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检索条件"任意字段=Conference on Simulation-Driven Modeling and Optimization"

共 1171 条记录，以下是61-70 订阅

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A Design Methodology of MMIC Power Amplifiers Using AI-driven Design Techniques 19

A Design Methodology of MMIC Power Amplifiers Using AI-drive...

引用

19th International conference on Synthesis, modeling, Analysis and simulation Methods, and Applications to Circuit Design, SMACD 2023

作者： Xue, Liyuan Fan, Haijun Ding, Yuan Liu, Bo University of Glasgow James Watt School of Engineering Glasgow United Kingdom Heriot-Watt University Institute of Sensors Signals and Systems Edinburgh United Kingdom

ISBN: (纸本)9798350332650

Designing a monolithic microwave integrated circuit (MMIC) power amplifier (PA) is challenging due to the involvement of multiple stages with tens of parameters and several types of simulations. To address this challenge, artificial intelligence (AI) techniques have gained significant attention. This paper presents an AI-driven design methodology for MMIC PAs, which incorporates a surrogate model-assisted global optimization algorithm, data-flow interface, and simulators. The proposed methodology is verified on a practical 3-stage PA design case that operates in 27-31 GHz and contains 30 variables. Notably, The case is successfully synthesized without an initial solution and exhibits good in-band performance consistency. The obtained results demonstrate the potential of AI-driven PA design for future applications. © 2023 IEEE.

关键词： Power amplifiers

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Information modeling for Data-driven Digital Twin simulation: Insights from Case Studies of Port Logistics and Urban Traffic Systems 43rd

Information Modeling for Data-Driven Digital Twin Simulation...

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43rd IFIP WG 5.7 International conference on Advances in Production Management Systems (APMS)

作者： Lim, Harry Kang, Hyoung Seok Kim, Duck Young Pohang Univ Sci & Technol Dept Ind & Management Engn Pohang South Korea Korea Automot Technol Inst Mat Technol R&D Div Cheonan South Korea

ISBN: (纸本)9783031716355;9783031716331;9783031716324

Digital twins, which enable the creation of virtual replicas of physical entities to simulate temporal changes and forecast future scenarios, have become increasingly vital in sectors such as logistics and traffic management. The deployment of digital twins can significantly enhance the efficiency of maritime operations and cargo handling, while also facilitating the strategic planning of road networks through the optimization of traffic flow and reduction of congestion. Recent advancements in international standards, particularly ISO 23247, have set forth comprehensive guidelines for the reference architecture, digital representation, and data exchange protocols of digital twins within the manufacturing sector. In this context, our study seeks to extend these standards to include applications in port logistics and traffic systems. This paper will offer practical guidelines for each specified domain through detailed case studies. Our approach entails a systematic method in which the necessary information for simulations is categorized. This is followed by a clear definition of the operational mechanisms of these simulations, culminating in the implementation of the simulation models. These models are crafted to optimize the scheduling processes for ships and cargo equipment, as well as to improve the management of traffic flow.

关键词： Digital Twin Framework & Architecture Information modeling Data-driven Discrete Event simulation Port Logistics and Traffic Systems

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Carbon Capture Optimisation for a Greener Tomorrow Using a Proxy Model-driven Numerical simulation

Carbon Capture Optimisation for a Greener Tomorrow Using a P...

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2024 SPE Nigeria Annual International conference and Exhibition, NAIC 2024

作者： Mensah, A.O. Osei, H. Lartey, P.K. SLB Ghana Petreoleum and Natural Gas Engineering Department University of Mines and Technology Ghana

ISBN: (纸本)9781959025474

As the urgency to combat climate change grows, the role of Carbon Capture, Utilization, and Storage (CCUS) in mitigating greenhouse gas emissions becomes increasingly pivotal for a more sustainable oil industry. However, the entrapment of CO2 in depleted reservoirs faces a significant challenge-cap rock integrity failure leading to CO2 leakages into surrounding aquifers. Previous studies have predominantly focused on CO2 storage modeling, neglecting optimization strategies that reduce leakage risks and how such optimization may impact storage performance. Consequently, limited knowledge exists on rock property sensitivity to cap rock leakage. This study centres on coupling flow-geomechanics simulation with proxy modeling to optimize CO2 storage and minimize leakage. A reservoir slab with varying permeability was modelled to investigate the efficiency of structural, stratigraphic, and residual trapping mechanisms. Incorporating a structural trapping mechanism alone resulted in 19.84% of CO2 entrapment. However, residual trapping contributed to 78.71% and solubility trapping led to 60.98% entrapment. The Barton-Bandis model was utilized to simulate cap rock integrity failure a sensitivity analysis was conducted. From this analysis, CO2 leakage through cap rock is mostly influenced by fracture-effective stress. Hence, there is a need to always monitor injection pressure since it can increase stress. The presence of a cap rock is integral to preventing the migration of captured CO2 Copyright © 2024, Society of Petroleum Engineers.

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Ensemble-based Offline Data-driven Evolutionary optimization for Operation Parameter of Blast Furnace 20

Ensemble-based Offline Data-driven Evolutionary Optimization...

引用

20th International conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Ye, Qiucheng Cai, Hongxia Bian, Yunqi School of Mechatronic Engineering and Automation Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China

ISBN: (纸本)9798350356328

Stable control of the iron-making process through the adjustment of operating parameters is essential to improve blast furnace productivity. However, due to the complex and dynamic nature of the reaction process, it is difficult to perform accurate modeling and simulation experiments for a blast furnace. This poses great difficulties in performing the evaluation of control strategies in real-time. Offline evolutionary optimization, as an optimization technique that relies only on historical data, enables the search for optimal operation parameters through the introduction of surrogates. Based on the characteristics of historical time-series data of the iron-making process, this study proposes an offline evolutionary optimization method based on advanced time-series surrogates. Ensemble strategy combining surrogates with different prediction time windows contributes robustness during the optimization process. Additionally, multiple sub-regions are maintained to enable extensive searching of the decision space with a multi-objective nature. The efficiency of the proposed pooling method is demonstrated through verification in actual production cases. © 2024 IEEE.

关键词： Blast furnaces

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modeling and simulation of Soft Robots driven by Embedded Artificial Muscles: an Example using Twisted-and-Coiled Actuators

Modeling and Simulation of Soft Robots Driven by Embedded Ar...

引用

American Control conference (ACC)

作者： Sun, Jiefeng Zhao, Jianguo Colorado State Univ Dept Mech Engn Ft Collins CO 80523 USA

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

Soft robots have been intensively investigated for manipulation and locomotion in recent years. However, the current state of soft robotics has significant design and development work but lags in modeling and control due to the difficulty in modeling them. In this paper, we present a physics-based analytical framework to model soft robots driven by Twisted-and-Coiled Actuators (TCAs), an artificial muscle that can he arranged in arbitrary shapes in the soft body of a soft robot to achieve programmable motions. The framework can model 1) the complicated routes of multiple TCAs in a soft body and 2) the coupling effect between the soft body and the TCAs during their actuation process. When not actuated, a TCA in the soft body is an antagonistic elastic element that restrains the magnitude of the motion and increases the stiffness of the robot. By stacking several modules together, we simulate the sequential motion of a soft robotic arm with three-dimensional bending, twisting, and grasping motion. The presented modeling and simulation approach will facilitate the design, optimization, and control of soft robots driven by TCAs or other types of artificial muscles.

关键词： Couplings Analytical models Computational modeling simulation Force Wires Stacking

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Recent Advances in ANN-Based Methods for Yield-driven Design of EM Structures

Recent Advances in ANN-Based Methods for Yield-Driven Design...

引用

IEEE MTT-S International conference on Numerical Electromagnetic and Multiphysics modeling and optimization (NEMO)

作者： Zhang, Jianan Zhang, Q. J. Southeast Univ State Key Lab Millimeter Waves Nanjing Peoples R China Carleton Univ Dept Elect Ottawa ON Canada

ISBN: (纸本)9781665486330

The design of microwave passive components should take into account the tolerances in the manufacturing process. This makes yield estimation and yield-driven design optimization essential steps in electromagnetic (EM) simulation-based microwave component design. Brute-force methods based on Monte Carlo (MC) analyses are not computationally feasible in practice due to the demand for a large number of EM simulations. Artificial neural networks (ANNs) hold a great potential to address this challenge. This paper reviews recent advances in ANN-based methods for the yield-driven design of EM structures, including a neuro-TF surrogate-assisted yield estimation method and the corresponding yield optimization algorithm. These advanced approaches are demonstrated by two microwave examples.

关键词： Artificial neural network neuro-transfer function statistical analysis EM structural uncertainties yield optimization

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Enhancing Continuous optimization with a Hybrid History-driven Firefly and Simulated Annealing Approach 19

Enhancing Continuous Optimization with a Hybrid History-Driv...

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19th International conference on Software Technologies, ICSOFT 2024

作者： Alizadeh, Sina Mouhoub, Malek Department of Computer Science University of Regina ReginaSK Canada

ISBN: (纸本)9789897587061

In this study, we propose a hybrid History-driven approach through collaboration between Firefly (FA) and Simulated Annealing (SA) algorithms, to improve the hybrid framework performance in finding the global optima in continuous optimization problems in less time. A Self-Adaptive Binary Space Partitioning (SA-BSP) tree is used to partition the search space of a continuous problem and guide the hybrid framework towards the most promising sub-region. To solve the premature convergence challenge of FA a"Finder - Tracker agents" mechanism is introduced. The hybrid framework progresses through three main stages. Initially, in the first phase, the SA-BSP tree is utilized within the FA algorithm as a unit of memory. The SA-BSP tree stores significant information of the explored regions of the search space, creates the fitness landscape, and divides the search space during exploration. Moving on to the second phase, a smart controller is introduced to maintain a balance between exploration and exploitation using HdFA and SA. During the third step, the search is limited to the most promising sub-region discovered. Subsequently, the SA algorithm employs the best solution’s information, including its fitness value and position, to efficiently exploit the limited search space. The proposed HdFA-SA technique is then compared against different metaheuristics across ten well-known unimodal and multimodal continuous optimization benchmarks. The results demonstrate HdFA-SA’s exceptional performance in finding the global optima solution while simultaneously reducing execution time. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Simulated annealing

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DATA-driven DESIGN OF HIGH ELECTRON MOBILITY TRANSISTOR DEVICES USING PHYSICS-INFORMED GAUSSIAN PROCESS modeling

DATA-DRIVEN DESIGN OF HIGH ELECTRON MOBILITY TRANSISTOR DEVI...

引用

ASME International Design Engineering Technical conferences / Computers and Information in Engineering conference (IDETC-CIE) / 49th Design Automation conference (DAC)

作者： Renteria, Anabel Xu, Yanwen Hamdan, Bayan Li, Zhou Cordero, Sergio Senesky, Debbie Wang, Pingfeng Univ Illinois Ind & Enterprise Syst Engn Urbana IL 61801 USA Stanford Univ Aeronaut & Astronaut Stanford CA 94305 USA

ISBN: (纸本)9780791887318

High electron-mobility transistors (HEMTs) have emerged as an attractive alternative for high-efficiency power systems, due to their good material properties to perform at high voltages, temperatures, and frequencies. For that reason, design optimization of HEMTs becomes imperative to ensure the quality and capability of the device when in service. There have been models derived from experimentation to guide the design of HEMTs. Nonetheless, due to its expensive manufacturing process, the relationship of the temperature channel with respect to the design parameters has not been investigated thoroughly. This paper presents a multiphysics finite element (FE) simulation to predict the HEMT's device maximum channel temperature when varying different design parameters. Furthermore, Gaussian Process (GP) based surrogate model was developed using the simulation results as the training database with adaptive sampling techniques for the optimization process. The proposed high-fidelity surrogate model effectively predicts the channel temperature of the HEMT device and enables an optimum search over the design space.

关键词： HEMT device design optimization multiphysics FE simulation surrogate model.

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2024 International conference on Materials Engineering and Intelligent Manufacturing, CMEIM 2024

2024 International Conference on Materials Engineering and I...

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2024 International conference on Materials Engineering and Intelligent Manufacturing, CMEIM 2024

The proceedings contain 28 papers. The topics discussed include: study on mechanical and thermal characteristics based on a new type of metal matrix composite cabin;data-driven optimization of material composition and casting processes for large ferritic nodular cast iron components in wind turbines;effect of modified coated aluminum powder on the explosive properties of emulsion explosive;study of the calculation IR and modeling on nitrile rubber;study on the trouser tearing characteristics of airship skin film materials;numerical simulation of underwater explosive shock wave with gas layer charge;gas layer charge influence law study of underwater explosion bubble pulsation;and research on the process parameters and welded joint quality of resistance spot welding of aluminum composite for car bodies.

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Integrated Data-driven Framework for Automatic Controller Tuning with Setpoint Stabilization Through Reinforcement Learning 19

Integrated Data-Driven Framework for Automatic Controller Tu...

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19th International conference on Software Technologies, ICSOFT 2024

作者： Mohajer, Babak Singh, Neelaksh Liebeskind, Joram BELIMO Automation AG Brunnenbachstrasse 1 Hinwil8340 Switzerland

ISBN: (纸本)9789897587061

We introduce a three-stage framework for designing an optimal controller. First, we apply offline black-box optimization algorithms to find optimal controller parameters based on a heuristically chosen setpoint profile and a novel cost function for penalizing control signal oscillations and direction changes. Then, we leverage cloud data to generate device-specific setpoint profiles and tune the controller parameters to perform well on the device with respect to the same cost function. Finally, we train a control policy on top of the offline tuned controller after deployment on device through an online learning algorithm to handle unseen setpoint variations. A novel reward function encouraging setpoint stabilization is added for preventing destabilization from coupling effects. Bayesian optimization and Nelder-Mead methods are used for offline optimization, and a state-of-the-art model free Reinforcement Learning algorithm namely Soft Actor-Critic is used for online optimization. We validate our framework using a realistic HVAC hydraulic circuit simulation. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Reinforcement learning

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