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ieee International Conference on Cyber Technology in automation, control, and Intelligent Systems

作者： Shijia Fu Haoyuan Sun Zheng Liu Honggui Han Yan Zhang Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing University of Technology Beijing China China Academy of Information and Communications Technology Beijing China

Influenced by the biochemical reactions and sensor characteristics, the wastewater treatment process (WWTP) has been regarded as a multirate sampling system. To deal with the asynchronous control problem caused by multirate sampling, a multimodel predictive control (MMPC) scheme is proposed to improve the operation performance of WWTPs. First, a multimodel predictive control structure is constructed such that the controlled variables with multirate sampling intervals of WWTPs are able to be tracked synchronously at each sampling instant. Second, the fuzzy neural networks (FNNs) are adopted as the carrier for multimodel to compute the synchronous prediction outputs of all controlled variables at each sampling instant. Moreover, an adaptive cost function is excogitated to obtain the optimal control action in which the adaptive parameter is to minimize the impact of forecast errors on the tracking effect. Ultimately, the experiment results on the benchmark simulation model 1 (BSM1) have verified the efficiency of the proposed MMPC.

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Fast convergent on-board Li-ion battery state of charge estimation via back-forth moving horizon estimation

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ieee Transactions on Power Electronics 2025年

作者： Liu, Zhihao Xiao, Yu Yuan, Yuan Xu, Xiaodong Luo, Biao Huang, Tingwen Ministry of Education School of Automation and Key Laboratory of Industrial Intelligence and Systems Central South University Changsha410083 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen China

Accurate state of charge (SOC) estimation is crucial for the safe operation of lithium-ion batteries (LIBs), yet existing methods are limited by sensitivity to initial SOC guess or high computational complexity. To address these challenges, this study proposes an onboard back-forth moving horizon estimation (BFMHE) framework for SOC estimation, achieving high precision, robustness, and computational efficiency compatible with microcontroller implementation. An efficient linear equivalent circuit model (ECM) is established, where the open-circuit voltage-SOC relationship is approximated by piecewise linear functions. The proposed framework employs two MHE estimators that iteratively operate in opposite directions over a finite time horizon, which enables rapid convergence. Moreover, this study provides a rigorous proof of the stability of the BFMHE method for the battery system with current input. The proposed approach is validated experimentally under various operating conditions and with different initial SOC guesses. Experimental results show that the proposed onboard BFMHE method achieves a maximum estimation error of less than 1% and quickly converges to the true SOC value, demonstrating strong robustness to initial estimation errors. © 1986-2012 ieee.

关键词： Lithium-ion batteries

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An efficient approach to closed-loop shape control of deformable objects using finite element models

An efficient approach to closed-loop shape control of deform...

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ieee International Conference on Robotics and automation (ICRA)

作者： Koessler, A. Filella, N. Roca Bouzgarrou, B. C. Lequievre, L. Corrales Ramon, J-A Univ Clermont Auvergne Inst Pascal CNRS Clermont Auvergne INP F-63000 Clermont Ferrand France Univ Santiago de Compostela Ctr Singular Invest Tecnoloxias Intelixentes CiTI Santiago De Compostela 15782 Spain

ISBN: (纸本)9781728190778

Robots are nowadays faced with the challenge of handling deformable objects in industrial operations. In particular, the problem of shape control, which aims at giving a specific deformation state to an object, has gained interest recently in the research community. Among the proposed solutions, approaches based on finite elements proved accurate and reliable but also complex and computationally-intensive. In order to mitigate these drawbacks, we propose a scheme for shape control that does not require to run a real-time simulation or to solve an implicit optimization problem for computing the control outputs. It is based on a partition of the nodal coordinates that allows deriving a control law directly from tangent stiffness matrices. This formulation is also coupled with the introduction of reduced finite element models. Simulation and experimental results in the context of linear deformable object manipulation demonstrate the interest of the proposed approach.

关键词： Deformable models Shape control Three-dimensional displays Shape computational modeling Estimation Finite element analysis

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The Future of Car automation Field with Smart Driverless Technologies 1

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International Conference on computational intelligence in Machine Learning, ICCIML 2021

作者： Thombare, Shreyas Shivajirao Department of Electrical Engineering Dr. D. Y. Patil Institute of Engineering and Technology Maharashtra Pune India

ISBN: (数字)9789811684845

ISBN: (纸本)9789811684838

This study investigates the challenges and opportunities particularly in current transportation policies that may arise to change existing mobility systems with the help of intelligent autonomous vehicle (IAV) technologies. India is already one of the highest levels of population, so all this leading to a massive increase in road traffic which leads to serious issues such as permanent traffic jams increased air pollution at harmful levels, accidents, or even human losses. Hence, technology and transportation infrastructure providers are needed to provide safer, large scalable, better flexible, and low-cost effective solutions to all these problems. This paper provides a comprehensive review of the relevant literature and explores intelligent transportation systems (ITS) which is one of the best solutions, and this offers traffic monitoring, guidance, or alerting by communication capabilities in between vehicles to avoid any accidents in terms of safety elements (Litman in Autonomous vehicle implementation predictions implications for transport planning, 2018;Pino et al. in ieee Access 6:17527–17532, 2018;Bagloee et al. in J Mod Transp 24(4):284–303, 2016;Aazam et al. in ieee Commun Mag, 2018 (in press);Gong et al. in ieee Trans Intell Transp Syst 19:390–401, 2017;Miettinen in Evolutionary algorithms in engineering and computer science: recent advances in genetic algorithms, evolution strategies, evolutionary programming, GE. Wiley, New York, 1999;Making Innov E-J, ***, Oct 2015;Campioni et al. in ieee Trans Veh Technol J Latex Class Files 14(8), 2015;El Zoghby et al. in ieee 68(2) ITVTAB, 2019;ieee International symposium on technology in society (ISTAS) proceedings, 2015;NCSL—National conference of state legislatures, database on autonomous vehicles legislation;Cai et al. in An empirical air-to-ground channel model based on passive measurements in LTE, 1140;Qin et al. in ieee Trans Veh Technol;Meng et al. in ieee Trans control Syst Technol 25:148

关键词： Autonomous vehicles

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Style-Specific Turkish Pop Music Composition with CNN and LSTM Network 19

Style-Specific Turkish Pop Music Composition with CNN and LS...

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19th ieee World symposium on Applied Machine intelligence and Informatics (SAMI)

作者： Tanberk, Senem Tukel, Dilek Bilgin NetRD Int R&D Istanbul Turkey Dogus Univ Comp Engn Dept Istanbul Turkey

ISBN: (纸本)9781728180533

The recent advance in artificial neural networks is an inspiration for automatic music generation. Deep learning algorithms help to produce pleasing melodies. They lead the creativity of musicians to be reproduced in digital environments. The proposed system learns from the Turkish popular music and then produces new music. In this study, our goal is to generate melody with a specific style, such as unforgettable soundtracks admired widely. We proposed a novel combination of convolutional neural network (CNN) and long short-term memory (LSTM) network for music generation. The experimental results reveal that the proposed combined deep model exhibits remarkable music quality compared to the lstm-only deep model or cnn-only deep model. We also conducted a survey to evaluate the quality of the generated music. The survey results show that the introduced model is capable of producing better quality and more pleasant music compared to other state-of-the-art music generation methods.

关键词： music generation deep learning convolutional neural network long short-term memory automation

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Learning to Solve Nonlinear Optimization Problem with Deep Reinforcement Learning

Learning to Solve Nonlinear Optimization Problem with Deep R...

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2022 ieee International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Gao, Yue Yang, Qiyue Wu, Huajian Sun, Ming Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence and AI Institute Shanghai China Shanghai Jiao Tong University Department of Automation Shanghai China

ISBN: (纸本)9781665481090

Nonlinear least-squares problems (NLS) are pop-ular in engineering and scientific fields. Traditional optimization methods such as Newton's method and Gaussian-Newton method (GN) suffer from the sensibility to initial values and the high computational complexity. In this paper, we propose LS-DDPG, a robust optimization method utilizing deep rein-forcement learning algorithms to solve nonlinear least-squares problems. The experiment results on synthetic data demonstrate that the proposed method outperforms Newton's method in terms of computation cost, convergence speed and initial values sensibility. In addition, LS-DDPG is utilized on model predictive control (MPC) problems for trajectory planning and tracking tasks in self-driving with longer prediction horizon and higher accuracy than baseline methods. © 2022 ieee.

关键词： Newton-Raphson method

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Adversarial Reinforcement Learning for Procedural Content Generation

Adversarial Reinforcement Learning for Procedural Content Ge...

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ieee Conference on Games (ieee CoG)

作者： Gisslen, Linus Eakins, Andy Gordillo, Camilo Bergdahl, Joakim Tollmar, Konrad SEED Redwood City CA 94065 USA Frostbite Elect Arts EA Stockholm Sweden

ISBN: (纸本)9781665438865

Training RL agents to solve novel environments is a notoriously difficult task. Here we present a new approach ARLPCG: Adversarial Reinforcement Learning for Procedural Content Generation, which procedurally generates and tests previously unseen environments with an auxiliary input as a control variable. The procedurally generated environments induces state diversity which increases the generalizability of the trained agents. ARLPCG deploys an adversarial model with one PCG RL agent (called Generator) and one solving RL agent (called Solver). The Generator receives a reward signal based on the Solver's performance, which encourages the environment design to be challenging but not impossible. To further drive diversity and control of the environment generation, we propose using auxiliary inputs for the Generator. The benefit is two-fold: Firstly, the Solver achieves better generalization through the Generator's generated challenges. Secondly, the trained Generator can be used as a creator of novel environments that, together with the Solver, can be shown to be solvable. We create two types of 3D environments to validate our model, representing two popular game genres: a third-person platformer and a racing game. In these cases, we show that ARLPCG has a significantly better solve ratio, and that the auxiliary inputs renders the levels creation controllable to a certain degree. For a video compilation of the results please visit https://***/z7q2PtVsT0I.

关键词： machine learning game testing procedural content generation automation computer games reinforcement learning adversarial

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High-speed Train Timetabling Based on Reinforcement Learning

High-speed Train Timetabling Based on Reinforcement Learning

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ieee symposium Series on computational intelligence (SSCI)

作者： Wanlu Yang Peng Jiang Shiji Song Department of Automation and BNRist Tsinghua University Beijing China

ISBN: (纸本)9781665487696

Chinese high-speed railway has developed rapidly in the more intelligent and automatic direction over the past few decades. In this paper, we consider the optimization problem of the train timetable for the high-speed railway to minimize the total train waiting time and total station occupied time. To deal with time-related constraints, we first establish the train operation environment based on Discrete Event Dynamic System (DEDS). Then, we reformulate the timetabling problem as a Markov Decision Process (MDP) problem and propose an improved Q-learning approach by redesigning Q-value function to solve the problem. Finally, we consider the Beijing-Shanghai high-speed railway as a numerical example, where the passenger flow and train running time are stochastic. We empirically show that our Q-learning method reduces over 30% total waiting time and 1.9% total occupied time compared with the well-known First-Come-First-Service (FCFS) scheduling strategy.

关键词： Q-learning Processor scheduling Markov processes Rail transportation Dynamical systems Optimization computational intelligence

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Integrated Autonomous Model System as Research Media

Integrated Autonomous Model System as Research Media

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International symposium on computational intelligence and Informatics

作者： László Horváth Institute of Applied Mathematics and Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary

ISBN: (纸本)9798350398830

This paper introduces concept and method of Integrated Autonomous Model System (IAMS) which is developed as organized content in dedicated collaborative virtual space (CVS).IAMS is a recent result and at the same time a possible answer to new requests by engineering related research. Development of IAMS in CVS uses capabilities offered by Modeling Sotware Platform (MSP). IAMS is devoted as model system which is capable of system, situation, behavior, and context representation. In its application proposed in this paper, IAMS is a purposeful experimental model serving research and represents results in applied informatics and applied mathematics in model environment suitable for development, validation, and experimental application of results. Research using IAMS is planned in the organization of a Virtual Research Laboratory (VRL). In this paper, first the relevant elements of recently modified concept of VRL are briefed to introduce contributions in this paper. Following this, IAMS is characterized as integrated media of research. Next, definition of global research organization in CVS using cloud organized services by research purpose configured MSP in the VRL is explained. Finally, PhD research program in functional organic shape modeling as means for the representation of living organizations in close connection with modeling of engineering shapes is introduced as example for key areas in future research at VRL.

关键词： Mechatronics Shape computational modeling Organizations Media Mathematical models Software

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INES 2022 - 26th ieee International Conference on Intelligent Engineering Systems 2022, Proceedings

INES 2022 - 26th IEEE International Conference on Intelligen...

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26th ieee International Conference on Intelligent Engineering Systems, INES 2022

ISBN: (纸本)9781665492096

The proceedings contain 44 papers. The topics discussed include: robotics out of the mainstream: special topics in agrifood and laboratory automation;time to start using checklists for reporting artificial intelligence in health care and biomedical research: a rapid review of available tools;prediction of photovoltaic energy production using machine learning methods in the RapidMiner application;increasing patient specificity of the recurrent neural network based insulin sensitivity prediction by transfer learning;autonomous non-technical surgical skill assessment and workload analysis in laparoscopic cholecystectomy training;evaluation of bit string fast reroute mechanism;effect of spectral resolution on the segmentation quality of magnetic resonance imaging data;non-dominated set of public service system designs using simulated annealing approach;time-varying parameter identification of a tumor growth model using moving horizon estimation;reasoning mechanism for the implementation of computational design synthesis;on the manipulability of the generalized triangle parallel delta robots;and robot instead of laboratory technicians - slide holder detection and 3D position determination by robotic arm.

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