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Frequency Domain Separation of DAS Multi-Source Aliased Signals

Frequency Domain Separation of DAS Multi-Source Aliased Sign...

2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Gu, Huaxin Liu, Shuaiqi Yu, Feihong Xu, Deyu Chen, Xingwei Shao, Liyang Wu, Yu Zhang, Haifeng College of Innovation and Entrepreneurship Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Electrical and Electronic Engineering Shenzhen China School of Information and Communication Engineering University of Electronic Science and Technology Chengdu China Research Institute of Tsinghua University Advanced Measurement and Control and Equipment R&d Center Chengdu China

ISBN: (纸本)9798350312614

In this paper, fast independent vector analysis (FastIVA) based on convolutional aliasing model is proposed to separate the aliased signals collected by distributed acoustic sensing (DAS) system, and the time-frequency entropy is used to judge the separation performance. The results show that the time-frequency entropy interval of the separated signals and the source signals correspond to each other, which means that the aliased signals are separated. This method is used to improve the accuracy of DAS system signal detection and recognition in complex real environment, and reduce the number of false alarm events. © 2023 IEEE.

关键词： convolutional aliasing model DAS FastIVA time-frequency entropy

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Research Advancements in Artificial Intelligence for Space Situational Awareness

Research Advancements in Artificial Intelligence for Space S...

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International Conference on Information science and Technology (ICIST)

作者： Pengrong Hou Zhelong Wang Yu Jiang Jun Zhu Qinghao Chu Jiaxi Li School of Control Science and Engineering Dalian University of Technology Dalian China Xi'an Satellite Control Center State Key Laboratory of Astronautic Dynamics Xi'an China

The increasing of satellite launch missions, collisions and disintegration events of space objects, and the vast number of near-Earth asteroids pose significant challenges to space traffic management. Space situational awareness aims to detect, track, classify, and characterize space objects, as well as assess space events, playing a crucial role in space traffic management. The application of artificial intelligence in space situational awareness has unique advantages, especially in enhancing comprehensive abilities such as knowledge, analysis, and decision-making. Its outstanding abilities in transfer learning and lifelong learning can rapidly address complex and dynamic space detection problems. This paper reviews and analyzes the research progress of AI in space situational awareness in the past five years, focusing on data collection, situational awareness, and decision-making. While discussing the advantages and limitations of technical applications, it addresses the feasibility of applying emerging technologies such as distributed machine learning, knowledge graphs, and multi-agent game theory to future space situational awareness research, considering the characteristics of space data and new requirements. This paper points out the effectiveness and necessity of artificial intelligence in space situational awareness, which can provide reference for strengthening space environmental understanding and promoting relevant research in space traffic management.

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Droop control for Microgrids with Variable Inverter Outputs: A Virtual Impedance-Based Adaptive Strategy

Droop Control for Microgrids with Variable Inverter Outputs:...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Hualin Zhang Shuwang Wang Baicheng Zhang Guanqiao Kong School of Electrical and Control Engineering Liaoning Technical University Huludao China School of Cyber Science and Engineering Southeast University Nanjing China CSSC Systems Engineering Research Institute Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

With the widespread application of renewable energy sources (RES) in islanded microgrids, the system instability caused by the intermittency of RES generation has also increased. To maintain stability during RES fluctuations, a novel adaptive droop control strategy utilizing adaptive virtual impedance is proposed in this study. This strategy allows the droop coefficients and virtual impedance to adaptively change based on the actual capacity of the inverters, thereby automatically adjusting the inverter output voltage and ensuring proper power sharing even as inverter capacity changes. Moreover, this strategy ensures appropriate power sharing under different load conditions and microgrid configurations. Finally, t he proposed strategy i s validated through MATLAB/Simulink simulations, showcasing its advantages over traditional droop control.

关键词： Adaptation models Renewable energy sources Reactive power Simulation Microgrids Inverters Stability analysis Impedance MATLAB Load modeling

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Non-line-of-Sight Recognition Algorithm Based on Deep Learning

Non-line-of-Sight Recognition Algorithm Based on Deep Learni...

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International Conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2022

作者： Zhang, Chengjun Wang, Hongmei Wang, Zhiwei Wang, Faguang Min, Minghui Li, Shiyin School of Information and Control Engineering China University of Mining and Technology Jiangsu Xuzhou221116 China Xuzhou First People’s Hospital Xuzhou221116 China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education and School of Cyber Science and Engineering Wuhan University Wuhan430072 China

ISBN: (纸本)9789819904150

With the development of the information age, large indoor places such as hospitals, mines and factories have higher and higher requirements for indoor positioning. GPS and other common outdoor positioning methods cannot meet the requirements of accurate indoor navigation. Ultra-wideband is widely used in indoor places because of its strong anti-interference and high accuracy. Accurate identification of errors in UWB indoor non-visual range helps to improve the data for positioning. Therefore, this study combined with convolutional neural network to solve the problem of low recognition rate of non-line-of-sight signals in indoor scenes, and proposed an improved convolutional neural network based on empirical mode decomposition (EMD), wavelet domain denoising (WD) and improved CNN) fusion model EMD-WD-CNN. First, the channel impulse response signal of UWB is denoised based on the EMD-WD algorithm, and then the denoised signal is used as the input of the CNN model for training. The comparison based on similar algorithms shows that the improved CNN model can significantly improve the line-of-sight and NLOS recognition rate of different media in indoor environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Empirical mode decomposition

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Experimental Study on the Thermal Performance of a Dual Compensation Chamber Loop Heat Pipe with Parallel Flow Vapor Line and Condenser

SSRN

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SSRN 2023年

作者： Fu, Jingwei Bai, Lizhan Zhang, Yunfei Lin, Guiping Laboratory of Fundamental Science on Ergonomics and Environmental Control School of Aeronautic Science and Engineering Beihang University Beijing100191 China

Dual compensation chamber loop heat pipe (DCCLHP) holds significant application potential in the aircraft thermal management because of its flexible installation and orientation-free operation. In this work, a DCCLHP with parallel flow vapor line and condenser was developed, aiming to enhance its maximum heat transfer capability without changing other component structures. To verify its startup performance and heat transfer capability, comprehensive experimental research has been carried out in five typical orientations. Experimental results show that the DCCLHP could realize the startup successfully under the heat load of 10-150 W at horizontal attitude. Its maximum heat transfer capability could reach greater than 600 W over a transport distance of 1.1 m, which was increased by 87.5% under the condition of only changing the line layout. The minimum thermal resistance of the system was only 0.086 °C/W. In addition, the heat load range in which the DCCLHP encountered startup failure at unfavorable attitudes was identified, and the responsible physical mechanism was analyzed. This work shows that DCCLHP with proper design can meet the application requirements in the aircraft thermal management with large heat load and varying attitude. © 2023, The Authors. All rights reserved.

关键词： Parallel flow

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Credible and Facile Fluorometric Detection of Soybean Trypsin Inhibitor Activity with a Water-Soluble Poly(diphenylacetylene) Derivative

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Transactions of Tianjin University 2024年第4期30卷 305-313页

作者： Jiao Tang Xiao Tang Jiaxiang Tang Wei Qi Qianwei Pan Jinhong Zeng Housheng Xia Jianping Zhou Zhongyi Sheng Junfeng Niu Zhejiang Tiantai Xianghe Industrial Co. Ltd.Taizhou 317200China Zhejiang Xianghe Track Fastener Research Institute Taizhou 317200China Shaoxing Institute for Food and Drug Control Shaoxing 312000China Key Laboratory of Chemical and Biological Processing Technology for Farm Products of Zhejiang Province School of Biological and Chemical EngineeringZhejiang University of Science and TechnologyHangzhou 310023China

Herein,a novel method for fl uorometric detection of soybean trypsin inhibitor(SBTI)activity based on a water-soluble poly(diphenylacetylene)derivative was *** quenching of the polymer via p-nitroaniline,produced from the trypsin-catalyzed decomposition of N-benzoyl-DL-arginine-4-nitroanilide hydrochloride(L-BAPA),was well described using the Stern-Volmer *** activity was quantitatively assessed based on changes in the fl uorescence intensity of the *** strategy has several advantages,such as high sensitivity and ease of ***,its applicability to other biochemical analyses is promising.

关键词： Fluorometric detection Soybean trypsin inhibitor Inhibition activity Fluorescence quenching

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ROLO-SLAM: Rotation-Optimized LiDAR-Only SLAM in Uneven Terrain with Ground Vehicle

arXiv

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arXiv 2025年

作者： Wang, Yinchuan Ren, Bin Zhang, Xiang Wang, Pengyu Wang, Chaoqun Song, Rui Li, Yibin Meng, Max Q.-H. School of Control Science and Engineering Shandong University Jinan China Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

LiDAR-based SLAM is recognized as one effective method to offer localization guidance in rough environments. However, off-the-shelf LiDAR-based SLAM methods suffer from significant pose estimation drifts, particularly components relevant to the vertical direction, when passing to uneven terrains. This deficiency typically leads to a conspicuously distorted global map. In this article, a LiDAR-based SLAM method is presented to improve the accuracy of pose estimations for ground vehicles in rough terrains, which is termed Rotation-Optimized LiDAR-Only (ROLO) SLAM. The method exploits a forward location prediction to coarsely eliminate the location difference of consecutive scans, thereby enabling separate and accurate determination of the location and orientation at the front-end. Furthermore, we adopt a parallel-capable spatial voxelization for correspondence-matching. We develop a spherical alignment-guided rotation registration within each voxel to estimate the rotation of vehicle. By incorporating geometric alignment, we introduce the motion constraint into the optimization formulation to enhance the rapid and effective estimation of LiDAR's translation. Subsequently, we extract several keyframes to construct the submap and exploit an alignment from the current scan to the submap for precise pose estimation. Meanwhile, a global-scale factor graph is established to aid in the reduction of cumulative errors. In various scenes, diverse experiments have been conducted to evaluate our method. The results demonstrate that ROLO-SLAM excels in pose estimation of ground vehicles and outperforms existing state-of-the-art LiDAR SLAM frameworks. Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetic levitation vehicles

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High-Precision Dimensional Measurement for Digital Crown Part of Smartwatch from Microscopic Image

High-Precision Dimensional Measurement for Digital Crown Par...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Hongqiang Lyu Yuhang Zhang Liang Bai Shengjun Xu Huangwei Zhong Yuan Liu Faculty of Electronic and Information Engineering School of Automation Science and Engineering Xi'an Jiao tong University Shaanxi China School of Information and Control Engineering Xi'an University of Architecture and Technology Shaanxi China

Digital crown is a precision part used for human-computer interaction in the smartwatch. The diameter of the digital crown determines its grade. Currently, mainly used Coordinate Measuring Machine(CMM) to measure the diameter of digital crown, but the measurement method has the problem of high cost and low efficiency. To address the problem, we propose a high-precision dimensional measurement system for digital crown part of smartwatch. The system consists of four modules: microscopic image acquisition module, central host, machine vision software and PLC control module. The image of digital crown is captured by the microscopic image acquisition module and sent to the software through the central host. In the machine vision software, we propose a defect detection algorithm using ResNet to filter defective digital crown in real time, and we design a circle detection algorithm based on edge point tracking to measure the diameter of digital crown. The measurement result is sent to the PLC control module through wireless transmission and control the mechanical suction cups to place the part. Finally, we create a dataset to test the performance of the system, the defect detection model achieves an accuracy of 97.33% and a runtime of 47ms, the dimensional measurement algorithm achieves a maximum error of 0.003878mm, a grading accuracy of 95.29%, and a measurement time of 465ms.

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Real-Time Migration Solution with Pre-Dumping Based on Docker Container Layering Feature

SSRN

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SSRN 2023年

作者： Tian, Pengxin Si, Guannan An, Zhaoliang Li, Jianxin Zhou, Fengyu School of Information Science & Electrical Engineering Shandong Jiaotong University Jinan250357 China School of Control Science & Engineering Shandong University Jinan250000 China

To ensure users always experience good performance when moving to different locations, a real-time migration solution for Docker containers has been proposed for mobile edge computing. To address excessive data transmission during mobile edge service migration, the container layering feature is used to achieve real-time container transmission. An image repository is created on the source node, with the image ID serving as the index to prevent repeated image layer pulling and reduce data transmission time. Additionally, to minimize service downtime, a pre-dumping mechanism is proposed, which enhances state information migration by iteratively transmitting data based on pre-copy and combining CRIU. Experimental results indicate that this solution enables real-time service migration, effectively reducing the total time and downtime of service migration, thereby ensuring service continuity. © 2023, The Authors. All rights reserved.

关键词： Containers

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Industrial Surface Defect Detection via Multi-scale Mask Cross-layer Fusion Network

Industrial Surface Defect Detection via Multi-scale Mask Cro...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ziao Liu Xuyang Li Lin Zhao Zhongquan Gao Yu Zheng School of Energy and Power Engineering Xi’an Jiaotong University Xi’an China School of Electrical and Control Engineering Shaanxi University of Science and Technology Xi’an China School of Cyber Engineering Xidian University Xi’an China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

This paper presents a multi-scale mask cross-layer fusion network (MMCF) for industrial surface defect detection. MMCF exploits the transformer’s ability to preserve global information, but replaces the large-kernel large-stride convolution in the patches embedding with a small-kernel small-stride convolution stacking to reduce redundancy. Moreover, MMCF uses different scale complementarily masks to occlude normal images and employs an encoder-decoder structure with cross-layer feature fusion to reconstruct the occluded regions. After smoothing the complementary reconstructed parts using pixel attention, merge them together to form a complete reconstructed image. The anomaly score is computed based on the gradient magnitude similarity between the input image and the reconstruction image. Experiments on the MVTec AD dataset with 15 product categories show that MMCF outperforms other surface defect detection methods in terms of AUC values.

关键词： Training Cross layer design Surface reconstruction Smoothing methods Convolution Stacking Termination of employment

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