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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

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ADRC Controller Design for Autonomous Vehicles Queuing Systems in Zero-Trust Environment 6

ADRC Controller Design for Autonomous Vehicles Queuing Syste...

引用

6th international conference on robotics, Control and Automation engineering, RCAE 2023

作者： Li, Xinrong Na, Yuhong Huang, Darong Zhu, Ling School of Mathematics and Statistics Chongqing Jiaotong University Chongqing China Anhui University Engineering Research Center of Autonomous Unmanned System Technology of Ministry of Education Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology Hefei China School of Information Science and Engineering Anhui University Chongqing Jiaotong University Engineering Research Center of Autonomous Unmanned System Technology of Ministry of Education Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology Chongqing Hefei China School of Information Science and Engineering Chongqing Jiaotong University Chongqing China

ISBN: (纸本)9798350315301

In recent years, the integration of advanced technologies in the Intelligent Connected Vehicle (ICV) field has attracted many scholars' interests, and their safety and efficiency improved significantly. However, with the increasing number of cyber attacks and security threats in modern vehicle networks, the communication environment of vehicle is deteriorated. To solve this problem, studying the topics in zero-trust environment instead of the classic full-trust environment is becoming a effective approach. Because the traditional centralized control system is considered to be insufficient to ensure the security and reliability of ICVs, the distributed system is applied to research the ICVs. In this paper, an active disturbance rejection control (ADRC) is utilized to decrease the unknown input perturbations brought by zero-trust environment to the ICV queuing system. And it also need to ensure the stability of the vehicle formation, and to mitigate the disturbances in the dynamic driving environment. The ADRC utilizes the principles of zero-trust architecture and active disturbance rejection control to enhance the robustness and security of the queue management of ICV s. In the end, the performance of the proposed ADRC is evaluated through extensive simulations and analysis, which is to demonstrate its effectiveness and superiority in maintaining a safe and efficient vehicle queue. © 2023 IEEE.

关键词： Autonomous vehicles

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Improving Adversarial Attacks On Graph Neural Networks With Honey Badger Optimization And Efficient Parallel Defense Strategies

Improving Adversarial Attacks On Graph Neural Networks With ...

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2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Singla, Abhishek Sunil, M.P. Dongre, Deepika Babu, V. Dinesh Mahajan, Sulabh Sharmila, P. Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Engineering and Technology Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Institute of Technology Department of Information Technology Coimbatore641105 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of S&h Chennai127 India

ISBN: (纸本)9798350388602

It is becoming very useful to learn from graphstructured data using Graph Neural Networks (GNNs);however, these networks are vulnerable to adversarial attacks. There are numerous other distinct ways in which these assaults can be performed, such as evasion, poisoning or perturbation. When such a situation happens the performance of the GNN models drastically reduces with high possibility of wrong forecasts or classifications which may not be required. Regulus and TensorMap are quite insecure;GNNs are more secure in areas of high risk such as healthcare, cybersecurity and financial institutions, thus, it is mandatory to secure GNNs. The goal of this work is to present a wide range of possible adversarial perturbations to provide a broader evaluation of the vulnerabilities of GNNs. In order to make larger-scale GNNs more immune to adversarial effects, the authors present a parallel defensive framework that includes adversarial training, graph sanitization, and resilient aggregation strategies. By incorporating this framework, the model resistance brought about by different adversarial approaches is significantly enhanced, and this is achieved alongside a preservation of adequate performance on classic tasks. © 2024 IEEE.

关键词： Adversarial machine learning

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Metro Route Optimization for Urban Public Transportation Using Q-Learning

Metro Route Optimization for Urban Public Transportation Usi...

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2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Dhiman, Richa Sushma Singh, Nongmeikapam Thoiba Grover, Richa Chandigarh University Department of Computer Science And Engineering Punjab Mohali India University Institute of Computing Chandigarh University Punjab Mohali India

ISBN: (纸本)9798350388602

Nowadays, efficient public transport is crucial, particularly in those where pollution and traffic congestion are significant issues. This study introduces a great approach to using the learning algorithm like Q-learning for finding the best route for passengers. Metro networks, which link many towns and areas, are essential to the survival of many cities. We present an approach that treats the whole metro map as a graph with edges connecting nodes. We enable the system to learn the most efficient path for passengers based on the rewards and punishments associated with different behaviors through the use of a Q-learning algorithm, which is also a reinforcement learning algorithm. As we know, this learning is stateless, rule-free, and feedback-based, as opposed to traditional routing techniques;it can learn the best rules for urban routing. The goal is to shorten the overall time taken to travel by using fewer stops. Through simulations and use of real-time data, we demonstrate the effectiveness of our method for determining the best subway routes, helping to deliver better public transportation and availability for commuters in urban environments. © 2024 IEEE.

关键词： Traffic congestion

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Deep Reinforcement Learning Based Backstepping Control for Underactuated AUV 8

Deep Reinforcement Learning Based Backstepping Control for U...

引用

8th IEEE international conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Chen, Hongxuan Bo, Tiechao Wang, Shufeng Tang, Guoyuan School of Naval Architecture and Ocean Engineering Huazhong University of Science and Technology CO Wuhan430074 China China Ship Development and Design Center CO Wuhan430074 China

ISBN: (纸本)9798350300178

This article renders the trajectory tracking of underactuated autonomous underwater vehicles (AUVs) in horizon plane. Considering it is hard to obtain dynamic model, a model-free controller is proposed based on deep reinforcement learning (DRL). To accelerate the deployment, backstepping method is adopted to simplify the model. Then a DRL agent is trained by deep deterministic policy gradient (DDPG) for the convergence of velocity. Simulation and comparison to another model-free control, i.e. backstepping PID, and a model-parameter-free control are conducted, verifying the effectiveness of the proposed control scheme. © 2023 IEEE.

关键词： Autonomous underwater vehicles

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Effectiveness of Security Export Control Ontology for Predicting Answer Type and Regulation Categories 24

Effectiveness of Security Export Control Ontology for Predic...

引用

8th international conference on advances in Artificial Intelligence, ICAAI 2024

作者： Rzepka, Rafal Obayashi, Akihiko Faculty of Information Science and Technology Hokkaido University Sapporo Japan Faculty of Engineering Hokkaido University Sapporo Japan

ISBN: (纸本)9798400718014

In this paper we present results of our experiments investigating if an expert knowledge graph can improve Large Language Models accuracy in predicting correct answer labels and regulations related to the topic of security export control. As the lack of related data prevents machine-learning or fine-tuning approaches, we implement prompt expansion by searching most relevant nodes of the graph and adding the expanded context to the prompt. Results of our experiments show that the addition improved answer type selection but clearly hamper the capability of finding a correct regulation category. © 2024 Copyright held by the owner/author(s).

关键词： Knowledge graph

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Pipe-finder: Adaptive, Lightweight Pipe Robot Integrating Origami Anisotropic Stiffness Structure

Pipe-finder: Adaptive, Lightweight Pipe Robot Integrating Or...

引用

2023 IEEE international conference on robotics and Biomimetics, ROBIO 2023

作者： Chen, Yifei Yang, Deyu Liu, Ziyu Yang, Boyu Wang, Hongqiang Southern University of Science and Technology Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Shenzhen518055 China Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China Southern Marine Science and Engineering Guangdong Laboratory Guangzhou510000 China

ISBN: (纸本)9798350325706

Pipeline inspection poses challenges that require robots to navigate complex environments. However, existing pipeline robots with the rigid bodies often face limitations in terms of efficiency, flexibility, and adaptability considering the significant size variations and complex terrains in pipelines. This paper introduces a novel design of wheel pipeline robots that addresses these limitations by incorporating the Origami Anisotropic Stiffness Structure (OASS). Inspired by the desert iguana's skin, the OASS offers a unique combination of rigidity and flexibility in different directions, making it an ideal skeletal framework for the robot with only 146 g weight. By integrating the OASS with PLA and resin materials, we enhance the robot's support and enable it to effectively navigate diverse pipeline configurations. Experimental results demonstrate the robot's capability to maneuver through pipelines of different diameters, U-shaped turns, obstacles, and even vertical sections. This research provides a promising solution for inspection tasks in complex pipeline systems. © 2023 IEEE.

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Prediction of spam reviews using feature-driven opinion mining deep learning model 5th

Prediction of spam reviews using feature-driven opinion mini...

引用

5th international conference on advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Sharma, Surya Prakash Singh, Laxman Sharma, Nagesh Khalid, Abdul Tiwari, Rajdev Dr. APJ Abdul Kalam Technical University UP Lucknow India School of Computer Science and Engineering and IT Noida Institute of Engineering and Technology UP Gr. Noida India Krishana Institute of Engineering and Technology Ghaziahabad India CHEEK Edunix Pvt. Ltd UP Noida India

ISBN: (纸本)9781032642987

Online product reviews strongly influence consumers’ purchases. Manufacturers and sellers value customer reviews because they impact their businesses. Scammers and spammers can post more fake reviews to promote fake products or criticize rival brands to harm companies and deceive consumers. Such behavior is called "review spamming". Differentiating fake online forum reviews from real ones is a difficult and open research problem. This study presents a CNN model that classifies reviews as spam or nonspam using deep learning. With 92.18% accuracy, the proposed model outperforms the state of the art. This paper shows how machine learning models affect product quality. © 2024 The Author(s).

关键词： Deep learning

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Implementing SLAM for Robot Navigation with Lidar in ROS Platform 4

Implementing SLAM for Robot Navigation with Lidar in ROS Pla...

引用

4th IEEE international conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Verma, Gaurav Kumar Ray, Ranjit Swar, Surya Narayan Meena, Gagan Deep Dept. of Electrical Engineering National Institute of Technology Patna India Underwater Robotics & Autonomous Group CSIR-Central Mechanical Engineering Research Institute Durgapur India Department of Computer Science & Engineering Gandhi Institute of Engineering and Technology University Gunupur India

ISBN: (纸本)9798331528614

This research covers the implementation of SLAM (Simultaneous Localization and Mapping) with Ouster OS0 LiDAR (Light Detection and Ranging) sensor in the Robot Operating System (ROS1) Noetic. Using the high-frequency capabilities of the ouster, this method works towards mapping and localization in structured and unstructured environments. The paper explains the Ouster sensor integration in the ROS1 Noetic environment, providing information on the sensor calibration, data acquisition, and pre-processing. The ROS ecosystem in Ubuntu 20.04 is set to process the data streams coming from the ouster LiDAR sensor which is further utilised to create map and localize the robot. The Ouster LiDAR sensor scans every object in its field of view (in 3D), the data is then used for the SLAM. SLAM is executed using the Direct Lidar Odometry (DLO) algorithm for mapping and simultaneously helping the robot with localization and enabling it to navigate through the environment smoothly and precisely. © 2024 IEEE.

关键词： SLAM robotics

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Text Representation Based on WT-GloVe Word Vector Weighting Model

Text Representation Based on WT-GloVe Word Vector Weighting ...

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2024 international conference on Intelligent Computing and robotics, ICICR 2024

作者： Xue, Cheng Fei, Rong Yao, Quanzhu School of Computer Science and Engineering Xi'An University of Technology Xi'an China School of Computer Science and Technology Xi'An University of Technology Xi'an China

ISBN: (纸本)9798350372274

The text representation issue in text classification is thoroughly examined in this paper, with a focus on handling massive volumes of text data. The paper suggests the word vector weighting model based on WT-GloVe in response to the issues with excessive sparsity and subpar classification performance that classic neural network text representation models frequently confront. To find important information in the text, the model first extracts features from the text using a feature weighting approach based on word spacing and inter-class contribution. This work then presents a filtering technique that tries to enhance the training quality and relevance of word vectors in order to address the issue of irrelevant words that may be introduced by the GloVe model in word vector training. This work effectively builds a weighted word vector model that can precisely capture the value of feature words and rich semantic information by combining the feature weighting technique with irrelevant word screening. According to experimental results, the model performs better in numerous text classification assignments when compared to existing models, demonstrating its efficacy in enhancing text representation performance. The paper enriches the field of text representation by offering a fresh approach and aiding in the advancement ofNatural Language Processing technology. ©2024 IEEE.

关键词： Semantics

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IoT-Enhanced Machine Learning Approach for Early Detection and Prediction of Urinary Tract Infection 2

IoT-Enhanced Machine Learning Approach for Early Detection a...

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2nd international conference on advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Anisha Sabharwal, Munish Tripathi, Rohit Department of Computer Science and Engineering Galgotias Univerisity Greater Noida India MRIIRS Faridabad India Department of Computer Science and Engineering IILM University Greater Noida India Department of Electronics Engineering JC Bose University of Science & Technology India

ISBN: (纸本)9798331541217

Millions of people worldwide suffer from urinary tract infections (UTIs), which are a common and recurring health problem. In order to limit possible health problems and stop the spread of disease, early identification and prompt management are essential. In order to overcome this issue, the Internet of Things and machine learning techniques are combined in the novel approach presented in this study. The suggested method continually monitors real-time vital signs and urine biomarkers using IoT-enabled wearable and smart sensors. Principle Component Analysis feature selection approaches are used to optimize the pertinent data inputs in order to improve the accuracy of UTI prediction. A centralized ML-driven processing unit is used to perform complex ML algorithms, such as the Ensemble Bagging Decision Tree Classifier classification model, to analyses and decipher the patterns of the gathered data. The technology reliably recognizes and anticipates UTIs, sending early warning signals to patients and medical professionals. In order to improve patient care and public health outcomes, our ML-IoT fusion in UTI detection offers a non-intrusive and proactive technique. The suggested EBDTC technique outperforms the existing approaches with an accuracy rate of 96.25%. The results of the study demonstrate how disruptive technology has the ability to successfully treat common health issues, expanding the area of healthcare diagnostics. © 2024 IEEE.

关键词： Feature Selection

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