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检索条件"任意字段=IEEE International Conference on Robotics, Intelligent Systems and Signal Processing"

共 7145 条记录，以下是61-70 订阅

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Parallel Simulation of Acoustic Characteristics for Near-Surface Targets

Parallel Simulation of Acoustic Characteristics for Near-Sur...

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2024 ieee international conference on Control Science and systems Engineering, ICCSSE 2024

作者： Li, Hao Hu, Jincheng Ji, Ziyu Wu, Xianyun School of Telecommunications Engineering Xidian University Xi'an China

ISBN: (纸本)9798331517199

This paper proposes a parallel acoustic characterization simulation method for near-surface targets. This method is based on the Shooting and Bouncing Ray (SBR)) method and allows for the high-precision simulation of near-surface target acoustic characteristics. Furthermore, it enables the acceleration of the simulation computation process of complex shape near-surface targets by utilizing a Graphics processing Unit (GPU) platform. By employing an equivalent decomposition of near-surface target acoustic field echoes, the task scale of 10 billion acoustic beam lines is exceeded on a multi-GPU cluster, thereby enabling the rapid and precise simulation of the acoustic characteristics of large near-surface targets. This approach has significant potential for a wide range of applications. © 2024 ieee.

关键词： Graphics processing unit

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VGRISys: A Vision-Guided Robotic intelligent System for Autonomous Instrument Calibration

VGRISys: A Vision-Guided Robotic Intelligent System for Auto...

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2023 ieee international conference on robotics and Biomimetics, ROBIO 2023

作者： Li, Zhenqi Wang, Hewei Li, Yijie Dev, Soumyabrata Zuo, Guoyu Beijing University of Technology Beijing-Dublin International College Beijing100124 China University College Dublin School of Computer Science Dublin Ireland Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China

ISBN: (纸本)9798350325706

With the increasing sophistication of musical instruments and the importance of precise tuning, a novel and efficient approach to instrument calibration is of significant necessity. This paper presents VGRISys, an innovative system leveraging robotics and artificial intelligence to facilitate the intricate process of instrument tuning. VGRISys incorporates a multi-axis robotic arm, meticulously engineered for fine-tuned physical interaction with various musical instruments, alongside an intelligent system that guides the robotic arm using real-time audio feedback analysis. In addition, it employs advanced signal processing and machine learning algorithms, allowing the system to identify pitch discrepancies and respond with appropriate corrective action. VGRISys remarkably reduces the time required for instrument tuning, thus improving efficiency and democratizing access to high-quality tuning. Furthermore, the system's machine learning capabilities allow it to refine its tuning process over repeated operations, providing a scalable solution to the ever-evolving complexities of instrument tuning. The exploration and development of VGRISys contribute significantly to the wider fields of robotics, artificial intelligence, and music technology. © 2023 ieee.

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Advanced Power Quality Management Through a PID-Based Dynamic Voltage Restorer 4

Advanced Power Quality Management Through a PID-Based Dynami...

引用

4th international conference on robotics, Electrical and signal processing Techniques, ICREST 2025

作者： Shib, Sowrov Komar Rahman, Md Tanvir Dipto, Durjoy Roy Shufian, Abu Hossain, Md Sajid American International University-Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798331530938

Voltage sags caused by faults in power systems pose a significant challenge to maintaining power quality and stability. This study investigates the performance of a Dynamic Voltage Restorer (DVR) equipped with a Proportional-Integral-Derivative (PID) controller in mitigating voltage sags. Simulations in MATLAB Simulink optimization demonstrate the effectiveness of DVR model in restoring nominal voltage levels during double-line-to-ground (DLG) faults. The DVR compensates for voltage sags by injecting corrective voltage, ensuring uninterrupted power delivery and stability. Analysis of input voltage and RMS voltage behavior shows a rapid recovery from a severe sag of ±100 V to nominal levels of ±325 V and 230 V RMS, respectively. The PID controller ensures smooth recovery without overshoots or oscillations, validating the precision level of the proposed model. The findings underscore the critical role in maintaining reliable power quality under fault conditions of DVR system, highlighting its potential as a robust solution for voltage sag mitigation in modern power systems. © 2025 ieee.

关键词： Uninterruptible power systems

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Analysing SCI Patients' EEG signal Using Manifold Learning Methods For Triple Command BCI Design 18

Analysing SCI Patients' EEG Signal Using Manifold Learning M...

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18th international conference on Innovations in intelligent systems and Applications (INISTA)

作者： Kucukselbes, Hezzal Sayilgan, Ebru Izmir Univ Econ Dept Elect & Elect Engn Izmir Turkiye Izmir Univ Econ Dept Mechatron Engn Izmir Turkiye

ISBN: (纸本)9798350368130

This study analyzed EEG signals from patients with spinal cord injuries by examining five different hand-wrist movements. The signal processing steps designed for an automatic and robust EEG-based BCI system were applied sequentially. Initially, 37 different features in the time, frequency, and time-frequency domains were extracted from the preprocessed signal. After trying widely used Manifold Learning (ML) methods in the literature, including t-Distributed Stochastic Neighbor Embedding (t-SNE), Local Linear Embedding (LLE), Multi-Dimensional Scaling (MDS), and ISOmetric Mapping (ISOMAP), we attempted the Spectral Embedding method, which has not yet been utilized in EEG signal analysis. The signals were then classified using three different machine-learning algorithms. The study compared classification performance using the accuracy metric. A multi-class classification method was employed specifically the triple classification method. The most successful performance was achieved by using the ISOMAP machine learning method and kNN classifier for the Pronation-Palmar Grasp-Hand Open combination, with an accuracy of 0.993 +/- 0.016. Other methods used were t-SNE, MDS, LLE, and Spectral Embedding, respectively. Regarding classifiers, the kNN, SVM, and Naive Bayes algorithms were found to be successful in that order. Based on these results, we propose a suitable methodology for designing a robust BCI system.

关键词： spinal cord injury eeg manifold learning bci spectral embedding machine learning

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Stabilization Through Contraction Based Output Feedback Control for a Class of Nonlinear systems

Stabilization Through Contraction Based Output Feedback Cont...

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2024 ieee international conference on intelligent signal processing and Effective Communication Technologies, INSPECT 2024

作者： Ranjan, Ravi Kumar Sharma, Bharat Bhushan Prajapati, Dipak J. National Institute of Technology Department of Electrical Engineering Hamirpur India GEC Modasa Mathematics Department Modasa India

ISBN: (纸本)9798350379525

This paper explores output feedback control strategies for stabilizing a particular category of nonlinear systems. Utilizing tools grounded in contraction theory, this approach offers a streamlined method to achieve stabilization of the targeted category of nonlinear systems. Certain assumptions are imposed on the associated nonlinearity of the systems to attain simple output feedback based control function. The methodology involves segmenting system dynamics into measurable and un-measurable states. A Suitable virtual system for the actual system is considered afterwards, which is stabilized with developed conteoller to ensure exponential stabilization of actual system states. Stabilization criteria are articulated in terms of controller gains. The approach is further extended to address stabilization under parametric uncertainties. Additionally, systematic adaptation laws for parameter estimation and stabilization are developed and discussed in detail. The efficacy of the developed stabilization scheme using output feedback is successfully validated using computational simulations. © 2024 ieee.

关键词： Stabilization

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signal processing for Acoustic Emission Signature Analysis and Defect Detection 26

Signal Processing for Acoustic Emission Signature Analysis a...

引用

26th international conference on Digital signal processing and its Applications, DSPA 2024

作者： Altay, Yeldos A. Kuzivanov, Dmitry O. Altay, Dinara A. Fedorov, Alexey V. Satbayev University Department of Robotics and Technical Means of Automation Almaty Kazakhstan ITMO University Faculty of Control Systems and Robotics Saint-Petersburg Russia Military Engineering Institute of Radio Electronics and Communications Department of Natural Sciences Almaty Kazakhstan

ISBN: (纸本)9798350361728

This article presents the results of implementing a digital signal processing method for analyzing the acoustic emission (AE) signature and detecting defects such as cracks and pores. The signal processing method is based on a cascade connection of digital high-pass filters and ensures the selection of informative signals and increases the signal-to-interference ratio. In order to control the formation of defects in the process of selective laser melting, the paper presents the results of experimental testing of the cascade filtration method for detecting defects in the internal structure such as cracks and pores. The signal components recorded during the development of defects during the growth of additive manufacturing products were extracted. AE signatures were analyzed and statistical relationships were assessed. The relationship between the parameters of the AE signal and the values of laser radiation power has been identified, which characterizes the process of defect formation. © 2024 ieee.

关键词： High pass filters

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Comparative Image processing and Object Detection in Aerial Search and Rescue 1

Comparative Image Processing and Object Detection in Aerial ...

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1st ieee international conference on Cognitive robotics and intelligent systems, ICC - ROBINS 2024

作者： Rawat, Sushil Rawat, Abhay Singh Singh Mewad, Vikas Vaidya, Himadri Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350372748

Aerial search and response plays an important role in finding and rescuing persons in need. Unmanned Aerial Vehicle (UAV) -acquired aerial images provide an intensive profile search area and facilitate identification of potential targets. To improve the effectiveness of aircraft Search and Rescue missions, this study compares many object detection algorithms and image processing methods. The investigated object detection methods are Mask R-CNN, SSD, and YOLOv8, and image processing techniques are contrast improvement and image brightness control. The effectiveness of these strategies has been evaluated by using real aerial images that include different targets such as humans, vehicles, and fire. The results show how well the object detection algorithms work to accurately distinguish targets, and how an attractive image processing approach improves the image performance of the best object detection model, as well as how the performance of the best object detection model with image processing can improve the performance of model. This research shows that combining image processing (contrast and brightness) with an optimal object detection algorithm (Mask R-CNN) significantly improves target detection in aerial search and rescue. While only Mask R-CNN performs best, adding image processing increases its accuracy to 0.92, mAP to 0.89, and makes it more reliable in finding people in need. This shows the potential of this joint approach to save people in air rescue missions. © 2024 ieee.

关键词： Antennas

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Sound Source Localization and Classification for Emergency Vehicle Siren Detection Using Resource Constrained systems 34

Sound Source Localization and Classification for Emergency V...

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34th international conference Radioelektronika (RADIOELEKTRONIKA)

作者： Munirathinam, Rakshaa Vitek, Stanislav Czech Tech Univ Dept Radioelect Prague Czech Republic

ISBN: (纸本)9798350362169

The present work introduces an intelligent system for sound source localization and classification, focusing on the critical task of detecting emergency vehicle sirens. Leveraging the STM32F411RE microcontroller and SPH0645LM4H microphones, the methodology combines generalized cross-correlation with phase transformation for precise sound source localization, alongside Mel Frequency Cepstral Coefficients (MFCC) for robust signal processing. A neural network model, expertly trained and deployed on the microcontroller, enables real-time sound classification. The integrated printed circuit board (PCB) with eight LEDs provides visual cues, enhancing emergency vehicle detection. This work contributes significantly to sound localization, digital signal processing, and embedded systems, ultimately improving safety and responsiveness in emergency scenarios. The work is vital for enhancing road safety and emergency response efficiency by providing an intelligent system that accurately identifies and localizes the sound of emergency vehicle sirens, minimizing response times and potential hazards for all road users.

关键词： Embedded System Sound localization Sound Classification MFCC Convolutional neural network Internet of Things

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Multi-Level Robotic Pouring Using Offline Reinforcement Learning

Multi-Level Robotic Pouring Using Offline Reinforcement Lear...

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2024 ieee international conference on robotics and Biomimetics, ROBIO 2024

作者： Zhang, Xiaoxiong Liu, Junwei Zhang, Wei School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Shenzhen518055 China

ISBN: (纸本)9781665481090

This paper presents a novel learning-based method for a robotic manipulator to achieve liquid pouring across different liquid levels using only visual sensors. Previous works have relied on either online reinforcement learning or imitation learning, which are limited by sim-to-real gaps or data efficiency. In this paper, we propose to combine supervised learning and offline reinforcement learning, utilizing a human demonstration dataset containing only successful pouring at the highest level. Specifically, our approach employs supervised learning for a visual classifier, transforming the visual input into a categorical distribution over liquid levels. The offline reinforcement learning method is applied to a binary-conditioned control policy, which takes a binary signal and the robot's proprioception as inputs to determine the action. The binary signal indicates whether the desired liquid level has been reached based on the target liquid level. Through experiments, our method demonstrates superior effectiveness compared to a state-of-the-art imitation learning algorithm. Moreover, tests in unseen scenarios and multiple liquid level commands verify the generalization and transferability of our approach. © 2024 ieee.

关键词： Self-supervised learning

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Advanced Security Mechanism for Real-Time 5G Healthcare Communication

Advanced Security Mechanism for Real-Time 5G Healthcare Comm...

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2024 ieee international conference on intelligent signal processing and Effective Communication Technologies, INSPECT 2024

作者： Meena, Vaishali Indra, Gaurav Indira Gandhi Delhi Technical University for Women Department of IT India

ISBN: (纸本)9798350379525

Securing smart healthcare systems is of utmost importance in the ever-changing realm of 5G communications. Existing security solutions frequently encounter challenges when safeguarding sensitive data in healthcare devices with limited resources, resulting in vulnerabilities in patient care networks. The paper presents a robust cybersecurity architecture for 5G-enabled healthcare. It employs Lightweight Cryptography (LWC) and Zero Trust Architecture to guarantee secure communication across the entire process, from device production to patient interaction. The suggested system exhibits robustness in the face of attacks such as replay, man-in-the-middle, and impersonation. Although communication costs have only increased by a modest 11%, the framework manages to decrease computing overhead by 12%, improving energy efficiency and protecting significant healthcare infrastructure. © 2024 ieee.

关键词： 5G mobile communication systems

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