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Rolling Bearing Fault Diagnosis Based on 1D Convolutional Neural Network and Kolmogorov–Arnold Network for Industrial Internet

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computers, Materials & Continua 2025年第6期83卷 4659-4677页

作者： Huyong Yan Huidong Zhou Jian Zheng Zhaozhe Zhou School of Computer Science and Information Engineering Chongqing Technology and Business UniversityChongqing400067China State Grid Chongqing Shibei Electric Power Supply Branch Chongqing400015China

As smart manufacturing and Industry 4.0 continue to evolve,fault diagnosis of mechanical equipment has become crucial for ensuring production safety and optimizing equipment *** address the challenge of cross-domain adaptation in intelligent diagnostic models under varying operational conditions,this paper introduces the CNN-1D-KAN model,which combines a 1D Convolutional Neural Network(1D-CNN)with a Kolmogorov–Arnold Network(KAN).The novelty of this approach lies in replacing the traditional 1D-CNN’s final fully connected layer with a KANLinear layer,leveraging KAN’s advanced nonlinear processing and function approximation capabilities while maintaining the simplicity of linear *** results on the CWRU dataset demonstrate that,under stable load conditions,the CNN-1D-KAN model achieves high accuracy,averaging 96.67%.Furthermore,the model exhibits strong transfer generalization and robustness across varying load conditions,sustaining an average accuracy of 90.21%.When compared to traditional neural networks(e.g.,1D-CNN and Multi-Layer Perceptron)and other domain adaptation models(e.g.,KAN Convolutions and KAN),the CNN-1D-KAN consistently outperforms in both accuracy and F1 scores across diverse load *** in handling complex cross-domain data,it excels in diagnostic *** study provides an effective solution for cross-domain fault diagnosis in Industrial Internet systems,offering a theoretical foundation to enhance the reliability and stability of intelligent manufacturing processes,thus supporting the future advancement of industrial IoT applications.

关键词： Bearing fault diagnosis Kolmogorov-Arnold network adaptivity under various working load transfer generalization

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High-quality Trajectory Generation for Autonomous Driving: A Lightweight Federated Learning-based Diffusion Model

High-quality Trajectory Generation for Autonomous Driving: A...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Gao, Runquan Kang, Jiawen Lai, Bingkun Xu, Minrui Sun, Geng Zhang, Tao Zhang, Weiting Yang, Dong Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computer Science and Engineering Singapore Jilin University College of Computer Science and Technology Changchun China Beijing Jiaotong University School of Software Engineering Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China

ISBN: (纸本)9798350351255

Vehicle trajectory data plays a pivotal role in simulation testing for autonomous driving. Hence, there exist well-established trajectory generation methods employing deep generative models to generate trajectories mapping the distribution of the original dataset, thereby augmenting existing trajectory datasets. However, these methods typically rely on large datasets gathered by governmental or organizational entities for central training, which may pose data privacy, security, and accessibility issues. Therefore, it is challenging to generate high-quality traffic trajectory data while preserving privacy which involves a delicate balance between these two objectives. To deal with this challenge, we introduce Federated Learning into the diffusion model and propose a Federated Learning-based diffusion model (FedDifftraj) to generate traffic trajectory data. Unlike existing central training methods, FedDifftraj aggregates model parameters uploaded by different vehicles and then updates a global model. Additionally, there is a substantial communication overhead incurred during the training of the federated diffusion model. Therefore, we quantize the local diffusion model before uploading it to the parameter server. Through extensive simulations on real-world datasets, FedDifftraj can generate high-quality traffic trajectory data that is consistent with the results of the central training while preserving privacy and reducing communication overhead by 93.74% when utilizing 8-bit quantization. © 2024 IEEE.

关键词： Differential privacy

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GMTS: GNN-based multi-scale transformer siamese network for remote sensing building change detection

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International Journal of Digital Earth 2023年第1期16卷 1685-1706页

作者： Xinyang Song Zhen Hua Jinjiang Li School of Infomation and electronic engineering Shandong Technology and Business UniversityInstitute of Network Technology(ICT)YantaiPeople's Republic of China School of Computer Science and Technology Shandong Technology and Business UniversityYantaiPeople's Republic of China

With the remarkable success of change detection(CD)in remote sensing images in the context of deep learning,many convolutional neural network(CNN)based methods have been *** the current research,to obtain a better context modeling method for remote sensing images and to capture more spatiotemporal characteristics,several attention-based methods and transformer(TR)-based methods have been *** research has also continued to innovate on TR-based methods,and many new methods have been *** of them require a huge number of calculation to achieve good ***,using the TR-based mehtod while maintaining the overhead low is a problem to be ***,we propose a GNN-based multi-scale transformer siamese network for remote sensing image change detection(GMTS)that maintains a low network overhead while effectively modeling context in the spatiotemporal *** also design a novel hybrid backbone to extract *** with the current CNN backbone,our backbone network has a lower overhead and achieves better ***,we use high/low frequency(HiLo)attention to extract more detailed local features and the multi-scale pooling pyramid transformer(MPPT)module to focus on more global features ***,we leverage the context modeling capabilities of TR in the spatiotemporal domain to optimize the extracted *** have a relatively low number of parameters compared to that required by current TR-based methods and achieve a good effect improvement,which provides a good balance between efficiency and performance.

关键词： Remote sensing(RS) change detection(CD) depthwise over-parameterized convolutional(DO-Conv) attention mechanism transformer graph convolution

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Isolation Enhancement in a Compact Four-Element MIMO Antenna for Ultra-Wideband Applications

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computers, Materials & Continua 2023年第4期75卷 911-925页

作者： Awais Khan Shahid Bashir Salman Ghafoor Hatem Rmili Jawad Mirza Ammar Ahmad Department of Electrical Engineering University of Engineering and TechnologyPeshawar25000Pakistan Department of Electrical Engineering University of Science and TechnologyBannu28100Pakistan School of Electrical Engineering and Computer Science(SEECS) National University of Sciences and Technology(NUST)Islamabad44000Pakistan Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah21589Saudi Arabia SEECS Photonic Research Group National University of Sciences and TechnologyIslamabad44000Pakistan

Mutual coupling reduction or isolation enhancement in antenna arrays is an important area of research as it severely affects the performance of an *** this paper,a new type of compact and highly isolated Multiple-Input-Multiple-Output(MIMO)antenna for ultra-wideband(UWB)applications is *** design consists of four radiators that are orthogonally positioned and confined to a compact 40×40×0.8 mm3 *** final antenna design uses an inverted L shape partial ground to produce an acceptable reflection coefficient(S11<−10 dB)in an entire UWB band(3.1–10.6)giga hertz(GHz).Moreover,the inter-element isolation has also been enhanced to>20 db for majority of the UWB *** antenna was fabricated and tested with the vector network analyzer(VNA)and in an anechoic chamber for scattering parameters and radiation ***,different MIMO diversity performance metrics are also measured to validate the proposed *** simulation results and the experimental results from the constructed model agree quite *** proposed antenna is compared with similar designs in recently published literature for various performance *** of its low envelope correlation coefficient(ECC<0.1),high diversity gain(DG>9.99 dB),peak gain of 4.6 dB,reduced channel capacity loss(CCL<0.4 b/s/Hz),and average radiation efficiency of over 85%,the proposed MIMO antenna is ideally suited for practical UWB applications.

关键词： Multiple input multiple output antennas envelope correlation coefficient defected ground structure ultra wide band diversity gain

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DEDF: An Enhanced Differential Evolution Algorithm with Dynamic-selection Framework in IIOT 29

DEDF: An Enhanced Differential Evolution Algorithm with Dyna...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhou, Zhou Fangmin Li, U. Liu U, Huazhong Changsha University School of Computer Science and Engineering Changsha China Hainan University School of Computer Science and Technology Haikou China

ISBN: (纸本)9798350330717

To solve the problems of slow convergence and limited prediction ability of the Differential Evolution (DE) algorithm, an enhanced DE algorithm with the trustworthiness of a dynamic-selection framework (denoted by DEDF) is proposed. DEDF develops a trusted framework containing five mutation strategies to realize the dynamic selection of mutation strategies. On the basis of the framework, the mutation factor, crossover factor, and local exit strategy are improved to balance the algorithm's local search and global search ability. A series of tests on the function set CEC2017 has been performed, and the findings show that compared with other benchmark algorithms, the DEDF has advantages in convergence speed and accuracy. The proposed algorithm DEDF can be effectively leveraged to address multi-objective optimization issues in IIOT. © 2023 IEEE.

关键词： differential evolution IIOT Neural network resource optimization trustworthiness of dynamic-selection framework

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Task Offloading and Approximate Computing in Solar Powered IoT Networks

IEEE Networking Letters

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IEEE Networking Letters 2024年第1期6卷 26-30页

作者： Zhan, Junfei Wu, Jiayi He, Tengjiao Chin, Kwan-Wu Jinan University College of Information Science and Technology Guangzhou510632 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia

This letter considers approximate computing and task offloading in a solar powered Internet of Things (IoT) network. Specifically, it addresses the novel problem of minimizing the energy consumption of IoT devices by either offloading their tasks or executing these tasks in approximate mode. To this end, this letter outlines a novel mixed integer linear program (MILP) that computes the minimum total energy required to execute tasks. It optimizes four key factors: (i) task offloading decision of devices, (ii) use of approximate computing by devices, (iii) channel allocation, and (iv) virtual machine (VM) assignment. Further, it outlines a novel solution that determines these factors using channel gain and energy arrival estimates obtained from digital twins (DTs). The results show that our DT-based solution uses at most 1.62x more energy than MILP. © 2019 IEEE.

关键词： Energy harvesting

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Polar Region Climate Dynamics: Deep Learning and Remote Sensing Integration for Monitoring Arctic and Antarctic Changes

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 582-595页

作者： Maniraj, Subramanian Pitchiah Rose, Jesuharan Dafni Arunachalam, Rohini Rangasamy, Kotteeswaran Patil, Vishal Ratansing Kathirvelu, Selvakumarasamy Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Computer Science and Engineering Anil Neerukonda Institute of Technology and Sciences Andhra Pradesh Visakhapatnam India Joseph’s College of Engineering Chennai 600119 India Department of CSE(AIML) Vishwakarma Institute of Technology Pune India Department of Electronics and Communication Engineering Saveetha School of Engineering (SIMATS) Chennai India

The polar regions of the Earth, specifically the Arctic and Antarctic, play a pivotal role in regulating the planet’s climate systems. These regions are highly sensitive to climate change, with observable shifts such as diminishing sea ice, retreating glaciers, and rising temperatures. Understanding these dynamics is essential for predicting broader global climate impacts. This paper presents a novel approach for monitoring and predicting these changes by integrating an attention-based transformer model with a 3D convolutional neural network (3D-CNN). The proposed model is designed to capture both the spatial features of satellite imagery and the temporal dynamics of climate data, offering enhanced predictive capabilities for key climate variables such as sea ice extent, glacier movement, and temperature variations. The dataset was split into 80% training and 20% testing with tenfold cross-validation, and the model was trained using the Adam optimizer. The results show that the proposed model achieved a remarkable accuracy of 97.43%, outperforming traditional CNN and RNN models. The model also demonstrated a precision of 98.12% for sea ice extent prediction, a 96.45% precision for glacier movement detection, and a mean squared error (MSE) of 0.35 °C for temperature predictions. These findings suggest that deep learning models, specifically the integration of transformer and 3D-CNN architectures, can significantly contribute to polar region climate monitoring, offering improved tools for scientific research and policymaking. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. corrected publication 2024.

关键词： 3D convolutional neural network Attention-based transformer Climate dynamics Deep learning Glacier movement Polar region Remote sensing Sea ice extent Spatial–temporal analysis Temperature prediction

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Black-box Word-level Textual Adversarial Attack Based On Discrete Harris Hawks Optimization 26

Black-box Word-level Textual Adversarial Attack Based On Dis...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Tianrui Niu, Weina Ding, Kangyi Yao, Lingfeng Cheng, Pengsen Zhang, Xiaosong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China School of Cyber Science and Engineering Sichuan University Chengdu China

ISBN: (纸本)9798350331684

Neural network-based applications are prone to being fooled by adversarial examples due to the natural vulnerability of deep neural networks (DNNs). Textual adversarial attacks are particularly challenging due to the discreteness between texts. The adversarial examples crafted by word-level textual attacks which are typically treated as optimization problems in black-box scenarios perform better in human evaluation. Existing approaches have struggled to balance the success rate with the time consuming, mainly because the chosen optimization algorithm is not efficient enough. In this paper, we propose a method to generate textual adversarial examples called Discrete Harris Hawk Optimization (DHHO). We set up three operations for handling discrete data, which are applied to each stage of the Harris Hawk Optimization (HHO) to enable it to solve optimization problems in discrete space. By attacking BiLSTM and BERT on two benchmark data sets, we conduct extensive experiments to evaluate our attack method with a success rate of up to 98% and a reduction of time is at least 50%. Moreover, the experimental results also show that our adversarial examples can ensure high quality and transferability. © 2023 IEEE.

关键词： Deep neural networks

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Opportunistic Relay Strategy for Body Area Networks

Opportunistic Relay Strategy for Body Area Networks

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Wu, Hongbo Jia, Yukuan Yan, Jintao Zhou, Sheng Niu, Zhisheng Chang, Zheng Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350350548

In wireless body area networks (WBANs), the deep channel fading between the nodes and the hub significantly impairs the reliability of end-to-end signal transmission. However, some nodes in WBANs necessitate high-priority data transmission with stringent latency and accuracy requirements. Retransmission is ineffective against channel fading and can result in increased communication overhead and extended transmission delays. This paper proposes an opportunistic relay strategy tailored to the characteristics of WBAN nodes with varying priorities. This strategy converts suitable low-priority nodes as relays during high-priority time slots to forward high-priority data to the hub when deep fading occurs. The relay can be opportunistically selected based on the channel condition and the power usage. With Lyanupov optimization, we maximize the transmission reliability while ensuring the extra power consumption of low-priority nodes are acceptable. Subsequently, simulations are conducted in the Network Simulator 3 (NS3) to validate the proposed relay selection strategy, showing that the proposed strategy effectively improves the reliability from 90.2% to 99.1% with an additional power consumption of 30%. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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Characterizing sEMG changes with muscle fatigue for lower-extremity prostheses and exoskeletons control 46

Characterizing sEMG changes with muscle fatigue for lower-ex...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Shang, Wenze Huang, Ruisen Lin, Yongji Liang, Fengyan Yin, Ming Lu, Xiao-Ping Wu, XinYu Gao, Fei Chinese Academy of Science Shenzhen Institute of Advanced Technology Guangdong Shenzhen China Hainan University Key Laboratory of Biomedical Engineering of Hainan Province School of Biomedical Engineering Haikou China Macau University of Science and Technology School of Computer Science and Engineering Taipa China

ISBN: (纸本)9798350371499

In powered lower limb prostheses and exoskeleton control, sEMG based methods have been widely explored and utilized due to non-invasive nature and ability to directly reflect user intentions. However, in practical use, neuromuscular fatigue (NMF) caused by long-term use can cause significant changes in sEMG signals. These issues are constraining sEMG's active prosthetics from the laboratory to real life. In this study, the changes in sEMG signals with muscle fatigue was investigated. sEMG data from one healthy subject and a below-knee amputee were logged and analyzed when they walked on a treadmill for a long time. They will experience from no NMF to NMF and then adapt to no NMF. A variational mode decomposition-based soft interval threshold (VMD-SIT) method was used to denoise the sEMG signals. Feature extraction methods were used to extract the features of the sEMG signal, and the distribution changes of these features during walking were analyzed. The results indicated that the NMF of the amputee was quite different from and more variable than that of the healthy subject, and the ratios of features' mean value of amputee and healthy are much higher in residual-limb side (12.35-26.18) than normal side (3.52-5.91). © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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