(RBFNN) based fixed-time control is designed to ensure position tracking. We have performed interaction tasks based on a Baxter robot for drawing on the plane and slope plane with different expected interaction forces and position trajectories. The experiment results indicate that the method performs well in terms of interaction force and trajectory tracking.

关键词： Mechatronics Automation Trajectory tracking Force Dynamics Position control Trajectory Robots Admittance control Convergence

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