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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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On Interval Perturbation of the ɑ-Symmetric Implicational Algorithm 2nd

On Interval Perturbation of the ɑ-Symmetric Implicational A...

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2nd International Conference on Artificial Intelligence Logic and Applications, AILA 2022

作者： Tang, Yiming Han, Jianghui Bao, Guangqing Chen, Rui Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Anhui Hefei230601 China Engineering Research Center of Safety Critical Industry Measure and Control Technology Ministry of Education Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9789811975097

Perturbation is an important property of fuzzy reasoning algorithms. It is an important task to estimate the upper and lower bounds of the output after a perturbation (such as an interval perturbation) is given to the input. In this study, the interval perturbation problem of a fuzzy reasoning algorithm, i.e., the ɑ-symmetric implicational algorithm, is researched systematically. For FMP (Fuzzy Modus Ponens) and FMT (Fuzzy Modus Tollens) problems of fuzzy reasoning, aiming at the case of a single rule, the estimation for the upper and lower bounds are obtained for the input interval disturbance, in which R-implications and (S, N)-implications are mainly employed. Then the stability is verified on account of the upper and lower bounds. Following that, for the multi-rule case, the upper and lower bounds of the output are also given, and the corresponding stability is validated. In conclusion, it is found that the ɑ-symmetric implicational algorithm has good stability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy inference

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Exploiting Knowledge Embedding to Improve the Description for Image Captioning 5th

Exploiting Knowledge Embedding to Improve the Description fo...

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5th China Conference on Knowledge Graph, and Semantic computing, CCKS 2020

作者： Song, Dandan Peng, Cuimei Yang, Huan Liao, Lejian Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Key Laboratory of Intelligent Information Technology School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9789811619632

Most existing methods for image captioning are based on the encoder-decoder framework which directly translates visual features into sentences, without exploiting commonsense knowledge available in the form of knowledge graph. Inspired by the success of information retrieval and question answering systems that leverage prior knowledge, we explore a knowledge embedding approach for image captioning. In this paper, we propose a Knowledge Embedding with Attention on Attention (KE-AoA) method for image captioning, which judges whether or how well the objects are related and augments semantic correlations and constraints between them. The KE-AoA method combines knowledge base method (TransE) and text method (Skip-gram), adding external knowledge graph information (triplets) into the language model to guide the learning of word vectors as the regularization term. Then it employs the AoA module to model the relations among different objects. As more inherent relations and commonsense knowledge are learned, the model can generate better image descriptions. The experiments on MSCOCO data sets achieve a significant improvement on the existing methods and validate the effectiveness of our prior knowledge-based approach. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Knowledge graph

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Toward UAV-Enabled Stereoscopic UL Heavy NOMA: Joint Resource Allocation and 3D Trajectory Design

Toward UAV-Enabled Stereoscopic UL Heavy NOMA: Joint Resourc...

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IEEE International Conference on Communications (ICC)

作者： Haiyong Zeng Rui Zhang Xu Zhu Yufei Jiang Zhongxiang Wei Fu-Chun Zheng Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

We study an unmanned aerial vehicle (UAV)-enabled uplink heavy non-orthogonal multiple access (NOMA) system in this paper, where the UL communication becomes extremely important in some hot spot areas such as live concerts or soccer stadiums, and investigate the joint optimization of bandwidth assignment (BA) and power allocation (PA) with UAV three-dimensional (3D) trajectory design to maximize the minimum average rate among all ground users, while meeting their heterogeneous rate requirements. More specifically, we propose a joint BA and PA algorithm by revealing that the inter-user interference in each NOMA group can be eliminated naturally while deriving the sum rate of users. The algorithm is proposed to get the optimal BA and PA solutions with the help of closed-form results and ellipsoid method. After that, in order to solve the UAV 3D trajectory design problem we introduce the elevation angle as a supplementary variable, and the successive convex approximation method is adopted to obtain the UAV 3D trajectory. The joint BA and PA algorithm and the UAV 3D trajectory design can be optimized alternatively, which leads to fast convergence and demonstrates a superior minimum average rate among users and user fairness performance over the previous methods.

关键词： NOMA Three-dimensional displays Stereo image processing Autonomous aerial vehicles Approximation algorithms Trajectory Resource management

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Dual arm coordination with coordination diagram based on teleoperation demonstration

Dual arm coordination with coordination diagram based on tel...

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2021 IEEE International Conference on Real-Time computing and Robotics, RCAR 2021

作者： Zuo, Guoyu Xu, Zichen Liu, Lu Gong, Daoxiong Li, Jianfeng Faculty of Information Technology Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781665436786

Efficient and collision-free coordination of two robot arms is increasingly needed in various service-oriented robotic applications. This paper proposes a dual-arm coordination algorithm to improve the efficiency of coordination by considering both robot's actions and operating sequences for tasks that require multiple operations with both arms. Teleoperation demonstration is performed to obtain the robot's human-like motion trajectories, so as to reduce the probability of collisions of the two robotic arms. The coordination diagram in time domain is designed to more clearly represent the situations of trajectory collisions and find the collision-free coordination action law. Simultaneously, a coordination pair generator (CPG) is designed to reorganize the operating sequences according to the characteristics of input trajectories in addition to the action coordination. The effectiveness and efficiency of our method are verified on the experiment of the drug sorting task in nursing homes on the ABB YuMi robot model. © 2021 IEEE.

关键词： Remote control

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Blockchain-Assisted Computation Offloading Collaboration: A Hierarchical Game to Fortify IoT Security and Resilience

SSRN

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SSRN 2023年

作者： Zhou, Zitao Ye, Fang Li, Yingsong Geng, Xiaoyu Zhang, Sitong Key Laboratory of Advanced Marine Communication and Information Technology College of Information and Communication Engineering Harbin Engineering University Harbin China National Key Laboratory of Underwater Acoustic Technology College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education College of Electronic and Information Engineering Anhui University Hefei China

With the rapid development of the Internet of Things (IoT), computation offloading collaboration has become an important solution to address the data processing challenges faced by resource-constrained devices. However, security and privacy issues have become a major concern in the computation offloading collaboration process. This paper proposes a blockchain-assisted computation offloading collaboration scheme, Blockchain-Assisted Computation Offloading Collaboration (BACOC). The scheme uses blockchain technology to assist computation offloading collaboration under IoT conditions to fortify security and resilience during the computation offloading process. At the same time, a hierarchical game model based on Stackelberg game theory and contract theory is adopted to determine the optimal service pricing strategy. Experimental and simulation results demonstrate the effectiveness and feasibility of the BACOC scheme. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Facial StO2-based Stress Recognition using Automatic Graph Generation and Dual-Stream GNN

Facial StO2-based Stress Recognition using Automatic Graph G...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yingjuan Jia Hanpu Wang Xinyu Liu Tong Chen College of Electronic and Information Engineering Southwest University China Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest University China Institute of Legal Psychology and Intelligent Computing Southwest University China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The recognition of stress responses in health, resilience, and psychopathology holds significant scientific importance. The biological information carried by the facial tissue oxygen saturation (StO2) is a useful indicator for stress recognition. Although graph-based methods have achieved state-of-the-art (SOTA), there is room for improvement. This study proposes an automatic graph generation method for facial StO2, replacing manual methods and enhancing the information of graphs. To further enhance the capability of graphs’ representation, a dimensionality reduction strategy for nodes is proposed. The strategy is implemented based on an novel objective function that can increase the inter-class distance and reduce the intra-class distance. After obtaining highly representative graphs, a dual-stream network integrating GAT and GCN is designed to extract stress-related features from the graphs, ultimately achieving the SOTA recognition accuracy.

关键词： Dimensionality reduction Accuracy Face recognition Manuals Feature extraction Linear programming Biology Convolutional neural networks Stress Resilience

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MixSA: Training-free Reference-based Sketch Extraction via Mixture-of-Self-Attention

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IEEE Transactions on Visualization and Computer Graphics 2024年 PP卷 PP页

作者： Yang, Rui Wu, Xiaojun He, Shengfeng Shaanxi Normal University School of Computer Sciences Xi'an China Shaanxi Normal University School of Computer Science The Key Laboratory of Intelligent Computing and Service Technology for Folk Song The Ministry of Culture and Tourism Xi'an710119 China Singapore Management University School of Computing and Information Systems Singapore Singapore

Current sketch extraction methods either require extensive training or fail to capture a wide range of artistic styles, limiting their practical applicability and versatility. We introduce Mixture-of-Self-Attention (MixSA), a training-free sketch extraction method that leverages strong diffusion priors for enhanced sketch perception. At its core, MixSA employs a mixture-of-self-attention technique, which manipulates self-attention layers by substituting the keys and values with those from reference sketches. This allows for the seamless integration of brushstroke elements into initial outline images, offering precise control over texture density and enabling interpolation between styles to create novel, unseen styles. By aligning brushstroke styles with the texture and contours of colored images, particularly in late decoder layers handling local textures, MixSA addresses the common issue of color averaging by adjusting initial outlines. Evaluated with various perceptual metrics, MixSA demonstrates superior performance in sketch quality, flexibility, and applicability. This approach not only overcomes the limitations of existing methods but also empowers users to generate diverse, high-fidelity sketches that more accurately reflect a wide range of artistic expressions. © 1995-2012 IEEE.

关键词： Image texture

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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MEFFGRN: Matrix enhancement and feature fusion-based method for reconstructing the gene regulatory network of epithelioma papulosum cyprini cells by spring viremia of carp virus infection

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Computers in Biology and Medicine 2024年 179卷 108835-108835页

作者： Wei, Pi-Jing Bao, Jin-Jin Gao, Zhen Tan, Jing-Yun Cao, Rui-Fen Su, Yansen Zheng, Chun-Hou Deng, Li Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University 111 Jiulong Road HefeiAnhui230601 China Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University 111 Jiulong Road HefeiAnhui230601 China Shenzhen Key Laboratory of Microbial Genetic Engineering College of Life Sciences and Oceanology Shenzhen University Guangdong Shenzhen518055 China School of Artificial Intelligence Anhui University 111 Jiulong Road HefeiAnhui230601 China

Gene regulatory networks (GRNs) are crucial for understanding organismal molecular mechanisms and processes. Construction of GRN in the epithelioma papulosum cyprini (EPC) cells of cyprinid fish by spring viremia of carp virus (SVCV) infection helps understand the immune regulatory mechanisms that enhance the survival capabilities of cyprinid fish. Although many computational methods have been used to infer GRNs, specialized approaches for predicting the GRN of EPC cells following SVCV infection are lacking. In addition, most existing methods focus primarily on gene expression features, neglecting the valuable network structural information in known GRNs. In this study, we propose a novel supervised deep neural network, named MEFFGRN (Matrix Enhancement- and Feature Fusion-based method for Gene Regulatory Network inference), to accurately predict the GRN of EPC cells following SVCV infection. MEFFGRN considers both gene expression data and network structure information of known GRN and introduces a matrix enhancement method to address the sparsity issue of known GRN, extracting richer network structure information. To optimize the benefits of CNN (Convolutional Neural Network) in image processing, gene expression and enhanced GRN data were transformed into histogram images for each gene pair respectively. Subsequently, these histograms were separately fed into CNNs for training to obtain the corresponding gene expression and network structural features. Furthermore, a feature fusion mechanism was introduced to comprehensively integrate the gene expression and network structural features. This integration considers the specificity of each feature and their interactive information, resulting in a more comprehensive and precise feature representation during the fusion process. Experimental results from both real-world and benchmark datasets demonstrate that MEFFGRN achieves competitive performance compared with state-of-the-art computational methods. Furthermore, study

关键词： Gene expression

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