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6th International Conference on Big Data Technologies, ICBDT 2023

作者： Yuan, Hao Liu, Kun Shi, Jiechuan Wang, Can Wang, Weiwei School of Information Science and Engineering University of Jinan Jinan250022 China China High-resolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China QiLu Research Institute Jinan250200 China

ISBN: (纸本)9798400707667

In recent years, the development of deep learning technology has led to widespread attention on Vision Transformer (ViT) as an emerging image classification method. Remote sensing image classification is an important task in the field of remote sensing, with extensive application prospects. This paper aims to explore the remote sensing image classification method based on Vision Transformer, addressing the limitations of traditional convolutional neural networks in terms of global perception capability, context information retrieval, and positional encoding. The classification performance of the Vision Transformer model is evaluated and compared on remote sensing datasets. Vision Transformer is a deep neural network model based on self-attention mechanism that can capture the global context information in images and has achieved remarkable performance in various computer vision tasks. Furthermore, experimental results demonstrate that the remote sensing image classification method based on Vision Transformer exhibits outstanding accuracy and generalization ability. Compared to traditional convolutional neural networks, it can better capture the global features in remote sensing images and has better scalability when dealing with large-scale remote sensing image data. Experimental results on different remote sensing image datasets show that the model performs well compared to state-of-the-art methods. Specifically, Vision Transformer achieves average classification accuracies of 95.41%, 98.26%, 93.74% and 95.25% on the AID, UC-Merced, NWPU-RESISC45 and Optimal31 datasets, respectively. © 2023 ACM.

关键词： Neural network models

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Compound Fault Diagnosis of Planetary Gearbox Using Capsule Network with Multi-Scale Convolutional Neural Network

Compound Fault Diagnosis of Planetary Gearbox Using Capsule ...

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2024 Prognostics and system Health Management Conference, PHM 2024

作者： Li, Guoyan Zhang, Jingbin Lu, Guoliang School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu61173 China Taiyuan University of Technology Key Laboratory of Advance Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China School of Mechanical Engineering Key Laboratory of High-efficiency and Clean Mechanical Manufacture of MOE Shandong University National Demonstration Center for Experimental Mechanical Engineering Education Jinan250061 China

ISBN: (纸本)9798350360585

Capsule network is an innovative deep learning-based model that uses neuron vectors as inputs and outputs of the network. It can achieve independent output of multiple classification labels, and solve the problem of decoupling recognition of compound fault components in planetary gearboxes. However, the single-scale feature extraction cannot fully explore multi-scale feature information in compound fault signals. This study proposes a capsule network combined with multi-scale convolutional feature fusion to diagnose the compound fault of planetary gearbox. The multi-scale features are extracted from STFT images of vibration signals using convolutional layers with different kernel sizes;secondly, a capsule network is designed to classify and identify the fault components by using the dynamic routing algorithm. The experiments are conducted to assess the performance of the proposed method, and the results indicates that it can effectively decoupling identify the compound faults with strong anti-interference and stability. © 2024 IEEE.

关键词： Dynamic routing algorithms

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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Optimal Tracking Performance of Networked control systems Under DoS and Its Applications

Optimal Tracking Performance of Networked Control Systems Un...

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2024 China Automation Congress, CAC 2024

作者： Ren, Xinyu Li, Bo Zhao, Feng Chen, Xiangyong Zhang, Xianhe Jiang, Xiaowei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (纸本)9798350368604

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output (MIMO) discrete networked control systems (NCSs) under the influence of Denial of Service (DoS) attack, channel noise, and bandwidth constraints. The channel noise is assumed to be additive Gaussian white noise (AWGN). Due to the randomness of DoS attack, a Bernoulli distribution is used for DoS modeling. Utilizing frequency domain analysis methods, coprime factorization, all-pass decomposition, and Youla parameterization, an explicit expression for the optimal tracking performance (OTP) of the system under the two-degree-of-freedom (TDOF) controller is derived. The analysis indicates that non-minimum phase (NMP) zeros, unstable poles (UPs), DoS attack, and network constraints affect the OTP of NCSs. Finally, the theoretical results are validated through simulations of MIMO NCS and vehicle control system. © 2024 IEEE.

关键词： White noise

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Real-Time Sleep Apnea Detection and Prediction From Single-Channel Airflow

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhang, Zirui Wu, Weiming Fu, Ziyu Han, Zhuo Zhang, Jinyuan Sun, Chen Ma, Dedong Wang, Cong Shandong University Center for Intelligent Medical Engineering School of Control Science and Engineering Jinan250061 China Qilu Hospital of Shandong University Department of Pulmonary and Critical Care Medicine Shandong Jinan250012 China Collaborative Innovation Center of Respiratory Therapy Shandong Research Institute of Industrial Technology Shandong Jinan250012 China

Sleep apnea (SA) is a widespread sleep disorder that causes oxygen desaturation and sleep fragmentation, affecting hundreds of millions worldwide. Real-time feedback on respiratory status is crucial for enabling timely intervention in treatment, improving therapeutic outcomes. This study introduces a novel method for real-time detection or prediction of SA events using airflow (AF) signals. By employing deterministic learning, a dynamical model of the respiratory system is constructed, from which two features are extracted: temporal dynamic trend features and current dynamical features. The former reflects airway vulnerability over time, though it struggles to pinpoint exact SA event timing. The latter indicates current airway patency but may misclassify brief AF restrictions as SA events. Combining these features with appropriate weighting balances their limitations, enabling both detection and prediction. In tests across multiple datasets, the method achieved a maximum detection accuracy of 91.1% and a prediction accuracy of 90%, demonstrating its potential for clinical application and superiority over retrospective diagnostic tools. © 1963-2012 IEEE.

关键词： Sleep research

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Natural Human Skin‑Inspired Wearable and Breathable Nanofiber‑based Sensors with Excellent Thermal Management Functionality

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Advanced Fiber Materials 2024年第6期6卷 1955-1968页

作者： Peng Wang Xiaodan Li Guifen Sun Guoqing Wang Qing Han Chuizhou Meng Zhonghe Wei Yang Li School of Mechanical Engineering University of JinanJinan 250022China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human‑Robot InteractionSchool of Mechanical EngineeringHebei University of TechnologyTianjin 300401China Shandong Provincial Key Laboratory of Network‑Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan 250022China Network Information Center Department of Computer ScienceQilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Integrated Circuits Shandong UniversityJinan 250101China

Wearable sensors have been rapidly developed for application in various human monitoring ***,the wearing comfort and thermal properties of these devices have been largely ignored,and these characteristics urgently need to be ***,we develop a wearable and breathable nanofiber-based sensor with excellent thermal management functionality based on passive heat preservation and active Joule heating *** multifunctional device consists of a micropatterned carbon nanotube(CNT)/thermoplastic polyurethane(TPU)nanofiber electrode,a microporous ionic aerogel electrolyte and a microstructured Ag/TPU nanofiber *** to the presence of a supercapacitive sensing mechanism and the appli-cation of microstructuration,the sensor shows excellent sensing performance,with a sensitivity of 24.62 ***,due to the overall porous structure and hydrophobicity of TPU,the sensor shows good breathability(62 mm/s)and water repellency,with a water contact angle of 151.2°.In addition,effective passive heat preservation is achieved by combining CNTs with high solar absorption rates(85%)as the top layer facing the outside,aerogel with a low thermal conductivity(0.063 W m-1 k-1)as the middle layer for thermal insulation,and Ag with a high infrared reflectance rate as the bottom layer facing the *** warming,this material yields a higher temperature than ***,the active Joule heat-ing effect is realized by applying current through the bottom resistive electrode,which can quickly increase the temperature to supply controlled warming on *** proposed wearable and breathable sensor with tunable thermal properties is promising for monitoring and heat therapy applications in cold environments.

关键词： Wearable sensor Breathable Personal thermal management Electrospun nanofibers Joule heating

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Quantum Neutrino Optimization Algorithm and Its Application in Fault Diagnosis of All-Electric Trawler Power systems

Quantum Neutrino Optimization Algorithm and Its Application ...

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Computer Applications and Information technology (ISCAIT), International Symposium on

作者： Yinchao Tan Xiao Kang JiaXin Jiang Weiwei Song Sun Zhi Yinta Li Liu Sheng Shandong Province Ship Control Engineering and Intelligent Systems Engineering Technology Research Center Weihai Ocean Vocational College Weihai China College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

The all-electric trawler power system exhibits complex characteristics such as multi-device coupling, multitimescale dynamic responses, and extensive heterogeneous data processing. Traditional fault diagnosis methods face challenges including slow convergence and susceptibility to local optima. To address these issues, this paper proposes a Quantum Neutrino Optimization Algorithm (QNOA) that integrates quantum computing and neutrino entanglement characteristics, aiming to enhance the accuracy and efficiency of fault diagnosis. QNOA employs quantum state array encoding to represent the solution space, leveraging quantum superposition states for parallel searches of high-dimensional data. By incorporating a neutrino spin regulation factor to dynamically adjust qubit probability amplitudes, the algorithm simulates the global entanglement behavior of neutrino weak interactions, thereby strengthening its global search capability and effectively balancing exploration and exploitation. Experimental results demonstrate that QNOA achieves over $30 \%$ reduction in average fitness values and more than $25 \%$ improvement in average convergence speed compared to traditional algorithms (e.g., Genetic Algorithm and Particle Swarm Optimization) on benchmark functions such as Sphere and Rosenbrock, particularly excelling in multimodal function optimization. Further application to fault diagnosis in all-electric trawler power systems-through equipment state encoding, objective function modeling, and simulation case studiesvalidates that QNOA can accurately locate diverse faults, including transformer short circuits, circuit breaker failures, and load overcurrents, with convergence to the optimal solution within 20-30 iterations. The findings confirm the high efficiency and robustness of QNOA in complex power system fault diagnosis, providing innovative technical support for intelligent ship operation and maintenance.

关键词： Fault diagnosis Neutrino sources Quantum entanglement Heuristic algorithms Power system dynamics Maintenance Circuit faults Power system faults Optimization Convergence

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Simultaneous enhancement of sensitivity, resonant frequency, and overload capability of piezoresistive accelerometers by heavily-doped fillet structures on sensing beams

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IEEE Sensors Journal 2025年第11期25卷 19138-19146页

作者： Xia, Yong Wang, Tao Lv, Yang Han, Xiangguang Yu, Mingzhi Tan, Renjie Huang, Mimi Jia, Chen Zhao, Libo Sun, Lin Yang, Ping Lu, Dejiang Xi'an Jiaotong University School of Mechanical Engineering State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies Xi'an710049 China Ningbo CRRC Times Transducer Technology Co. LTD Department of Basic Research and Development Ningbo315021 China Research Institute for Intelligent Sensing Technology and System School of Instrument Science and Technology State Key Laboratory for Manufacturing Systems Engineering State Industry-Education Integration Center for Medical Innovations International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies Xi'an Jiaotong University Xi'an710049 China Shandong Laboratory of Advanced Materials and green Manufacturing at Yantai Yantai264000 China

High sensitivity, high natural frequency, and high overload capability are desired for accelerometers used in industrial applications, but simultaneously enhancing all is challenging. Here, via designing novel heavily-doped fillet structures, we prove the sensitivity, resonant frequency, and overload capability can be simultaneously enhanced. The enhancements are attributed to regulations of stress concentration and effective sensing regions by the heavily-doped fillets. The sensors with and without fillets are designed, fabricated, packaged, and tested. The results show a sensitivity of 0.31±0.02 mV/g@5V, a natural frequency of 37.87±2.09 kHz, and an overload of 10040.17±531.76 g for sensors with 10-μm fillets, which are superior to 0.27±0.01 mV/g@5V, 34.13±0.90 kHz, and 7368.55±843.92 g for those no-fillet counterparts. Additionally, the developed sensor further exhibited feasibility in air-bearing vibration monitoring. The proposed design is envisioned to give insights into the novel designs of high-performance stress-based sensors, including accelerometers, pressure sensors, and strain gauges. © 2001-2012 IEEE.

关键词： Strain gages

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High-gain signal-on PEDOT:PSS organic photoelectrochemical transistor biosensing modulated by a MXene/MOFs/NiO Schottky heterojunction

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Science China Chemistry 2023年第2期66卷 578-585页

作者： Zheng Li Yi-Tong Xu Jin Hu Ting Wang Fang-Qing Liu Hong Zhou Guang-Xu Chen Peng Lin Wei-Wei Zhao Jing-Juan Xu Hong-Yuan Chen State Key Laboratory of Analytical Chemistry for Life Science School of Chemistry and Chemical EngineeringNanjing UniversityNanjing210023China Shenzhen Key Laboratory of Special Functional Materials&Guangdong Research Center for Interfacial Engineering of Functional Materials College of Materials Science and EngineeringShenzhen UniversityShenzhen518060China Shandong Key Laboratory of Biochemical Analysis College of Chemistry and Molecular EngineeringQingdao University of Science and TechnologyQingdao266042China School of Environment and Energy State Key Laboratory of Luminescent Materials and DevicesGuangdong Provincial Key Laboratory of Atmospheric Environment and Pollution ControlSouth China University of TechnologyGuangzhou510006China

Herein we report a high-gain signal-on poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(PEDOT:PSS)organic electrochemical transistor(OECT)biosensing using an accumulation-mode PEDOT:PSS OECT modulated by a light-fueled MXene/MOFs/Ni O Schottky *** such a system,the MXene/MOFs/Ni O Schottky heterojunction exhibited superior gating effect,as it not only enabled the fast-directional charge transfer but also guaranteed the maximal accessibility of the electrolyte to topped 2D MXene with large surface *** linkage with a bi-enzyme cascade system,the quinone derivatives produced by the cascade reaction of alkaline phosphatase(ALP)and tyrosinase(TYR)could serve as effective electron acceptors for the representative Ti_(3)C_(2)/PCN-224/Ni O heterojunction,underpinning an innovative method for sensitive detection of ALP activity with a low detection limit of 0.001 U L^(-1).Remarkably,the as-developed system demonstrated a remarkable current gain as high as near 10^(4),which to our knowledge is the highest one among existing OECT biosensory *** work represents a generic protocol to develop the novel signal-on PEDOT:PSS OECT platform towards biochemical detection and beyond.

关键词： signal-on PEDOT:PSS organic electrochemical transistor biosensing Schottky heterojunction

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Development of Longitudinal-Lateral-Roll Vehicle Dynamic Modelling Based on Instantaneous Screws

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IFAC-PapersOnLine 2024年第29期58卷 184-189页

作者： Guofeng. Zhou Yaqi. Gao Yafei. Wang Xulei. Liu College of Engineering Science and Technology Shanghai Ocean University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan Chengdu China

This paper presents a new theoretical method for the dynamic analysis of a vehicle coupling the longitudinal, lateral and roll motions. First, the nonlinear full-vehicle model is modelled in the global coordinate system and vehicle coordinate system, respectively. Second, the instantaneous screws are applied to represent the cornering forces acting on the vehicle system and describe the kinematic and static relations of the full-vehicle model. Based on these relations the generalize coordinates and forces are determined for the dynamic analysis. The dynamic equations are derived by the using of Lagrangian formula. Finally, two numerical examples are illustrated for the validity of the proposed method. Compared with Software Carsim it is shown that the theoretical method is effective for the dynamic analysis of a vehicle.

关键词： nonlinear full-vehicle model longitudinal motion lateral motion roll motion instantaneous screws dynamic analysis

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