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A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

Computers, Materials & Continua 2023年第6期75卷 5987-6006页

作者： Mohamad Afendee Mohamed Talal Bonny Aceng Sambas Sundarapandian Vaidyanathan Wafaa Al Nassan Sen Zhang Khaled Obaideen Mustafa Mamat Mohd Kamal Mohd Nawawi Faculty of Informatics and Computing Universiti Sultan Zainal AbidinMalaysia Department of Computer Engineering University of SharjahSharjah26666UAE Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaIndonesia Research and Development Centre Vel Tech UniversityAvadiChennaiIndia School of Artificial Intelligence and Automationand Key Laboratory of Image Processingand Intelligent Control of Education Ministry of ChinaHuazhong University of Scienceand TechnologyWuhan430074China Institute of Strategic Industrial Decision Modeling School of Quantitative SciencesUniversiti Utara Malaysia06010UUM SintokKedahMalaysia

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and *** paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium *** proposed chaotic system has two quadratic,two cubic and two quartic nonlinear *** is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium *** is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium *** have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and *** an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium *** proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)*** results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

关键词： Chaos chaotic systems hidden attractors multi-stability bifurcation speech cryptosystem FPGA design

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Trajectory Selection Optimization for Behavior-based Aerial-Ground Robots Heterogeneous System: A Predictive Approach

Trajectory Selection Optimization for Behavior-based Aerial-...

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2021 China automation Congress, CAC 2021

作者： Chen, Jianfei Chen, Yutao Liu, Zihang Huang, Jie College of Electrical Engineering and Automation Fuzhou University Fuzhou350108 China Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350108 China Fuzhou No.1 High School Fuzhou35108 China

ISBN: (纸本)9781665426473

To address the problem of unpredictable trajectory caused by sensing limitation of UGV, in this paper, a heterogeneous multi-agent behavioral control architecture with predictive function is proposed, in which UAV intervenes in the decision-making of UGV. The basic task functions of UGV and UAV are defined respectively and the intervention signal of UAV is connected to the task function of UGV in null-space-based control framework to solve the problem of multi-task conflict. The UAV platform adopts the nonlinear model predictive control to predict the trajectory according to the environment data collected in advance and the current state of the UGV. The prediction results are input into the decision model to produce the final intervention signal and sent to the UGV platform. The UGV adjusts the task strategy in time to ensure the selection of the optimal trajectory for the UGV. Finally, the simulation results show the effectiveness of the proposed cooperative control method. © 2021 IEEE

关键词： Unmanned aerial vehicles (UAV)

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Eigenvector of Gram-Matrix based Open-Fault Diagnosis Method for Two-level Three-Phase Inverter

Eigenvector of Gram-Matrix based Open-Fault Diagnosis Method...

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International Conference on Power Electronics and Motion control (IPEMC)

作者： Yang Zhou Jin Zhao Yang Liu Lijun Sun Ministry of Education School of Artificial Intelligence and Automation Key Laboratory of Imaging Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China China Electronic Product Reliability and Environmental Testing Research Institute Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology Guangzhou China

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

This paper studies the power switch open-circuit(OC) fault in two-level three-phase (2L3P) voltage source inverter (VSI). According to the topological symmetry of the inverter, when it works in healthy conditions, its phase currents are standard sine waves with only 120-degree phase difference, and the similarity between phase currents is high. When OC fault occurs, the current waveforms of both normal and fault phases will be distorted, the similarity will be changed. Therefore, this paper introduces the Gram matrix in image processing to measure the similarity and proposes a fast and computationally efficient diagnostic method based on the eigenvector of Gram-Matrix. Physical experiment shows that both single-switch fault and multiple-switch fault on the same leg can be effectively detected and located.

关键词： Legged locomotion Fault diagnosis Voltage source inverters Switches Computational efficiency Maintenance Velocity measurement

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Cascading Failures in Interdependent Directed Networks Under Localized Attacks

SSRN

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SSRN 2023年

作者： Lv, Mengyu Pan, Linqiang Liu, Xueming Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Many real-world systems interact with one another through dependency links, which reduces the system robustness. Most previous studies on the robustness of interdependent networks focus on undirected networks, and the related studies on directed networks are limited to random or targeted attacks. However, some failure scenarios can not be described by these two kinds of attacks, such as earthquakes, floods, and epidemics, where systems are attacked in a local range. In this work, we develop a theoretical framework for analyzing the robustness of interdependent directed networks under localized attacks. We find that for degree homogeneous networks, network robustness under localized attacks is similar to that under random attacks. There are four phase transitions in the phase diagram of the network, and a four-phase transition point and a two-phase transition point are found. For degree heterogeneous networks, localized attacks are more likely to lead to collapse than random attacks. As the coupling strength between networks increases, the interdependent networks first show a second order phase transition, and then a hybrid phase transition, and a first order phase transition at last. Furthermore, as the degree heterogeneity decreases, the robustness of networks first increases and then decreases, showing a local robustness maximum. The findings could help understand network robustness and enable better design of robust interdependent systems. © 2023, The Authors. All rights reserved.

关键词： Phase diagrams

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Event-Triggered Distributed Multitarget Tracking With Unknown Clutter Rate and Detection Profile

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zheng, Litao Yang, Feng Shi, Lihong Guo, Zhengyu Cai, Yunze Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Northwestern Polytechnical University School of Automation Xi'an710129 China Key Lab of Information Fusion Technology Ministry of Education of China Xi'an710129 China National Key Laboratary of Air-based Information Perceptian and Fusion Luoyang China State Key Laboratory of Submarine Geoscience Shanghai201913 China

This paper addresses communication bandwidth reduction and energy efficiency enhancement of a peer-to-peer sensor network for distributed multitarget tracking under unknown clutter rate and detection profile. We propose an event-triggered distributed bootstrap cardinalized probability hypothesis density (CPHD) filter, where the local filter consists of an estimator that estimates the clutter and detection parameters, and a tracker that estimates the number and states of targets. Specifically, the proposed approach enforces each sensor to broadcast its local posterior density only when it has high estimation quality and significant information gain, wherein the former is determined by the outputs of the local estimator and tracker, while the latter is measured via the Jensen-Shannondivergence. Then, we develop a modified weighted arithmetic average fusion method to separately fuse the cardinality distributions and intensity functions, in which the fusion weights are constructed based on estimation qualities and triggering thresholds, which is incorporated into the distributed flooding scheme for distributed averaging. Additionally, the Gaussian mixture implementations of the proposed event-triggered distributed bootstrap CPHD filter are given, where only Gaussian components with high weights are applied, thus reducing the effect of false alarms and, consequently, achieving a partial consensus across the network. Finally, the effectiveness of the proposed algorithm is validated through simulation experiments. © 1965-2011 IEEE.

关键词： Gaussian distribution

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A novel method of constructing high-dimensional digital chaotic systems on finite-state automata

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Chinese Physics B 2020年第9期29卷 233-243页

作者： Jun Zheng Han-Ping Hu School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Information Processing and Intelligent Control Ministry of EducationWuhan 430074China

When chaotic systems are implemented on finite precision machines, it will lead to the problem of dynamical degradation. Aiming at this problem, most previous related works have been proposed to improve the dynamical degradation of low-dimensional chaotic maps. This paper presents a novel method to construct high-dimensional digital chaotic systems in the domain of finite computing precision. The model is proposed by coupling a high-dimensional digital system with a continuous chaotic system. A rigorous proof is given that the controlled digital system is chaotic in the sense of Devaney's definition of chaos. Numerical experimental results for different high-dimensional digital systems indicate that the proposed method can overcome the degradation problem and construct high-dimensional digital chaos with complicated dynamical properties. Based on the construction method, a kind of pseudorandom number generator (PRNG) is also proposed as an application.

关键词： high-dimensional digital chaotic system dynamical degradation anti-control pseudorandom number generator

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A Method for Detecting the Reducers’ Grease Failure Based on Vibration Signal Analysis

A Method for Detecting the Reducers’ Grease Failure Based o...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Ditong Wang Hong Zhang Ying Zheng Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan China National Key Laboratory of Science and Technology on Multispectral Information Processing and School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Real time detection of the lubricating grease’s effectiveness is the best method to solve wear faults, which can achieve the purpose of predictive maintenance and improve the service life of mechanical equipment. A new online detection method for gearbox lubricating grease failure is proposed to address the issues about long offline detection cycles and high labor costs associated with phased sampling analysis. Compared with online detection methods such as inductive and photoelectric, our method is difficult to detect the specific physicochemical parameters of the grease. However, it has the advantage of simple operation without touching the grease or changing the structure of the tested object. It only needs to attach the vibration sensor table to the mechanical arm reducer to measure and analyze the vibration signal. This article briefly analyzes the impact of grease failure on vibration signals, and then we introduce a novel approach that uses support vector machines along with feature extraction through wavelet packet decomposition for our detection methodology. Next transfer the commonly used spectral image recognition algorithms for temporal signal classification to achieve failed grease detection as a baseline and compare with our traditional machine learning methods. The experimental findings indicate that the recognition accuracy of both methods are above 90%, with traditional machine learning performing better on this task.

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A Reinforcement Learning PID Compound Protocol Design Method for Ultrasonic Motor control*

A Reinforcement Learning PID Compound Protocol Design Method...

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control Conference (ANZCC), Australian and New Zealand

作者： Qiang Wang Yunlong Yang Xiongjun Wu Jinqi Wang Guanyu Liu The 802 Institute of Shanghai Academy of Space Flight Technology The Eighth Academy of China Aerospace Science and Technology Corporation National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering Shanghai Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Aiming at overcoming drawback brought by the nonlinear and time-varying characteristics of ultrasonic motor, this paper proposed a PID compound controller of ultrasonic motor based on reinforcement learning to improve the dynamic performance and adaptability of the system. In practice, the output control value of the reinforcement learning controller is observed in general to be uncertain, and sometimes even destroys the learning strategy, which makes the system unstable. To overcome this limitation, this paper ultilized the idea of expert rules to improve the controller performance, and the Lyapunov stability is also guaranteed. The simulation results show that the improved composite control algorithm can significantly improve the learning efficiency, and meanwhile shorten the number of learning iterations. At the same time, the composite controller based on expert rules significantly improves the control performance of the system, in terms of reducing the rise time and adjustment time of the system, as well as reducing the steady-state error of the system.

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Hanjing Wang, Meineng Zhang, Luping Huang, Yu-An Li, Yangyuan Huang, Yabing Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Hubei Wuhan430074 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Yichun University School of Mathematics and Computer Science Yichun336000 China East China University of Technology School of Information Engineering Nanchang330013 China Northwestern Polytechnical University School of Computer Science Xi'an710129 China Renmin Hospital Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350386226

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Incipient Gradual Fault Detection via Transformed Component and Dissimilarity Analysis

Incipient Gradual Fault Detection via Transformed Component ...

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industrial Cyber-Physical Systems (ICPS)

作者： Lingxia Mu Wenzhe Sun Youmin Zhang Nan Feng Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Mechanical Industrial & Aerospace Engineering Concordia University Montreal Canada

This paper proposes a novel method named recursive transformed component dissimilarity analysis (RTCDA) combining dissimilarity analysis algorithm and traditional sliding window technique for detecting incipient gradual faults. Firstly, orthogonal transformed components (TCs) corresponding to a new set of data in the sliding window are obtained using a recursive algorithm based on rank-one modification. Then, to quantitatively estimate the distribution difference of TCs, the dissimilarity index between TCs of the new dataset and that of referenced dataset is calculated. The distribution of TCs changes more dramatically than that of original data after a small quantitative bias in the original data. Compared with original data, TCs are more sensitive to tiny quantitative variation of dataset. Finally, case studies on a numerical example and a practical industrial fed-batch penicillin fermentation process are carried out to evaluate the performance of RTCDA method for incipient gradual fault detection.

关键词： Fault detection Cyber-physical systems Indexes Data mining

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