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The current predictive control method of switched reluctance motor based on commutation interval subdivision

The current predictive control method of switched reluctance...

引用

PELS Students and Young Professionals symposium (SYPS), ieee

作者： Chen Tao Qiu Zhijian School of Mechatronics Engineering and Automation Shanghai University Shanghai China

For the large torque ripple of switched reluctance motors paper proposes an improved predictive current control method. The method inherits the advantages of simple logic and flexible control of traditional model predictive control and divides an electric cycle into nine sectors according to the switching reluctance motor conduction principle, including the phase change sector and single-phase conduction sector, in which the phase change sector is divided into two parts and the cost function is introduced to turn off the phase current. Different switching states and cost functions are selected according to the sectors to be selected. The new algorithm reduces the computational effort and the optimized cost function enables a better torque ripple suppression effect. A simulation model is built in Matlab/Simulink to simulate and analyze the proposed algorithm, and the conventional model predictions are compared with it. The results show that the proposed method has a better torque ripple suppression effect than the conventional model prediction.

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Data-driven Fault Diagnosis Method Based on Fusion of Multi-direction and Multi-source Signals 5

Data-driven Fault Diagnosis Method Based on Fusion of Multi-...

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5th International Conference on Intelligent Autonomous Systems, ICoIAS 2022

作者： Yang, Yang Wang, Junda Li, Ning Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

As a pivotal mechanical component of turbine drive-chain, gearbox suffers from various accidents and fault diagnosis could remarkably enhance its reliability and maintain running safety and lifetime. Vibration-based analysis on single-channel is mainly adopted in previous diagnostic, which cannot catch full vibration inner information in gearbox sensors and bring model performance down. In this paper, a novel method based on the fusion of multi-direction and multi-source signals and the convolutional neural network (MDMS-CNN) is presented. This method can obtain multi-dimensional feature maps from MDMS and preserve the time correlation among different signal sequences. First, features from MDMS are extracted via a corrosion conversion method that converts signals into two-dimensional (2-D) gray images. Then, homologous images are spliced into same channels, while heterogeneous images are superimposed to different channels. Finally, fusion features are input into CNN for fault classification. In experiments, the method is validated on a gearbox diagnosis case. Results have shown that the proposed method achieves 99.86% recognition accuracy and faster convergence speed compared to others. © 2022 ieee.

关键词： Failure analysis

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Bayesian inference for neural network based high-precision modeling

Bayesian inference for neural network based high-precision m...

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ieee symposium Series on computational intelligence (SSCI)

作者： Jorge Morales Wen Yu Departamento de Control Automático CINVESTAV-IPN (National Polytechnic Institute) Mexico City Mexico Departamento de Control Automático CINVESTAV-IPN Mexico City Mexico

ISBN: (纸本)9781665487696

In this paper we implement neural network structure and Bayesian inference in order to improve performance black-box modeling for unknonw nonlinear systems. This kind of structure works in batch form passing both the identification and the statistical training. Two nonlinear systems and two data sets of seismic information from regions of Italy and Mexico are used to evaluate the methods. The results are satisfied.

关键词： Training Backpropagation computational modeling Closed box Artificial neural networks Cost function Bayes methods

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Automatic Difficulty Balance in Two-Player Games with Deep Reinforcement Learning

Automatic Difficulty Balance in Two-Player Games with Deep R...

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ieee symposium on computational intelligence and Games, CIG

作者： Simão Reis Rita Novais Luís Paulo Reis Nuno Lau Artificial Intelligence and Computer Science Laboratory (LIACC) Faculty of Engineering University of Porto Porto Portugal Institute of Electronics and Informatics Engineering of Aveiro (IEETA) University of Aveiro Aveiro Portugal

Regardless of the goal of a game, it should be a pleasant and fun experience for its players. For some games to be enjoyable, the level of difficulty must be carefully calibrated, otherwise, players will feel bored or frustrated. Multiplayer scenarios in particular, where one player’s satisfaction might not translate to the enjoyment of other players and poses extra challenges in balancing the difficulty. The performance of one player is relative to the opponent, versus single-player scenarios where we can fully control the environment. We propose an AI automation framework for difficulty balancing in two-player games, where balancing is seen as a Reinforcement Learning task. A Game Master (GM) agent learns how to use handicap game mechanics, signaled by a reward function that evaluates a weighted combination of aesthetic criteria that encourages dramatization and allows a player in the lead to go back and a player in the rear to catch up, creating the desired rubber banding effect that balances out skill gaps. The quality of the games with the trained GM embedded is examined by measuring the same aesthetic criteria on the resulting games, and by analyzing the resulting changes in the game.

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Fluxing Optimisation and control Improvements at the Kalgoorlie Nickel Smelter

Fluxing Optimisation and Control Improvements at the Kalgoor...

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1st ieee Colombian Conference on Applications in computational intelligence (ColCACI)5th International symposium on Nickel and Cobalt (Ni-Co)

作者： Grimsey, David Grimsey, Eric Bjorklund, Peter BHP Nickel West Nickel West Kalgoorlie Smelter NK Smelterman Dr Feysville WA 6431 Australia Curtin Univ WA Sch Mines Kalgoorlie WA 6431 Australia Outotec Finland Oy Rauhalanpuisto 9 FI-02231 Espoo Finland

ISBN: (纸本)9783030656478;9783030656461

The Nickel West Kalgoorlie Smelter (NKS) treats a blended nickel sulphide concentrate from multiple ore bodies containing a high portion of magnesia in an integrated flash smelter. Fluxing and related control strategies have been an area of continuous improvement. Recent improvements in slag liquidus control have been achieved through the application of powerful thermodynamic modelling software (FACTSage) combined with improved process measurements and implementation of a highly automated control model (Outotec Process Advisor).

关键词： Slag chemistry Fluxing Nickel smelting Process control FACTSage Outotec process advisor

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The True Online Continuous Learning automation (TOCLA) in a continuous control benchmarking of actor-critic algorithms

The True Online Continuous Learning Automation (TOCLA) in a ...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Frost, Gordon Vallejo, Marta Heriot Watt Univ Sch Engn & Phys Sci Edinburgh Midlothian Scotland

ISBN: (纸本)9781728125473

Reinforcement learning problems are often discretised, use linear function approximation, or perform batch updates. However, many applications that can benefit from reinforcement learning contain continuous variables and are inherently non-linear, for example, the control of aerospace or maritime robotic vehicles. Recent work has brought focus onto online temporal difference methods, specifically for using non-linear function approximation. In this paper, we evaluate the Forward Actor-Critic against the regular Actor-Critic, and Continuous Actor-Critic Learning automation. We also propose and evaluate a new algorithm called True Online Continuous Learning automation (TOCLA) which combines these two approaches. The chosen benchmark problem was the MountainCarContinuous-v0 environment from OpenAI Gym, which represents a further step in complexity over the benchmark used to test the Forward Actor Critic in previous works. Our results demonstrate the superiority of TOCLA in terms of its sensitivity to hyper-parameter selection compared with the Forward Actor Critic, Continuous Actor-Critic Learning automation, and Actor Critic algorithms.

关键词： Reinforcement Learning Actor-Critic TOCLA CACLA Forward Actor-Critic Nonlinear Function Approximation

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Artificial intelligence and Energy Efficiency Revolution in Electric Consumption Management

Artificial Intelligence and Energy Efficiency Revolution in ...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Róbert Štefko Marek Bobček Department of Electric Power Engineering Facultz of Electrical Engineering and Informatics Technical University of Košice Košice Slovak Republic

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

The article proposes energy-saving measures in sports stadiums, emphasizing the Košice Football Arena (KFA) as a case study. In recent years, increasing energy consumption and prices have necessitated sustainable solutions, particularly in buildings subject to stricter energy efficiency regulations. Energy independence, achieved through passive design and active systems like efficient lighting and renewable sources, is crucial. Smart management systems, bolstered by artificial intelligence (AI), offer enhanced efficiency. Sports stadiums, with their unique energy demands, require attention for energy savings and renewable integration, driven by legal mandates and government-supported renovations. AI facilitates better optimization, forecasting, and management of energy systems, as seen in the articles referenced. For instance, AI-driven algorithms outperform generic algorithms in energy system optimization. Moreover, AI and interactive virtual simulations support ecological building transformations, aiding in understanding and optimizing energy systems. The proposed measures for the KFA encompass energy sustainability, management, analysis of electricity consumption, and the application of AI. These measures aim to optimize energy consumption, enhance energy efficiency, and reduce operating costs. The deployment of smart meters and AI-driven systems enables real-time monitoring and adaptive control, contributing to overall energy savings.

关键词： Training Renewable energy sources Electricity Energy measurement Lighting Energy efficiency Artificial intelligence

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Automated Patient-Specific Left Ventricular Simulations for Cardiac Function Evaluation Using Image-Based computational Fluid Dynamics and Color Flow Imaging

Automated Patient-Specific Left Ventricular Simulations for ...

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ieee symposium (IUS) Ultrasonics

作者： Vincent Bryon Sigurd Vangen Wifstad Thomas Grønli Jieyu Hu Lasse Løvstakken Department of Circulation and Medical Imaging Norwegian University of Science and Technology Trondheim Norway

Cardiovascular disease (CVD) is a leading cause of global mortality, necessitating improved diagnostic and treatment strategies. The integration of computational techniques and artificial intelligence holds promise for enhancing cardiovascular health assessment. computational fluid dynamics (CFD) models offer detailed insights into heart function, particularly for patient-specific (PS) simulations. However, challenges in automation hinder its clinical applicability. We present an automated pipeline for modeling left ventricle (LV) flow patterns based on PS data. The pipeline involves preprocessing, CFD simulation, and postprocessing stages. Deep learning aids in LV and mitral valve segmentation, reducing manual effort. CFD simulations, using ANSYS FLUENT, capture intraventricular flow dynamics. Temporal evolution showcases vortex interactions and propagation, aligning with established patterns. However, limitations arise due to 2D constraints, impacting vortex dynamics. Velocity overestimation, attributed to the 2D assumption, underscores the need for improved segmentation and measurement accuracy. Despite challenges, this study signifies progress toward automated patient-specific 2D simulations for cardiovascular assessment. Further research should explore 2D ventricular wall movement comparisons and refine the pipeline for enhanced accuracy and clinical relevance.

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

Exploring the Integration of Differential Equations in Neura...

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International symposium on computational intelligence and Informatics

作者： Lehel Dénes-Fazakas Kata Sándor-Rokaly Laszló Szász Henrik Csuzi Levente Kovács László Szilágyi György Eigner University Research and Innovation Center Physiological Controls Research Center Obuda 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 Budapest Hungary Keleti Károly Faculty of Business and Management Obuda University Budapest Hungary

ISBN: (数字)9798350353433

ISBN: (纸本)9798350353440

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.

关键词： Deep learning computational modeling Biological system modeling Scalability Neural networks Predictive models Mathematical models Stability analysis Numerical models Numerical stability

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Neural Potential Field for Obstacle-Aware Local Motion Planning

Neural Potential Field for Obstacle-Aware Local Motion Plann...

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ieee International Conference on Robotics and automation (ICRA)

作者： Muhammad Alhaddad Konstantin Mironov Aleksey Staroverov Aleksandr Panov Center of Cognitive Modeling Moscow Institute of Physics and Technology Dolgoprudny Russia Artificial Intelligence Research Institute Moscow Russia Federal Research Center "Computer Science and Control" Moscow Russia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Model predictive control (MPC) may provide local motion planning for mobile robotic platforms. The challenging aspect is the analytic representation of collision cost for the case when both the obstacle map and robot footprint are arbitrary. We propose a Neural Potential Field: a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, and robot footprint. The differentiability of our model allows its usage within the MPC solver. It is computationally hard to solve problems with a very high number of parameters. Therefore, our architecture includes neural image encoders, which transform obstacle maps and robot footprints into embeddings, which reduce problem dimensionality by two orders of magnitude. The reference data for network training are generated based on algorithmic calculation of a signed distance function. Comparative experiments showed that the proposed approach is comparable with existing local planners: it provides trajectories with outperforming smoothness, comparable path length, and safe distance from obstacles.

关键词： Costs Trajectory planning computational modeling Computer architecture Predictive models Vectors Planning

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