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8th International symposium on Computer Science and Intelligent control, ISCSIC 2024

作者： Li, Hao Wang, Honghua HoHai University School of Electrical and Power Engineering Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Nanjing Changzhou China HoHai University College of Artificial Intelligence and Automation Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Changzhou China

ISBN: (纸本)9798350380286

A finite element model of a switched reluctance generator is established in ANSYS software. The transient characteristics of the switched reluctance generator are simulated, and the influence of structural parameters such as stator embrace, rotor embrace, stator yoke thickness, and winding turns, as well as control parameters such as initial excitation voltage, speed, turn-on angle, and turn off angle on the output characteristics of the switched reluctance generator are analyzed. This provides a basis for parameter optimization and actual operation control of the switched reluctance generator. © 2024 ieee.

关键词： control parameters finite element model structural parameters switched reluctance generator

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Exploring the Integration of Differential Equations in Neural Networks: Theoretical Foundations, Applications, and Future Directions 24

Exploring the Integration of Differential Equations in Neura...

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24th ieee International symposium on computational intelligence and Informatics, CINTI 2024

作者： Dénes-Fazakas, Lehel Sándor-Rokaly, Kata Szász, Laszló Csuzi, Henrik Kovács, Levente Szilágyi, László Eigner, György University Research and Innovation Center Physiological Controls Research Center Óbuda University Budapest Hungary John von Neumann Faculty of Informatics Biomatics and Applied Artificial Intelligence Institute Óbuda University Budapest Hungary Applied Informatics and Applied Mathematics Doctoral School Óbuda University Bécsi street 96/B BudapestH-1034 Hungary Keleti Károly Faculty of Business and Management Obuda University Budapest Hungary

ISBN: (纸本)9798350353433

Differential equations have become increasingly integral to the development of neural network architectures, providing a continuous-time framework that enhances both the theoretical understanding and practical application of deep learning models. This review explores the mathematical foundations of integrating ordinary and partial differential equations (ODEs and PDEs) into neural networks, with a focus on Neural Ordinary Differential Equations (Neural ODEs) and Physics-Informed Neural Networks (PINNs). We discuss the theoretical insights gained from this integration, including stability, convergence, and generalization, and examine key applications in time-series forecasting, generative modeling, control systems, and biological systems. Despite their promise, these models face challenges related to computational complexity, stability, scalability, and interpretability. Future research directions include the development of specialized numerical solvers, enhancing model robustness, scaling to high-dimensional problems, and improving interpretability. This paper provides a comprehensive overview of the current state and future prospects of differential equation-based neural networks, highlighting their potential to advance both theoretical and applied machine learning. © 2024 ieee.

关键词： Neural network models

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Learning-Based Hierarchical Adaptive Congestion control with Low Training Overhead 22

Learning-Based Hierarchical Adaptive Congestion Control with...

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22nd ieee International symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Hu, Jinbin Zhou, Zikai Rao, Shuying Peng, Yujie Bao, Bowen Ruan, Chang Changsha University of Science and Technology ChangSha China

ISBN: (纸本)9798331509712

Most congestion control mechanisms perform well in specific network environments, but none can consistently deliver good performance across all scenarios. Recently proposed frameworks based on reinforcement learning can flexibly select congestion control algorithms to adapt to dynamic changes in network conditions. However, frequently altering the congestion control mechanisms during relatively stable periods of the network actually leads to instability and unnecessary computational overhead. In this paper, we propose a hierarchical adaptive congestion control algorithm (HACC) to be resilient to the varying network. HACC dynamically selects the appropriate congestion control mechanism only when the current congestion control algorithm is not suitable for the current network state, rather than changing the congestion control scheme every training cycle to ensure network stability. The simulation results show that under different realistic workloads, HACC significantly reduces the computational overhead and improves throughput. Specifically, HACC reduces average overhead by 31% and improves throughput by up to 47%, 35%, 23%, and 15% compared to Cubic, Reno, BBR, and Antelope, respectively. © 2024 ieee.

关键词： Deep reinforcement learning

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Parallel Learning:Overview and Perspective for computational Learning Across Syn2Real and Sim2Real

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ieee/CAA Journal of Automatica Sinica 2023年第3期10卷 603-631页

作者： Qinghai Miao Yisheng Lv Min Huang Xiao Wang Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Artificial Intelligence Anhui UniversityHefei 266114China Qingdao Academy of Intelligent Industries Qingdao 230031China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE

The virtual-to-real paradigm,i.e.,training models on virtual data and then applying them to solve real-world problems,has attracted more and more attention from various domains by successfully alleviating the data shortage problem in machine *** summarize the advances in recent years,this survey comprehensively reviews the literature,from the viewport of parallel ***,an extended parallel learning framework is proposed to cover main domains including computer vision,natural language processing,robotics,and autonomous ***,a multi-dimensional taxonomy is designed to organize the literature in a hierarchical ***,the related virtual-toreal works are analyzed and compared according to the three principles of parallel learning known as description,prediction,and prescription,which cover the methods for constructing virtual worlds,generating labeled data,domain transferring,model training and testing,as well as optimizing the strategies to guide the task-oriented data generator for better learning *** issues remained in virtual-to-real are ***,the future research directions from the viewpoint of parallel learning are suggested.

关键词： Machine learning parallel learning parallel systems sim-to-real syn-to-real virtual-to-real

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ColO-RAN: Developing Machine Learning-Based xApps for Open RAN Closed-Loop control on Programmable Experimental Platforms

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ieee TRANSACTIONS ON MOBILE COMPUTING 2023年第10期22卷 5787-5800页

作者： Polese, Michele Bonati, Leonardo Doro, Salvatore Basagni, Stefano Melodia, Tommaso Northeastern Univ Inst Wireless Internet Things Boston MA 02115 USA

Cellulaar networks are undergoing a radical transformation toward disaggregated, fully virtualized, and programmable architectures with increasingly heterogeneous devices and applications. In this context, the open architecture standardized by the O-RAN Alliance enables algorithmic and hardware-independent Radio Access Network (RAN) adaptation through closed-loop control. O-RAN introduces Machine Learning (ML)-based network control and automation algorithms as so-called xApps running on RAN Intelligent controllers . However, in spite of the new opportunities brought about by the Open RAN, advances in ML-based network automation have been slow, mainly because of the unavailability of large-scale datasets and experimental testing infrastructure. This slows down the development and widespread adoption of Deep Reinforcement Learning (DRL) agents on real networks, delaying progress in intelligent and autonomous RAN control. In this paper, we address these challenges by discussing insights and practical solutions for the design, training, testing, and experimental evaluation of DRL-based closed-loop control in the Open RAN. To this end, we introduce ColO-RAN, the first publicly-available large-scale O-RAN testing framework with software-defined radios-in-the-loop. Building on the scale and computational capabilities of the Colosseum wireless network emulator, ColO-RAN enables ML research at scale using O-RAN components, programmable base stations, and a "wireless data factory." Specifically, we design and develop three exemplary xApps for DRL-based control of RAN slicing, scheduling and online model training, and evaluate their performance on a cellular network with 7 softwarized base stations and 42 users. Finally, we showcase the portability of ColO-RAN to different platforms by deploying it on Arena, an indoor programmable testbed. The lessons learned from the ColO-RAN implementation and the extensive results from our first-of-its-kind large-scale evaluation highlight

关键词： O-RAN network intelligence 5G/6G deep reinforcement learning Colosseum

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The implementation of Thread Network for a Smart Factory

The implementation of Thread Network for a Smart Factory

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Castro, Camille Mavic Tupas Sharma, Anurag Kumar, Dhivya Sampath Abidi, Khalid Kim, Noori Newcastle Univ Newcastle Upon Tyne Tyne & Wear England Singapore Inst Technol Singapore Singapore

ISBN: (纸本)9781665487689

In an excessively busy factory, a shortage of manpower is more likely to happen due to the overload of orders which may lead to immoderate overtime for workers. This is a customary hindrance to a factory's higher production aims where the initial practice yielded a long lead time for packing of servers that are ready for delivery. The packing of servers includes the manual search of a specific server in multiple trolley racks, which may take an average of 7 hours for an order. Readily available information such as the Internet-of-Things (IoT) can be fully utilized to implement automation in such companies. The proposed solution is to implement the Pack Predictive Scheduling (PPS) System for a Smart Factory and track these travelling servers by attaching a router to it that has indicative LED lighting connected to it for easier detection. It also encompasses the study of standardized networking protocols and the necessary steps taken to achieve the desired interoperability.

关键词： IoT Industry 4.0 automation Thread Network Mesh Network Constrained Application Protocol

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Achieving Reinforcement Learning Speedup via Quantum Kernel Estimation 29

Achieving Reinforcement Learning Speedup via Quantum Kernel ...

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29th Annual ieee International symposium on Technology and Society (ISTAS)

作者： Gilvarry, John Emeakaroha, Vincent C. Munster Technol Univ Comp Sci Dept Cork Ireland

ISBN: (纸本)9798350324860

Reinforcement learning is playing a crucial role in sustainable developments and enabling wide applications in our daily lives ranging from self-driving cars, robotics, automation, personalised product suggestions, user notifications and many more. Kernel-based reinforcement learning holds theoretical guarantees of convergence to a unique solution for decision function approximation. This has motivated the development of implementations that exploit the advantage of kernel methods whilst overcoming the 'curse of dimensionality'. The theoretical properties of kernel-based methods also support the combination of learning agents in loosely coupled systems into a single combined model for composable learning. Learning speedup is achievable by introducing expert knowledge to the algorithm through the kernel, which reduces variance by increasing bias and thus learning speed. Despite the theoretical promise of kernel based reinforcement learning methods, and continued evolution of more efficient algorithms, practical implementation are not prevalent. Research in quantum machine learning has successfully applied quantum algorithms to achieve computational speed-ups for various machine-learning tasks, and has also identified potential quantum advantage through the identification of patterns not accessible to classical data mapping. To date, this has not been applied to kernel based reinforcement learning. Recent work in the field of quantum learning suggests an important duality between quantum machine learning and kernel machine learning methods. Therefore, this paper proposes a framework to complement kernel-based reinforcement learning with quantum generated feature maps for function approximation to improve learning speed. This has positive consequences for optimisation of reinforcement learning techniques already in use to support sustainability targets in applications such as industry innovation and infrastructure, power generation management, data centre cooling and s

关键词： Kernel Based Reinforcement Learning Quantum Machine Learning Quantum Kernel Estimation Artificial intelligence Sustainable Development

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Redundant robot control with learning from expert demonstrations

Redundant robot control with learning from expert demonstrat...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Ramirez, Jorge Yu, Wen Natl Polytech Inst Dept Control Automat IPN CINVESTAV Mexico City DF Mexico IPN Dept Control Automat CINVESTAV Mexico City DF Mexico

ISBN: (纸本)9781665487689

This paper proposes a biased exploration based reinforcement learning, which uses expert experiences to avoid the exploration of all states. The method is applied to control redundant robots with expert experiences. A 7-degree-of-freedom robot manipulator is used in experiments. The results show that expert demonstrations based robot control works well.

关键词： reinforcement learning learning from expert demonstration redundant robot control

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Real-Time Artificial intelligence Text Analysis for Identifying Burnout Syndromes in High-Performance Athletes 22

Real-Time Artificial Intelligence Text Analysis for Identify...

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2024 ieee 22nd World symposium on Applied Machine intelligence and Informatics, SAMI 2024

作者： Biro, Attila Janosi-Rancz, Katalin Tunde Szilagyi, Laszlo UMFST G.E. Palade Targu Mures Romania University of Malaga Spain IBIMA Malaga Spain Sapientia University Department of Mathematics-Informatics Tg. Mureş Romania Obuda University Physiological Controls Research Center Budapest Hungary Sapientia University Computational Intelligence Research Group Tg. Mureş Romania

ISBN: (纸本)9798350317206

Predicting burnout from texts by using AI necessitates an interdisciplinary approach, combining sports science, psychology, and machine learning. This research is essential to understanding the psychological signs of burnout and the methods to gauge it, coupled with expertise in NLP and sentiment analysis techniques. Collecting athletes' textual data, like logs or interviews, is crucial, along with possible metrics of stamina or fatigue. The role of AI, in particular significantly advanced models like BERT or GPT-3, is to analyze these texts, discerning underlying sentiments that may indicate burnout. AI can identify impending burnout patterns by correlating text-derived sentiments with physical metrics. This study investigated the feasibility of an AI-driven approach to proactive intervention, allowing timely measures to prevent burnout. Additionally, it allows athletes to customize their training to their mental and physical health, thus extending their careers. AI promises to prioritize athletes' physical and mental well-being, improving performance and team dynamics. A series of experiments were conducted on two datasets to validate this model. The results are promising. © 2024 ieee.

关键词： Sentiment analysis

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ieee 23rd International symposium on computational intelligence and Informatics, CINTI 2023 - Proceedings

IEEE 23rd International Symposium on Computational Intellige...

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23rd ieee International symposium on computational intelligence and Informatics, CINTI 2023

ISBN: (纸本)9798350342949

The proceedings contain 68 papers. The topics discussed include: how do generation Z young people view academic ethics modelling the user polarization of echo chambers, using network science methods;fuzzy rule-based estimation of students' skills;the double life of John von Neumann;regional level investigation of EU-funded H2020 collaborations via social network analysis;mastering control: a digital toolkit for understanding PID controllers;neuromorphic spikes generated by the retinal circuit emulator for training 3-dimentinal convolutional neural networks to reconstruct natural images;agile and communication within a team according to a primary research;simulation of fluvial erosion;and analysis of information security among Hungarian technical higher education students.

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