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2022 Chinese automation Congress, CAC 2022

作者： Wu, Dongchang Xue, Wei Yang, Xinya Li, Tin Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems Wuhan430074 China

ISBN: (纸本)9781665465335

With the development of radar technology, frequency modulated continuous wave (FMCW) radar has been used for non-contact vital signs detection. In order to suppress the environmental noise and interference of breathing harmonics on heartbeat signal, this paper proposes a new vital sign detection method based on scale-space representation (SSR) and empirical wavelet transform (EWT) for the FMCW radar. First, a low-pass filter is set for the high-frequency noise elimination outside the frequency range of vital sign signals. Second, SSR is used to adaptively segment the spectrum of signals to obtain the initial frequency boundaries, and the kurtosis of the spectrum is applied to segment the spectrum again and obtain the final frequency boundaries to further eliminate noise. Third, the empirical wavelet filter bank is constructed by using the determined boundaries and EWT is used to decompose the signals to several components. Finally, according to the frequency ranges of components and the correlation between the components and vital sign signals, the components are selected to reconstruct the breathing and heartbeat signals. The experimental results show that the proposed method achieves a better detection performance than the classical EWT and empirical mode decomposition (EMD). © 2022 IEEE.

关键词： Wavelet transforms

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Simulation and optimization of inchworm-like pneumatic crawling actuator based on ABAQUS software 7

Simulation and optimization of inchworm-like pneumatic crawl...

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7th International Conference on Robotics, control and automation Engineering, RCAE 2024

作者： Mu, Junqi Zong, Xiaofeng Wei, Yiyang Hou, Xuping China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan430074 China

ISBN: (纸本)9798350355642

The actuator, serving as the fundamental power source of a soft robot, functions as its central component. The field of soft robotics has garnered increasing research attention since its inception. Pneumatic crawling robots, being an essential branch within the realm of soft robotics, possess notable characteristics such as rapid response time, cost-effectiveness, and human-computer interaction safety. Consequently, they hold significant research value. Among various types of crawling robots that have garnered considerable interest from researchers is the inchworm crawling robot. This paper aims to address the design and development aspects pertaining to inchworm actuators. By utilizing ABAQUS simulation software and taking displacement as the reference parameter, an analysis was conducted to investigate the impact of various geometric parameters on actuator displacement. Additionally, optimization of these geometric parameters was performed. The values of aspect ratio, length-to-width ratio, vertex angle and gap depth suitable for inchworm-like actuator are obtained. These findings are presented for the first time in the field of crawling soft robots and provide a theoretical basis for the development of pneumatic inchworm-like actuators. © 2024 IEEE.

关键词： Aspect ratio

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Remote Sensing Image Classification Based on Lightweight Network and Pruning

Remote Sensing Image Classification Based on Lightweight Net...

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2022 Chinese automation Congress, CAC 2022

作者： Wang, Zexi Xue, Wei Chen, Kehui Ma, Shaopeng School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665465335

In recent years, deep neural network has continuously improved the performance of remote sensing image classification. Though The deep neural network model is powerful, it is difficult to deploy on resource-constrained hardware platforms such as mobile terminal devices and embedded devices due to the large number of network parameters. In this paper, a novel lightweight network (LW-Net) model and a network pruning method are used for remote sensing image classification. This LW-Net model adopts a net block unit to obtain more characteristic graphs with less computational complexity. The proposed network pruning method uses the sparsity regularization on the influence factor in BN layer to automatically identified and pruned unimportant channels to make the model structure simpler. Experimental results demonstrate compared with traditional deep neural networks, the proposed model with the network pruning method can greatly reduce the computational complexity and model parameters with a little accuracy loss. © 2022 IEEE.

关键词： Deep neural networks

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Signal discrimination with wavelet algorithm and Pearson correlation-based algorithm in f-OTDR systems 2

Signal discrimination with wavelet algorithm and Pearson cor...

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2020 2nd International Conference on Electronics and Communication, Network and Computer Technology, ECNCT 2020

作者： Mo, Dai Li Yu, Zhi Hua Cai, Yun Fei Liu, Rui School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China

This paper presents a novel method of distinguishing signal, the way expected to reduce the nuisance alarm rate since the high nuisance alarm rate will restrict the capability of phase-sensitive optical time-domain reflection technology. The proposed method includes two parts: wavelet positioning mutation to obtain the perturbation area and Pearson correlation algorithm to directly convert the intensity of the perturbation into a useful amplitude. This technique avoids the use of irrelevant data in these differential signals and provides a simple and feasible new approach for distinguishing signal and optimizing the positioning speed of f-OTDR systems. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Time domain analysis

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Predefined-Time Synchronization of Inertial Memristive Neural Networks

Predefined-Time Synchronization of Inertial Memristive Neura...

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2021 International Conference on Neuromorphic Computing, ICNC 2021

作者： Zeng, Kan Wang, Leimin School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9781665412872

This article investigates the predefined-time synchronization (PTS) of inertial memristive neural networks (IMNNs). First, we reduce the order of the system and design an effective controller for the error system. Furthermore, several new criteria for PTS of IMNNs are obtained and a user-defined settling time is provided. Lastly, the effectiveness of the results is proved by simulation experiment. © 2021 IEEE.

关键词： Synchronization

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Radar Vital Signs Detection Method Based on Variational Mode Decomposition and Wavelet Transform

Radar Vital Signs Detection Method Based on Variational Mode...

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2021 China automation Congress, CAC 2021

作者： Ma, Shaopeng Xue, Wei Chen, Kehui Wang, Zexi School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665426473

To deal with noise interference in frequency modulated continuous wave (FMCW) radar vital signs and the interference of breathing harmonics on the heartbeat signal, a vital signs detection method based on variational mode decomposition (VMD) and wavelet transform is proposed. First, VMD is applied to decompose the vital signs into a series of intrinsic mode function (IMF) components. Then, the IMF components with spectral peaks in the frequency range of breathing and heartbeat are selected to reconstruct the breathing and heartbeat signals respectively. Finally, the wavelet transform threshold method is used to remove the noise in the breathing and heartbeat signals. The experimental results show the proposed method can overcome the influence of breathing harmonics, accurately extract the breathing and heartbeat signals, and effectively improve the signal-to-noise ratio and detection accuracy of heart rate. © 2021 IEEE

关键词： Variational mode decomposition

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Road Underground Target Detection in Ground Penetrating Radar Images Based on YOLO Models

Road Underground Target Detection in Ground Penetrating Rada...

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2023 China automation Congress, CAC 2023

作者： Li, Ting Xue, Wei Yang, Xinya Wu, Dongchang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350303759

Ground penetrating radar (GPR) is extensively employed for subsurface road target detection, offering benefits such as convenience, nondestructive testing, rapid data acquisition, and superior resolution. Despite these advantages, interpreting GPR data often depends on the expertise of professionals, resulting in low detection efficiency and low accuracy. To address these challenges, this study introduces an intelligent detection technique for GPR images, utilizing an enhanced YOLOv5 framework. First, considering the problems of the small amount of GPR image datasets and the unclear characteristics caused by the complex underground media, a Dense-C3 module is built by utilizing the structure of DenseNet to enhance the network's capability for extracting features. Subsequently, a channel and spatial hybrid attention module is introduced into the backbone for feature refinement and improving the efficiency. Finally, the multi-class focal loss function is devised to enhance the precision in cases of imbalanced sample classes. Experimental results show that the proposed model surpasses the original YOLOv5 model and various contemporary advanced models. © 2023 IEEE.

关键词： Efficiency

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An adaptive hybrid optimizer based on particle swarm and differential evolution for global optimization

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Science China(Information Sciences) 2010年第5期53卷 980-989页

作者： XIN Bin 1,2 ? , CHEN Jie 1,2* , PENG ZhiHong 1,2 & PAN Feng 1,2 1 School of automation, Beijing Institute of Technology, Beijing 100081, China 2 key laboratory of complex system intelligent control and Decision, Ministry of Education, Beijing 100081, China 1. School of Automation Beijing Institute of Technology Beijing 100081 China2. Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing 100081 China

This paper presents extensive experiments on a hybrid optimization algorithm (DEPSO) we recently developed by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). The hybrid optimizer achieves on-the-fly adaptation of evolution methods for individuals in a statistical learning way. Two primary parameters for the novel algorithm including its learning period and population size are empirically analyzed. The dynamics of the hybrid optimizer is revealed by tracking and analyzing the relative success ratio of PSO versus DE in the optimization of several typical problems. The comparison between the proposed DEPSO and its competitors involved in our previous research is enriched by using multiple rotated functions. Benchmark tests involving scalability test validate that the DEPSO is competent for the global optimization of numerical functions due to its high optimization quality and wide applicability.

关键词： global optimization statistical learning differential evolution particle swarm optimization hybridization adaptation rotated function

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Multi-UAV Coverage Path Planning Method Based on Improved Particle Swarm Optimization

Multi-UAV Coverage Path Planning Method Based on Improved Pa...

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2024 China automation Congress, CAC 2024

作者： Zhou, Yan Xiong, Yonghua School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350368604

The widespread application of unmanned aerial vehicle (UAV) has made the coverage path planning problem for multiple UAV systems a research hotspot. In this paper, we propose a multi-UAV solution to achieve complete coverage of multi-region. Based on this solution, we present an improved particle swarm optimization (PSO) algorithm that combines PSO algorithm, greedy strategy, and back-and-forth (BAF) algorithm. Its purpose is to plan the optimal paths for multi-UAV while achieving the shortest total distance and the highest UAV cooperation efficiency with the minimum number of UAVs, thereby enhancing the multi-UAV coverage cooperation and path optimization capabilities. Simulation results demonstrate the feasibility and superiority of this algorithm. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Uniform Stability of Fractional-order Memristive Neural Networks with Hybrid Time-Varying Delays

Uniform Stability of Fractional-order Memristive Neural Netw...

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2021 International Conference on Neuromorphic Computing, ICNC 2021

作者： Hu, Xiaofang Wang, Leimin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665412872

This article discusses the uniform stability of Caputo fractional-order memristive neural networks (FMNNs) with discrete delay and distributed delay. By virtue of fractional-order Razumikhin-type theorem, interval matrix method, differential inclusion and set-valued mapping, a LMI-based standard is acquired to certify uniform stability of the addressed FMNNs. Lastly, the validity of the constructed criterion is manifested by an example. © 2021 IEEE.

关键词： Time delay

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