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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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EBVS: An Efficient Pseudonymous Batch Verification Scheme for Vehicular Networks 2nd

EBVS: An Efficient Pseudonymous Batch Verification Scheme fo...

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2nd International Conference on Data Security and Privacy Protection, DSPP 2024

作者： Wang, Ping Wang, Shuokang College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen518060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Guangdong Shenzhen518060 China

ISBN: (纸本)9789819785452

With the rapid development of intelligent transportation systems, the security of vehicular network communication architectures has become a focal point of research. This paper introduces a vehicular network communication system based on cloud computing and blockchain technology, aimed at enhancing the efficiency and security of vehicle privacy protection and message authentication. The system architecture comprises five core components: the Traffic Safety Management Center (TSMC), Cloud Node Servers (CNS), Roadside Units (RSU), Vehicles (V), and the Traffic Safety Blockchain (TSB). In the system initialization phase, public-private key pairs based on elliptic curves and bilinear mappings are generated to provide fundamental encryption and authentication mechanisms. During the identity registration stage, vehicles acquire a pseudonymous identity (PID), used for subsequent message authentication, ensuring vehicle privacy while maintaining identity legitimacy. Through vehicle message authentication and the traffic safety blockchain, the system facilitates efficient batch processing of messages and immutable data records, enhancing the transparency and credibility of traffic data. Furthermore, the system conducts a detailed analysis of potential external threats and malicious vehicle behaviors, proposing corresponding countermeasures such as enhanced data integrity monitoring, adaptive risk management, and collaborative governance strategies. These measures enable the system not only to effectively defend against external attacks but also to monitor and prevent malicious vehicle operations in real-time, thereby ensuring the safe and stable operation of intelligent transportation systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Risk management

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PPS-SLAM: Dynamic Visual SLAM with a Precise Pruning Strategy

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Computers, Materials & Continua 2025年第2期82卷 2849-2868页

作者： Jiansheng Peng Wei Qian Hongyu Zhang College of Automation Guangxi University of Science and TechnologyLiuzhou545000China Department of Artificial Intelligence and Manufacturing Hechi UniversityHechi546300China Key Laboratory of AI and Information Processing Education Department of Guangxi Zhuang Autonomous RegionHechi546300China Guangxi Key Laboratory of Sericulture Ecology and Applied Intelligent Technology School of Chemistry and BioengineeringHechi UniversityHechi546300China

Dynamic visual SLAM (Simultaneous Localization and Mapping) is an important research area, but existing methods struggle to balance real-time performance and accuracy in removing dynamic feature points, especially when semantic information is missing. This paper presents a novel dynamic SLAM system that uses optical flow tracking and epipolar geometry to identify dynamic feature points and applies a regional dynamic probability method to improve removal accuracy. We developed two innovative algorithms for precise pruning of dynamic regions: first, using optical flow and epipolar geometry to identify and prune dynamic areas while preserving static regions on stationary dynamic objects to optimize tracking performance;second, propagating dynamic probabilities across frames to mitigate the impact of semantic information loss in some frames. Experiments show that our system significantly reduces trajectory and pose errors in dynamic scenes, achieving dynamic feature point removal accuracy close to that of semantic segmentation methods, while maintaining high real-time performance. Our system performs exceptionally well in highly dynamic environments, especially where complex dynamic objects are present, demonstrating its advantage in handling dynamic scenarios. The experiments also show that while traditional methods may fail in tracking when semantic information is lost, our approach effectively reduces the misidentification of dynamic regions caused by such loss, thus improving system robustness and accuracy.

关键词： Visual SLAM dynamic SLAM YOLOv8

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DKP-SLAM:A Visual SLAM for Dynamic Indoor Scenes Based on Object Detection and Region Probability

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Computers, Materials & Continua 2025年第1期82卷 1329-1347页

作者： Menglin Yin Yong Qin Jiansheng Peng College of Automation Guangxi University of Science and TechnologyLiuzhou545000China Department of Artificial Intelligence and Manufacturing Hechi UniversityHechi546300China Key Laboratory of AI and Information Processing Education Department of Guangxi Zhuang Autonomous RegionHechi546300China Guangxi Key Laboratory of Sericulture Ecology and Applied Intelligent Technology School of Chemistry and BioengineeringHechi UniversityHechi546300China

In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and *** paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic ***,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed ***,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points *** process divides the image into low dynamic,suspicious dynamic,and high dynamic *** the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these *** with geometric methods,it detects and removes the dynamic *** final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.

关键词： Visual SLAM dynamic scene YOLOX K-means++clustering dynamic probability

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Multi-objective Balanced Task Offloading in Vehicular Networks Based on Edge Computing 27th

Multi-objective Balanced Task Offloading in Vehicular Networ...

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27th International Conference on pattern recognition, ICPR 2024

作者： Wang, Lingjiao Meng, Lingwei Guo, Hua Xiangtan University Hunan Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China

ISBN: (纸本)9783031783821

The increasing number of vehicular networking devices and application demands has made the limited computing and communication resources a significant challenge. The heuristic task offloading strategy mechanism was proposed to improve the efficiency of task offloading in vehicular networks. This strategy mechanism utilized the Nondominated Sorting Genetic Fireworks Algorithm (NSGFA) based on the characteristics of the problem, integrated consideration of the multi-target balance between system offloading costs and load balancing. Experimental data results demonstrate that this method performs well in reducing latency, energy consumption, and load balancing, effectively enhancing the quality of service for vehicular network users. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computation offloading

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Unsupervised Domain Transfer for Object Classification in 3D Point Clouds via Hierarchical Prompt Learning

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IEEE Signal processing Letters 2025年 32卷 1750-1754页

作者： Luo, Huan Fu, Kaiwei Fang, Lina Fuzhou University College of Computer and Data Science China Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing China Ministry of Education Key Laboratory of Spatial Data Mining & Information Sharing Fuzhou350003 China Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing Haixi Institute Fujian Quanzhou362216 China

Traditional object classification in 3D point cloud scenes relies heavily on large-scale labeled training data, which is both time-consuming and labor-intensive to obtain. Unsupervised Domain Transfer (UDT) mitigates this challenge by transferring knowledge from a labeled source domain to an unlabeled target domain. However, existing UDT-based methods often require complex neural architectures and substantial computational resources. This letter proposes a novel UDT framework that integrates hierarchical prompt learning with a 3D foundational model. The proposed method consists of a modality alignment stage and an unsupervised transfer stage. In the modality alignment stage, cross-modal hierarchical prompts are employed to align the Visual-Language (V-L) modality in the 3D foundational model through a V-L coupling module. In the unsupervised transfer stage, cross-domain hierarchical prompts and a Target-Source (T-S) coupling module facilitate the alignment of multi-scale contextual information across domains, ensuring efficient and accurate knowledge transfer. Extensive experiments conducted on multiple datasets collected from various laser scanners demonstrate the effectiveness of our proposed approach. © 1994-2012 IEEE.

关键词： Visual languages

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Complementary Dual-Branch Network for Space-Time Video Super-Resolution 27th

Complementary Dual-Branch Network for Space-Time Video Supe...

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27th International Conference on pattern recognition, ICPR 2024

作者： Tian, Ming Li, Tianyi Luo, RongSheng Gao, Changxin Sang, Nong National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783031781247

Space-time video super-resolution aims to simultaneously increase the space-time resolution of low-resolution and low frame-rate videos. Existing deep learning-based methods have made notable strides, predominantly achieving space-time video super-resolution through the relatively simple integration of modules for video super-resolution and video frame interpolation sub-tasks. However, these methods typically do not fully exploit the inherent relationships between the two sub-tasks. To address this limitation, we propose a Complementary Dual-Branch Network designed to better explore the interdependence of the two sub-tasks. Specifically, our dual-branch architecture facilitates mutual enhancement between video super-resolution and video frame interpolation sub-tasks within each branch and provides mutual guidance between the two branches. Additionally, we introduce a simple yet effective strategy for the rough estimation of optical flow, incorporating Flow-Guided Deformable Alignment into space-time video super-resolution to achieve precise motion estimation. In addition, we use an RNN-based Backward and Forward Recurrent module to ensure that all frames can utilize the information of the whole sequence. It is more efficient and memory saving compared to the currently popular bidirectional LSTM module. Experimental results on several datasets show that our method achieves superior accuracy and requires fewer parameters compared to state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical flows

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High-order consensus graph learning for incomplete multi-view clustering

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Applied Intelligence 2025年第6期55卷 1-25页

作者： Guo, Wei Che, Hangjun Leung, Man-Fai College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China Faculty of Science and Engineering Anglia Ruskin University CambridgeCB1 1PT United Kingdom

Incomplete Multi-View Clustering (IMVC) aims to partition data with missing samples into distinct groups. However, most IMVC methods rarely consider the high-order neighborhood information of samples, which represents complex underlying interactions, and often neglect the weights of different views. To address these issues, we propose a High-order Consensus Graph Learning (HoCGL) model. Specifically, we integrate a reconstruction term to recover the incomplete multi-view data. High-order proximity matrices are constructed, and the self-representation similarity matrices and multiple high-order proximity matrices are learned mutually, allowing the similarity matrices to incorporate complex high-order information. Finally, the consensus graph representation is derived from the similarity matrices through a self-weighted strategy. An efficient algorithm is designed to solve the proposed model. The excellent clustering performance of the proposed model is validated by comparing it with eight state-of-the-art models across nine datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Consensus algorithm

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Underwater Image Restoration Based on Improved SwinIR Model 13

Underwater Image Restoration Based on Improved SwinIR Model

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13th International Conference on intelligent Control and information processing, ICICIP 2025

作者： Lu, Yanke Li, Fang Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

Image restoration is a classic foundational visual task, aimed at recovering damaged images, such as those affected by compression, blurring, or noise, to high-definition clarity. Although current image enhancement techniques largely rely on convolutional neural networks, the efficient capabilities of Transformers demonstrated in complex visual tasks have not yet been extensively applied in the field of image restoration. This paper presents an optimized image restoration strategy based on the SwinIR model, which is applied to the task of underwater image restoration. In the shallow feature extraction phase, deep separable convolution technology is employed;while in the deep feature extraction phase, a Spatial Gated Feedforward Network (SGFN) is utilized. The experimental data suggests that the novel approach surpasses conventional techniques in effectiveness and has effectively minimized both the model's parameter quantity and computational demands. © 2025 IEEE.

关键词： Convolution

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Toward High-Accuracy and Low-Latency Group Vehicle Trajectory Prediction With Linear UNet-Enhanced Fully Connected Spatial–Temporal Graph Neural Network

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IEEE Internet of Things Journal 2025年第9期12卷 11797-11811页

作者： Zhang, Zirui Huang, Xumin Yu, Rong Wu, Maoqiang Kang, Jiawen Xie, Shengli Guangdong University of Technology School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China South China Normal University School of Electronic Science and Engineering Foshan528225 China Guangdong University of Technology School of Automation 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China

Group vehicle trajectory prediction (GVTP) is important for analyzing the traffic states and optimizing the traffic management. However, existing studies have performance bottlenecks in the prediction accuracy and inference latency. To tackle the problems, we propose a linear UNet-enhanced fully connected spatial–temporal GNN (LUFC-STGNN) for GVTP. First, a spatial graph is constructed by integrating prior-based and data-driven methods to capture both explicit and implicit spatial interactions between the vehicles. After that, a comprehensive temporal graph is created to capture the varying strengths of temporal interactions between all vehicles throughout historical timestamps. Furthermore, a fully connected spatial–temporal graph combining the spatial and temporal graphs is introduced to extract the effective spatial–temporal interaction features of the vehicles through the graph convolution operation. Finally, a linear UNet-based temporal dependency encoder (LU-TDE) is designed to further enhance the model’s ability of capturing the potential temporal patterns in the vehicle interactions. The encoder with linear complexity explores the multiscale temporal dependencies from the spatial–temporal interaction features but also reducing the inference latency. Experiments results based on real-world datasets show that compared to state-of-the-art models, our model reduces the average root mean square error over the 5-s prediction horizon by 31% and 10% on the NGSIM and HighD datasets, while reducing the inference latency by at least 1.26 times. © 2014 IEEE.

关键词： Prediction models

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