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Effective single-mode model of laser cavity dynamics 11

Effective single-mode model of laser cavity dynamics

引用

11th Vienna international conference on Mathematical modelling, MATHMOD 2025

作者： Tarra, Lukas Deutschmann-Olek, Andreas Kugi, Andreas Automation and Control Institute TU Wien Gußhausstraße 27-29 Vienna1040 Austria Austrian Institute of Technology Vienna1210 Austria

This paper presents an effective single-mode model of laser cavity dynamics, focusing on pulsed lasers, which significantly improves the computational efficiency of state-of-The-Art models while exhibiting a comparable model accuracy. The phenomenon of gain-narrowing is discussed starting from a spatially lumped model with a spectrum of multiple longitudinal modes. Based on this information, effective mode parameters are derived, which convert the multi-mode behavior to time-varying dynamics for the total intracavity power. A simulation study for a cavity-dumped Nd:YAG laser shows that the simple model agrees well with the original model. The results presented in this work can be utilized for laser optimization and controller design for a wide range of photonic systems where a compromise between model accuracy and complexity is desired. Copyright © 2025 The Authors.

关键词： Stochastic systems

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Intelligent control algorithms for industrial automation systems

Intelligent control algorithms for industrial automation sys...

引用

2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sarathkumar, D. Anil, A.R. Sivadasan, J. Jayakumar, T. Manivel, Murugesan Anandhakumar, C. Engineering Kongu Engineering College Department of Electrical and Electronics Tamil Nadu 638 060 India Sree Buddha College of Engineering Pattoor Department of Computer Science &engineering Kerala Alappuzha India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Sri Ramakrishna Institute of Technology Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641010 India

ISBN: (纸本)9798331529833

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem. © 2025 IEEE.

关键词： Genetic algorithms

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Generative Artificial intelligence Techniques for modelling Open Source Threat intelligence Verticals

Generative Artificial Intelligence Techniques for Modelling ...

引用

international conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Hou, Cong Su, Yuyue Tian, Qi Hu, Xinrui Lei, Huibing Ning, Haoxuan Xia, Yu Research Institute State Grid Jibei Electric Power Co. Ltd. Beijing China School of Electrical and Control Engineering Shaanxi University of Science and Technology Shaanxi Xi’an China

ISBN: (纸本)9789819620456

With the continuous development of power system distribution network automation, network security issues are becoming more and more prominent, in which the collection and analysis of open source threat intelligence is a key link to ensure the safe and stable operation of power systems. With the development of artificial intelligence technology, the application of artificial intelligence generated content (AIGC) has brought significant changes to open source intelligence. The application of generative AI technology in power system open source threat intelligence was focused in this paper, and the implementation methods and challenges of automated collection, fusion and analysis of open source threat intelligence using generative AI technology were introduced, summarized and discussed. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Power system analysis

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Artificial intelligence in Robotics: Revolutionizing Retail Store automation and Beyond 22

Artificial Intelligence in Robotics: Revolutionizing Retail ...

引用

22nd international Learning and Technology conference, L and T 2025

作者： Ali, Khalufi Nasser Jazan University College of Business Department of Management and Marketing Jazan Saudi Arabia

ISBN: (纸本)9798331532215

AI in robotics involves the application of efficient algorithm and computational techniques that allows robots to operate independently, learn from the environment and make efficient decisions. However, in current studies, mostly contact methods that cannot be learned and do not respond to change are used despite the progress made in this regard. These approaches often fail to respond in real-time to а certain issue, which can result in inefficiency and low overall operational performance. To overcome these shortcomings, this paper presents a new technique in which DRL (Deep Reinforcement Learning) is used for motion control of a robotic arm and limb in retail automation. DRL allows the robotic system to learn the best motion profiles from playing a game with its surroundings with reduced chances of a less optimum setting due to changing environmental conditions. The most important peculiarities of the proposed approach include integration of enhanced neural structures and realization of automatic learning and decision-making. The effectiveness of the DRL approach is shown to be considerably higher than that of traditional ones, with accuracy reaching 99.3%. Such outcomes speak to the prospects to transform robotics through DRL knowledge and skills to enhance real-time adaptation and increase effectiveness. Lastly, this research enhances the existing literature in AI & Robotics and accordingly establishes the foundation for further developments of intelligent automation systems in various fields. © 2025 IEEE.

关键词： Deep reinforcement learning

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Identification of Wiener-Hammerstein Model Using Stochastic Variational Bayesian Learning 17th

Identification of Wiener-Hammerstein Model Using Stochastic ...

引用

17th international conference on Intelligent Robotics and Applications

作者： Li, Junhao Zhang, Fukai Wang, Cong Liu, Yuehu Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Peoples R China

ISBN: (纸本)9789819607822;9789819607839

The complexity of probability estimation has limited the application of Bayesian learning in nonlinear system identification. This paper addresses Wiener-Hammerstein (WH) nonlinear process identification in the presence of process noise and measurement noise, we propose a Stochastic Variational Inference (SVI) method inspired by stochastic optimization. The SVI method leverages probabilities of intermediate variables to estimate natural gradients of model parameters and updates the posterior probabilities of hidden variables. Compared to the traditional Variational Inference (VI) method, our proposed approach significantly reduces computational complexity. The effectiveness of the SVI method is verified by two numerical simulations and the WH benchmark problem, thereby providing a fresh perspective for efficiently identifying nonlinear systems with large-scale uncertain data.

关键词： Bayesian learning Stchastic optimization nonlinear system identification Wiener-Hammerstein model

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A Novel Study on modelling and Adaptive Optimal control of a Tubular Reactor Based on Gaussian Processes 19th

A Novel Study on Modelling and Adaptive Optimal Control of a...

引用

19th international conference on Artificial intelligence: Methodology, Systems, and Applications, AIMSA 2024

作者： Grancharova, Alexandra Xie, Junhong Kocijan, Juš Department of Industrial Automation University of Chemical Technology and Metallurgy Sofia Sofia Bulgaria Department of Systems and Control Jozef Stefan Institute Ljubljana Slovenia University of Nova Gorica Rožna Dolina Slovenia

ISBN: (纸本)9783031815416

Number of engineering systems can be characterized as complex since they have a dynamic and nonlinear behaviour incorporating a stochastic uncertainty. On the other hand, as a machine learning method, Gaussian processes (GP) provide a practical, probabilistic approach to learning in kernel machines. This makes them very suitable for obtaining probabilistic, nonparametric black-box models of stochastic nonlinear dynamic systems. In this paper, a novel study on the modeling and adaptive optimal control of a tubular reactor is made by using Gaussian processes. Such reactor is a typical example of a nonlinear distributed parameters system, whose first-principles dynamic models consists of partial differential equations. The purpose is to obtain a nonlinear autoregressive models with exogenous input (NARX) of the output concentration and temperature of the reactor by applying a GP modeling approach. The identified surrogate models are then used to design an adaptive model predictive controller to achieve optimal performance of the reactor despite of the stochastic changes in the feed temperature. The performance of the adaptive MPC based on GP models is studied by simulation experiments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Predictive control systems

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IEEE Joint 19th international Symposium on computational intelligence and Informatics and 7th international conference on Recent Achievements in Mechatronics, automation, Computer Sciences and Robotics, CINTI-MACRo 2019 - Proceedings

IEEE Joint 19th International Symposium on Computational Int...

引用

19th IEEE Joint international Symposium on computational intelligence and Informatics and 7th international conference on Recent Achievements in Mechatronics, automation, Computer Sciences and Robotics, CINTI-MACRo 2019 - Proceedings

ISBN: (纸本)9781728156255

The proceedings contain 36 papers. The topics discussed include: mixed-order Sugeno model to predict the resultant force in the milling process for honeycomb sandwich;stoichiometry control of the two gas reactive sputtering process;demand side management electric energy consumption data processing architectures within internet of things context;development of an LQ regulator for longitudinal vehicle control of an automated vehicle;parking lot exploration strategy;a semi-automated generation of entity relationship diagram based on morphosyntactic tagging from the requirements written in a Serbian natural language;data analytics for health-care risk predictions based on ensemble classifiers and subjective projection;and data analytics for health-care risk predictions based on ensemble classifiers and subjective projection.

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Proceedings of 2021 IEEE international conference on Robotics, automation, Artificial-intelligence and Internet-of-Things, RAAICON 2021

Proceedings of 2021 IEEE International Conference on Robotic...

引用

2021 IEEE international conference on Robotics, automation, Artificial-intelligence and Internet-of-Things, RAAICON 2021

ISBN: (纸本)9781665478694

The proceedings contain 24 papers. The topics discussed include: suspension system modelling and control for an electric vehicle driven by in-wheel motors;a comprehensive study and modern take on Intel 8086 like modular trainer board system;a comparison of chaotic key sequence and blockchain hasher for the privacy protection of the Internet of things image data;smart diaper: non-stop diaper monitoring system;TOS: a relative metric approach for model selection in machine learning solutions;a simple SDR based method to spoof low-end GPS aided drones for securing locations;an analytical approach to predict the COVID-19 death rate in Bangladesh utilizing multiple regression and seir model;designing and implementing robot-web-suite, a cloud based robotics platform;and quadcopter dynamic modeling and stability control design using hardware in loop.

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IEEE Joint 22nd international Symposium on computational intelligence and Informatics and 8th international conference on Recent Achievements in Mechatronics, automation, Computer Science and Robotics, CINTI-MACRo 2022 - Proceedings

IEEE Joint 22nd International Symposium on Computational Int...

引用

Joint 22nd IEEE international Symposium on computational intelligence and Informatics and 8th IEEE international conference on Recent Achievements in Mechatronics, automation, Computer Science and Robotics, CINTI-MACRo 2022

ISBN: (纸本)9798350398823

The proceedings contain 64 papers. The topics discussed include: noise-based logic versus quantum supremacy;modeling cerebro-vascular autoregulation after postural change perturbations;a review on emotion based harmful speech detection using machine learning;comparison of automatic question generation techniques;transparent slide detection and gripper design for slide transport by robotic arm;proposing a new model for estimation of oil rate passing through wellhead chokes in an Iranian heavy oil field;real-time multi-user 3D visualization software in medicine;evaluation of the use of an intelligent system in the calibration of a refined car-following model;inertial sensor-based movement classification with dimension reduction based on feature aggregation;and on the simulation of lower order control strategies for higher order systems.

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Prediction of Rolling Force in Hot Rolling Process Based on Rollformer Model 16th

Prediction of Rolling Force in Hot Rolling Process Based on ...

引用

16th international conference on modelling, Identification and control, ICMIC 2024

作者： Li, Qing Jiang, Pengyuan Yuan, Li School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China

ISBN: (纸本)9789819617760

In the steel manufacturing industry, the precise forecasting of rolling forces in the hot rolling process is vital for enhancing production efficiency and ensuring product quality. Traditional forecasting methods exhibit limitations when addressing the nonlinearity and variability of industrial data, and current deep learning models are slow to respond to real-time data, with room for improvement in predictive accuracy within dynamic production settings. This paper introduces the Rollformer deep learning model, which adeptly captures intricate nonlinear patterns through the self-attention mechanism's nonlinear transformation capabilities, and boosts sensitivity and adaptability to data variability via adaptive weight distribution. The Rollformer model also pioneers the Roll Self-attention mechanism, leveraging probabilistic sparsity to diminish computational complexity and accelerate the model’s response. Additionally, it employs a generative decoder for swift long-sequence predictions, thereby augmenting the model’s predictive precision. Validated with extensive real-world data from steel mills, the Rollformer model demonstrates superior predictive accuracy and response speed when compared to other Transformer-based deep learning models such as Autoformer and Conformer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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