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Autonomous Control of Simulated Aircraft Based on Deep Reinforcement Learning Driven by Limited State Information 3rd

Autonomous Control of Simulated Aircraft Based on Deep Rei...

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

作者： Shen, Xinglong Liu, Yang Zhao, Dequn Beijing University of Technology Chaoyang Beijing100000 China Beijing Institute of Technology Beijing China Beijing Institute of Astronautical Systems Engineering Beijing China

ISBN: (纸本)9789819624676

The autonomous control of aircraft poses a challenging high-dimensional continuous control problem, extensively utilized in drones, autonomous driving systems, and flight simulators. Reinforcement Learning (RL), especially with the latest advancements in deep neural networks, empowers agents to excel in complex continuous control tasks. The research employed the RL framework to model aircraft heading and altitude control as a Markov decision process, simulated via JSBSim. Leveraging the Proximal Policy Optimization (PPO) algorithm alongside a tailored reward function, the agent adeptly learns to attain target altitude and heading at a designated speed through training with an exhaustive set of state variables. Efforts were made to trim down the number of state variables, pinpointing a refined set highly linked to altitude control. This streamlined selection maintains performance while slashing computational complexity and boosting convergence speed. It offers a potent approach for autonomous aircraft control with limited state information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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An Empirical Study of Machine Learning for Business Enterprises Management of Cloud computing Services 9th

An Empirical Study of Machine Learning for Business Enterpri...

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9th international conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Reddy, D. Jayanarayana Krishna, D. Vamshi Vali, S. Sharmas Tharun, E. Kumar, M. Vamsi Department of CSE G. Pullaiah College of Engineering and Technology Kurnool India G. Pullaiah College of Engineering and Technology Kurnool India

ISBN: (纸本)9783031440830

Without a question, two of the maximum significant technologies to reach conventional IT in recent years are computing and big statistics analytics. The surprising convergence of the two technologies is yielding potent outcomes and advantages for enterprises. The delivery of IT services by so-called cloud firms and the relationship between enterprises and IT resources are already being affected by cloud computing. Recent developments in information and communication technology have made possible a new approach to data analysis known as "Big Data". Nevertheless, the large quantity of computer resources needed for big data analysis means that many small and medium-sized businesses cannot afford to embrace big data technologies at this time. Affordances in business analytics, cloud computing data security are all part of the concept. Technique (KPCA-LDA-XGB) is used to conduct the empirical research. Using a structural equation model built using Partial Least Squares, this theory is experimentally evaluated with data from 316 businesses. Business analytics and the decision-making affordances of safety are positively moderated by data-driven ethos and IT business process integration. The findings of this research provide practical guidelines for organisations looking to advance their computing data safety organisation with the usage of analytics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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A Novel Wireless Radio technology Paradigm for Next Generation Wireless Networks 4

A Novel Wireless Radio Technology Paradigm for Next Generati...

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4th international conference on Signal Processing and Communication, ICSPC 2023

作者： Kasinathan, Prabakaran Angulakshmi, M. Iswarya, K. Elangovan, D. Chanthirasekaran, K. Parthasarathy, G. School of Computing Veltech Rangarajan Dr. Sagunthula R & D Institute of Science and Technology Department of Cse Chennai India Vellore Institute of Technology Vellore Campus School of Information Technology and Engineering Tamil Nadu India Panimalar Engineering College Dept. of Cse Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Ece Chennai India Reva University School of C&it Bangalore India

ISBN: (纸本)9798350300772

A fifth-generation network is the need of the people in the year 2020 and it had been deployed all over the world. The drawbacks of the fifth-generation network include data rate, efficiency, reliability, processing, coverage globally, density. This has led to the progress of technology. There had been an increased need for a high-security system (RAT) has been developed. The next-generation wireless network is expected to have coverage globally, higher security along with intelligence, and improved efficiency in terms of energy, spectrum, and cost. Thus, this paper is intended in developing a novel radio technology for the seamless wireless network. The advent of the Radio Access technology into the next-generation wireless network seems to include more users when compared to the existing system of networks. This technology has proven. In this paper, we have discussed the design scenario, expectations from the 6G networks, energy efficiency, bit error rate, and spectral efficiency. This paper also includes the primary technology used and an overview of the current project and researches that are being held. There had been an improved method for searching and automatically computing using artificial intelligence for convergence of the local optimal solution. © 2023 IEEE.

关键词： Spectrum efficiency

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A Vision-based Indoor Positioning Systems utilizing Computer Aided Design Drawing 22

A Vision-based Indoor Positioning Systems utilizing Computer...

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28th Annual international conference on Mobile computing and Networking (ACM MobiCom)

作者： Yoo, Dae-ha Shan, Gaoyang Roh, Byeong-hHee Ajou Univ Dept AI Convergence Network Suwon South Korea Ajou Univ Inst Informat & Commun Suwon South Korea

ISBN: (纸本)9781450391818

In recent years, with the increase in users' demand for location services, the research of Indoor Positioning Systems (IPS) has attracted much attention. Many researchers proposed schemes to estimate the user's location based on the Received Signal Strength Indicator (RSSI) values of wireless technologies. However, the RSSI value is affected by signal interference seriously. This causes the accuracy of the positioning to be greatly reduced. To solve this problem, we use Computer Vision (CV) to replace traditional solutions in this paper. CV is known for its high performance and low complexity. The proposed scheme is capable of inferring the current location of users in the possible candidates from the interior structural features of buildings captured by cameras and Computer-Aided Design (CAD) drawing.

关键词： Indoor Positioning System Artificial Intelligence Vision and Scene Understanding

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Improving Support Vector Machine Performance with Advanced Kernel Methods 7

Improving Support Vector Machine Performance with Advanced K...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Mittal, Mudit Al-Jawahry, Hassan M. Varshney, Neeraj Kumar, Sunil Prashanth Michaelson, Jacob Reddy, R. Akhilesh Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

In many different fields, Support Vector Machines (SVMs) have shown to be an effective tool for regression and classification problems. When using support vector machines (SVMs), the kernel function - which specifies the feature space where the data is linearly separable - has a significant impact on the performance of the model. In this research, we look at how to improve SVM performance by creating and using new kernel approaches. We contrast the effectiveness of conventional kernels like the linear, polynomial, and radial basis function (RBF) kernels with new kernel functions designed to capture complicated data structures. Extensive studies on benchmark datasets show that classification accuracy, convergence speed, and generalisability are significantly improved. Applications in image recognition, bioinformatics, and financial forecasting are well-suited to the suggested kernels because to their exceptional performance in dealing with high-dimensional and non-linearly separable data. Research and practitioners in the area may benefit greatly from this study's thorough examination of the computational complexity and actual implementation of these sophisticated kernel approaches. It offers useful insights for optimising SVM performance. © 2024 IEEE.

关键词： Support vector regression

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Cloud-Enhanced Machine Learning: Exploring the Synergy for Intelligent Automation and Optimization 15th

Cloud-Enhanced Machine Learning: Exploring the Synergy for I...

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15th international conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Kumar, Naween Dash, Yajnaseni Abraham, Ajith Choudhary, Rakesh Kumar Pandey, Shriyansh School of Computer Science Engineering and Technology Bennett University UP Greater Noida India School of Artificial Intelligence Bennett University UP Greater Noida India School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810855

The convergence of cloud computing and machine learning (ML) is evolving in the new era of technological innovation. This paper elaborates on the symbiotic relationship between these two fields, highlighting how cloud infrastructure enables scalable, flexible, and cost-efficient ML development and deployment. At the same time, ML enhances cloud services through intelligent automation, optimization, and data-driven insights. This study of synergy presents transformative and revolutionizing opportunities in the industry and opens doors to evolved ML models with the help of cloud environments. Additionally, the paper mentions best practices, challenges, and future trends in integrating ML and cloud technologies, providing a comprehensive guide for students, professionals, and researchers. Also, the pre-existing research on the subject has been reviewed thoroughly and summarized, making this study a guide for researchers. Through performance analyses, we illustrate the practical benefits and certain pitfalls of this integration, highlighting valuable insights for leveraging the full potential of ML and cloud synergy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Intelligent computing

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Swarm Intelligence for Multi-Robot Coordination in Agricultural Automation 10

Swarm Intelligence for Multi-Robot Coordination in Agricultu...

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10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Abhang, L.B. Gummadi, Annapurna Changala, Ravindra Vuyyuru, Veera Ankalu Sabareesh, R. Raj, I. Infant Pravara Rural Engineering CollegeLoni under. Sppu University Automation and Robotics Dept Pune India Hyderabad India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Ecsotics Farm Foods Private Limited Bangalore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India

ISBN: (纸本)9798350384369

The research investigates the optimization of multi-robot coordination in agricultural automation through Particle Swarm Optimization. The methodology unfolds in four key aspects: firstly, by harnessing swarm intelligence principles, the approach enhances coordination among multiple robots in agricultural settings, fostering collaboration and dynamic task allocation in response to real-time changes. Secondly, the integration of Particle Swarm Optimization (PSO) introduces a powerful optimization technique, enabling the algorithm to dynamically adapt the swarm's configuration. This adaptability optimizes task allocation and individual robot positions, enhancing overall efficiency. Thirdly, the methodology systematically tackles scalability, adaptability, and robustness challenges in agricultural automation. By synergizing swarm intelligence and PSO, it ensures efficient scalability with an increasing number of robots and responsiveness to evolving agricultural demands. The holistic design makes it well-suited for practical implementation in real-world agricultural scenarios. A series of experiments varying PSO parameters were conducted, revealing nuanced relationships between iterations, swarm size, and coefficients and their impact on convergence time and fitness metrics. The outcomes demonstrate the trade-offs involved in selecting these parameters for efficient coordination in agricultural tasks. A comparative analysis with ABO and ACO highlights PSO's superior performance, achieving remarkable fitness value of 98% in the context of robotic swarm applications. © 2024 IEEE.

关键词： Scalability

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Design and Implementation of AI Machine Vision Inspection System with Multi-Optical Images for OLED Film Manufacturing 8

Design and Implementation of AI Machine Vision Inspection Sy...

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8th IEEE/ACIS international conference on Big Data, Cloud computing, and Data Science, BCD 2023

作者： Kim, Tae-Yong Oh, Seongjin Lee, So-Min Lee, Sungkook Hwang, Sangyoon Jeong, Jongpil Sungkyunkwan University Dept. of Smart Factory Convergence Suwon Korea Republic of Sungkyunkwan University Dept. of Culture and Technology Suwon Korea Republic of Sungkyunkwan University Dept. of Mechanical Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Chemical Engineering Suwon Korea Republic of

ISBN: (纸本)9798350373615

Machine vision technology is one of the main areas of rapid development in the modern world and is becoming increasingly important in various industries. This innovative technology uses the fusion of computer vision and artificial intelligence to process and analyze visual information, enabling the development of automated inspection systems and the automation of processes. In particular, machine vision technology is playing an increasingly important role in OLED film production and quality control. Currently, OLED film production is mainly based on visual inspection, but this method has various problems. It is labor-intensive and relies on humans, which limits the accuracy and consistency of the inspection. It is also difficult to identify defective elements with irregular shapes or sizes. To overcome these issues and revolutionize the field of OLED film production and quality control, we propose an OLED Film Inspection Architecture with Machine vision. This architecture utilizes advanced cameras and sensors to analyze the surface of OLED films in detail and automatically identify defective elements. In addition, by integrating multiple optical systems and AI deep learning algorithms, it can perform accurate inspections under various lighting conditions and detect defects in real time. By utilizing these advanced technologies, quality control and production efficiency can be greatly improved, which is expected to drive innovation in OLED film production. This research provides a high level of technical content and provides a bright outlook for the future of machine vision technology in the industrial field. © 2023 IEEE.

关键词： Inspection

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GWO-GEA: A Black-box Method for Generating Audio Adversarial Examples 9

GWO-GEA: A Black-box Method for Generating Audio Adversarial...

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9th international conference on Image, Vision and computing, ICIVC 2024

作者： Wang, Zhenyu Xu, Xiaowei Wang, Xiaodong Zhou, Huasong College of Computer Science and Technology Ocean University of China Qingdao China

ISBN: (纸本)9798350385991

In the context of launching adversarial assaults on Automatic Speech Recognition (ASR) systems, the intricacies of the target model, including its architecture and parameters, remain obscured to the attacker. Predominantly, assault techniques have been tailored for white-box models, leading to a notable struggle in crafting adversarial examples for black-box models, often marked by inefficiencies and low success probabilities. In response to this predicament, we introduce an innovative approach for generating voice adversarial examples specifically designed for black-box models. This method, dubbed GWO-GEA, integrates the Grey Wolf Optimizer with Gradient Estimation algorithms, leveraging these optimization techniques to enhance the generation process. This paper presents a method that fully leverages the fast convergence of the Grey Wolf algorithm and the fine optimization of objectives by the Gradient Estimation algorithm, allowing for the rapid and efficient generation of high-quality adversarial voice samples. This paper’s adversarial sample generation methods for two different scenarios are tested and analyzed on three public speech datasets: The Speech Commands Dataset, Mozilla Common Voice dataset, and LibriSpeech. The results show significant improvements in attack success rate, adversarial sample generation quality, and generation time compared to common methods. Furthermore, we have demonstrated on multiple commercial APIs that the adversarial examples generated by our method have good transferability and can adapt well to real-world attack scenarios. © 2024 IEEE.

关键词： Generative adversarial networks

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Multi-agent distributed deep reinforcement learning algorithm in cloud environment 10

Multi-agent distributed deep reinforcement learning algorith...

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10th IEEE Joint international Information technology and Artificial Intelligence conference, ITAIC 2022

作者： Gong, Shuai Cheng, Hang Cao, Wanwan Yu, Dongbo Chen, Chao State Grid Anhui Electric Power Co.LTD. Information & Telecommunication Branch Hefei China Anhui Mingsheng Hengzhuo Technology Co. Ltd. Hefei China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

In recent years, deep reinforcement learning technology has been continuously applied in the field of cloud computing, making it possible to provide effective and optimized decision-making strategies for complex and high-dimensional multi-agent systems. However, in complex multi-agent scenarios, the existing multi-agent depth The reinforcement learning algorithm not only has a slow convergence speed, but also the stability of the algorithm cannot be guaranteed. In this paper, a multi-agent distributed deep deterministic policy gradient algorithm based on value distribution is proposed, which introduces the idea of value distribution into multi-agent scenarios and preserves expectations. Return complete distribution information, so that the agent can obtain more stable and effective learning signals;Introduce multi-step rewards to improve the stability of the algorithm;Introduce a distributed data generation framework to decouple experience data generation and network update, so that it can make full use of computing resources and speed up the convergence of the algorithm. Experiments show that the algorithm proposed in this paper has better stability and convergence speed in multiple continuous/discrete controlled multi-agent scenarios, and the decision-making ability of the agent has also been significantly improved. Enhance. © 2022 IEEE.

关键词： Fault detection

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