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3rd International Conference on Electrical, Electronics, Information and communication Technologies, ICEEICT 2024

作者： Padmalaya, G. Athmanesan, T. Senthilkumar, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Electrical and Communication Department of Electronics and Communication Engineering Tamil Nadu Avadi600062 India Pondicherry University Government of India Centre for Pollution Control and Environmental Engineering School of Engineering and Technology Puducherry Kalapet605014 India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

In this research work, a GO-ZnO nanocomposite-based electrochemical sensor was used as a modified electrode for enhanced electrochemical behavior. The electrochemical sensor characteristics were analyzed using cyclic and impedance voltammetry. Cyclic voltammetry confirmed the electrochemical behavior of the modified electrode. The cyclic voltammograms for GO, ZnO and GO-ZnO nanocomposite were investigated, and observed that peaks are highly independent on behavioral potential peaks. Their physio-chemical characteristics were analyzed using X-ray diffraction (XRD) analysis and HR-scanning electron Microscopy. On examining the electrochemical characteristics of obtained nanomaterials found to exhibit its behavior towards electrochemical sensor applications for detecting sulfamethoxazole. © 2024 IEEE.

关键词： Cyclic voltammetry

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Image-denoising algorithm based on improved K-singular value decomposition and atom optimization

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CAAI Transactions on Intelligence technology 2022年第1期7卷 117-127页

作者： Rui Chen Dong Pu Ying Tong Minghu Wu College of Information&Communication Engineering Nanjing Institute of TechnologyNanjingChina College of Electric Power Engineering Nanjing Institute of TechnologyNanjingChina Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of technologyWuhanChina

The traditional K-singular value decomposition(K-SVD)algorithm has poor imagedenoising performance under strong *** image-denoising algorithm is proposed based on improved K-SVD and dictionary atom ***,a correlation coefficient-matching criterion is used to obtain a sparser representation of the image *** dictionary noise atom is detected according to structural complexity and noise intensity and removed to optimize the ***,non-local regularity is incorporated into the denoising model to further improve image-denoising *** of the simulated dictionary recovery problem and application on a transmission line dataset show that the proposed algorithm improves the smoothness of homogeneous regions while retaining details such as texture and edge.

关键词： singular value smoothness

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FPGA-Oriented Target Detection Accelerator Design

FPGA-Oriented Target Detection Accelerator Design

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Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Weizhe Huang School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

Target detection is widely applied in fields such as face recognition, autonomous driving, and industrial automation. However, when deploying target detection models based on convolutional neural networks on resource-constrained embedded platforms, issues like high computational complexity, long execution times, and significant on-chip memory usage arise. To address these challenges, this paper presents a target detection accelerator design based on Field Programmable Gate Array (FPGA). Firstly, the computational workload of convolutional neural networks is reduced by merging convolutional layers with batch normalization layers. Secondly, to mitigate on-chip storage space concerns, on-chip data fixed-point quantization is employed to decrease on-chip memory consumption. Subsequently, to tackle the issue of long execution times, a convolution calculation module based on the Winograd algorithm is designed, which reduces computation latency. Finally, a dual-buffer pipeline architecture is devised to optimize memory bandwidth. Experimental results demonstrate that the proposed solution accelerates YOLOv4-tiny on the ZYNQ-7020, achieving a performance of 21.35 GOP/s. The energy efficiency is 32.56 times higher than CPU and 9.76 times higher than GPU. Additionally, the convolution calculation module in this paper reduces inference time by 15% compared to the sliding window convolution calculation method.

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ADAPTIVE DETECTORS WITH ENHANCED SELECTIVITY PROPERTIES IN THE COMPOUND GAUSSAIN CLUTTER WITH GERNERALIZED INVERSE GAUSSIAN TEXTURE

ADAPTIVE DETECTORS WITH ENHANCED SELECTIVITY PROPERTIES IN T...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Zeyu Wang, Zhirui Sun, Xiaona State Key Laboratory of Networking and Switching Technology School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Institute of Radio Measurement Beijing China

ISBN: (纸本)9781839539954

In this paper, the problem of adaptive detection of signals in the compound Gaussian clutter when the signal mismatch occurs is discussed. The texture follows the generalized inverse Gaussian distribution which includes inverse Gaussian, inverse gamma and gamma as three special examples. To increase the mismatch rejection performance, the binary hypothesis test is modified and a fictitious signal is introduced under the null hypothesis. Two adaptive mismatch selective detectors are derived according to the maximum a posteriori test and generalized likelihood ratio test. The performance evaluation shows that the two proposed adaptive detectors have stronger mismatch rejection performance than the conventional detector. © The Institution of engineering & technology 2023.

关键词： Clutter (information theory)

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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The Preparation Method, Resistive Switching Mechanism and Application Prospects of Memristor 7

The Preparation Method, Resistive Switching Mechanism and Ap...

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7th International Conference on Electrical, Mechanical and Computer engineering, ICEMCE 2023

作者： Li, Jiao Zhao, Ximei Lei, Tengfei Chai, Lin Wang, Shi Yu, Yuna Collaborative Innovation Center of Memristive and Computing Application Qilu Institute of Technology Jinan250200 China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan250200 China College of Computer and Communication Engineering Qilu Institute of Technology Jinan250200 China

ISBN: (纸本)9798350382877

Memristors have also become one of the most promising research hotspots for solving data storage and computing problems faced in the big data era. As an emerging electronic device, memristor has potentially important applications in information storage and processing. This paper constructs a rod-shaped TiO2-x nanoscale memristor with FTO/TiO2-x/Cu structure, whose reliability is based on the memory behavior and its conversion mechanism. Research progress including materials, preparation methods, resistive switching mechanism and application prospects are described. First, the constituent materials of memristor are introduced, focusing on metal oxides and organic materials such as titanium oxide (TiO2). The choice of these materials is critical to memristor performance and applications. Secondly, the advantages and limitations of the preparation methods are discussed. Then the resistance switching mechanism of memristor was studied in depth, focusing on the generation principle and adjustment mechanism of memristor effect. These include important concepts such as charge migration, Schottky barrier formation and oxygen vacancy drift, which help to understand the electrical properties of memristors. In addition, we explore the prospects of memristors in practical applications. Finally, past and current work in this field is summarized, and the prospects and challenges of memristorrelated research are prospected. © 2023 IEEE.

关键词： Memristors

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Secrecy Performance Analysis of Hybrid RF-FSO Cooperative Satellite-Aerial-Terrestrial Networks with imperfect CSI 2

Secrecy Performance Analysis of Hybrid RF-FSO Cooperative Sa...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Ma, Yuanyuan Chen, Han Xu, Zhan Zhi, Ruxin Ma, Chunshuai Zheng, Yiyang Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems Ministry of Information Industry Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing China No.723 Research Institute China State Shipbuilding Corporation Yangzhou China Beijing Information Science and Technology University Beijing China Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331515669

This study examines the secrecy outage performance of uplink transmission within a hybrid cooperative satellite-aerial-terrestrial network (SATN) that integrates radio-frequency (RF) and free-space optical (FSO) technologies. Specifically, in the proposed cooperative SATN, a terrestrial source (S) conveys its information to a satellite receiver (D) with the assistance of an aerial relay (R), while a group of eavesdropping aerial terminals (Eves) attempts to intercept the confidential data. RF and FSO transmission methods are employed over the S-R and R-D links, respectively, both subject to imperfect channel state information (CSI). By considering the randomness of R, D, and Eves, and utilizing a stochastic geometry framework, we investigate the secrecy outage performance of the considered SATN. A closed-form analytical expression for the end-to-end secrecy outage probability is derived. Finally, simulations are conducted to validate the accuracy of the analysis. © 2024 IEEE.

关键词： Stochastic systems

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Transformer-Based Macroscopic Regulation for High-Speed Railway Timetable Rescheduling

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1822-1833页

作者： Wei Xu Chen Zhao Jie Cheng Yin Wang Yiqing Tang Tao Zhang Zhiming Yuan Yisheng Lv Fei-Yue Wang Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Signal&Communication Research Institute China Academy Railway SciencesBeijing 100081China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway *** such cases,train timetables need to be ***,timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization *** paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based ***,the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer,providing the better understanding of overall operation in the high-speed railway ***,a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast *** experiments on various delay scenarios are *** results demonstrate the effectiveness of the proposed method in comparison to other popular methods.

关键词： High-speed railway reinforcement learning train timetable rescheduling Transformer

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3D Imaging Sensing and Prediction technology of Large Component Surface with Multi-source Sensing 6

3D Imaging Sensing and Prediction Technology of Large Compon...

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6th Conference on Frontiers in Optical Imaging and technology: Applications of Imaging Technologies

作者： Sun, Jing Li, Lijuan Lin, Xuezhu Guo, Lili Liu, Yue Yan, Dongming Key Laboratory of Optoelectronic Measurement and Control and Optical Information Transmission Technology Ministry of Education College of Optoelectronic Engineering Changchun University of Science and Technology Changchun130022 China Zhongshan Research Institute of Changchun University of Science and Technology Guangdong Zhongshan528400 China

ISBN: (数字)9781510679757

ISBN: (纸本)9781510679740

In order to solve the problem that the surface deformation of large components cannot be photographed in real time due to the influence of assembly stress during assembly, a multi-source sensor surface deformation sensing and 3D imaging prediction technology for large components is proposed, which combined laser tracking, structured light detection and optical fiber sensing monitoring. Firstly, the multi-source sensor system is analyzed, and the multi-source sensor fusion sensing field is constructed. Secondly, the unified model of heterogeneous data is established to complete the accurate conversion of optical fiber monitoring wavelength to space point coordinates. Thirdly, multi-source data fusion is realized based on Gaussian process fusion algorithm to complete 3D imaging prediction of component surface deformation. Finally, a large skin component is taken as an example to simulate the assembly deformation experiment. The results show that the root-mean-square error is less than 0.1mm compared with the actual results. Based on the measured data of structured light scanning and multi-source sensing technology, a real-time sensing and prediction method for surface deformation imaging of large components is proposed. This method not only simplifies the scanning mode of structured light, but also provides a new idea for dynamic model building. © 2024 SPIE.

关键词： Forecasting

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Lightweight recognition method of infrared sensor image based on deep learning method 24

Lightweight recognition method of infrared sensor image base...

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8th International Conference on control engineering and Artificial Intelligence, CCEAI 2024

作者： Ren, Ze Tian, Xinling Qin, Guan Zhou, Dongdong China Energy Group International Engineering Consulting Co. Ltd. Beijing China Optoelectronics Business Department Beijing Institute of Control Engineerin Beijing100190 China Optoelectronic Measurement and Intelligent Perception Zhongguancun Open Lab Space Optoelectronic Measurement and Perception Lab Beijing Institute of Control Engineering Beijing100190 China Collaborative Innovation Center of Steel Technology University of Science and Technology Beijing 30 Xueyuan Road Haidian District Beijing100083 China

ISBN: (纸本)9798400707971

Space infrared sensors have become the "eyes"of space activities, there is currently no established identification method applied to the embedded deployment of space infrared sensor, despite it being a crucial device for aerospace detection due to the influence of deep learning target detection model embedded deployment difficulty, large sample demand, model parameter weight, low computing efficiency, and other factors. In this paper, the YOLOv5 detection model's network structure and activation function are lightened up. Next, the algorithm model's adaptive learning rate optimization algorithm is used to build the lightened-up YOLOv5 infrared image target recognition model. And the image samples were preprocessed with the intention of addressing the poor image quality brought on by background interference and multi-pose acquisition. Then, a method of sample amplification is suggested in light of the issues with the deep learning model. Consequently, the deep learning lightweight model applied in this study is one-third smaller than the conventional model, which dramatically increases target detection speed without significantly lowering detection rate. In addition to investigates ideas for the embedded deployment and recognition of infrared images in small sample space. © 2024 ACM.

关键词： Deep learning

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