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Leakage Localization of Gas Pipelines Based on Modal Acoustic Emission and Cross-power Spectrum Peak Optimization

IEEE Sensors Journal 2025年第11期25卷 19982-19992页

作者： Zhang, Wenbiao Gong, Kezheng Cui, Xiwang North China Electric Power University School of Control and Computer Engineering Beijing102206 China Beijing Information Science and Technology University School of Instrument Science and OptoElectronics Engineering Beijing100192 China

Leakage accidents in natural gas pipelines bring huge property losses and pose serious safety risks. Therefore, faster and more accurate leakage localization is of great significance. In this paper, a new method based on modal acoustic emission (MAE) and peak optimization of cross-power spectrum is proposed to enhance the performance of leakage localization. When a pipeline leakage occurs, the sensors installed on the pipelines receive the acoustic emission (AE) signals, which have various modes. Instead of extracting a single mode through complex algorithms, the proposed method directly extracts the cross-power spectrum of a specified mode by choosing the appropriate window function and window parameters to locate the leakage with velocity information from dispersion curves. Gaussian, Hamming and Blackman window functions are compared in the extraction of the cross-power spectrum of L(0,1) mode, among which the Blackman window function is found to be the most effective. The artificial fish swarm algorithm combined with the iteration criterion for peak optimization is used to obtain suitable window parameters. Finally, the localization error of the above method is less than 5.2%. In addition, the performance of the iteration criterion for peak optimization of cross-power spectrum combined with other intelligent swarm algorithms are investigated. The results show that the maximum errors of other intelligent methods are still less than 9%, demonstrating the great compatibility of the proposed method. © 2001-2012 IEEE.

关键词： Acoustic emissions

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Dynamic Layered Clustering Routing Protocol Based on Hybrid-Optimized Neural Networks for UWSNs

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IEEE Internet of Things Journal 2025年第11期12卷 16155-16170页

作者： Lu, Xinmiao Yang, Longyue Wu, Qiong Zhu, Yuna Zou, Yixin Shi, Yunbo Harbin University of Science and Technology School of Measurement-Control Technology and Communications Engineering Harbin150000 China Heilongjiang Network Space Research Center School of Computer Science and Technology Harbin150000 China

To address the limitations of traditional flat routing in large-scale Underwater Wireless Sensor Networks (UWSNs), and to tackle challenges such as long delays, low bandwidth, and high error rates encountered by sensor nodes in underwater environments, this paper proposes a dynamic layered clustering routing protocol based on a hybrid optimized backpropagation neural network (PSB-NN), referred to as PSBDR. The protocol introduces non-uniform vertical layering of the network based on water depth and assigns different communication and sensing radii to nodes according to their initial layer. A trained hierarchical prediction model dynamically adjusts the virtual layer assignments of nodes throughout the network's operation. Additionally, a method is implemented for isolated nodes to join clusters via relay nodes, and a priority mechanism, along with a dynamic weight update strategy, is employed to select the optimal next hop during routing. This protocol effectively balances the workload across nodes at varying depths and maximizes the utilization of residual energy in the nodes, contributing to an extending network lifetime. Simulation results demonstrate that PSBDR enhances network performance by improving load balancing, prolonging network longevity, reducing end-to-end delay, and increasing data collection reliability. © 2014 IEEE.

关键词： Network layers

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Relation-wise transformer network and reinforcement learning for visual navigation

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Neural Computing and Applications 2024年第21期36卷 13205-13221页

作者： He, Yu Zhou, Kang School of Computer Science and Artificial Intelligence Huanghuai University Zhumadian463000 China Henan Key Laboratory of Smart Lighting Zhumadian463000 China Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots Henan463000 China School of Computer Science City University of Hong Kong 999077 Hong Kong School of Computer Science Wuhan University Wuhan430072 China

The task of object goal navigation is to drive an embodied agent to find the location of a given target only using visual observation. The mapping from visual perception of observation determines the navigation actions. Heterogeneous relationships in the observation are the essential part of the scene graph, which can guide the agent to find the target more easily. In this work, we propose a novel Heterogeneous Zone Graph Visual Transformer formulation for graph representation and visual perception. It consists of two key ideas: (1) Heterogeneous Zone Graph (HZG) that explores the heterogeneous target-related zones graph and spatial information. It allows the agent to navigate efficiently. (2) Relation-wise Transformer Network (RTNet) that transforms the relationship between previously observed objects and navigation actions. RTNet extracts rich nodes and edges features as pays more attention to the target-related zone. We model self-attention on the node-to-node encoder and cross-attention on the edge-to-node decoder. We evaluate our methods on the AI2THOR dataset and show superior navigation performance. Code and datasets can be found inhttps://***/zhoukang12321/RTNet_VN_2023. © The Author(s) 2024.

关键词： Navigation

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DCL-depth: monocular depth estimation network based on iam and depth consistency loss

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Multimedia Tools and Applications 2025年第8期84卷 4773-4787页

作者： Han, Chenggong Lv, Chen Kou, Qiqi Jiang, He Cheng, Deqiang The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China

The self-supervised monocular depth estimation algorithm obtains excellent results in outdoor environments. However, traditional self-supervised depth estimation methods often suffer from edge blurring in complex textured regions and the loss of depth information in pixels within weakly-textured areas. To enhance the perception ability of the deep network for complex textured areas and the accuracy of depth estimation in weakly-textured regions, the following methods are proposed in this paper. First of all, the image activity measure (IAM) is used to segment the image features. Based on the multi-directional distribution of image contours, the network's perception ability has been improved, resulting in effective enhancement of depth estimation in complex regions. Furthermore, a new loss function called depth consistency loss (DCL) is proposed, which is based on recursive recurrent networks. The DCL aims to measure the similarity between the output images of the first-order network and the second-order network, and the network's constraint on weak-texture regions has been strengthened. By employing this approach, the accuracy of estimating depth information in weakly-textured regions can be enhanced. Through adequate experimentation on the public indoor datasets, the results show that our network outperforms the compared algorithms in terms of accuracy and visualization of predicted depth. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Complex networks

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Uncertainty and disturbance estimator-based model predictive control for wet flue gas desulphurization system

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Chinese Journal of Chemical Engineering 2024年第3期67卷 182-194页

作者： Shan Liu Wenqi Zhong Li Sun Xi Chen Rafal Madonski School of Energy and Environment Key Laboratory of Energy Thermal Conversion and Control of Ministry of EducationSoutheast UniversityNanjing 210096China Faculty of Automatic Control Electronics and Computer ScienceSilesian University of TechnologyGliwise 44-100Poland

Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution *** desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system *** address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this *** UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant *** viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear *** obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled *** the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and ***,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.

关键词： Desulphurization system Disturbance rejection Model predictive control Uncertainty and disturbance estimator Nonlinear system

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Corwdsourced Task Recommendation via Link Prediction 18th

Corwdsourced Task Recommendation via Link Prediction

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Yu, Song Pan, Qingxian Li, Li School of Computer and Information Science Southwest University Chongqing400715 China School of Computer and Control Engineering Yantai University Shandong264005 China

ISBN: (纸本)9789819996391

Mobile crowdsourcing (MCS) can solve problems that are difficult for computers to solve accurately or efficiently. Current crowdsourcing workers face the challenges of overload, task recommendation is presented to deal with the above issue. The existing MCS task recommendation methods only consider the workers themselves, without taking the potential relationships between workers and workers and tasks, which may fail to grasp the long-term behavioral preferences of workers and cannot solve the cold start problem efficiently. To solve the above problems, this paper proposes a task recommendation method based on heterogeneous graph link prediction. First, we try to obtain the connection relationships between crowdsourcing tasks and workers and build a worker-task heterogeneous graph accordingly. Then, majority voting and DeepWalk are used to generate initial node features of workers. Second, we design two efficient message passing mechanisms to aggregate and update node features between task and worker nodes to explore the potential relationships between nodes. Finally, we obtain attention weights among nodes and use Bi-GRU to capture the long-term behavioral preferences of workers and recommend appropriate tasks to workers based on the similarity between workers and tasks to improve perceptual quality. Evaluations are conducted on seven real datasets. Experimental results show that our method is superior to the state-of-the-art baselines. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Message passing

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Computing Challenges of UAV Networks: A Comprehensive Survey

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computers, Materials & Continua 2024年第11期81卷 1999-2051页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Xianxuan Lin Farman Ali Ahmad Ali AlZubi School of Computer Science&Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science Community CollegeKing Saud UniversityRiyadh11495Saudi Arabia

Devices and networks constantly upgrade,leading to rapid technological ***-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information *** network types,including sensor networks and 5G mobile networks,support this ***,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/*** topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future ***,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility *** also explores the prospects of UAV systems and presents state-of-the-art solutions.

关键词： Autonomous vehicles UAV systems UAV transmission UAV in healthcare future of UAV system security measures

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Heterogeneous Image Knowledge Driven Visual Perception

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 255-257页

作者： Lan Yan Wenbo Zheng Fei-Yue Wang IEEE the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China the School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430070China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous *** the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

关键词： Visual visual knowledge

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Two-Way Neural Network Performance PredictionModel Based onKnowledge Evolution and Individual Similarity

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computer Modeling in Engineering & sciences 2024年第2期138卷 1183-1206页

作者： Xinzheng Wang Bing Guo Yan Shen College of Computer Science Sichuan UniversityChengdu610065China School of Information Science and Engineering Guilin University of TechnologyGuilin541004China School of Control Engineering Chengdu University of Information TechnologyChengdu610225China

Predicting students’academic achievements is an essential issue in education,which can benefit many stakeholders,for instance,students,teachers,managers,*** with online courses such asMOOCs,students’academicrelateddata in the face-to-face physical teaching environment is usually sparsity,and the sample size is *** makes building models to predict students’performance accurately in such an environment even *** paper proposes a Two-WayNeuralNetwork(TWNN)model based on the bidirectional recurrentneural network and graph neural network to predict students’next semester’s course performance using only theirprevious course *** experiments on a real dataset show that our model performs better thanthe baselines in many indicators.

关键词： computer education performance prediction deep learning

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Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation 38

Efficient Adaptation of Pre-trained Vision Transformer via H...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Dong, Wei Sun, Yuan Yang, Yiting Zhang, Xing Lin, Zhijun Yan, Qingsen Zhang, Haokui Wang, Peng Yang, Yang Shen, Hengtao College of Information and Control Engineering Xi'an University of Architecture and Technology China School of Computer Science Northwestern Polytechnical University China School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Tongji University China

A common strategy for Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViTs) involves adapting the model to downstream tasks by learning a low-rank adaptation matrix. This matrix is decomposed into a product of down-projection and up-projection matrices, with the bottleneck dimensionality being crucial for reducing the number of learnable parameters, as exemplified by prevalent methods like LoRA and Adapter. However, these low-rank strategies typically employ a fixed bottleneck dimensionality, which limits their flexibility in handling layer-wise variations. To address this limitation, we propose a novel PEFT approach inspired by Singular Value Decomposition (SVD) for representing the adaptation matrix. SVD decomposes a matrix into the product of a left unitary matrix, a diagonal matrix of scaling values, and a right unitary matrix. We utilize Householder transformations to construct orthogonal matrices that efficiently mimic the unitary matrices, requiring only a vector. The diagonal values are learned in a layer-wise manner, allowing them to flexibly capture the unique properties of each layer. This approach enables the generation of adaptation matrices with varying ranks across different layers, providing greater flexibility in adapting pre-trained models. Experiments on standard downstream vision tasks demonstrate that our method achieves promising fine-tuning performance. © 2024 Neural information processing systems foundation. All rights reserved.

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