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16th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2023

作者： Liu, Chunming Tian, Yu Tian, Shulin Fan, Jiangjun Li, Yubo Zhang, Shengyi Yu, Xiaoyu School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

ISBN: (纸本)9798350327144

The problem of sampling rate conversion in wideband Zero Intermediate Frequency (Zero-IF) receiver is considered. The purpose of digital resampling technology is to realize digital signal sampling rate conversion, which is widely used in bandwidth filter design in spectrum analysis, noise suppression in modulation and demodulation technology, etc. However, in most usage scenarios, the required sampling rate conversion is not integer multiple conversion. Combined with the system architecture requirements of wideband Zero-IF receivers, this paper designs an arbitrary magnification resampling algorithm with relatively little hardware resource consumption and relatively high accuracy. After the test of the system platform, the feasibility of the arbitrary magnification resampling algorithm designed in this paper is verified, which provides a guarantee for the correct detection and analysis of subsequent signals. © 2023 IEEE.

关键词： Signal sampling

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Expressed and Private Opinion Dynamics with Group Pressure and Liberating Effect

Expressed and Private Opinion Dynamics with Group Pressure a...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Peng, Yuan Dong, Jianglin Zhao, Yiyi Hu, Jiangping School of Automation Engineering University of Electronic Science and Technology of China Chengdu China School of Business Administration South-western University of Finance and Economics Chengdu China

ISBN: (纸本)9798350337020

This paper introduces the liberating effect under group pressure into the Hegselmann-Krause (HK) model and proposes a novel expressed and private opinion dynamics model. Agents in the group hide their honest opinions because of the group pressure, and each agent has a private and expressed opinion. The liberating effect is divided into two stages. In the first stage, one agent in the group is the first to liberate when the number of times it feels pressure exceeds a specific limit and the cumulative pressure is the maximum. The liberating agent will express its opinion authentically, with private opinion consistent with the expressed opinion. In the second stage, the other agents in the group are influenced by the liberating neighbors and also liberate until the group evolution reaches a stable state. Through simulations, we study the effects of confidence level and pressure threshold on group opinion evolution. The experimental results show that both confidence level and pressure threshold are critical. All agents liberate when they are smaller than the critical value;when they are greater than the critical value, the liberating effect disappears. We also find that the liberating effect can accelerate the group opinion evolution. © 2023 IEEE.

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Neural Network Radionuclide Identification Algorithm Based on Exponential Smoothing

Neural Network Radionuclide Identification Algorithm Based o...

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2022 International Conference on Computation, Big-Data and engineering, ICCBE 2022

作者： Wang, Tang He, Xin Ge, Liangquan Li, Fei Gu, Yi Gu, Zhixing Chengdu University of Technology College of Nuclear Technology and Automation Engineering Chengdu China Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province Chengdu China

ISBN: (数字)9781665470551

ISBN: (纸本)9781665470551

The development of nuclear energy and the application of nuclear technology is inseparable from the detection of radionuclides. Considering the incomplete utilization of ?-spectral information in traditional spectral analysis methods, the accuracy is not high. In this study, exponential smoothing is performed on the energy spectrum signal of length 1024 to eliminate the influence of the statistical fluctuation of the detector, and then it is used as the input of the neural network to realize the full spectrum analysis. The method avoids the complex feature extraction process and can identify a single radionuclide in real time. © 2022 IEEE.

关键词： Radioisotopes

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Analysis and Design of Surgical Instrument Localization Algorithm

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Computer Modeling in engineering & Sciences 2023年第10期137卷 669-685页

作者： Siyu Lu Jun Yang Bo Yang Zhengtong Yin Mingzhe Liu Lirong Yin Wenfeng Zheng School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Resource and Environment Engineering Guizhou UniversityGuiyang550025China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325000China Department of Geography and Anthropology Louisiana State UniversityBaton Rouge70803USA

With the help of surgical navigation system,doctors can operate on patients more intuitively and *** positioning accuracy and real-time performance of surgical instruments are very important to the whole *** this paper,we analyze and design the detection algorithm of surgical instrument location mark,and estimate the posture of surgical *** addition,we optimized the pose by ***,the algorithm of location mark detection proposed in this paper and the posture analysis data of surgical instruments are verified and analyzed through *** final result shows a high accuracy.

关键词： Surgical navigation system surgical instruments positioning positioning mark detection algorithm matching algorithm posture analysis

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A Family of Single-Stage Multiport Inverters for Hybrid Renewable Energy Generation 2

A Family of Single-Stage Multiport Inverters for Hybrid Rene...

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2nd IEEE International Power Electronics and Application Symposium, PEAS 2023

作者： Huang, Zhengqin Zhou, Dehong Shen, Zewei Zou, Jianxiao Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350313765

Hybrid renewable energy generation (HREG) with single-stage multiport (SSM) inverters features high efficiency, small size, high reliability, and a long lifespan. While the limited variety of SSM inverters currently available is impeding the progress of HREG development. To alleviate this issue, a power flow unit combination-based topology method is proposed in this paper to develop more SSM inverters for HREG, where the features of target topologies are revealed by analyzing the operation states and power flow of HREG, and the final topologies can be constructed by combining units to meet the features. Under the proposed topology derivation method, a family of SSM inverters is derived. The analysis of operational mode and the corresponding experimental outcomes for derived topologies have been presented. © 2023 IEEE.

关键词： hybrid renewable energy generation Multiport inverter multisource single-stage conversion topology derivation

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Analysis of Teaching Influencing Factors and Teaching Reform of Numerical Analysis Course Based on BP Neural Network 5

Analysis of Teaching Influencing Factors and Teaching Reform...

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5th IEEE Eurasian Conference on Educational Innovation, ECEI 2022

作者： Qin, Qingao Li, Fei Gu, Yi Gu, Zhixing Ge, Liangquan Chengdu University of Technology College of Nuclear Technology and Automation Engineering Chengdu China Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9781665433181

Numerical analysis course has important guiding significance for data analysis ability of science and engineering graduate students. Based on BP neural network, we study the teaching influencing factors and teaching reform analysis of numerical analysis course. The teaching quality evaluation of numerical analysis is constructed based on BP neural network. The course of numerical analysis is evaluated from the aspects of classroom teaching, extracurricular development, students' knowledge reserve, and teaching results. 0.820675 is the related coefficient between the predicted value of the neural network and the actual value of questionnaire results, which shows that the constructed teaching quality evaluation model is reliable. Through the related coefficient analysis of the data, the main factors affecting the quality of teaching are obtained to provide new direction and orientation for teaching reform. © 2022 IEEE.

关键词： Teaching

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Overview of Fault Diagnosis in High Voltage Direct Current Transmission Systems

Overview of Fault Diagnosis in High Voltage Direct Current T...

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IEEE Asia Power and Energy engineering Conference (APEEC)

作者： Zhuofan Zhang Dongsheng Cai Olusola Bamisile Qi Huang The College of Energy Chengdu University of Technology Chengdu China The College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China

ISBN: (数字)9798350373479

ISBN: (纸本)9798350373486

This review aims to provide a comprehensive literature overview of the field of fault diagnosis in high-voltage direct current (HVDC) transmission systems. The goal is to summarize and analyze the current state, trends, and key issues in this area. A retrospective examination of the developmental history reveals significant progress in technical innovations related to fault diagnosis in HVDC transmission systems. Additionally, emerging technologies such as big data and artificial intelligence have been introduced, carrying significant implications for the advancement of fault diagnosis in these systems. The paper discusses and elucidates various fault diagnosis methods proposed for HVDC transmission systems, outlining the specific implementation principles and advantages of each approach.

关键词： Fault diagnosis Technological innovation Reviews HVDC transmission High-voltage techniques Power system stability Routing

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Target tracking method of Siamese networks based on the broad learning system

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CAAI Transactions on Intelligence technology 2023年第3期8卷 1043-1057页

作者： Dan Zhang C.L.Philip Chen Tieshan Li Yi Zuo Nguyen Quang Duy Navigation College Dalian Maritime UniversityDalianChina Innovation and Entrepreneurship Education College Dalian Minzu UniversityDalianChina Computer Science and Engineering College South China University of TechnologyGuangzhouChina Department of Computer and Information Science Faculty of Science and TechnologyUniversity of MacaoMacaoChina School of Automation Engineering University of Electronic Science and Technology of ChinaChengduChina Faculty of Navigation Vietnam Maritime UniversityHaiphongVietnam

Target tracking has a wide range of applications in intelligent transportation,real‐time monitoring,human‐computer interaction and other ***,in the tracking process,the target is prone to deformation,occlusion,loss,scale variation,background clutter,illumination variation,etc.,which bring great challenges to realize accurate and real‐time *** based on Siamese networks promotes the application of deep learning in the field of target tracking,ensuring both accuracy and real‐time ***,due to its offline training,it is difficult to deal with the fast motion,serious occlusion,loss and deformation of the target during ***,it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time *** broad learning system(BLS)has a simple network structure,high learning efficiency,and strong feature learning *** at the problems of Siamese networks and the characteristics of BLS,a target tracking method based on BLS is *** method combines offline training with fast online learning of new features,which not only adopts the powerful feature representation ability of deep learning,but also skillfully uses the BLS for re‐learning and re‐*** broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on,so as to change the selection of the Siamese networks search area,solve the problem that the search range cannot meet the fast motion of the target,and improve the *** results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.

关键词： broad learning system siamese network target tracking

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Linux-Based Oscilloscope Kernel Driver Software Design 16

Linux-Based Oscilloscope Kernel Driver Software Design

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16th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2023

作者： Guo, Lianping Luo, Jiying Yu, Tian Zhang, Shengyi Liu, Chunming Yu, Xiaoyu School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

ISBN: (纸本)9798350327144

This paper implements a PXI Express kernel driver that complies with the Interchangeable Virtual Instruments (IVI) specification based on the self-developed digital oscilloscope hardware and the Galaxy Kirin Linux operating system produced in China, which is compatible with both PCI and PCIe buses. The integrity of device connectivity and register access between the digital oscilloscope hardware module and software was verified through the user interface, and the driver is smooth to use and can meet the design requirements. © 2023 IEEE.

关键词： Galaxies

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Domain Contrastive Learning for Multi-Site Alzheimer's Disease Classification 20

Domain Contrastive Learning for Multi-Site Alzheimer's Disea...

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20th International Computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2023

作者： Fiasam, Linda Delali Rao, Yunbo Sey, Collins Aggrey, Esther Stacy E. B. Kodjiku, Seth Larweh Browne, Judith Ayekai Danso, Juliana Mantebea Ukwuoma, Chiagoziem C. Gyarteng, Emmanuel S. A. School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Zhejiang Gongshang University China School of Computer Science and Technology University of Electronic Science and Technology of China University China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

ISBN: (纸本)9798350318982

In the field of medical imaging, accurately classifying Alzheimer's Disease (AD) poses a substantial challenge, primarily due to the inherent variability in imaging data across different clinical sites. This paper introduces a novel approach, Domain Contrastive Learning (DCL), to address this challenge by enhancing the robustness and transferability of AD classification models using multi-site Magnetic Resonance Imaging (MRI) data. DCL leverages a unique contrastive learning technique within an autoencoder's latent space, enabling the extraction of domain-invariant features essential for accurate diagnosis. We rigorously evaluate the proposed network on brain images from three distinct imaging sites, employing a Domain Generalization (DG) training protocol to ensure robustness and generalizability across varying data sources. Our model is evaluated on three binary classification tasks: AD vs. Normal Control (NC), AD vs. Mild Cognitive Impairment (MCI), and MCI vs. NC. Results demonstrate that DCL outperforms existing methods, especially in AD vs. NC classification, achieving higher accuracy, sensitivity, and specificity with 0.51%, 1.54% and 1.68% increments respectively. This underscores DCL's potential as a robust and generalizable method for enhanced AD diagnosis, marking a significant step forward in medical imaging challenges in a multi-site context. © 2023 IEEE.

关键词： Magnetic resonance imaging

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