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2021 6th International Symposium on Advances in Electrical, Electronics and Computer Engineering, ISAEECE 2021

作者： Qin, Yao Wei, Bin Liu, Shuo Li, Mingxing Cai, Chengxin Hou, Tiangang College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou450001 China

In this paper, a new lossless free space method for measuring the complex relative permittivity of dielectric material by reflection method is presented. When this method is used to measure the complex relative permittivity of dielectric material, there is no multi-value problem. The measurement system used in this method consists of a horn lens antenna and a vector network analyzer(VNA). In this paper, HFSS microwave simulation software is used to simulate the complex relative permittivity measurement method proposed in this paper. The simulation results show that this method can accurately measure the complex relative permittivity of dielectric material. © Published under licence by IOP Publishing Ltd.

关键词： Dielectric materials

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Numerical simulation study of free space method for measuring sample permittivity 6

Numerical simulation study of free space method for measurin...

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2021 6th International Symposium on Advances in Electrical, Electronics and Computer Engineering, ISAEECE 2021

作者： Qin, Yao Liu, Shuo Wei, Bin Li, Mingxing Cai, Chengxin Hou, Tiangang College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou450001 China

The composition and performance of the material can be expressed by the permittivity. The paper introduces the measurement principle of free space method and TRL calibration principle, the calculation formula of the sample permittivity is theoretically analysed, a horn lens antenna is designed to simulate the emission of plane waves, and numerical simulation software is used for simulation and optimization. In the working frequency range, the voltage standing wave ratio of the antenna is less than 1.49, and the maximum gain is 21.8dB. A free space measurement system is established in the software to measure the sample permittivity with different thicknesses, and compare the calculated value with the actual value. The results show that the root mean square error of the real part is less than 5.3%, and the root mean square error of the imaginary part is less than 1.3%. The correctness and applicability of the formula for calculating the permittivity are also proved. © Published under licence by IOP Publishing Ltd.

关键词： Permittivity

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An Anomaly Detection Model for ADS-B Systems Based on Improved GAN and LSTM Networks 21

An Anomaly Detection Model for ADS-B Systems Based on Improv...

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21st IEEE International Conference on Communication technology, ICCT 2021

作者： Guo, Xinying Zhu, Chunhua Yang, Jing Xiao, Yan Henan University of Technology Key Laboratory of Grain Information Processing and Control Zhengzhou China Zhengzhou Locaris Technology Co. LTD Zhengzhou China

ISBN: (纸本)9781665432061

Automatic Dependent Surveillance-Broadcast (ADS-B) is an important part of the next generation air transportation systems, but its protocol does not provide relevant authentication and data encryption, so it is extremely vulnerable to various spoofing attacks. Therefore, it is necessary to develop a set of ADS-B data anomaly detection models. According to the strong temporal dependence and fast update rate of ADS-B data, we initiatively introduce a novel anomaly detection model based on Improved Generative Adversarial Network and long short-term memory networks (IGAN-LSTM). In the model, IGAN mainly improves the generator G architecture of GAN through upgrading the commonly used encoder architecture to encoder-decoder-encoder architecture, which captures the training data distribution within both data space and latent space. IGAN then utilizes encoding losses based on latent spatial features to determine whether a data sample is anomalous or not. At the same time, to model ADS-B data with the temporal dependence, we use the LSTM networks as the main architecture of the model, which is able to make use of long-term temporal dependence and avoid the vanishing gradient problem during training. Experimental results show that the proposed model has a higher area under the receiver operating characteristic curve and the precision recall curve than the other baseline methods from the literature, and reveal that the model has better performance of anomaly detection of ADS-B data. © 2021 IEEE.

关键词： Generative adversarial networks

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Adaptive Spectral Broadening Filter-Based Phased Array Focused Ultrasound Imaging for High-Echo Target Detection

SSRN

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SSRN 2024年

作者： Lou, Cuijuan Zhou, Quan Li, Ranyang Liu, Wufeng Li, Yan Yuchi, Ming Ding, Mingyue Key Laboratory of Grain Information Processing and Control Henan University of Technology Henan Key Laboratory of Grain Photoelectric Detection and Control China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China Department of Automation School of Automation Wuhan University of Technology Wuhan430070 China Department of Biomedical Engineering School of Life Science and Technology Key Laboratory of Molecular Biophysics of the Ministry of Education Medical Ultrasound Laboratory Huazhong University of Science and Technology Wuhan430074 China

Noise suppression and resolution improvement are important issues in high-echo target ultrasound imaging detection. To address this problem, this paper proposes a phased array focused ultrasound imaging method based on an adaptive spectral broadening filter. The proposed method first standardizes the received signals after fixed-point transmission by subtracting the mean of the received aperture signals and dividing by the standard deviation of the received aperture signals. Then, spectralbroadening was achieved through covariance and temporal autocorrelation coefficient calculations. Finally, the variance factor proposed for the first time is calculated as a weight from standardized received data after fixed-point transmission focusing to obtain the final scanline data. To evaluate the performance of the proposed method in high-echo target ultrasound imaging detection, experimental phantom images and two views of the interventricular septum including the parasternal long-axisviews and the apical four-chamber views, are reconstructed. In three scenarios, some finer structures of the targets in the images by the proposed method can be more easily identified compared to delay and sum (DAS), square-neighborhood robust signal mean-to-standard-deviation factor (SN-RSMSF), and coherence factor beamforming (CF). For the experimental phantom data, the image resolution measured by -20dB mainlobe width of the proposed method can reach 14.2 percents of DAS, 18.3 percents of SN-RSMSF and 18.3 percents of CF. The proposed method also outperforms the other three methods in terms of noise suppression. The proposed method can be used to detect diseases that involve high-echo targets such as stones, heart valves, and calcifications. © 2024, The Authors. All rights reserved.

关键词： Ultrasonic imaging

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Addr-Net: Attention Mechanism-Based Dual-Stream Deformable Medical Image Registration Network

SSRN

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SSRN 2024年

作者： Fan, Chao Zhu, Xinru Peng, Bincheng Xuan, Zhihui Zhu, Zhentong School of Artificial Intelligence and Big Data Henan University of Technology Henan Province Zhengzhou City China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Province Zhengzhou City China School of Information Science and Engineering Henan University of Technology Henan Province Zhengzhou City450001 China

Medical image registration is crucial for tumor growth monitoring, radiation therapy, and disease diagnosis. Recently, U-Net type networks have become widely used in unsupervised image registration to predict dense deformation fields. However, for image data with large deformations, these networks are prone to interference from outliers and task-irrelevant regions, leading to suboptimal registration accuracy and difficulty in maintaining the continuity of the deformation field. To address this issue, we propose a novel model called ADDR-Net. ADDR-Net leverages dual encoders and incorporates a Cross-Perceptual Attention Module (CPA) alongside a Multi-dimensional Attention Module (MDA) to enhance registration. Unlike traditional methods that concatenate fixed and moving images into a single encoder, ADDR-Net uses dual encoders to separately extract features from each image, facilitating explicit feature matching between image pairs. The CPA module, embedded within the encoder, enhances the extraction of global features over long distances. Meanwhile, the MDA module, embedded within the skip connection, filters redundant features and extracts local refined features, thereby improving the U-shaped network architecture. Experimental results demonstrate that ADDR-Net outperforms state-of-the-art methods. Specifically, compared to the Transformer-based TransMorph method, ADDR-Net increases the Dice scores on two publicly available brain datasets, IXI and OASIS, by 1.4% and 3.5%, respectively. © 2024, The Authors. All rights reserved.

关键词： Network architecture

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Acquisition and Analysis of Rotating Machinery Rotor Vibration Signal by Non-Contact Laser Sensing

SSRN

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SSRN 2024年

作者： Yang, Shangjun Wu, Jiali Ke, Chenghu Zhang, Feng Zhu, Chunhua Ke, Xizheng Key Laboratory of Grain Information Processing and Control [Henan University of Technology Ministry of Education Zhengzhou China College of Information Science and Engineering Henan University of Technology Zhengzhou China Henan Engineering Laboratory of Grain Condition Intelligent Detection and Application Henan University of Technology Zhengzhou China School of Information Engineering Xi’an University Xi’an China School of Electronic Information Engineering Xi’an Technological University Xi’an China School of Automation and Information Engineering Xi’an University of Technology Xi’an China

Conventional high-speed rotating machinery uses a contact structure to realize data transmission between rotating parts and fixed parts. Mechanical wear and vibration noise can interfere with the subsequent processing and fault diagnosis of vibration signals. This paper designs a contactless vibration signal transmission system for rotating machinery using laser sensing. It analyzes the non-coincidence error between the optical axis and the rotating axis, which causes the effective photosensitive area and practical power of the detector to change periodically with the frequency of the rotational speed, resulting in the vibration signal presenting various harmonics with the rotational speed angular frequency as the fundamental wave. A convenient time-frequency joint analysis method is proposed using fractional Fourier transform in time-frequency domain projection localization. Using mixed programming techniques of C# and Matlab, an upper computer interface for data storage, processing, network publishing, is developed to form a complete vibration signal processing system. © 2024, The Authors. All rights reserved.

关键词： Rotating machinery

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Weed detection based on improved Yolov5 8

Weed detection based on improved Yolov5

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8th International Conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Yu, ZiZheng Nie, ZiJun Hu, Yong Qi Yan, HuangBin Lou, LiJun Yin, Zhijian Yang, Zhen Jiangxi Science and Technology Normal University School of Communications and Electronics Nanchang China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9798350302455

The scale of grain production affects human life and development. With the continuous expansion of cultivated land, the reproductive ability of weeds to mutiply gradually increases, which affects the growth of crops. If weeds are not treated properly managed, they can also allow spread indiscriminately in the field and reduce crop yields. The growth ability of weeds is stronger than that of crops, and there are many types of weeds with a wide of species diversity. To solve the problem that the existing weed detection methods cannot detect and classify weeds accurately and quickly, a deep learning method based on improved Yolov5 was designed for weed detection. By replacing the 3 × 3 convolution with multi-head self-attention (MHSA) in the Yolov5's backbone, the accuracy of weed detection is improved. The experimental results show that the improved Yolov5 weed detection algorithm achieves 51.4% accuracy. Compared with the original Yolov5 model, the calculation amount is 3 points less than the original, and total time required is slightly shorter, which improves the accuracy of weed classification and positioning. © 2023 IEEE.

关键词： Crops

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Actively tunable dual-broadband graphene-based terahertz metamaterial absorber

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Chinese Physics B 2021年第12期30卷 495-502页

作者： Dan Hu Tian-Hua Meng Hong-Yan Wang Mai-Xia Fu School of Physics and Electrical Engineering Anyang Normal UniversityAnyang 455000China Department of Physics and Electronics Science Shanxi Datong UniversityDatong 037009China School of Education Information Technology and Communication Anyang Normal UniversityAnyang 455000China College of Information Science and Engineering Henan University of TechnologyKey Laboratory of Grain Information Processing and ControlMinistry of EducationZhengzhou 450001China

A tunable metamaterial absorber(MA)with dual-broadband and high absorption properties at terahertz(THz)frequencies is designed in this *** MA consists of a periodic array of flower-like monolayer graphene patterns at top,a SiO_(2)dielectric spacer in middle,and a gold ground plane at the *** simulation results demonstrate that the designed MA has two wide absorption bands with an absorption of over 90%in frequency ranges of 0.68 THz-1.63 THz and 3.34 THz-4.08 THz,and the corresponding relative bandwidths reach 82.3%and 20%,*** peak absorptivity of the absorber can be dynamically controlled from less than 10%to nearly 100%by adjusting the graphene chemical potential from 0 eV to 0.9 ***,the designed absorber is polarization-insensitive and has good robustness to incident *** a high-performance MA has broad application prospects in THz imaging,modulating,filtering,etc.

关键词： metamaterial graphene absorber terahertz

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Scheduling Algorithm for Low Energy Consumable Parallel Task Application Based on DVFS 1

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Liu, Xun Zhao, Hui College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Henan University Kaifeng475000 China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

With the continuous improvement of various high-performance computing systems, various data centers had also been fully expanded. Energy consumption and actual performance measurement were very important indicators, which were also key issues in how to judge parallel calls for some tasks in high-performance computer systems. Modern processors were basically equipped with software control functions such as DVFS (Dynamic Voltage Frequency Scaling), in the actual system operation to ensure that the system could ensure the reasonable operation of the system while reducing energy consumption indicators. This paper considered how the designed scheduling algorithm first divides tasks reasonably to ensure that the maximum completion time and energy consumption of the processor were sufficiently reduced when the directed acyclic graph was executed. Then considered making reasonable adjustments to the processor frequency using DVFS technology to adapt to the task while ensuring the critical path of the task. At the end of the article, make sure that the experimental verification algorithm could ensure that the task was completed and could reduce the energy consumption during task execution as much as possible. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy utilization

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An Anomaly Detection Model for ADS-B Systems Using a LSTM-based Variational Autoencoder 3

An Anomaly Detection Model for ADS-B Systems Using a LSTM-ba...

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3rd IEEE International Conference on Civil Aviation Safety and information technology, ICCASIT 2021

作者： Guo, Xinying Zhu, Chunhua Yang, Jing Xiao, Yan Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education College of Information Science and Engineering Henan University of Technology Zhengzhou China Zhengzhou Locaris Technology Co. Ltd Zhengzhou China

ISBN: (纸本)9781665425186

Automatic Dependent Surveillance-Broadcast (ADS-B) is an important part of the next generation air transportation system, but its protocol does not provide relevant authentication and data encryption, so it is extremely vulnerable to various spoofing attacks. Therefore, it is necessary to develop a set of ADS-B data anomaly detection models. According to the strong temporal dependence and fast update rate of ADS-B data, we initiatively introduce a novel anomaly detection model based on variational autoencoder and long short-term memory networks (VAE-LSTM). In the model, we focus on reconstructing the expected distribution of ADS-B data, which is called reconstruction probability. At the same time, to model ADS-B data with the temporal dependence, we use the LSTM networks as the main architecture of the model, which is able to make use of long-term temporal dependence and avoid the vanishing gradient problem during training. Experiments show that the proposed model has a higher area under the receiver operating characteristic curve and the precision-recall curve than the other baseline methods from the literature, and reveal that the model has better performance of anomaly detection of ADS-B data. © 2021 IEEE

关键词： Long short-term memory

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