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Asymptotically Optimal Sequence Sets With Low/Zero Ambiguity Zone Properties

IEEE Transactions on Information Theory 2025年第6期71卷 4785-4796页

作者： Tian, Liying Song, Xiaoshi Liu, Zilong Li, Yubo Northeastern University School of Computer Science and Engineering Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China University of Essex School of Computer Science and Electronics Engineering ColchesterCO4 3SQ United Kingdom Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China

Sequences with low/zero ambiguity zone (LAZ/ZAZ) properties are useful in modern communication and radar systems operating over mobile environments. This paper first presents a new family of ZAZ sequence sets motivated by the "modulating" zero correlation zone (ZCZ) sequences which were first proposed by Popovic and Mauritz. We then introduce a second family of ZAZ sequence sets with comb-like spectrum, whereby the local Doppler resilience is guaranteed by their inherent spectral nulls in the frequency domain. Finally, LAZ sequence sets are obtained by exploiting their connection with a novel class of mapping functions. These proposed unimodular ZAZ and LAZ sequence sets are cyclically distinct and asymptotically optimal with respect to the existing theoretical bounds on ambiguity functions. © 1963-2012 IEEE.

关键词： Frequency domain analysis

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Enhanced Genetic Algorithms Based Optimization of ARX-Laguerre Model Parameters: Application to 2-Dof Helicopter

Enhanced Genetic Algorithms Based Optimization of ARX-Laguer...

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2024 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2024

作者： Guerfel, Mohamed Abdelwahed, Imen Ben Jaffel, Ines Bouzrara, Kais Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Electronics department Tunisia National Engineering School of Monastir University of Monastir Laboratory of Automatic Control Signal and Image Processing Tunisia Higher Institute of Applied Sciences and Technology of Kairouan University of Kairouan Tunisia

ISBN: (纸本)9798350389838

This paper explores the application of genetic algorithms to optimize ARX-Laguerre model parameters. It outlines the ARX-Laguerre modeling process and genetic algorithm principles. A fitness function is chosen and the impact of noise on it is addressed. A new methodology for optimizing Laguerre poles using genetic algorithms is proposed. It is used in modeling a real 2-Degree-of-Freedom helicopter offering valuable insights for real-world applications. © 2024 IEEE.

关键词： Genetic algorithms

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Fast Node Selection of Networked Radar Based on Transfer Reinforcement Learning 5th

Fast Node Selection of Networked Radar Based on Transfer R...

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5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022

作者： Cao, Yanjun Wang, Yuan Guo, Jingjing Han, Li Zhang, Chao Zhu, Jin Zhang, Tianyang Zhang, Xiangrong The 54th Research Institute of China Electronics Technology Group Corporation Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Shaanxi Xian710071 China National Laboratory of Radar Signal Processing Xidian University Xian710071 China

ISBN: (纸本)9783031149023

The networked radar system can synthesize different echo signals received by various radars and realize the cooperative detection of multiple radars, becoming more and more critical for data fusion sharing and network collaboration. However, due to the large number and wide range of nodes in the networked radar system, there exists a redundancy problem in radar node assignment, which causes additional resource consumption and slows down the task execution speed of radar node selection. To solve the above problem, this paper proposes a fast radar node selection method based on transfer reinforcement learning to quickly select the optimal and minimum node resources. The proposed method devises a novel reward function for the Monte Carlo Tree and a different termination state of iteration to select the minimize the number of radar nodes. In order to further accelerate the selection of radar nodes, transfer reinforcement learning is presented to fully leverage the previous knowledge. Experimental results show that our proposed method can quickly select the optimal and minimum radar nodes in a brief period, significantly improving the speed of radar node selection in the networked radar. © 2022, IFIP International Federation for Information processing.

关键词： Reinforcement learning

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On the use of DPCA in process fault detection and identification

On the use of DPCA in process fault detection and identifica...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Mohamed Guerfel Hassani Messaoud Electronics Department Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia Laboratory of Automatic Control Signal and Image Processing (LARATSI) National Engineering School of Monastir University of Monastir Monastir Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

This paper proposes an innovative Dynamic Principal Component Analysis (DPCA) scheme to perform fault detection and identification (FDI) for systems affected by process faults. In this scheme, a new modeling method is proposed to fix the data lag and the number of principal components to retain. A new structuration method is introduced to identify the process fault. It is based on the computation of common angles between residual subspaces of different modes. This structuration yields a concise set of detection indices, specifically sensitive to certain process faults while remaining unsensitive to others. The proposed FDI scheme is successfully applied to a three tank affected by several process faults.

关键词： Fault diagnosis Automation Fault detection Computational modeling Systems modeling Data models Principal component analysis

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image Forest Fire Segmentation Using Dirichlet Process Mixture Model

Image Forest Fire Segmentation Using Dirichlet Process Mixtu...

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Advances in electronics, Control and Communication Systems (ICAECCS), International Conference on

作者： Mohammed Khorchef Naim Ramou Yamina Boutiche Abderrezak Guessoum Nabil Chetih Research Centre in Industrial Technologies (CRTI) P.O.Box 64 Cheraga Algiers Algeria Dept. of Electronics Signal Processing and Image Laboratory (LATSI) University of Blida 1 Blida Algeria

image analysis in the detection of forest fires aims to minimize damage by timely intervention to limit the damage. In this work, we will focus on image segmentation nonparametric Bayesian models used in forest fire detection. This model defines a probability distribution over functional spaces (of infinite dimension). We have therefore chosen to study the processes of Chinese restaurants in what is called mixture models. We used Gibbs sampling methods to generate the weights used in the representation of the Dirichlet process. Simulations were performed on signals, synthetic and real images, the results are promising.

关键词： image segmentation image analysis Communication systems Process control Forestry Mixture models Sampling methods

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Enhanced Genetic Algorithms Based Optimization of ARX-Laguerre Model Parameters: Application to 2-Dof Helicopter

Enhanced Genetic Algorithms Based Optimization of ARX-Laguer...

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Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Mohamed Guerfel Imen Ben Abdelwahed Ines Jaffel Kais Bouzrara Electronics department Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Tunisia Laboratory of Automatic Control Signal and Image Processing National Engineering School of Monastir University of Monastir Tunisia Higher Institute of Applied Sciences and Technology of Kairouan University of Kairouan Tunisia

ISBN: (数字)9798350389838

ISBN: (纸本)9798350389845

关键词： Green energy Helicopters Noise 2-DOF Transfer functions Noise measurement Optimization Genetic algorithms

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Enhancing Fault Diagnosis of Uncertain Grid-Connected Photovoltaic Systems using Deep GRU-based Bayesian optimization

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IFAC-PapersOnLine 2024年第4期58卷 449-454页

作者： Zahra Yahyaoui Mansour Hajji Majdi Mansouri Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine 1200 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M Hamed Bougara of Boumerdes Boumerdes Algeria Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir Tunisia

The efficacy of photovoltaic systems is significantly impacted by electrical production losses attributed to faults. Ensuring the rapid and cost-effective restoration of system efficiency necessitates robust fault detection and diagnosis (FDD) procedures. This study introduces a novel interval-gated recurrent unit (I-GRU) based Bayesian optimization framework for FDD in grid-connected photovoltaic (GCPV) systems. The utilization of an interval-valued representation is proposed to address uncertainties inherent in the systems, the GRU is employed for fault classification, while the Bayesian algorithm optimizes its hyperparameters. Addressing uncertainties through the proposed approach enhances monitoring capabilities, mitigating computational and storage costs associated with sensor uncertainties. The effectiveness of the proposed approach for FDD in GCPV systems is demonstrated using experimental application.

关键词： Bayesian optimization fault detection fault diagnosis gated recurrent units grid-connected PV systems interval-data representation uncertainties

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CNN based Emotion Detection in Cross Linguistic Children speech 24

CNN based Emotion Detection in Cross Linguistic Children spe...

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24th International Congress on Acoustics, ICA 2022

作者： Lyakso, Elena Frolova, Olga Ruban, Nersisson Mekala, A. Mary Joseph Raj, Alex Noel Matveev, Anton Matveev, Yuri The Child Speech Research Group St. Petersburg State University St. Petersburg Russia School of Electrical Engineering VIT Vellore India School of Information Technology & Engineering VIT Vellore India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronics Engineering College of Engineering Shantou University China

The goal of this cross-linguistic study is to analyze the speech of children in the age group of 8-12 years and automatic classification of children's emotions from various speech data in Russian and Tamil languages. A standardized approach for the collection of speech (spontaneous and acting speech - emotional words, phrases, and meaningless texts) was used. Two emotional child speech corpora include the emotional speech of 95 Russian children and 40 Indian children were created. Annotation of the emotional speech was carried out in four categories "joy - neutral - sadness – anger" by two speech specialists of the same nationality of the child. The set of 2505 labeled audio files of Russian children and 418 labeled audio files of Tamil children were used. The SVM classifier shows slightly better results in Russian language, and the MLP classifier in Tamil. Inter-Cultural approach (mixed dataset of Russian and Indian speech) revealed that the accuracy of recognition of all emotions remains higher. In Cross-Cultural approach, on samples of Tamil speech, anger state is recognized better, and in samples of Russian speech – sad state. Using 5 Layered CNN Model, the accuracy was revealed for Russian speech is higher than for Indian children. © 2022 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Emotion Recognition

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Real-Time Fault Detection and Diagnosis Method for Industrial Chemical Tennessee Eastman Process

Real-Time Fault Detection and Diagnosis Method for Industria...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Khadija Attouri Majdi Mansouri Mansour Hajji Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Institut Superieur des Sciences Appliques et de Technologie de Kasserine Kairouan University Tunisia The Department of Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M Hamed Bougara of Boumerdes Boumerdes Algeria The Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The accurate detection and diagnosis of faults are critical for maintaining optimal operation and ensuring the reliability of industrial processes. Notably, the topic of online fault detection and diagnosis has recently presented a significant challenge. This work mainly deploys a neural network technique for the comprehensive detection and diagnosis of faults within the Tennessee Eastman Process (TEP) on a low-computational power system, the Raspberry Pi board. The devolved methodology showcases a remarkable level of accuracy (94.50%) in diagnosing the various TEP faults, affirming its robustness and effectiveness. To elevate the practical applicability of the proposed approach, a meticulous investigation into the implementation of the suggested approach on a Raspberry Pi 4 card was undertaken. The successful realization of this implementation not only highlights the adaptability of the approach but also paves the way for its seamless integration into practical industrial applications.

关键词： Accuracy Embedded systems Fault detection Neural networks Electrical fault detection Real-time systems Robustness Power system reliability Information technology Chemicals

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Real-Time Fault Detection Scheme for Industrial Chemical Tennessee Eastman Process

Real-Time Fault Detection Scheme for Industrial Chemical Ten...

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International Conference on Control, Decision and Information Technologies (CoDIT)

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The key idea behind this study is to integrate a moving window dynamic PCA (MW-DPCA) methodology for fault detection within the Tennessee Eastman process (TEP) into a low-computational power system, the Raspberry Pi 4 card, for real-time application. Indeed, the paramount importance of real-time fault detection (FD) in intricate industrial processes presents a critical challenge. Various data-driven techniques have been developed to ensure safety, maintain operational stability, and optimize productivity in such processes. Principal Component Analysis (PCA) is a fundamental data-driven technique that utilizes dimensionality reduction to extract the most informative features from high-dimensional data, simplifying analysis and potentially revealing underlying fault patterns. However, PCA primarily focuses on static relationships and may miss crucial temporal dynamics for fault identification. This is where dynamic PCA (DPCA) excels. By incorporating lagged values of variables, DPCA captures the temporal evolution of features, enabling a more comprehensive understanding of process behavior and improving the detection of faults involving dynamic changes. In order to address the stochastic measurements, a moving average filter tool is also employed. The results obtained and the successful realization of this implementation demonstrate the adaptability of the approach and pave the way for its seamless integration into practical industrial applications.

关键词： Fault detection Power system dynamics Power system stability Feature extraction Electrical fault detection Real-time systems Stability analysis Robustness Safety Principal component analysis

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