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SEFormer:A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis

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Computers, Materials & Continua 2025年第1期82卷 1417-1437页

作者： Hongxing Wang Xilai Ju Hua Zhu Huafeng Li State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and AstronauticsNanjing210016China School of Computer Science and Engineering Nanyang Technological UniversitySingapore639798Singapore

Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain ***,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis ***,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault ***,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative *** complexity results in high computational costs and limited industrial *** tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault ***,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration ***,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global ***,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and *** study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.

关键词： CNN-Transformer separable multiscale depthwise convolution efficient self-attention fault diagnosis

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Topology Identification for Distribution Systems Based on Bayesian Nonparametric Model and Gaussian Mixture Ensemble Kalman Filter 9

Topology Identification for Distribution Systems Based on Ba...

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9th Asia Conference on Power and Electrical engineering, ACPEE 2024

作者： Zhang, Tingting Zhang, Wen Shandong University School of Control Science and Engineering Jinan China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical Engineering Jinan China

ISBN: (纸本)9798350309638

To ensure the secure and reliable operation of active distribution systems (ADSs), it is essential to efficiently and accurately identify their time-varying topology. This paper proposes an estimation-based topology identification (TI) method for ADSs considering topology changes. The TI problem is formulated by a forecasting-aided estimation model, which is depicted by a discrete-time switching state-space model. The topology change process is described by a Bayesian nonparametric model that allows TI with limited knowledge of topology libraries. Then the Gaussian mixture ensemble Kalman filter (GM-EnKF) is employed to carry out the TI, where the underlying probability density is approximated by using Gaussian mixture model. In this case, the proposed method become capable of handling the unavoidable non-Gaussian noises in realistic distribution systems. Test results on various ADSs validates the effectiveness of the proposed GM-EnKF-based TI method on accurate and efficient topology identification. © 2024 IEEE.

关键词： Topology

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Synergistic effects of peroxydisulfate on UV/O_(3) process for tetracycline degradation:Mechanism and pathways

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Chinese Chemical Letters 2024年第10期35卷 408-413页

作者： Fengrui Yang Debing Wang Xinying Zhang Jie Zhang Zhichao Wu Qiaoying Wang State Key Laboratory of Pollution Control and Resource Reuse Shanghai Institute of Pollution Control and Ecological SecuritySchool of Environmental Science and EngineeringTongji UniversityShanghai 200092China Fujian Provincial Engineering Research Center of Rural Waste Recycling Technology College of Environment&Safety EngineeringFuzhou University.Fuzhou 350108China

Tetracycline(TC)as a typical emerging pollutant is becoming a serious threat to the environment and human health.A combined advanced oxidation technology of UV/Ozone(O_(3))/peroxydisulfate(PDS)process was developed to explore an efficient and economic treatment process of TC in ***,the reactive sites and transformation pathways of TC were explored and the toxicity of the intermediates was quantified with a quantitative structure-activity relationship(QSAR)*** degradation performance of TC was substantially enhanced in UV/O_(3)/PDS process with a kobsof 0.0949 min-1,which was 2.3 times higher than UV/O_(3)and 3.2 times than sole *** results demonstrated that there was a superior synergistic effect of PDS on UV/O_(3)processes for the degradation of *** paramagnetic resonance(EPR)analysis and quenching experiments show that·OH,SO_(4)·-,O_(2)·-and1O_(2)all contributed to TC degradation in the UV/O_(3)/PDS process and exhibited a synergistic effect,which inhibited the generation of harmful *** addition,the UV/O_(3)/PDS system can effectively degrade TC in a wide range of substrate concentrations and pH,and also showed excellent adaptability to various concentrations of anions(Cl-and HCO_(3)-).This study proves the feasibility of UV/O_(3)/PDS process for treating TC contaminated wastewater with complicated water matrix.

关键词： UV/ozonation/peroxydisulfate Synergistic effect Tetracycline Degradation Wastewater

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circ2CBA: prediction of circRNA-RBP binding sites combining deep learning and attention mechanism

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Frontiers of Computer science 2023年第5期17卷 217-225页

作者： Yajing GUO Xiujuan LEI Lian LIU Yi PAN School of Computer Science Shaanxi Normal UniversityXi’an 710119China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitations in feature *** view of this,we propose a method named circ2CBA,which uses only sequence information of circRNAs to predict circRNA-RBP binding *** have constructed a data set which includes eight ***,circ2CBA encodes circRNA sequences using the one-hot ***,a two-layer convolutional neural network(CNN)is used to initially extract the *** CNN,circ2CBA uses a layer of bidirectional long and short-term memory network(BiLSTM)and the self-attention mechanism to learn the *** AUC value of circ2CBA reaches *** of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.

关键词： circRNAs RBPs CNN BiLSTM self-attention mechanism

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TD-SAS: A Trust-Aware and Decentralized Speed Advisory System for Energy-Efficient Autonomous Vehicle Platoons

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-16页

作者： Li, Shike Li, Jianbin Liang, Yin Zhang, Hengyang Wu, Sixing Wang, Shen Cheng, Long School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Science University College Dublin Ireland

Autonomous vehicle platooning benefits significantly from the Consensus Speed Advisory System (CSAS), an emerging technology that recommends a consensus speed to reduce energy consumption. However, managing trust to ensure system security and identify malicious nodes poses a considerable challenge in these autonomous environments. Furthermore, while CSAS optimizes the total energy consumption of the platoon, it may inadvertently result in increased energy use for specific vehicles, discouraging their continued participation and hindering an efficient formation of a platoon. This paper proposes a trust-aware and decentralized speed advisory system (TD-SAS) to address these challenges. TD-SAS employs a consortium blockchain for managing trust nodes, providing non-repudiation and tamper resistance for reputation data. Capitalizing on this platform, we use a multi-weight subjective logic model for precise reputation value calculation. Additionally, we present a trust-aware consensus speed recommendation scheme capable of adapting its recommendations to vehicular reputation variations. To mitigate the potential disincentives for vehicles experiencing increased energy consumption, we incorporate an incentive mechanism to encourage their re-engagement with TD-SAS. Comprehensive security analysis and extensive simulation experiments confirm the robustness and effectiveness of TD-SAS, emphasizing its potential for enhancing the energy efficiency and security of autonomous vehicle platoons. IEEE

关键词： Blockchain

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Graph Dual Adversarial Network for Hyperspectral Image Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 922-932页

作者： Cheng, Yuhu Chen, Yang Kong, Yi Philip Chen, C.L. Wang, Xuesong China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of EducationSchool of Information and Control Engineering Xuzhou221116 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China

An end-to-end unsupervised domain adaptation method for hyperspectral image (HSI) classification based on a graph dual adversarial network is proposed in this article. First, in order to extract the domain-invariant features of the source and target domains, the rich spectral information and spatial position of HSI are used to construct a spectral-spatial nearest neighbor graph, which is input into the graph convolutional network. Then, a prototype adversarial strategy is proposed, which uses the labeled data of the source domain to reliably calculate the feature prototypes of different classes. Through the prototype adversarial strategy, the distances between different prototypes are appropriately extended, so that the clusters of different classes are far away from their respective decision boundaries, and the discriminability of features are also enhanced. The dual adversarial strategy is composed of the prototype adversarial strategy and the domain adversarial strategy. It is worth noting that the dual adversarial strategy does not require feature extractor and discriminator to work in turn, which can be implemented through a gradient reversal layer. Finally, on the basis of adapting the overall features of both the domains via the domain adversarial strategy, the source- and target-domain features are further adapted by minimizing the correlation alignment loss of each class of samples. Experimental results on two real HSI datasets of Botswana and Kennedy Space Center show the effectiveness of our proposed method. © 2020 IEEE.

关键词： Spectroscopy

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Simulation of the Heat Transfer during the Casting Process by Mirror U-Net Models

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Defect and Diffusion Forum 2024年 433卷 97-104页

作者： Kang, Jinwu Zhao, Qichao Wang, Jiwu Han, Xiao Yang, Yahui School of Materials Science and Engineering Key Laboratory for Advanced Materials Processing Technology Tsinghua University Beijing100084 China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China

Deep learning has achieved great progress in image recognition, segmentation, semantic recognition, and game theory. It also shows potential to solve scientific computing such as simulation problems in engineering. On the other hand, the numerical simulation method requires constitutive modelling, involves a huge computation volume and takes a long time. In this paper, two mirror U-Net models were proposed for the simulation of the heat transfer during the casting process. These models include an upper U-Net branch for the treatment of the geometries of casting, mold, and chill, and a lower U-Net branch for the treatment of the initial temperature field. Their difference is whether the bottoms of upper and lower U-Nets are shared. These two branches tackle the problems involving the input of a geometrical model which consists of three types of materials and the input of an initial or current temperature field image. These models were trained and validated with a big database with hundreds of casting shapes. The prediction results show that the average accuracy reaches 98.8%. © 2024 Trans Tech Publications Ltd, Switzerland.

关键词： Semantic Segmentation

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A robust RGB-D visual odometry with moving object detection in dynamic indoor scenes

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IET Cyber-Systems and Robotics 2023年第1期5卷 79-88页

作者： Xianglong Zhang Haiyang Yu Yan Zhuang College of Mechanical and Electronic Engineering Dalian Minzu UniversityDalianChina School of Control Science and Engineering Dalian University of TechnologyDalianChina

Simultaneous localisation and mapping(SLAM)are the basis for many robotic *** the front end of SLAM,visual odometry is mainly used to estimate camera *** dynamic scenes,classical methods are deteriorated by dynamic objects and cannot achieve satisfactory *** order to improve the robustness of visual odometry in dynamic scenes,this paper proposed a dynamic region detection method based on RGBD ***,all feature points on the RGB image are classified as dynamic and static using a triangle constraint and the epipolar geometric constraint ***,the depth image is clustered using the K-Means *** classified feature points are mapped to the clustered depth image,and a dynamic or static label is assigned to each cluster according to the number of dynamic feature ***,a dynamic region mask for the RGB image is generated based on the dynamic clusters in the depth image,and the feature points covered by the mask are all *** remaining static feature points are applied to estimate the camera ***,some experimental results are provided to demonstrate the feasibility and performance.

关键词： dynamic indoor scenes moving object detection RGB-D SLAM visual odometry

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Variations in source-specific soil organic matter components across 32 forest sites in China

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Carbon Research 2024年第1期3卷 1-16页

作者： Sun, Guodong Wang, Mengke Zhang, Qiang Wang, Yinghui Yang, Yuanxi Xu, Shan Wang, Junjian State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Marine Sciences Sun Yat-Sen University Zhuhai519082 China Zhuhai519082 China

Forest soils store substantial amounts of carbon in various soil organic matter (SOM) components due to high plant litter inputs and active microbial turnover. However, the variations in plant- and microbial-derived SOM components in surface and subsurface forest soils across a wide geographic scale remain poorly understood. This study investigated the SOM components from aboveground and belowground plant inputs and fungal and bacterial necromass in surface (soil0–5 cm) and subsurface (soil5–10 cm) soils across 32 forest sites in China and analyzed their relationships with climate and edaphic factors. Compared to soil0–5 cm, soil5–10 cm exhibited lower soil organic carbon content and cutin biomarker concentration but higher concentrations of fungal necromass carbon and lignin phenols. Higher mean annual precipitation led to higher concentrations of cutin and suberin biomarkers in soil0–5 cm and soil5–10 cm, respectively. Higher soil organic carbon content was associated with lower plant-derived lignin biomarkers, higher lignin oxidation degrees, and increased microbial necromass-derived amino sugars across sites, highlighting the pivotal role of microbial necromass in SOM stabilization. Additionally, both fungal and bacterial necromass decreased with increasing mineral weathering across sites. These insights improve the understanding of environmental drivers of source-specific carbon storage in forest soils. Graphical Abstract: (Figure presented.) © The Author(s) 2024.

关键词： Amino sugars

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation science and engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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