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Air Purification robotics using Cloud and Deep Q Networks for Autonomous systems 1

Air Purification Robotics using Cloud and Deep Q Networks fo...

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1st international conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart systems, ISTEMS 2024

作者： Reddy, Elangovan Guruva Damodhar, T. S. Balaji Yuvaraj, S. Vathani, B. Santha Sivakumar, S. Malathi, N. Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram India Madanapalle Institute of Technology and Science Department of Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Mahalashmi Women's College of Arts and Science Department of Management Studies Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

Rising urban air pollution poses serious health dangers. Through robots, cloud computing, and deep reinforcement learning, this research proposes a new air purification method. The suggested autonomous system analyzes real-time air quality parameters and optimizes air purification tactics using cloud-based data processing. Advanced sensors and purification processes allow robotic agents to intelligently adapt to shifting pollution patterns in dynamic settings. Robots use Deep Q Networks (DQN) to learn and change their purifying tactics based on past data and environmental input. Cloud computing and deep learning improve air purification efficiency and enable real-time pollution response decision-making. The autonomous system is scalable and adaptable to varied urban environments since it requires little human involvement. The cloud-based design lets autonomous agents communicate and coordinate to improve air quality. Experimental findings show that the suggested strategy improves air purification over previous approaches. This system advances environmental robotics and lays the groundwork for intelligent, autonomous systems that handle urban air quality and pollution issues. © 2024 ieee.

关键词： robotics

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A toolbox for neuromorphic perception in robotics 8

A toolbox for neuromorphic perception in robotics

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8th international conference on Event-Based Control, Communication and signal processing (EBCCSP)

作者： Dupeyroux, Julien Stroobants, Stein de Croon, Guido C. H. E. Delft Univ Technol Micro Air Vehicle Lab MAVLab Delft Netherlands Delft Univ Technol Fac Aerosp Engn Micro Air Vehicle Lab Delft Netherlands

ISBN: (数字)9781665453493

ISBN: (纸本)9781665453493

The third generation of artificial intelligence (AI) introduced by neuromorphic computing is revolutionizing the way robots and autonomous systems can sense the world, process the information, and interact with their environment. Research towards fulfilling the promises of high flexibility, energy efficiency, and robustness of neuromorphic systems is widely supported by software tools for simulating spiking neural networks, and hardware integration (neuromorphic processors). Yet, while efforts have been made on neuromorphic vision (event-based cameras), it is worth noting that most of the sensors available for robotics remain inherently incompatible with neuromorphic computing, where information is encoded into spikes. To facilitate the use of traditional sensors, we need to convert the output signals into streams of spikes, i.e., a series of events (+ 1,- 1) along with their corresponding timestamps. In this paper, we propose a review of the coding algorithms from a robotics perspective and further supported by a benchmark to assess their performance. We also introduce a ROS (Robot Operating System) toolbox to encode and decode input signals coming from any type of sensor available on a robot. This initiative is meant to stimulate and facilitate robotic integration of neuromorphic AI, with the opportunity to adapt traditional off-the-shelf sensors to spiking neural nets within one of the most powerful robotic tools, ROS.

关键词： Neuromorphic engineering Neural networks signal processing algorithms signal processing Robot sensing systems Sensor systems Robustness

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An Efficient Learning Algorithm for Phase Shift Optimization in RIS-Aided MISO Wireless systems 15

An Efficient Learning Algorithm for Phase Shift Optimization...

引用

15th ieee international conference on Wireless Communications and signal processing, WCSP 2023

作者： Tian, Jiawen Guan, Wenwen Li, Xiao Tsiftsis, Theodoros A. Liu, Hongwu Jinan University School of Intelligent Systems Science & Engineering Zhuhai China Southeast University National Mobile Communications Research Laboratory Nanjing China University of Thessaly Dept. of Informatics & Telecommunications Lamia Greece Shandong Jiaotong University School of Information Science & Electrical Engineering Jinan China

ISBN: (纸本)9798350324662

Due to dynamic change of the wireless environment, devising the local phase shift matrix for reconfigurable intelligent surface (RIS) can be a thought-provoking task. Therefore, in this paper, we come up with a deep reinforcement learning (DRL)-based beamforming optimization algorithm for RIS-aided multiple-input single-output communication systems. To be precise, we exploit a DRL-based structure based on twin-delayed-deep-deterministic-policy-gradient (TD3) algorithm to automatically adjust the phase shift of each unit at RIS to maximize the downlink received signal-to-noise ratio (SNR). Simulation results show that the raised TD3 algorithm performs a higher received SNR than the conventional DRL algorithm with a reduced running time. © 2023 ieee.

关键词： Deep reinforcement learning (DRL) passive phase shift design reconfigurable intelligent surface (RIS) TD3 algorithm

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Research on the treatment method of COVID-19 sequelae with intelligent soft manipulator based on haptic feedback

Research on the treatment method of COVID-19 sequelae with i...

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2023 ieee international conference on Real-Time Computing and robotics, RCAR 2023

作者： Huang, Zixin Li, Xinpeng Deng, Jie Li, Yun Wan, Xiao Wang, Lejun Nankai University School of Electrical and Information Engineering Wuhan Institute of Technology Collage of Artificial Intelligence China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan China Hubei University of Chinese Medicine Hubei Provincial Hospital of Traditional Chinese Medicine Wuhan China Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing China

ISBN: (纸本)9798350327182

Soft manipulator has the advantages of high degree of freedom, strong adaptability, high safety, good man-machine interaction ability, and has a wide range of application prospects in the medical field. In the treatment of traditional Chinese medicine, acupoint therapy can effectively treat diseases and avoid the side effects of drug therapy. Acupoint therapy can be used to treat the sequelae of the elderly in the post-COVID-19 era. Inspired by this, this paper designs and produces an intelligent soft manipulator. Such manipulator can realize the function of acupoint physiotherapy for part of human body through the pressure drive, and realize the real-time feedback function through the sensor to feedback the pressure signal to the upper position machine. Such manipulator provides a feasible treatment method for the sequelae of COVID-19 through physical therapy. © 2023 ieee.

关键词： COVID-19

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Isolation Forest Based Anomaly Detection and Fault Localization for Solar PV System 3

Isolation Forest Based Anomaly Detection and Fault Localizat...

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3rd international conference on robotics, Electrical and signal processing Techniques, ICREST 2023

作者： Kabir, Shaharier Shufian, Abu Zishan, Md. Saniat Rahman American International University-Bangladesh Department of Electrical and Electronic Engineering Dhaka1229 Bangladesh Institute of Energy Technology Chittagong University of Engineering & Technology Chattogram4349 Bangladesh

ISBN: (纸本)9798350346435

The decrease in fossil fuel reserves has prompted a global move toward distributed energy resources. For this reason, solar PV power generation has recently gained much attention as a feasible renewable energy source. However, large-scale generation is challenging if there are anomalies in individual solar PV panels. This will reduce the efficiency of the PV system and create a potential fire hazard. In this perspective, the anomaly detection technique discloses system anomalies accurately and effectively. Identified anomalies will localize the event for an improved generation. This paper addresses the performance analysis of using the isolation forest technique to identify anomalies in the PV system and the rule-based fault localization technique to identify defective panel events. In the developed model, the isolation forest technique found around 453 anomalies in 45,740 observations, and approximately six panels indicated a fault in the system. The accuracy score is found to be approximately 0.9886. The proposed fault detection method will help detect the faults in solar power systems. © 2023 ieee.

关键词： Fault detection

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Protecting Multiple Types of Privacy Simultaneously in EEG-Based Brain-Computer Interfaces

Protecting Multiple Types of Privacy Simultaneously in EEG-B...

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2024 ieee international conference on systems, Man, and Cybernetics, SMC 2024

作者： Meng, Lubin Jiang, Xue Jia, Tianwang Wu, Dongrui School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Wuhan China

ISBN: (纸本)9781665410205

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred input signal in non-invasive BCIs, due to its convenience and low cost. EEG-based BCIs have been successfully used in many applications, such as neurological rehabilitation, text input, games, and so on. However, EEG signals inherently carry rich personal information, necessitating privacy protection. This paper demonstrates that multiple types of private information (user identity, gender, and BCI-experience) can be easily inferred from EEG data, imposing a serious privacy threat to BCIs, To address this issue, we design perturbations to convert the original EEG data into privacy-protected EEG data, which conceal the private information while maintaining the primary BCI task performance. Experimental results demonstrated that the privacy-protected EEG data can significantly reduce the classification accuracy of user identity, gender and BCI-experience, but almost do not affect at all the classification accuracy of the primary BCI task, enabling user privacy protection in EEG-based BCIs. © 2024 ieee.

关键词： Differential privacy

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Generating Manipulation Sequences using Reinforcement Learning and Behavior Trees for Peg-In-Hole Task

Generating Manipulation Sequences using Reinforcement Learni...

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2022 ieee international conference on systems, Man, and Cybernetics, SMC 2022

作者： Xu, Jiahua Lin, Yunhan Zhou, Haotian Min, Huasong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Reinforcement Learning (RL), a method of learning skills through trial-and-error, has been successfully used in many robotics applications in recent years. We combine manipulation primitives (MPs), behavior trees (BTs), and reinforcement learning to propose an algorithm for peg-in-hole tasks, which speeds up the convergence of the RL model and enhance the adaptability of the dynamic environment. Manipulation primitives are used as actions for RL, which can reduce the gap between control instruction and robotic actions and speed up the convergence of the RL model. Behavior trees are used as robot behavior control, which makes robots can actively adapt to the changes in the environment. In experiments, RL-BT, from the combination of RL and BT, is designed for the peg-in-hole task in the Gazebo simulation environment by using UR5 as the actuator. The experiments are conducted on a simple peg and a complex multi-hole peg by three aspects, which include convergence speed verification experiment, adaptability of dynamic environment experiment, and algorithm robustness experiment. The experiment result proves that our RL-BT can speed up the convergence and adapt to the changes in the environment. © 2022 ieee.

关键词： Reinforcement learning

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Enhanced VLSI Speech Enhancement systems Utilizing Adaptive LMS Filters and Machine Learning Techniques for Echo Cancellation

Enhanced VLSI Speech Enhancement Systems Utilizing Adaptive ...

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2024 international conference on intelligent systems for Cybersecurity, ISCS 2024

作者： Reddy, V. K. Vasudeva Kavya, Yeturu Prasad, Patchuru Krishna, Kanchi Bala Harika, D. Tirupati India School of Engineering Electronic Communication Engineering Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798350375237

The project 'Enhanced VLSI Speech Enhancement systems Utilizing Adaptive LMS Filters and Machine Learning Techniques for Echo Cancellation' integrates advanced signal processing and machine learning to improve speech quality in Very Large-Scale Integration (VLSI) environments. The cornerstone of this system is the innovative use of a Convolutional Neural Network (CNN) to dynamically predict the parameters for an adaptive Least Mean Squares (LMS) filter, which is then simulated in a Verilog environment. The methodology begins in a Matlab environment, where audio processing libraries are used to collect and preprocess a vast array of audio data, creating a diverse training set. A CNN model is meticulously developed to interpret this data and forecast optimal filter settings. These predicted parameters are then translated into a hex file format, compatible with the ModelSim environment for hardware simulation. In parallel, a Verilog model of the LMS filter is crafted, designed to adapt its parameters in real-time, simulating echo cancellation within a VLSI context. The hex audio files are processed through the filter in ModelSim, with the CNN-derived parameters guiding the adaptive filtering process. Post-simulation, the output is converted back to MP3 format using a Matlab script for practical evaluation of the system's performance. The project showcases a unique synergy between software intelligence and hardware functionality, culminating in a highly adaptive system capable of real-time speech enhancement. This system not only achieves superior echo cancellation but also lays the groundwork for sophisticated, real-world VLSI applications where audio clarity is paramount. © 2024 ieee.

关键词： Machine learning

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Unveiling Effectiveness: Advanced Vehicle Tracking and Detection systems in Action 4

Unveiling Effectiveness: Advanced Vehicle Tracking and Detec...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Aeri, Manisha Purohit, Kamlesh C. Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350387490

This comprehensive paper delves into recent break-throughs in intelligent Transportation systems (ITS), with a focus on advancements in traffic control and short-term traffic flow prediction. The application of Reinforcement Learning (RL) for adaptive traffic signal control is explored, showcasing its efficacy in dynamically optimizing traffic flow. Short-term traffic flow prediction algorithms, emphasizing accuracy and adaptability, leverage a macroscopic urban road network model enriched with real-time loop detector data. The proposed method demonstrates accurate predictions through rigorous simulation experiments on the CORSIM platform. Additionally, the integration of a Graph Neural Network (GNN) for spatial-temporal traffic flow prediction captures intricate spatial dependencies and temporal patterns in interconnected road networks. Addressing the challenges of rising urban populations and increased vehicular density, the paper proposes a real-time road traffic density calculation system using live camera feeds and AI. This system, employing image processing and the YOLO (You Only Look Once) algorithm, dynamically adjusts traffic lights based on vehicle density to alleviate congestion, enhance transit speed, and reduce pollution. Computer Vision and the YOLO algorithm facilitate live traffic density estimation and vehicle categorization, optimizing green signal timings for more effective traffic flow management compared to static systems. This synthesis of RL, network models, prediction algorithms, and real-time density calculations contributes to augmenting traffic management and forecasting capabilities in intelligent Transportation systems. © 2024 ieee.

关键词： Graph neural networks

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Automated Sewer Pipe Defect Classification Using Inspection Robot Imagery and Deep Neural Network 12

Automated Sewer Pipe Defect Classification Using Inspection ...

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12th RSI international conference on robotics and Mechatronics, ICRoM 2024

作者： Goharinezhad, Saleh Hadi, Alireza Mirzaei, Sayeh School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology University of Tehran Advanced Service Robots Laboratory Department of Mechatronics Tehran Iran School of Engineering Science College of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9798331529734

Sewer systems play a critical role in transporting wastewater. Efficient maintenance requires accurate identification of sewer pipe defects, traditionally performed through manual interpretation of Closed-Circuit Television (CCTV) images from robot inspections. This manual process is time-consuming, labor-intensive, and susceptible to human error. While conventional computer vision methods (machine learning methods) have been explored, they often involve extensive image pre-processing and intricate feature extraction. In this study, we propose an automated approach for classifying sewer pipe defects in Iran using deep learning, specifically leveraging the ResNet50 architecture for defect classification. We applied this deep convolutional neural network (CNN) model to automatically detect three common defect types as root intrusions, deposits, and open joints within a dataset 4630 images extracted from CCTV inspection videos of sewer pipes. Our model achieved an average testing accuracy of 96%, highlighting its potential for automating the interpretation of sewer CCTV footage. © 2024 ieee.

关键词： Convolutional neural networks

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