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2023 International Congress on Ultrasonics, ICU Beijing 2023

作者： Chou, Xingxing Ren, Wei Han, Yueqi Wang, Wei Wang, Jin School of Electrical and Control Engineering Shaanxi University of Science and Technology Xi'an710021 China

Quantitative ultrasound (QUS) has been widely used in non-destructive evaluation of bone health in research and clinical practice. To make a more accurate bone evaluation, the transmission characteristics of ultrasound in the bone need to be understood in detail. In the two-dimensional finite element model, cancellous bone is usually simulated by a non-porous structure solid or by approximating bone trabeculae as ellipses, which is different from real bone. However, although the error of the model constructed by bone CT images is small, it needs to be based on real bone samples, and the samples are limited. Therefore, a modeling method of cancellous bone based on four-parameter random growth method was proposed in this paper, and on this basis, numerical simulation of ultrasonic transmission was carried out. Firstly, based on the four-parameter random growth method, the aggregation algorithm is used to concentrate discrete pixels and smooth the edge of pores. Meanwhile, the built-in algorithm ensures the same porosity before and after processing to reduce the discrete pore structure. Secondly, based on COMSOL to establish the simulation model of ultrasonic propagation in cancellous bone, we analyzed the change of acoustic field distribution, discussed the correlation between the porosity of cancellous bone and backscattering coefficient (BSC) based on the ultrasonic backscattering method, and compared the experimental results of CT scan images of bone samples. The experimental results show that the cancellous bone modeling method in this paper has the same conclusion as the method based on CT images, which verifies the feasibility of this method. This method can generate a geometric model of the cancellous bone microstructure with specified porosity and different bone trabecular distribution, which is similar to the real bone structure, and can be directly imported into the finite element software to facilitate the study of bone microstructure related problems. It p

关键词： Ultrasonic transmission

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An Adaptive control Method Based on Radial Basis Function Neural Network for Variable Stiffness Actuator 25

An Adaptive Control Method Based on Radial Basis Function Ne...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Wu, Xiujun Wang, Chen Xu, Changxian Liu, Keping Wang, Gang Sun, Zhongbo Changchun University Of Technology Department Of Control Science And Engineering Changchun China School Of Electrical And Information Engineering Jilin Engineering Normal University Department Of Control Science And Engineering Changchun China

ISBN: (纸本)9798350340266

Rehabilitation robots based on variable stiffness elastic actuators exhibit strong human-robot interaction char-acteristics due to the specific features of the actuators, including flexible drive and variable stiffness output capabilities. However, to ensure the robot can achieve rehabilitation actions smoothly, safely, and precisely, accurate modeling and control of the variable stiffness elastic actuators becomes a burning question. In order to address this challenge, a dynamic model is established considering the actuator's own gravity factor and the influence of disturbances, and a nominal dynamic model is designed. Accurate tracking of output location and stiffness is reached by designing a radial basis function (RBF) network to approximate the actual dynamic model. Through simulation experiments, it is verified that the block approximation RBF network adaptive controller designed in this article has lower actuator tracking error and higher control accuracy than the proportional integral derivative (PID) controller. © 2024 IEEE.

关键词： Radial basis function networks

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A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 377-390页

作者： Honghao Zhu MengChu Zhou Yu Xie Aiiad Albeshri IEEE School of Computer and Information Engineering Bengbu UniversityBengbu 233030China School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China Helen and John C.Hartmann the Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewark NJ 07102 USA College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

关键词： Credit card fraud detection(CCFD) dandelion algorithm(DA) feature selection normal sowing operator

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Nighttime image dehazing using color cast removal and dual path multi-scale fusion strategy

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Frontiers of computer science 2022年第4期16卷 147-159页

作者： Bo Wang Li Hu Bowen Wei Zitong Kang Chongyi Li School of Physics and Electronic-Electrical Engineering Ningxia UniversityYinchuan750021China School of Computer Science and Engineering Nanyang Technological UniversitySingapore639798Singapore

Nighttime image dehazing aims to remove the effect of haze on the images captured in nighttime,which however,raises new challenges such as severe color distortion,more complex lighting conditions,and lower *** of estimating the transmission map and atmospheric light that are difficult to be accurately acquired in nighttime,we propose a nighttime image dehazing method composed of a color cast removal and a dual path multi-scale fusion *** first propose a human visual system(HVS)inspired color correction model,which is effective for removing the color deviation on nighttime hazy ***,we propose to use dual path strategy that includes an underexposure and a contrast enhancement path for multi-scale fusion,where the weight maps are achieved by selecting appropriate exposed areas under Gaussian *** experiments demonstrate that the visual effect of the hazy nighttime images in real-world datasets can be significantly improved by our method regarding contrast,color fidelity,and *** addition,our method outperforms the state-of-the-art methods qualitatively and quantitatively.

关键词： nighttime image dehazing color cast removal dual path multi-scale fusion

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Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

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Chinese Physics B 2023年第11期32卷 401-407页

作者：马璐任彦霖何爱军程德强杨小冬 School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Suzhou Vocational and Technical College Suzhou 234000China School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China

Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free ***, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.

关键词： epilepsy EEG signal horizontal visibility graph complex network

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A High-Performance 220-290 GHz Micromachined Waveguide Switch Based on Interference Between MEMS Reconfigurable Surfaces

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IEEE Transactions on Terahertz science and Technology 2024年第2期14卷 188-198页

作者： Karimi, Armin Shah, Umer Yu, Suxian Oberhammer, Joachim Kth Royal Institute of Technology Micro and Nanosystems Technology Division School of Electrical Engineering and Computer Science StockholmSE-100 44 Sweden

This article presents a highly integrated novel silicon micromachined single-pole-single-throw waveguide switch based on two microelectromechanically reconfigurable switching surfaces (MEMS-RSs), which allows optimizing the switching performance by tuning the interference between the two such MEMS-RSs utilizing integrated electrostatic comb-drive actuators. The switch prototype is implemented with axially aligned standard WR-3.4 waveguide ports with a total footprint of 3 mm×3.5 mm×1.2 mm. The measured blocking (off) state insertion loss (isolation) and return loss, measured between two standard WR-3.4 waveguide flanges, are 28.5-32.5 dB and better than 0.7 dB, and the propagating (on) state insertion and return losses are 0.7-1.2 dB and better than 17 dB in the 220-290 GHz frequency band, respectively. The measured results were in excellent agreement with the simulation data, implying 27.5% fractional bandwidth, which is very close to a full waveguide band performance. For further investigations, two variants of the switching circuit with only a single MEMS-RS and without any MEMS-RSs have also been fabricated. The single MEMS-RS switch achieved the off-state isolation, on-state insertion loss, and return loss of only 11.5-12.5 dB, 0.8-1.3 dB, and better than 12 dB from 220 to 274 GHz, respectively, which clearly indicates the drastic performance improvement of the interference-based double MEMS-RS switch design. Moreover, measurement of the waveguide-only reference structure showed that the waveguide section alone attributed to 0.2-0.5 dB of the measured on-state insertion loss of the double MEMS-RS switch, and the rest is due to the introduction of the MEMS-RSs inside the waveguides. © 2011-2012 IEEE.

关键词： Millimeter waves

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Detection of healthy and pathological heartbeat dynamics in ECG signals using multivariate recurrence networks with multiple scale factors

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Chinese Physics B 2023年第10期32卷 273-282页

作者：马璐陈梅辉何爱军程德强杨小冬 School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Suzhou Vocational and Technical College Suzhou 234000China School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China

The electrocardiogram(ECG)is one of the physiological signals applied in medical clinics to determine health *** physiological complexity of the cardiac system is related to age,disease,*** the investigation of the effects of age and cardiovascular disease on the cardiac system,we then construct multivariate recurrence networks with multiple scale factors from multivariate time *** propose a new concept of cross-clustering coefficient entropy to construct a weighted network,and calculate the average weighted path length and the graph energy of the weighted network to quantitatively probe the topological *** obtained results suggest that these two network measures show distinct changes between different *** is because,with aging or cardiovascular disease,a reduction in the conductivity or structural changes in the myocardium of the heart contributes to a reduction in the complexity of the cardiac ***,the complexity of the cardiac system is *** that,the support vector machine(SVM)classifier is adopted to evaluate the performance of the proposed *** of 94.1%and 95.58%between healthy and myocardial infarction is achieved on two ***,this method can be adopted for the development of a noninvasive and low-cost clinical prognostic system to identify heart-related diseases and detect hidden state changes in the cardiac system.

关键词： electrocardiogram signals multivariate recurrence networks cross-clustering coefficient entropy multiscale analysis

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ACDD: Automated COVID Detection using Deep Neural Networks

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IEIE Transactions on Smart Processing and Computing 2023年第6期12卷 518-525页

作者： Raza, Ghulam Musa Shoaib, Muhammad Kim, Byung-Seo Department of Software and Communications Engineering Hongik University Sejong Korea Republic of School of Electrical Engineering and Computer Science National University of Science and Technology Islamabad Pakistan

December 2019 witnessed the outbreak of a novel coronavirus, thought to have started in the Chinese city of Wuhan. The situation worsened owing to its quick spread across the globe, leading to a worldwide pandemic that became known as COVID-19. To suppress the pandemic, early detection of positive COVID-19 patients has become highly important. There is a lack of precise automated tool kits available for use in diagnosing medical conditions, so auxiliary diagnostic tools are in high demand. Important information about this virus can be extracted from X-ray images, which can be used in conjunction with advanced artificial intelligence. This study addresses the unavailability of physicians in remote areas, and the complex algorithm proposed in this study can find potential matches for patients in rural areas who need care. This could help to improve access to care for those who need it most. The purpose of this study is to develop a novel model that can automatically detect COVID-19 by utilizing chest X-ray images. The proposed model incorporates binary and multi-class classification and can be employed by radiologists for timely detection of the COVID-19 virus in an effective manner. © 2023 The Institute of Electronics and Information Engineers.

关键词： COVID-19

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A Low-Complexity Diversity-Preserving Universal Bit-Flipping Enhanced Hard Decision Decoder for Arbitrary Linear Codes

IEEE Open Journal of Vehicular Technology

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IEEE Open Journal of Vehicular Technology 2024年 5卷 1-22页

作者： Bere, Praveen Sai Khan, Mohammed Zafar Ali Hanzo, Lajos Department of Electrical Engineering Indian Institute of Technology Hyderabad Hyderabad India School of Electronics and Computer Science University of Southampton Southampton United Kingdom

V2X (Vehicle-to-everything) communication relies on short messages for short-range transmissions over a fading wireless channel, yet requires high reliability and low latency. Hard-decision decoding sacrifices the preservation of diversity order, leading to pronounced performance degradation in fading channels. By contrast, soft-decision decoding retains diversity order, albeit at the cost of increased computational complexity. We introduce a novel enhanced hard-decision decoder termed as the Diversity Flip decoder (DFD) designed for preserving the diversity order. Moreover, it exhibits ‘universal’ applicability to all linear block codes. For a $\mathscr {C}(n,k)$ code having a minimum distance ${d_{\min }}$, the proposed decoder incurs a worst-case complexity order of $2^{({d_{\min }}-1)}-1$. Notably, for codes having low ${d_{\min }}$, this complexity represents a significant reduction compared to the popular soft and hard decision decoding algorithms. Due to its capability of maintaining diversity at a low complexity, it is eminently suitable for applications such as V2X (Vehicle-to-everything), IoT (Internet of Things), mMTC (Massive Machine type Communications), URLLC (Ultra-Reliable Low Latency Communications) and WBAN (Wireless Body Area Networks) for efficient decoding with favorable performance characteristics. The simulation results provided for various known codes and decoding algorithms validate the performance versus complexity benefits of the proposed decoder. Authors

关键词： Vehicle to vehicle communications

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A Comprehensive Study on Lightweight Convolution Techniques for Malicious Traffic Synthesis in Diffusion Models

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IEEE Access 2025年 13卷 88306-88317页

作者： Li, Fuhao Udoidiok, Ifiok Zhang, Jielun University of North Dakota School of Electrical Engineering and Computer Science Grand ForksND58202 United States

Network security relies on effective and accurate malicious traffic detection, which is increasingly important for edge devices. As computing resources are distributed across many devices, detecting malicious traffic at the edge becomes crucial. Additionally, the lack of high-quality malicious traffic data hinders the effectiveness of AI models in training. Furthermore, the limited computational power of edge devices requires lightweight generative models, making it challenging to implement diffusion models for data augmentation. In this paper, we conduct a comprehensive study on lightweight convolution techniques for malicious traffic synthesis in diffusion models. We evaluate the performance of three classic lightweight convolutional architectures in generating malicious traffic data. Two popular datasets, USTC-TFC2016 and N-BaIoT, are utilized to train our generative models, with the generated data being employed to train various classifiers for a thorough performance evaluation. The experimental results demonstrate that the investigated lightweight approaches can efficiently generate high-quality and diverse malicious traffic data for enhancing the performance of intrusion detection systems while the computational complexity is reduced to around 19.69% of the baseline. © 2013 IEEE.

关键词： Network intrusion

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