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共 15478 条记录，以下是171-180 订阅

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State Estimation and control of Switched Neural Networks with Mode-Dependent Hybrid Dwell Time 3rd

State Estimation and Control of Switched Neural Networks wit...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Li, Dan Wang, Jinling Li, Jiarong Northwest Normal University Lanzhou730070 China Xinjiang University Urumqi830046 China Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789819616930

The focus of this paper lies in the state estimation and control of switched neural networks (SNNs) under mode-dependent hybrid dwell time (MDHDT). First, a novel MDHDT switching machanism is constructed based on average dwell time (ADT) and mode-dependent average dwell time (MDADT). Then a mode-dependent time scheduler is designed by introducing fixed period and a mode-dependent maximum integer. Moreover, a stability criterion to guarantee the global uniform exponential stability (GUES) of the switched closed-loop system is given by constructing mode-dependent multiple Lyapunov function. Finally, the authenticity of the obtained conclusion is validated through data simulation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lyapunov functions

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An eBPF/XDP-Based Architecture for Efficient SOME/IP Service Discovery 11

An eBPF/XDP-Based Architecture for Efficient SOME/IP Service...

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11th international conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Lim, Seong Hyeon Oh, Sung Bhin Cha, Syng Keun Do, Young Soo Lim, Se Jeong Choi, Hyeok Jun Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798331509293

The rapid evolution of Software-Defined Vehicles (SDVs) necessitates efficient and scalable communication mechanisms between in-vehicle Electronic control Units (ECUs). SOME/IP, particularly its Service Discovery component (SOME/IP-SD), has emerged as a critical protocol for enabling dynamic service-oriented communication in this context. However, traditional user-space implementations of SOME/IP-SD suffer from performance limitations, primarily due to excessive context switching overhead and CPU resource consumption, which can hinder real-time performance in automotive applications. This paper introduces a novel kernel-based architecture for SOME/IP-SD processing that leverages the power of eBPF/XDP. The proposed architecture offloads SOME/IP-SD packet processing to the kernel, significantly reducing context switching and optimizing system resource utilization. By processing SOME/IP service discovery at the kernel level with eBPF/XDP, the architecture minimizes latency and improves overall performance through early packet filtering and efficient data sharing between kernel and user space via eBPF maps. The proposed architecture aims to enhance the responsiveness of service discovery, a critical requirement for real-time applications in SDVs. This kernel-level approach demonstrates the potential of eBPF/XDP in optimizing automotive network protocols and is expected to contribute to the advancement of real-time communication systems in next-generation vehicles. © 2025 IEEE.

关键词： eBPF/XDP SOME/IP Service Discovery SOMEI/P

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Adaptive Overhead Glass Curtain Wall Cleaning Robot Design 24

Adaptive Overhead Glass Curtain Wall Cleaning Robot Design

引用

2024 international conference on Industrial Automation and Robotics, IAR 2024

作者： Jiang, Tao Chen, Huadong Yang, Zixi Zhou, Haolin School of Mechanical and Electrical Engineering Chengdu University of Technology Sichuan Chengdu China

ISBN: (纸本)9798400711268

In the rapid development of modern cities, glass curtain wall structures are widely used in many high-rise buildings and large-scale comprehensive transportation hubs, becoming an important means of beautifying architectural space and enhancing the city’s image. The beautification and cleaning of curved curved glass curtain wall has become the focus and difficulty of the world’s metropolis to rectify the city appearance. In this paper, an adaptive overhead glass curtain wall cleaning robot is innovatively designed, featuring self-adaptive wall surface, strong adsorption capacity, sewage recycling and purification, compact structure, long energy-saving and environmentally friendly single working time, etc. It can stably adsorb and travel on curved curved surfaces, and flexibly, quickly and stably complete the automatic cleaning operation of overhead glass curtain wall. This paper focuses on solving the problem of surface adaptability of cleaning brush head and adsorption marching, and provides an efficient sewage recycling and purification program, which provides ideas for green urban beautification and construction development, and has important social significance, engineering practical value of a wide range of market application prospects. Adaptive high altitude glass curtain wall cleaning robot is a kind of professional cleaning robot that integrally applies the results of many technologies such as intelligent control, mechanical design and material science, which is an extended application of wall climbing robot, and its basic composition includes the robot’s mechanical system and the control system, and this chapter will elaborate the overall design scheme and the design ideas of the robot based on the functions and principles of these two parts. © 2024 Copyright held by the owner/author(s).

关键词： Tall buildings

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Enhancing IoT-Driven Smart Home Security and Automation with a GCN Model 3

Enhancing IoT-Driven Smart Home Security and Automation with...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Ganti, Sreelakshmi Vankayalapati, Ravi Kumar Krishnamoorthy, P. Thakare, Pallavi S. Nayak, Ullal Akshatha Vignesh, P. Geethanjali College of Engineering and Technology Department of Electronics and Communication Engineering Medchal India Equinix TX United States Sasi Institute of Technology & Engineering Department of Computer Science and Engineering West Godavari India Dr D. Y. Patil Institute of Technology Department of Information Technology Pimpri India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bangalore India Kcg College of Technology Department of Mathematics Chennai India

ISBN: (纸本)9798331508456

A network of computing devices and sensors, the Internet of Things (IoT) rapidly exchanges data to assess circumstances and propose new services. Intelligent home technologies rely on IoT. Contemporary smart home systems offer automatic lighting control, smoke detectors, temperature sensors, and smart lock installations. But these technologies raise serious privacy and security concerns. Security flaws let hackers access sensitive data, sendoff false alarms, and take control monitoring systems. Smart home technology is vulnerable to many threats because consumers are wary of it. Recommended steps are preprocessing, sensing, and model training. Preparing raw, unstructured data for analysis. DHT11 humidity and temperature sensors and HC-SR501 PIR motion detectors collect data. It trained the model using Multiscale DGCN data. Better than GCN and BiLSTM, Multiscale DGCN performs. It uses Internet of Things-based smart home security data well, with an average accuracy rate of 93.87%. Identifying outliers in sensor data solves major smart home security challenges. Increasing smart home system accuracy reduces security concerns and boosts user trust, which may drive adoption. © 2025 IEEE.

关键词： Smart homes

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Designing Low-Power Industrial Networks with the Internet of Things 5th

Designing Low-Power Industrial Networks with the Internet of...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Kannagi, A. Ansari, Tabish Nasurudeen Ahamed, N. Alam, Intekhab Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

The Internet of Things (IoT) revolution is evolving conventional commercial networks to fulfill the demands of present-day technological advancements. Low-electricity business networks are becoming increasingly vital to allow a green communique between linked systems and remote monitoring and management of gadgets. Designing and deploying low-electricity networks for business programs brings many demanding situations, which include energy control, conversation latency, and cease-to-give-up safety. This summary will offer a top-level view of the layout issues while implementing IoT and occasional-power business networks, in addition to the capability of high-quality practices for ensuring low-strength, comfortable, and reliable communications among connected devices. Furthermore, IoT techniques for localizing network accessibility, improving facts accuracy, and boosting community performance may be discussed. Subsequently, some of the trendy trends and trends in the industrial networking area could be highlighted. Low-power business networks are getting increasingly important to allow the green conversation of connected structures and remote tracking of devices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Capability Management Strength

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Multimodal Semi-active Dynamic Vibration Absorber Array with Ultra-Wideband Vibration Reduction Characteristics Based on Magnetorheological Elastomers

Multimodal Semi-active Dynamic Vibration Absorber Array with...

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international conference on Applied Nonlinear Dynamics, Vibration and control, ICANDVC 2024

作者： Ren, Hang Zhao, Dan Dong, Liqiang Xie, Binbin Liu, Shaogang Wang, Zezheng Jin, Yanji College of Mechatronic Engineering Harbin Engineering University Harbin150001 China North Automatic Control Technology Institute Taiyuan030000 China

ISBN: (纸本)9789819633166

The hydrodynamic excitation force of the propeller causes vibration in the ship's propulsion shaft system, which is transmitted to the hull through the bearing seat, causing sound radiation and reducing its acoustic stealth ability. Semi active control technology has broadband vibration control performance and is very suitable for vibration control of propulsion shaft hull coupling system. This article proposed a conceptual model of a multimodal semi-active dynamic vibration absorber array based on magnetorheological elastomer materials with ultra-wideband vibration control characteristics, to control the longitudinal and transverse vibration of propulsion shaft hull coupling system. An anisotropic magnetorheological elastomer with excellent magnetorheological properties was prepared, with a shear storage modulus between 0.244 ~ 1.56 MPa in the magnetic field range of 0 ~ 1000 mT. In addition, a vibration absorber array based on magnetorheological elastomers with a longitudinal natural frequency shift range of 671.8 Hz in the range of 20.1 ~ 728 Hz and a transverse natural frequency shift range of 7.8 ~ 189 Hz was also constructed. Finally, a simulation model of the propulsion shaft hull coupling system was established, and the proposed vibration absorber array was installed to verify its vibration reduction performance. The results showed that the transverse vibration VAL of the shaft ship coupling system decreased by 6 dB in the 7.9 ~ 199.1 Hz, and the longitudinal vibration VAL decreased by 5 dB in the 20.1 ~ 728 Hz. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine vibrations

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Robust Learning-Based Impedance control of Robotic Manipulators in Unknown Environments 16th

Robust Learning-Based Impedance Control of Robotic Manipula...

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16th APCA international conference on automatic control and Soft computing, controlO 2024

作者： Nazmara, G. Reza Neto, Pedro Aguiar, A. Pedro Faculty of Engineering SYSTEC-ARISE Research Center for Systems and Technologies University of Porto Porto4200-465 Portugal Department of Mechanical Engineering - POLO II University of Coimbra Coimbra3030-788 Portugal

ISBN: (纸本)9783031817236

This research focuses on implementing Machine Learning Impedance control for robot arms to ensure both effective and safe interaction within unknown environments. The designed Machine Learning system comprises two key steps. Firstly, a Neural Network Classifier framework categorizes attributes by assigning labels to various impedance behaviors. Secondly, employing a regularization technique, the system determines the appropriate impedance behavior as a command signal for the internal control loop. Utilizing a backstepping-based algorithm alongside Taylor’s parameter adaptation within the control loop facilitates achieving effective interaction. To validate the efficacy of this control approach, the proposed method undergoes testing and simulation on a two-link robot manipulator across varying environmental stiffness levels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robotic arms

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Radius-Based Authentication: UFrames’ Novel Approach to Prevent Account Sharing 5th

Radius-Based Authentication: UFrames’ Novel Approach to Pre...

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5th international conference on computing and Network Communications, CoCoNet 2023

作者： Vardhan, D. Sai Tanmayi, Doddapaneni Rakesh, Kakaraparthi Sreevidya, B. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819745395

OTT platforms such as Netflix and Amazon Prime have been in the market for more than a decade. They have come to full-fledged usage especially during the lockdown. The main reason for their magnanimous success is ease of use and feasibility to access unlimited content anywhere and anytime with basic Internet connection. The features in OTT have made accessing the content and viewing the content easy and delightful for the users. However, the "sharing account" option enabled for a single user to access the content from multiple devices is instead being used to share one account among multiple users, negatively affecting the business of OTT creators. In this paper, the focus is to build an OTT platform that proves advantageous to both users and providers. Besides the prevailing features that are found across platforms, including sign-in and sign-up functionalities, categorized content selection, and payment options, the proposed system enables automatic sign-out when the non-primary device strays beyond range from the primary device, content uploading, and more. Furthermore, there is a proposal to implement a GPS technology-based mechanism aimed at preventing users from sharing their accounts with others. The proposed approach conceptually and experimentally proves to be beneficial to the OTT platforms from a business perspective. The scheme is able to prevent users from sharing the account beyond a geographical limit, thus improving the profit. At the same time, these stringent requirements are developed in such a way that a balance is made between user experience, ease of use experience along with improved security measures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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Application of Recurrent Neural Networks for Predictive Modeling of electrical Submersible Pumps in Oil Extraction 11

Application of Recurrent Neural Networks for Predictive Mode...

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11th Vienna international conference on Mathematical Modelling, MATHMOD 2025

作者： Rebello, Carine Menezes Costa, Erbet Almeida Luiz De Abreu, Odilon Santana Reges, Galdir Viena, Vinicius Guimares Mendes, Tefilo Paiva Fontana, Marcio Ribeiro Bacellar, Francisco Raphael Ribeiro, Marcos Pellegrini Foresti, Bernardo Pereira Schnitman, Leizer Nogueira, Idelfonso B.R. Department of Chemical Engineering Norwegian University of Science and Technology Norway Department of Chemical Engineering Federal University of Bahia Bahia Brazil Department of Electrical and Computer Engineering Federal University of Bahia Bahia Brazil CENPES Petrobras R&D Center Ilha do Fundo Rio de Janeiro Brazil

This study presents a mathematical modeling framework that leverages recurrent neural networks (RNNs), specifically echo state networks (ESNs) and long short-Term memory (LSTM) architectures, for the predictive modeling of electrical submersible pumps (ESPs) in offshore oil extraction operations. Utilizing real operational data from an offshore oil field, the research addresses the inherent complexity and nonlinear dynamics of ESP systems by employing these recurrent structures to capture and represent the temporal dependencies within the data. Key challenges, such as data noise, variability, and limited diversity, are systematically tackled to ensure robust dynamic modeling. A comparative analysis evaluates the performance of ESN and LSTM models under these constrained data conditions, aiming to identify the superior model in terms of predictive accuracy and resilience. The modeling approach emphasizes the formulation, parameterization, and validation processes essential for effective ESP optimization and control in real-world industrial settings. Findings reveal the distinct strengths and limitations of each RNN variant when applied to offshore operational data. Copyright © 2025 The Authors.

关键词： Offshore oil well production

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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

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2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

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