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Exploring Dynamics and Hardware Implementation of an Enhanced 5D Hyperchaotic Memristive System Inspired by Sprott-C System

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Complex System Modeling and Simulation 2025年第1期5卷 34-45页

作者： Abdulmajeed Abdullah Mohammed Mokbel Fei Yu Yumba Musoya Gracia Bohong Tan Hairong Lin Herbert Ho-Ching Iu School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha 410114China School of Electronic Information Central South UniversityChangsha 410083China School of Electrical Electronic and Computer EngineeringUniversity of Western AustraliaPerth 6009Australia

This paper proposes a novel 5D hyperchaotic memristive system based on the Sprott-C system configuration,which greatly improves the complexity of the system to be used for secure communication and signal processing.A critical aspect of this research work is the introduction of a flux-controlled memristor that exhibits chaotic behavior and dynamic responses of the *** this respect,detailed mathematical modeling and numerical simulations about the stability of the system’s equilibria,bifurcations,and hyperchaotic dynamics were conducted and showed a very wide variety of behaviors of great potential in cryptographic applications and secure data ***,the flexibility and efficiency of the real-time operating environment were demonstrated,and the system was actually implemented on a field-programmable gate array(FPGA)hardware platform.A prototype that confirms the theoretical framework was presented,providing new insights for chaotic systems with practical ***,we conducted National Institute of Standards and technology(NIST)testing on the proposed 5D hyperchaotic memristive system,and the results showed that the system has good randomness.

关键词： Sprott-C system field-programmable gate array(FPGA)implementation hyperchaotic system memristor numerical simulation

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A Multi-Task Optimization-based Heterogeneous Expensive Multi-Objective Evolutionary Algorithm 3

A Multi-Task Optimization-based Heterogeneous Expensive Mult...

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3rd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing technology, AIoTC 2024

作者： He, Chunlin Zhang, Yong School of Information and Control Engineering China University of Mining and Technology Xu Zhou China

ISBN: (纸本)9798331530280

Multi-objective optimization problems with different evaluation costs for performance metrics or objective values are commonly seen in the real world. Such problems are known as heterogeneous expensive multi-objective optimization problems (HE-MOPs). Although there are some researches available, the heterogeneity among objectives can cause search bias, which affects the performance of these methods on HEMOPs. To address this, we propose a Multi-Task Optimizationbased Heterogeneous Expensive Multi-Objective Evolutionary Algorithm (MT-HEMOEA). This algorithm firstly uses a multilayer sampling method based on objective evaluation costs to obtain a uniformly distributed sample set on objectives space. Then we employ multi-task exploitation strategy focused on convergence and multi-task exploration strategy focused on diversity to reduce search bias and improve sample diversity. Experimental results on WFG test problems show that the proposed algorithm achieves higher-quality optimal solution sets compared to four existing heterogeneous multi-objective optimization algorithms. © 2024 IEEE.

关键词： Optimization algorithms

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Design and implementation of an electromagnetic tracing intelligent vehicle based on STC32 3

Design and implementation of an electromagnetic tracing inte...

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3rd International Conference on Algorithms, Microchips, and Network Applications, AMNA 2024

作者： Zhang, Zhaocheng Shen, Mo Guo, Hongtao School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China

ISBN: (纸本)9781510680098

This article takes the National College Student Smart Car Competition as the background and designs and produces an electromagnetic tracking car based on the STC32 chip. On the basis of not violating the competition rules, in order to improve the running stability and speed of the car, the hardware uses a DRV8701E driver board to drive the motor and a four-inductor arrangement to collect the inductance signal;the tracking algorithm uses a ratio algorithm and Fuzzy PID handles electromagnetic values. The experimental results show that the car has good tracking effect and strong anti-interference ability, and can successfully complete various requirements in smart car competitions. © 2024 SPIE.

关键词： Microcontrollers

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Low-Light Image Enhancement Network Based on Hierarchical Residual and Attention Mechanism 43

Low-Light Image Enhancement Network Based on Hierarchical Re...

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43rd Chinese control Conference, CCC 2024

作者： Xu, Shengjun Nan, Chi Yang, Hua Xi'an University of Architecture and Technology School of Information and Control Engineering Shanxi 710055 China

ISBN: (纸本)9789887581581

In order to solve the problems of noise artifacts, texture blurring and color imbalance of existing low-light image enhancement methods, a low-light image enhancement network based on hierarchical residual and attention mechanism (HRAMNet) is proposed. Firstly, a hierarchical residual module (HRM) is designed as a feature extractor, which expands the network receptive field using hierarchical residual connections and extracts irregular features using asymmetric convolutional groups in its branches to improve shallow feature extraction. Secondly, rich multi-scale local features are captured by attention-based residual dense block (ARDB), and a dual soft-threshold channel attention module (DSCA) is utilized to suppress noise interference. Finally, the multi-feature fusion module (MFFM) is proposed to facilitate the fusion of deep and shallow global features, and the channel pixel attention block (CPA) is utilized to recover the color and texture details of the feature map. The experimental results show that the PSNR of the proposed network reaches 22.7973 dB and SSIM reaches 0.8748 on the LOL dataset, and 21.7538 dB and 0.8536 on the MIT-Adobe FiveK dataset, which effectively improves the quality of the visualization of the low-light image after enhancement. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Image enhancement

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Failure Mode Analysis and Infrared Image Recognition of Crystalline Silicon PV Module 5

Failure Mode Analysis and Infrared Image Recognition of Crys...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Li, Ziyi Wang, Lijie Sun, Jianbo Lu, Xinyu Zhu, Xianwen School Of Measurement And Control Technology And Communication Engineering Harbin University Of Science And Technology China

ISBN: (纸本)9798400709272

In this paper, the fault analysis of crystalline silicon photovoltaic modules is studied. The Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) methods are used to analyze the failure mode and its causes. Based on the results of fault analysis, the infrared image analysis and recognition methods are studied for the two fault modes of infrared hot spot and component shedding of Photovoltaic(PV) modules. The YOLOv5s image recognition method based on clustering improvement and feature enhancement is discussed. The experimental results show that the YOLOv5s image recognition algorithm with clustering improvement and feature enhancement improves the training effect of the model by using the EIOU loss function to adaptively adjust the confidence loss balance coefficient, and the detection speed (Frame Per Second, FPS) can reach 42.37 FPS;by adding InRe feature enhancement modules before each detection layer, the extraction ability of target features is improved. The mean Average Precision (mAP) of hot spot and component shedding are 94.85 % and 90.67 %, respectively, which can fully meet the needs of UAV automatic inspection. © 2023 ACM.

关键词： Silicon

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Determination of quantum toric error correction code threshold using convolutional neural network decoders

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Chinese Physics B 2022年第1期31卷 136-142页

作者： Hao-Wen Wang Yun-Jia Xue Yu-Lin Ma Nan Hua Hong-Yang Ma School of Sciences Qingdao University of TechnologyQingdao 266033China School of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China

Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum *** order to find the best syndrome of the stabilizer code in quantum error correction,we need to find a fast and close to the optimal threshold *** this work,we build a convolutional neural network(CNN)decoder to correct errors in the toric code based on the system research of machine *** analyze and optimize various conditions that affect CNN,and use the RestNet network architecture to reduce the running *** is shortened by 30%-40%,and we finally design an optimized algorithm for CNN *** this way,the threshold accuracy of the neural network decoder is made to reach 10.8%,which is closer to the optimal threshold of about 11%.The previous threshold of 8.9%-10.3%has been slightly improved,and there is no need to verify the basic noise.

关键词： quantum error correction toric code convolutional neural network(CNN)decoder

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A SOC Estimation Method for Lithium-ion Batteries Based on Optuna optimized Long Short-Term Memory Neural Network 4

A SOC Estimation Method for Lithium-ion Batteries Based on O...

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4th International Conference on Electronic information engineering and Computer communication, EIECC 2024

作者： Li, Hao China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou China

ISBN: (纸本)9798331534622

Estimating a battery's state of charge (SOC) accurately is essential for determining an electric vehicle's safety and remaining range. In this paper, a battery SOC (state of charge) estimation method based on Optuna optimised LSTM is proposed. In response to the problem of low efficiency and difficulty in achieving global optimization through manual parameter tuning in traditional methods, this paper applies the Optuna automated hyperparameter tuning framework and combines it with a random search algorithm to optimize key hyperparameters in the LSTM model. The research results indicate that the optimal setting (Look back: 43, Epoch: 175, Batch size: 65, Learning rate: 0.0029) achieved the best estimation accuracy. Finally, the performance of the model was validated on two different lithiumion battery datasets. The experimental results showed that the LSTM model based on Optuna optimization exhibited good generalization ability and high accuracy under different temperature and dynamic load conditions, significantly improving the reliability and accuracy of SOC estimation. © 2024 IEEE.

关键词： State of charge

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CPD-Based Channel Estimation for IRS-Assisted MIMO Communicaton 23

CPD-Based Channel Estimation for IRS-Assisted MIMO Communica...

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7th International Conference on Digital Signal Processing, ICDSP 2023

作者： Huang, Lei Gong, Xiaofeng Lin, Qiuhua School of Information and Communication Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781450398626

Intelligent reflecting surface (IRS) have emerged as an innovative technology and are integrated into existing wireless communication systems for enhancing coverage and capacity. The acquisition of accurate channel state information is essential, but challenging due to the high dimensionality. Leveraging the derived tensor structure and uniqueness conditions for third-order CPD with a known factor matrix, a novel algebraic algorithm is proposed to solve the problem of channel estimation for IRS-assisted MIMO communication system. Moreover, a version of ALS that imposes 2 regularisation on alternate optimization is proposed to obtain performance gains. Our proposed algebraic algorithm can be effective with low complexity at limited training overhead and large-scale IRS, which performs well in terms of estimation accuracy. Its output is valuable as an initialisation for iterative algorithms, especially in some demanding system settings. Numerical results demonstrated the effectiveness and usefulness of our proposed algorithms. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Channel estimation

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LSTM-Based Method for Characteristic Mode Tracking

LSTM-Based Method for Characteristic Mode Tracking

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Li, Hui Qu, Chang School of Information and Communication Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781733509657

Characteristic mode (CM) theory has been widely used for antenna design and scattering problems. However, it is always challenging to correctly track the eigenmodes for complicated structures over a wide frequency band. Due to the typical time-series features of the mode data, a method based on long short-term memory neural network (LSTM) is proposed for mode tracking. Different modes at each frequency are distinguished and classified by learning the internal features between the mode data, which can overcome the mode degradation problem and thus achieve good tracking performance. The proposed method is validated by a U-slot patch antenna. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Brain

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Enhancing Synthesized Speech Detection with Dual Attention Using Features Fusion

Enhancing Synthesized Speech Detection with Dual Attention U...

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2023 International Conference on Computer Applications technology, CCAT 2023

作者： Wang, Bo Ma, Yanyan Tang, Yeling Wang, Rui Zhang, Maozhen Dalian University of Technology School of Information and Communication Engineering Dalian China

ISBN: (纸本)9798350301540

Automatic Speaker Verification (ASV) is a system based on speech recognition used for identity verification. However, with the continuous improvement of synthetic speech generation technology, the quality of generated speech is also getting higher and higher. This allows some malicious actors to use synthetic speech to deceive, which poses a serious threat to ASV. Therefore, we need to continuously improve the authenticity identification to cope with this challenge. Although many synthetic speech detection algorithms have been proposed, their generalization performance is still not ideal. This means that the performance of ASV systems may suffer in the face of previously unseen synthetic speech attacks. Therefore, researchers need to further explore new methods for detecting synthetic speech attacks and strengthen the robustness of ASV systems against synthetic speech attacks. In order to extract more abundant and reliable speech features, this paper proposes a dual-attention network, specifically combining the features extracted by Wav2vec with traditional speech features Logmel through self-attention, and inputting them into a Resnet network with convolutional block attention module (CBAM). The results have shown that our method has achieved a very competitive performance on the Asvspoof 2021 LA and DF datasets, with a t-DCF of 0.3008 on LA and an EER of 3.9% on DF, indicating good generalization performance. © 2023 IEEE.

关键词： Speech recognition

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