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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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A Simple Controllable Left-Right-Hand Circularly Polarized Antenna for GPS L2 Using Arc-like Slots

A Simple Controllable Left-Right-Hand Circularly Polarized A...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Lulu Meng Sixian Qian Zhixiang Huang Yingsong Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

A single-layer, polarization adjustable circular-polarization (CP) antenna with four arc-like slots has been designed for GPS L2 band. The created antenna uses four arc-like slots to tune the phase difference to form a CP antenna, where the arc-like slots with a specific size relationship are etched on the patch. By adjusting the radius of the arc-like slots, Left-handed -circular-polarization (LHCP) and Right-handed-circular-polarization (RHCP) can be realized easily with simple structure. Simulations and optimizations show that the constructed CP-antenna has a good axial-ratio bandwidth of 10 MHz and impedance-bandwidth of 40 MHz and 30 MHz for LHCP and RHCP application.

关键词： Polarization Slot antennas Bandwidth Directive antennas Antenna feeds Optimization Global Positioning System

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Linear Frequency Modulated signal Denoising Based on Symplectic Geometric Mode Decomposition and Block Thresholding

Linear Frequency Modulated Signal Denoising Based on Symplec...

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International Conference on Communication Technology (ICCT)

作者： Jincheng Ni Jin Zhao Xianliang Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education AnHui University AnHui University Heifei China

In this paper, a novel and comprehensive signal denoising method is proposed by combining Symplectic Geometric Modal Decomposition (SGMD) and Block Thresholding denoising. The proposed approach involves a three-step process: first, the signal is decomposed into a set of Symplectic Geometric Components (SGCs) using SGMD. Subsequently, each SGC is subjected to Block-Thresholding denoising. Finally, the denoised SGCs are recombined to obtain the denoised linear frequency modulation (LFM) signal. The experimental verification demonstrates the effectiveness of the SGMD-BT method in denoising LFM signals. This novel approach offers a fresh solution for the processing and analysis of LFM signals, holding significant application potential and research importance.

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Adversarial Example Generation Method Based on Probability Histogram Equalization

Adversarial Example Generation Method Based on Probability H...

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第42届中国控制会议

作者： Youqing Fang Jingwen Jia Yuhai Yang Wan-Li Lyu Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui University

CNNs(Convolutional Neural Networks) have a good performance on most classification tasks,but they are vulnerable when meeting adversarial *** and design of highly aggressive adversarial examples can help enhance the security and robustness of *** transferability of adversarial examples is still low in black-box ***,an adversarial example method based on probability histogram equalization,namely HE-MI-FGSM(Histogram Equalization Momentum Iterative Fast Gradient Sign Method) is *** each iteration of the adversarial example generation process,the original input image is randomly histogram equalized,and then the gradient is calculated to generate adversarial perturbations to mitigate overfitting in the adversarial *** effectiveness of the method is verified on the ImageNet *** with the advanced method I-FGSM(Iterative Fast Gradient Sign Method) and MI-FGSM(Momentum I-FGSM),the attack success rate in the adversarial training network increased by 27.9% and 7.7% on average,respectively.

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Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models

Evolutionary Multi-Objective Neural Architecture Search for ...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Shangshang Yang Cheng Zhen Ye Tian Haiping Ma Yuanchao Liu Panpan Zhang Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures.

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Improving Transferability Reversible Adversarial Examples Based on Flipping Transformation

Improving Transferability Reversible Adversarial Examples Ba...

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作者： Youqing Fang Jingwen Jia Yuhai Yang Wanli Lyu Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University

Adding subtle perturbations to an image can cause the classification model to misclassify, and such images are called adversarial examples. Adversarial examples threaten the safe use of deep neural networks, but when combined with reversible data hiding(RDH) technology, they can protect images from being correctly identified by unauthorized models and recover the image lossless under authorized models. Based on this, the reversible adversarial example(RAE) is rising. However, existing RAE technology focuses on feasibility, attack success rate and image quality, but ignores transferability and time complexity. In this paper,we optimize the data hiding structure and combine data augmentation technology,which flips the input image in probability to avoid overfitting phenomenon on the dataset. On the premise of maintaining a high success rate of white-box attacks and the image's visual quality, the proposed method improves the transferability of reversible adversarial examples by approximately 16% and reduces the computational cost by approximately 43% compared to the state-of-the-art method. In addition, the appropriate flip probability can be selected for different application scenarios.

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A Dual-Band Compact Four-Element MIMO Antenna for Sub-6G Applications 13

A Dual-Band Compact Four-Element MIMO Antenna for Sub-6G App...

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13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Pan, Tao Sun, Yufa Anhui University Key Lab Of Intelligent Computing Signal Processing Ministry Of Education Hefei230601 China

ISBN: (纸本)9781665434379

A compact Sub-6GHz multiple-input-multiple-output (MIMO) antenna is presented in this paper. The recommended MIMO antenna is electrically small (38mm × 38 mm× 1.6 mm). For good isolation and miniaturized size, four antenna elements are deposed orthogonally to each other. Four isolated grounds are devised to diminish the surface wave coupling as well as each ground is etched with orthogonal symmetric stub to decrease the near field coupling. Simulation outcomes conform to the return loss requirement of superior to 10 dB and insertion loss superior to 16 dB over the entire 3.24-3.68 and 4.77-5.04 GHz bandwidths (covering Sub-6GHz band in China). © 2021 IEEE.

关键词： Surface waves

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A Wideband Six-Port 5G MIMO Mobile Phone Antenna 13

A Wideband Six-Port 5G MIMO Mobile Phone Antenna

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13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Shi, Ce Sun, Yufa Anhui University Key Lab Of Intelligent Computing Signal Processing Ministry Of Education Hefei230601 China

ISBN: (纸本)9781665434379

This paper introduces a compact wideband 6 port multiple-input multiple-output (MIMO) antenna for the 5G handset. Each antenna elements uses coupling feed, and I-shaped grounding structure is added to the unit to obtain better impedance matching and enhance the isolation from nearby antenna units. The 5G MIMO antenna is designed with a F-type antenna to cover 3.685.27 GHz. The simulation results show that it has good impedance matching, bandwidth and isolation with extremely small ground clearance. © 2021 IEEE.

关键词： 5G mobile communication systems

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DDoS Detection and Defense Based on FLAD and SDN 20

DDoS Detection and Defense Based on FLAD and SDN

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Dong, Jie Fang, Wenyu Zheng, Wanling Liu, Jinkun Liu, Yanhua College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China

ISBN: (纸本)9798350356328

In order to achieve more efficient and accurate DDoS detection while ensuring data privacy, this paper proposes a DDoS detection method based on FLAD. Firstly, this paper uses the FLAD algorithm to train a global DDoS detection model without leaving local traffic data, protecting the privacy and security of traffic data between different hosts, and improving aggregation efficiency by dynamically adjusting the aggregation weights to adapt to different sub-dataset increments. Secondly, a DDoS traffic detection method based on the integration of LSTM and CNN is proposed, which extracts and analyzes the temporal correlation of traffic data by calculating the statistical characteristics of traffic data within a time period, to achieve real-time detection of traffic feature data. Again, combined with the concept of SDN, real-time defense against DDoS based on ODL-API is implemented, and precise matching of DDoS detection results with network entity information is achieved, realizing the technology of real-time and precise issuance of multiple flow rules, effectively blocking DDoS malicious attack traffic, protecting important entities in the topology, and maintaining stable traffic in the topology. This paper focuses on solving the detection problem of DDoS traffic data increments and uneven data distribution through the FLAD algorithm. Experimental results show that the proposed method improves the accuracy of DDoS attack detection by more than 4% and the F1 Score by more than 7% compared to the FedAvg aggregation algorithm. © 2024 IEEE.

关键词： Differential privacy

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A Circularly Polarized Antenna for GPS L1 band with U-Shaped-Like Strips

A Circularly Polarized Antenna for GPS L1 band with U-Shaped...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Meng, Lulu Huang, Zhixiang Li, Yingsong Anhui University School of Electronic and Information Engineering Hefei230601 China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn230088 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9781665452366

A U-shaped-like parasitic strip loading antenna is proposed and designed, which can work in L1 band of GPS with circular polarization (CP) characteristics. The antenna consists of ground plane, dielectric, radiation patch and U-shaped-like parasitic strips using a simple coaxial feed probe. The asymmetric U-shaped-like strips are to realize CP. The simulation and optimization results for the modeled directional CP antenna show that the axial ratio bandwidth is 10MHz and the impedance bandwidth is 30MHz, which can meet the requirements for GPS L1 band. © 2022 IEEE.

关键词： Circular polarization

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