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4th International Signal Processing, Communications and engineering Management Conference, ISPCEM 2024

作者： Wang, Yinan Zhao, Yang Gong, Lizhou Yu, Yewei Liu, Hanxu School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China Global R&D Center China FAW Corporation Limited Changchun130013 China

ISBN: (纸本)9798331528676

Driver fatigue detection serves as an essential tool in the process of human-machine co-driving, capable of promptly alerting intelligent systems to assume control of the vehicle. The detection of driver fatigue based on EEG signals currently stands as a primary method for assessing the mental state of individuals. However, at the same time, there are still some problems with EEG signals, such as being prone to noise interference and low cross-subject recognition rate. In addressing the aforementioned issues, this study introduces a method for modeling driver fatigue through the fusion of features extracted from EEG and EOG signals. The proposed model incorporates high-precision EEG equipment and good cross-subject EOG equipment as input. Comprising EEGNet, SANet (Spatial Attention Network), SCONV (and Separable Convolutional Network), the model employs a multi-modal approach to assess driver performance and quantify fatigue. The model effectiveness was verified on the SEED-VIG data set. The test results show that the recognition accuracy of this method reaches 91%, which is better than the currently popular model. At the same time, the cross-subject recognition accuracy test was conducted, and the average accuracy reached 87%, which proved the generalization performance of the model across subjects and verified the effectiveness of the model. ©2024 IEEE.

关键词： Fluorescent screens

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Dual-Class Leader-Following Modeling for Collective Motion of Multi-Agent Systems 43

Dual-Class Leader-Following Modeling for Collective Motion o...

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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Bo Li, Haoqi Meng, Bin Hu, Jiangping Ghosh, Bijoy Kumar Beijing Institute of Control Engineering Beijing100080 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China University of Electronic Science and Technology of China Huzhou313000 China Texas Tech University Department of Mathematics and Statistics LubbockTX79409 United States

ISBN: (纸本)9789887581581

Leader-follower collective motion is a key multi-agent network application. However, conventional methods struggle with high dynamics or communication interferences and fail to maintain consistent motion. To address this issue, a resilient dual-class cooperative following (DCCF) model is proposed. This model uses two movement strategies for the different follower categories. Leader-connected followers use a single-tracking control based on velocity or direction, whereas leader-disjoint followers employ a modified relative speed for single tracking. The DCCF model may be more suitable for practical use than existing ones. Simulations under degraded conditions demonstrate its superior tracking performance and robustness over traditional methods. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： collective motion dual-class cooperative following leader-follower tracking Multi-agent network

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XVAE-mViT: Explinable Hybrid Artificial Intelligence Framework for Predicting COVID-19 from Chest X-Ray and CT Scans 7

XVAE-mViT: Explinable Hybrid Artificial Intelligence Framewo...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Addo, Daniel Al-Antari, Mugahed A. Zhou, Shijie Sarpong, Kwabena Butun, Ertan Talo, Muhammed Abualkebash, Humam Ukwuoma, Chiagoziem C. School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Sejong University DaeyangAI Center Department of Artificial Intelligence Seoul Korea Republic of Firat University Department of Computer Engineering Elaziǧ Turkey Firat University Department of Software Engineering Elazig Turkey Kocaeli University Department of Mechatronics Engineering Kocaeli Turkey The College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan China

ISBN: (纸本)9798350342154

The COVID-19 virus has rapidly spread as a global pandemic, causing significant impacts on public health, economies, and daily life worldwide. Accurately and quickly predicting COVID-19 is crucial to maintaining stronger healthcare systems. This paper introduces a novel hybrid model of artificial intelligence that combines the benefits of the Variational Auto-Encoder (VAE) with the attention mechanism based on the Vision Transformer (ViT). The novel encoder network is structured with four sequential blocks, each involving residual connections of two multiscale kernel depth-wise separable convolution (MKnDSC) modules. The mobile ViT is coupled with the V AE to serve as the classification head for predicting COVID- 19 using chest X-ray (CXR) and computed tomography (CT) scan modalities. We achieved promising classification results with overall accuracies of 96.16% and 95.42% using CXR and CT images, respectively. The proposed hybrid AI framework appears to be a practical solution, especially considering its lightweight structure of 2.15 million parameters and 0.68 FLOPs. © 2023 IEEE.

关键词： COVID-19

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Improved Bayesian Denoising Algorithm for Signal of Centrifugal Microfluidic Immunoassay System 19

Improved Bayesian Denoising Algorithm for Signal of Centrifu...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Shi, Yuxing Guo, Jinhong Ye, Peng Xie, Jiawen Qiaoge, Bayin Wang, Chuang School of Automation Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China University of Birmingham Birmingham United Kingdom University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Signal denoising technology is one of the key technologies that need to be broken through in centrifugal microfluidic immunoassay system. Among various signal denoising technologies, wavelet threshold denoising is commonly used, while the traditional wavelet threshold denoising algorithms have difficulty in adapting to the wavelet coefficient distribution of noise at different scales. To solve this problem, firstly, an improved Bayesian threshold method was proposed to solve the problem of inaccurate threshold caused by its noise variance estimation algorithm. Secondly, for the defects of soft and hard threshold function, we proposed to use weighted threshold function and semi soft threshold function. Finally, the proposed improved Bayesian threshold method was simulated with various threshold functions, and their denoising performance was compared. The simulation results showed that compared with the existing traditional wavelet threshold denoising algorithms (adaptive threshold combined with hard threshold function, the SNR was 75.21db) and the traditional Bayesian threshold denoising algorithm (the SNR was increased to 76.99db), the denoising algorithm based on the combination of improved Bayesian threshold method and hard threshold function proposed in this paper can greatly adapt to the distribution of wavelet coefficients of noise under different scales and had strong denoising ability and peak feature retention ability. It improved the SNR to 83.31db and reduced the root mean square error. Its comprehensive denoising parameter Dr reached 0.71 which achieved satisfactory results in denoising of spectral signal. This method is expected to be a powerful means to improve the sensitivity of centrifugal microfluidic immunoassay system. © 2022 IEEE.

关键词： Signal denoising

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Research on Neutron and Gamma Discrimination Method Based on Anti-Aliasing Pulse Shaping

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SSRN 2023年

作者： Zhao, Yang Wang, Min Hu, Biao Tan, Liang Liang, Zhiyang Chen, Fengli Liang, Jiangtao Liu, Xianghe Faculty of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610059 China

The detectors commonly used in neutron measurement are sensitive to both neutrons and gamma, so it is necessary to distinguish neutrons from gamma rays. Traditional pulse shape discriminators (PSD) include charge comparison method and pulse gradient analysis method, which are representative and need less calculation. In addition, there are digital PSD techniques based on pulse shaping. In this paper, the anti-aliasing shaping method based on Z transform is presented, and the method is applied to the neutron and gamma pulses obtained from simulation and experiment, and the anti-aliasing shaping method is realized, which proves the correctness of the anti-aliasing shaping method. In addition, neutron and gamma pulses are simulated, and the discrimination of neutron and gamma is realized by pulse shaping ratio method, which further verifies the feasibility of the shaping method. At last, the neutron and gamma pulses from Am-Be neutron source detected by EJ276 are analyzed, and the discrimination effects of the three methods are compared: (1) charge comparison method, (2) pulsed gradient analysis method, (3) pulse shaping ratio method. The experimental results show that the pulse shaping ratio method is superior to the charge comparison method and the pulse gradient method. © 2023, The Authors. All rights reserved.

关键词： Neutrons

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An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge

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Automotive Innovation 2022年第2期5卷 134-145页

作者： Huanyang Huang Jinhao Meng Yuhong Wang Lei Cai Jichang Peng Ji Wu Qian Xiao Tianqi Liu Remus Teodorescu College of Electrical Engineering Sichuan UniversityChengduChina Faculty of Computer Science and Engineering Shanxi Key Laboratory for Network Computing and Security TechnologyXi’an University of TechnologyXi’anChina Smart Grid Research Institute Nanjing Institute of TechnologyNanjingChina Department of Automation University of Science and Technology of ChinaHefeiChina Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjinChina Department of Energy Technology Aalborg University9220 AalborgDenmark

In the long-term prediction of battery degradation,the data-driven method has great potential with historical data recorded by the battery management *** paper proposes an enhanced data-driven model for Lithium-ion(Li-ion)battery state of health(SOH)estimation with a superior modeling procedure and optimized *** Gaussian process regression(GPR)method is adopted to establish the data-driven estimator,which enables Li-ion battery SOH estimation with the uncertainty level.A novel kernel function,with the prior knowledge of Li-ion battery degradation,is then introduced to improve the mod-eling capability of the *** for the features,a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high *** the first stage,an optimal partial charging voltage is selected by the grid search;while in the second stage,the principal component analysis is conducted to increase both estimation accuracy and computing *** of the proposed method are validated on two datasets from different Li-ion batteries:Compared with other methods,the proposed method can achieve the same accuracy level in the Oxford dataset;while in Maryland dataset,the mean absolute error,the root-mean-squared error,and the maximum error are at least improved by 16.36%,32.43%,and 45.46%,respectively.

关键词： Li-ion battery State of health Gaussian process regression Kernel function Feature optimization

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Non-fragile memory sampled-data LFC for semi-Markov jump interconnected multi-area power systems: A looped functional approach

Non-fragile memory sampled-data LFC for semi-Markov jump int...

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第43届中国控制会议

作者： Yulong Li Xiaoqing Li Zhinan Peng Kaibo Shi School of Electrical Engineering and Automation Hefei University of Technology School of Automation Engineering University of Electronic Science and Technology of China School of Information Science and Engineering Chengdu University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The problem of memory sampled-data load frequency control(LFC) for semi-Markov jump interconnected multi-area power systems(IMAPSs) has been elaborately presented in this work. Firstly, the semi-Markov jump process(MJP) is introduced to model the dynamical random mutations of the IMAPSs. Subsequently, a memory sampled-data LFC method concerning signal transmission delay caused by geographical isolation for the semi-Markov jump IMPASs is accordingly delivered. By considering the parameter fluctuations in practical operation, the non-fragile issue for the proportional-integral(PI) controller is included to meet the actual operation scenario. After that, a mode-dependent two-side looped Lyapunov–Krasovskii functional is properly adopted to acquire enhanced flexibility of the derived sufficient condition in the shape of linear matrix inequalities(LMIs), which guarantees the stochastic stability with prescribed H∞ performance. Finally, a numerical example is exhibited to validate the effectiveness of the developed control strategy.

关键词： Semi-Markov jump interconnected multi-area power systems(IMAPSs) memory sampled-data load frequency control(LFC) non-fragile proportional-integral(PI) strategy

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Preliminary Development and Verification of a Openfoam-Based Multi-Physics Coupling Safety Analysis Code for Lead-Based Reactor

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SSRN 2023年

作者： Ou, Wenlan Gong, Zhengyu Gu, Zhi Xing Pan, Qiwen Zhang, Ling Dai, Jianing College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610059 China

To overcome the complex multi-dimensional thermal hydraulic problems involved in LFR, CFD-based multi-physics coupling strategy has been treated as an effective solution. As a free open-source CFD code, OpenFOAM is undoubtedly a great platform to perform the multi-physics coupling simulations based on complicated coupling algorithms. By employing an explicit coupling framework, a new program for fluid-solid coupling solution of LFR fuel pin channel is developed based on OpenFOAM. The self-developed point kinetics and fuel pin heat conduction models are coupled to the modified icoFoam of OpenFOAM. Verifications are conducted by transient (BT, BOP and TOP) simulations of flow and heat transfer coupling behaviors in a single fuel pin channel of LFR, comparing with benchmarks-verified HC-PK-CFD code based on ANSYS Fluent. The results agree well with the ones provided by HC-PK-CFD code in ANSYS Fluent, demonstrating its ability of conducting coupled simulations involved in safety analysis of LFR with sufficient accuracy. © 2023, The Authors. All rights reserved.

关键词： Simulation platform

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Adaptive Consensus Quantized Control for a Class of High-Order Nonlinear Multi-Agent Systems With Input Hysteresis and Full State Constraints

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1574-1589页

作者： Guoqiang Zhu Haoqi Li Xiuyu Zhang Chenliang Wang Chun-Yi Su Jiangping Hu the School of Automation Engineering Northeast Electric Power UniversityJilin 132012 the Jilin Province International Research Center of Precision Drive and Intelligent Control Jilin 132012China the School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China the Department of Mechanical and Industrial Engineering Concordia UniversityQCMontrealH3B 1R6Canada the School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China(

For a class of high-order nonlinear multi-agent systems with input hysteresis,an adaptive consensus output-feedback quantized control scheme with full state constraints is *** major properties of the proposed control scheme are:1)According to the different hysteresis input characteristics of each agent in the multi-agent system,a hysteresis quantization inverse compensator is designed to eliminate the influence of hysteresis characteristics on the system while ensuring that the quantized signal maintains the desired value.2)A barrier Lyapunov function is introduced for the first time in the hysteretic multi-agent *** constructing state constraint control strategy for the hysteretic multi-agent system,it ensures that all the states of the system are always maintained within a predetermined range.3)The designed adaptive consensus output-feedback quantization control scheme allows the hysteretic system to have unknown parameters and unknown disturbance,and ensures that the input signal transmitted between agents is the quantization value,and the introduced quantizer is implemented under the condition that only its sector bound property is *** stability analysis has proved that all signals of the closed-loop are semi-globally uniformly *** Star Sim hardware-in-the-loop simulation certificates the effectiveness of the proposed adaptive quantized control scheme.

关键词： Adaptive quantized control barrier Lyapunov function input hysteresis multi-agent systems

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RELIABILITY-BASED MULTIDISCIPLINARY DESIGN OPTIMIZATION METHOD UNDER ORIGINAL PROBABILITY MODEL FOR WIND TURBINE STRUCTURE 14

RELIABILITY-BASED MULTIDISCIPLINARY DESIGN OPTIMIZATION METH...

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14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2024

作者： Meng, Debiao Yang, Hengfei Nie, Peng Su, Xiaoyan Yang, Shiyuan School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan China School of Automation Engineering Shanghai University of Electric Power Shanghai China Faculty of Engineering University of Porto Porto Portugal

ISBN: (纸本)9781837242672

As an optimization strategy considering both uncertainty and discipline coupling, Reliability based Multidisciplinary Design Optimization (RBMDO) has been widely studied. While RBMDO is utilized across various fields, its application and overarching frameworks are scant in the context of wind turbine structures. This study introduces an RBMDO approach tailored for wind turbine structures. The method incorporates a strategy for resolving the Most Probable Point (MPP) issue based on the original probability model, which is particularly useful for addressing challenges encountered when RBMDO relies on the First Order Reliability Method that may result in multiple MPPs. To alleviate the computational demands of RBMDO, collaborative optimization, sequential optimization and reliability analysis are integrated into the methodology. The effectiveness of this approach is demonstrated through the design optimization of a 3 MW onshore wind turbine tower, considering the uncertainties in material properties, load conditions and design variables. © The Institution of engineering & technology 2024.

关键词： Design aids

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