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Controlled-source electromagnetic noise attenuation via a deep convolutional neural network and high-quality sounding curve screening mechanism

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Geophysics 2025年第3期90卷 WA125-WA140页

作者： Liu, Yecheng Li, Diquan Li, Jin Zhang, Xian Central South University Monitoring Ministry of Education Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Changsha China Hunan Provincial Key Laboratory of Non-ferrous Resources and Geological Hazard Detection Changsha China Central South University School of Geoscience and Info-physics Changsha China Hunan Normal University College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan University of Finance and Economics School of Information Technology and Management Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology Changsha China

Strong noise is one of the biggest challenges in controlled-source electromagnetic (CSEM) exploration, which severely affects the quality of the recorded signal. We develop a novel and effective CSEM noise attenuation method that integrates a deep convolutional neural network (DCNN) with a high-quality sounding curve screening mechanism. This method first uses a DCNN to learn the large-scale noise characteristics of CSEM measured data and build a DCNN denoising model for attenuating the noise. Because some impulse noise remains after DCNN denoising, the high-quality sounding curve screening mechanism is innovatively applied to optimize the DCNN denoised data. The core of this method is the smoothness value (SV), which quantitatively evaluates the smoothness of the sounding curve (apparent resistivity curve). In effect, the mechanism segments the overall DCNN denoised data and calculates the corresponding sounding curve and its SV for each segment. The highest-quality sounding curve can be screened out by comparing the SVs of all curves as well as obtaining the highest-quality data segment present in the overall DCNN denoised data. Finally, our method is validated with synthetic and field data, demonstrating its feasibility and effectiveness in attenuating large-scale CSEM noise. The quality of CSEM data, which is heavily affected by noise, is significantly improved after processing by our method. © 2025 Society of Exploration Geophysicists. All rights reserved.

关键词： Deep neural networks

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An Improved Back Projection Algorithm for Terahertz Imaging

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IEEE Sensors Journal 2025年第10期25卷 17761-17768页

作者： Wang, Guanwen Qi, Feng Shenyang University of Technology School of Information Science and Engineering Shenyang110870 China Key Laboratory of Liaoning Province in Terahertz Imaging and Sensing Shenyang110169 China Shenyang Institute of Automation Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110169 China

In this article, an analytical algorithm based on inverse matrix solution is proposed to perform imaging based on near-field terahertz (THz) scan data. The computational framework of conventional back projection algorithms (BPAs) comprises four principal stages. The initial procedure discretizes the target region via structured mesh partitioning. This was followed by the subsequent computation of wave propagation time delays for each discrete pixel element. Phase correction operations were then performed to compensate for waveform distortions. Ultimately, image reconstruction was achieved through coherent integral of back propagated signals. In contrast to the conventional BPA, this proposed approach reconstructed the signal propagation model. It achieves this by transforming the back projection process into an inverse problem solution, which is then used to solve the forward propagation of the signal. It can solve the inverse problem more accurately by obtaining the actual reflectivity of each pixel of the target. The computed results are closer to the electromagnetic (EM) field distribution of the real scene, reducing the error caused by energy in the subaperture signal unrelated to the imaging region compared to BPA. The radial resolution has, moreover, been improved by over 30%. Those absorbers, which are generally invisible in radar imaging due to very weak reflections, can be visualized beyond classical BPA. In the imaging process of complex targets, enhanced precision in EM calculations facilitates more comprehensive reconstructions. The algorithm can be used for elaborate environment imaging and sensing in the future. © 2001-2012 IEEE.

关键词： Radar imaging

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Time-Varying Formation Tracking Control of Heterogeneous Multi-Agent Systems With Intermittent Communications and Directed Switching Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems Control theory Autonomous systems Stability

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Robust Trajectory Design and Task Scheduling With Data Compression in Industrial Internet of Things Assisted by UAV

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IEEE Internet of Things Journal 2025年第9期12卷 12501-12511页

作者： Li, Bin Zhu, Xiao Wang, Junyi Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Guilin University of Electronic Technology Key Laboratory of Cognitive Radio and Information Processing Ministry of Education Guilin541004 China Guilin University of Electronic Technology School of Information and Communication Guilin541004 China

Data compression technology is able to reduce data size, which can be applied to lower the cost of task offloading in mobile edge computing (MEC). This article addresses the practical challenges for robust trajectory and scheduling optimization based on data compression in the uncrewed aerial vehicle (UAV)-assisted MEC, aiming to minimize the sum energy cost of terminal users while maintaining robust performance during UAV flight. Considering the nonconvexity of the problem and the dynamic nature of the scenario, the optimization problem is reformulated as a Markov decision process (MDP). Then, a randomized ensembled double Q-learning (REDQ) algorithm is adopted to solve the issue. The algorithm allows for higher feasible update-to-data ratio, enabling more effective learning from observed data. The simulation results show that the proposed scheme effectively reduces the energy consumption while ensuring flight robustness. Compared to the PPO and A2C algorithms, energy consumption is reduced by approximately 21.9% and 35.4%, respectively. This method demonstrates significant advantages in complex environments and holds great potential for practical applications. © 2014 IEEE.

关键词： Energy utilization

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RIS-Aided Cell-Free Massive MIMO Systems With Low-Resolution ADCs: Uplink Performance Analysis and Optimization

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IEEE Internet of Things Journal 2025年第11期12卷 18249-18265页

作者： Xiong, Youzhi Tang, Liu Sun, Sanshan Yang, Songjie Liu, Li Mao, Sun Zhang, Zhongpei Wei, Ning Sichuan Normal University Laboratory of Information Intelligent Processing and Communications College of Physics and Electronic Engineering Chengdu610101 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Sichuan Normal University College of Computer Science Chengdu610101 China

This article investigates the uplink performance of reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems over spatially correlated Rayleigh fading channels. We consider multiple RISs and low-resolution analog-to-digital converters (ADCs) to improve the system energy efficiency (EE). We first provide an aggregated channel estimation technique with less pilot overhead. By exploiting the statistical channel state information (CSI), we further optimize the RISs’ phase shifts with the goal of minimizing the total normalized mean square error (NMSE) of the estimated aggregated channels. Subsequently, we derive the closed-form expression of the uplink spectral efficiency (SE) for quantization-aware minimum mean-square error (MMSE) combining. Third, based on the closed-form SE expression and power consumption model, we formulate and solve an optimization problem that maximizes the uplink EE under the constraints of transmit power and total ADC quantization bits. Specifically, by leveraging the Dinkelbach transform, Lagrangian dual transform, and fractional programming (FP) techniques, an alternating optimization (AO)-based algorithm is proposed to jointly obtain the bit allocation (BA) scheme among all access points (APs) and the uplink power control (PC) strategy for all users. Finally, numerical results validate the correctness of the closed-form SE expression and show the effectiveness of the proposed optimization methods for phase shift design and EE maximization. © 2014 IEEE.

关键词： Mean square error

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Collaborative optimization for multirobot manufacturing system reliability through integration of SysML simulation and maintenance knowledge graph

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Journal of Manufacturing Systems 2025年 80卷 749-775页

作者： Zhou, Jian Zheng, Lianyu Wang, Yiwei School of Mechanical Engineering and Automation Beihang University Beijing100191 China MIIT Key Laboratory of Intelligent Manufacturing Technology for Aeronautics Advanced Equipments Ministry of Industry and Information Technology Beijing100191 China Beijing Key Laboratory of Digital Design and Manufacturing Technology Beijing100191 China

In the rapidly advancing field of industrial automation, the reliability and maintenance of multirobot manufacturing systems are crucial. This paper proposes a collaborative optimization method for the reliability of multirobot system, combining SysML (System Modeling Language) model simulation with an operational and maintenance knowledge graph, aiming to ensure the reliable operation of multirobot manufacturing systems. The SysML model provides a comprehensive framework to represent the system architecture, workflows, and key parameters, identify critical components and potential bottlenecks, and perform detailed reliability analysis. Simultaneously, by embedding intelligent algorithms, the operational and maintenance knowledge graph enables automatic detection of operational anomalies and intelligent generation of maintenance strategies for industrial robots. By integrating the SysML model with the operational and maintenance knowledge graph, a collaborative optimization framework for the reliability of multirobot system is constructed. This framework not only dynamically adjusts key parameters in the simulation model, enhancing the accuracy and real-time performance of system reliability assessments, but also optimizes maintenance strategies based on system simulation indicators to ensure the reliable operation of multirobot system. Case studies validate that the proposed method improves the reliability of multirobot manufacturing systems, demonstrating that the combination of SysML simulation and the operational and maintenance knowledge graph can effectively address the complexity of modern manufacturing systems, offering significant reference value. © 2025 The Society of Manufacturing Engineers

关键词： Knowledge graph

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Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM

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Tsinghua Science and technology 2025年第1期30卷 68-86页

作者： Yahang Qin Zhenni Li Shengli Xie Bo Li Ming Liu Victor Kuzin School of Automation Guangdong University of Technologyand also with Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete ManufacturingGuangzhou 510006China School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Information Processing and System Integration of IoTMinistry of EducationGuangzhou 510006China Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologHong Kong 999077China Academician of Russian Engineering Academy Moscow Moscow 125009Russia

Multipath signal recognition is crucial to the ability to provide high-precision absolute-position services by the BeiDou Navigation Satellite System(BDS).However,most existing approaches to this issue involve supervised machine learning(ML)methods,and it is difficult to move to unsupervised multipath signal recognition because of the limitations in signal *** by an autoencoder with powerful unsupervised feature extraction,we propose a new deep learning(DL)model for BDS signal recognition that places a long short-term memory(LSTM)module in series with a convolutional sparse autoencoder to create a new autoencoder ***,we propose to capture the temporal correlations in long-duration BeiDou satellite time-series signals by using the LSTM module to mine the temporal change patterns in the time ***,we develop a convolutional sparse autoencoder method that learns a compressed representation of the input data,which then enables downscaled and unsupervised feature extraction from long-duration BeiDou satellite series ***,we add an l_(1/2) regularizer to the objective function of our DL model to remove redundant neurons from the neural network while ensuring recognition *** tested our proposed approach on a real urban canyon dataset,and the results demonstrated that our algorithm could achieve better classification performance than two ML-based methods(e.g.,11%better than a support vector machine)and two existing DL-based methods(e.g.,7.26%better than convolutional neural networks).

关键词： convolutional sparse autoencoder BeiDou Navigation Satellite System(BDS) long short-term memory(LSTM) multipath classification

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Masking Cascaded Self-attentions for Few-Shot Font-Generation Transformer 17th

Masking Cascaded Self-attentions for Few-Shot Font-Generati...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Ma, Jing Xiang, Xiang He, Yan National Key Lab of Multi-Spectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819609161

Few-shot Font Generation (FFG) is a practical technology widely used in designing artistic characters, handwriting imitation, and identification, etc., which aims to generate realistic font images with a few reference samples. Typically, convolutional neural networks (CNNs) are employed to learn the representations of style and content from the font images. However, owing to the locality of convolutional operations, CNNs are not good at capturing the global structure of fonts, which leads to the generated images with blurry components and distorted space layouts. To address this problem, we consider cascading self-attention modules to exploit long-range dependencies for font generation and propose a transformer-based approach called FGTr. Following the style-content disentanglement paradigm, FGTr contains two different transformer encoders to extract the style and content sequences. A multi-layer transformer decoder is adopted to merge the two sequences and generate target images. In order to smoothen the transition of patch edges, we utilize a Local Self-Attention Mask (LSAM) to restrict the attention scope of each patch to a fixed-size sliding window, which plugs into the Transformer with no extra parameters. We also propose an Auxiliary Generation Module (AGM) in favor of generating glyphs closer to the real. Extensive experiments demonstrate the effectiveness and superiority of our method compared with state-of-the-art CNN-based models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution

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Decoupling the nonthermal effect of current by electrically assisted crystal plasticity modeling of Ni-based single crystal superalloys

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材料科学技术（英文版） 2025年第1期204卷 91-103页

作者： Jia Gao Hongwei Li Xin Zhang Guangda Shao Zhiyu Xiang Xinxin Sun State Key Laboratory of Solidification Processing School of Materials Science and EngineeringNorthwestern Polytechnical UniversityXi'an 710072China Shaanxi Key Laboratory of High-performance Precision Forming Technology and Equipment Northwestern Polytechnical UniversityXi'an 710072China Shaanxi Key Laboratory of High-performance Precision Forming Technology and Equipment Northwestern Polytechnical UniversityXi'an 710072China School of Mechanical and Electrical Engineering Northwestern Polytechnical UniversityXi'an 710072China Ministry of Industry and Information Technology Key Laboratory of Aeroengine Technology Northwestern Polytechnical UniversityXi'an 710072China

The electrically assisted(EA)deformation process has received considerable attention in recent years,ac-companied by research on current-induced deformation ***,there are still challenges in eliminating thermal effects,which have prevented a comprehensive understanding of the underlying current-induced *** for a single crystal(SC)in research provides advantages in decou-pling the nonthermal effect of electric current at smaller scales and eliminating the complex interactions that exist in polycrystalline ***,the innovation of this work lies in decoupling the non-thermal effect of electric current and conducting a comprehensive analysis of anisotropic deformation and mechanisms within a Ni-based SC with different crystallographic axes and various current directions dur-ing electrically assisted tensile simulation.A significant tension axis direction in the SC during EA tension was induced by the combination of a higher current direction factor(|cos θ|)and a dimensionless factor for the current density(Jα/Jα0|)along the[100]*** stress drop within the SC due to the nonthermal effect of electric current generally increased with increasing current *** was attributed to the increased dislocation density differences and decreased *** increased stress anisotropy of the SC at a current direction of 45° was attributed to fewer activated(111)slip systems and the pinning effect of more dislocations within these *** study advances our understanding of the thermal and nonthermal effects of electric current and offers valuable insights for the informed application of EA deformations in industrial and aerospace settings with SC superalloys.

关键词： Electrically assisted(EA)tension Single crystal superalloys Nonthermal effect Anisotropy Tension axis Current direction

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Optical performance monitoring of mode division multiplexing signal based on multi-task convolutional neural network 7

Optical performance monitoring of mode division multiplexing...

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7th Optics Young Scientist Summit, OYSS 2024

作者： He, Ting Chang, Huan Gao, Ran Wang, Fei Guo, Dong Beijing Institute of Technology No.5 Zhongguancun South Street Haidian District Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (数字)9781510688377

ISBN: (纸本)9781510688360

Mode division multiplexing (MDM) optical fiber communication technology enhances bandwidth utilization and communication capacity to address the increasing demand for high-capacity data transmission in the era of rapid network traffic growth. For MDM optical fiber communication systems affected by various impairments, optical performance monitoring (OPM) can effectively characterize the impairments in received optical signals, directly reflecting the transmission signal state and channel conditions, and help ensure efficient and stable network operation. In this paper, we propose a multi-task convolutional neural network (MT-CNN) aided OPM method for MDM signals. The proposed MTCNN combines convolutional neural networks and a multi-task learning framework to extract the transmission link information and predict the optical signal-to-noise ratio (OSNR) of few-mode channels synchronously. Simulation results show that the proposed MT-CNN-based OPM scheme can achieve accurate OSNR performance evaluation for MDM fiber transmission signals. © 2025 SPIE.

关键词： Convolutional neural networks

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