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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51-60 订阅

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2024 IEEE Latin American Electron Devices conference, LAEDC 2024

2024 IEEE Latin American Electron Devices Conference, LAEDC ...

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2024 IEEE Latin American Electron Devices conference, LAEDC 2024

ISBN: (纸本)9798350361292

The proceedings contain 26 papers. The topics discussed include: performance analysis and compact modeling of 12-nm P-channel bulk FinFETs at cryogenic operating temperatures for quantum computing applications;encapsulated solar cells based on PM6:Y7 with inverted architecture: degradation study;modeling and simulation of vertical organic permeable base transistors;variability-aware circuit design: Monte Carlo simulation versus noise analysis;FPGA based image transmission system in VHF and digital processing of the received signal using python;scalability perspective of nanotube TFET capacitorless DRAM in sub-20 nm regime;laser-induced graphene electrodes for urine osmolarity estimation through electrochemical impedance spectroscopy;artificial neural network driven optimization for analog circuit performance;and the impact of solvent on enhancing efficiency in PBDB-T-2F:Y6-O non-fullerene organic solar cells.

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BIOMECHANICAL IDENTIFICATION AND simulation OF PASSIVE ANKLE JOINTS IN ANYBODY 38

BIOMECHANICAL IDENTIFICATION AND SIMULATION OF PASSIVE ANKLE...

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38th ECMS International conference on Modelling and simulation, ECMS 2024

作者： Kleppe, Adam L. Shayestehpour, Mohammadamin Bjelland, Øystein Cyber-Physical Systems Laboratory Department of ICT and Natural Sciences NTNU - Norwegian University of Science and Technology Norway

Virtual biomechanical simulation models enable safe exploration of new treatment methods. This paper presents a data-driven force-dependent kinematics model within the AnyBody modeling system that simulates biomechanical stability of the passive ankle complex. A simulation model was developed in AnyBody based on the Twente Lower Extremity Model, and model parameters were identified by using a sequential least squares quadratic programming optimization method with results from ex vivo biomechanical experiments. The final root mean squared error was 2.29 degrees for rotation tests, and 0.26 mm for the lateral translation test. ©ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.

关键词： Quadratic programming

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EFFICIENCY-driven SUPERVISED LEARNING REGRESSORS IN POWER modeling AND optimization OF VERTICAL AXIS WIND TURBINES 18

EFFICIENCY-DRIVEN SUPERVISED LEARNING REGRESSORS IN POWER MO...

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ASME 18th International conference on Energy Sustainability (ES)

作者： Dorosti, Ehsan Shabani, Amir Vijayaraghavan, Krishna Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9780791887899

Of all the clean energy sources, wind power stands as the most widely available and employed form. Besides the Horizontal Axis Wind Turbines (HAWT), Vertical Axis Wind Turbines (VAWT) are attracting significant attention. This study focuses on optimizing a 12-kW lift-based 3-blade VAWT by introducing an optimal diffuser to enhance performance using two optimization procedures. To do that, a nonsymmetric diffuser in the shape of NACA4405 is introduced to the VAWT which is simulated using Computational Fluid Dynamics (CFD) and validated with experimental data. To optimize the power coefficient (Cp) of the VAWT, adjustments will be made to the position and orientation of both the upper and lower walls of diffuser. Two optimization methods were employed: one involves direct optimization, where a Genetic Algorithm (GA) is integrated with CFD. The second method utilizes Machine Learning (ML) models, such as Gaussian Process Regression (GPR), Support Vector Regression (SVR), and AdaBoost, fitted to the data set extracted from CFD. These ML models then replace CFD and are used in optimization. Results show that optimal diffuser can contribute to 27% increase in Cp. When employing GPR and AdaBoost, the optimal Cp values closely match those from direct optimization while optimization using ML models reduces computational costs by nearly 78% fewer simulation runs.

关键词： Vertical Axis Wind Turbine optimization Genetic Algorithm Machine Learning Gaussian Process Regression Support Vector Regression AdaBoost Augmentation Devices Diffuser Surrogate Model

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simulation-driven optimization for Supply Chain Agility

Simulation-Driven Optimization for Supply Chain Agility

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2023 IEEE International conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Chaudhary, Ish Kumar, Amol Borkakaty, Bidisha Rajeev Ranjan, N. Ranakoti, Omprakash Data Science & Insights Genpact Bangalore India

ISBN: (纸本)9798350327816

Supply chain management is a critical aspect of modern business operations. The retail manufacturers are grappling to predict customer demand, inventory, transportation, and production cost with ability to replicate real-world supply chain processes therefore it is essential for businesses to remain competitive in today's global marketplace. The motivation behind this research is to analyze and optimize supply chain operations through simulation. It aims to review simulation modeling techniques applied in the supply chain industry, including discrete event simulation, agent-based simulation, and system dynamics modeling. Furthermore, highlights the advantages of simulation modeling in supply chain management, including the ability to capture real-time data, simulate multiple scenarios, and evaluate the impact of different strategies on performance metrics. This simulation framework will help to model the supply chain operation of multiple dairy products for period of eight weeks, including varying demand and supply for different products which differ across the US region. It will allow us to provide insights for the future by capturing real data with abrupt changes in demand at the plant level. A comparative study on total cost (Fixed cost + Transportation cost) across the plant with and without optimization at different scenarios is presented for better realization of ease in making decisions at the business level. Holistically, this research effort provides retail manufacturers to make accurate predictions, optimize resource allocation, and enable real-time decision support. © 2023 IEEE.

关键词： Decision support systems Inventory Management Risk Assessment simulation Modelling Supply chain optimization

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Cavitation performance analysis and optimal design of the portable electric-driven axial flow pump 17

Cavitation performance analysis and optimal design of the po...

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17th Asian International conference on Fluid Machinery, AICFM 2023

作者： Tian, Ding Lu, Rong Zhu, Han Liu, Jinfeng Zhang, Yong Wei, Guangjuan Li, Gangyuan Si, Qiaorui Research Center of Fluid Machinery Engineering and Technology Jiangsu University Zhenjiang212013 China Jiangsu Xugong Construction Machinery Institute Co. Ltd. Xuzhou221004 China

Based on the ISIGHT optimization platform, the impeller parameterization modeling and simulation process of the portable axial flow pump is carried out through CFturbo, ICEM and CFX software, and the impeller is optimized with the help of multi-island genetic algorithm to target the two optimization objectives of efficiency and head. The optimization results show that the hydraulic efficiency increased by 6.3% under design conditions. At the same time, compared with the original solution, the optimized cavitation performance is significantly improved. Additionally, overall entropy production is significantly reduced. Field test results show that the head deviation is 1.42% and the efficiency deviation is 4.52% under design conditions, proving that the optimization results are reliable. © Published under licence by IOP Publishing Ltd.

关键词： Cavitation

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Multiresolution Dynamic Mode Decomposition Based modeling of Wastewater Treatment Process 4

Multiresolution Dynamic Mode Decomposition Based Modeling of...

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4th modeling, Estimation, and Control conference (MECC)

作者： Prakash, Om Huang, Biao Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada

The wastewater treatment process (WWTP) has now become an increasingly important process in order to accommodate sustainable goals and mitigate the water crisis. modeling of WWTP is crucial to performing estimation, control, and optimization. Since WWTP displays large-scale, multiscale behavior, a data-driven multiresolution dynamic mode decomposition (MrDMD) based approach fits well to uncover spatiotemporal structure at high resolution. In the current work, we propose incorporating exogenous variables (control) in the MrDMD framework. Further, we illustrate how slow mode reconstructions at different decomposition levels uniquely capture the process's multiscale behavior. Additionally, we demonstrate the efficacy of the proposed approach on benchmark WWTP. We also compare its performance with the dynamic mode decomposition-based model. Copyright (c) 2024 The Authors.

关键词： Wastewater treatment processes Nonlinear system identification Discrete event modeling and simulation Dynamic mode decomposition

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Towards Effective Traffic Signal Safety and optimization Using Fisheye Video 9

Towards Effective Traffic Signal Safety and Optimization Usi...

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9th International conference on Vehicle Technology and Intelligent Transport Systems (VEHITS)

作者： Sengupta, Rahul Banerjee, Tania Chen, Ke Karnati, Yashaswi Ranka, Sanjay Rangarajan, Anand Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA

ISBN: (纸本)9789897586521

Most traffic authorities across the US usually collect high-resolution (10 Hz) loop detector and signal state data and video data. The multiple modalities of data that are readily available can be utilized for better traffic operations management and improving safety. In this work, we show that the fusion of widely deployed loop detector data with trajectory information collected through video cameras can augment intersection safety and operational efficiency analysis. The additional information that can be extracted from the object's (vehicle and pedestrian) trajectory derived from video data when fused with signal state data leads to several interesting safety analyses. Data analysis shows a significant variance in turn-movement counts, pedestrian behaviors, vehicle composition, etc., temporally (hour-of-day, day-of-week, etc.) and spatially (approach-wise). We present a simulation-based approach for customizing signal timing plans based on the traffic behavior at the intersections at various times. When used to drive simulations in demand generation, we show that the fused data calibrating the simulation parameters can lead to potential improvements in existing signal timing plans that match reality and can greatly help improve intersection safety and operational efficiency by providing planners with data-driven insights.

关键词： simulation and modeling Data Mining and Data Analysis modeling simulation Control of Pedestrians and Cyclists

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Data-driven NARX-based digital twin thread modelling of Boost converter inductance current under closed-loop output voltage control 21

Data-driven NARX-based digital twin thread modelling of Boos...

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IEEE 21st International Power Electronics and Motion Control conference (PEMC)

作者： Nalepa, Radoslaw Najdek, Karol Wroclaw Univ Sci & Technol Dept Elect Power Engn Wroclaw Poland

ISBN: (纸本)9798350385236;9798350385243

This paper introduces a pioneering approach to optimizing input and output delays in Nonlinear Autoregressive with Exogenous Inputs (NARX) Neural Network (NN) models through the use of Mutual Average Information (MAI), enhancing the dynamic modeling of power converters. Our research advances the development of digital twins by employing a data-driven methodology that significantly improves model accuracy for systems under variable loads. The novelty of our work lies in applying the MAI for delays selection in NARX-NN models, a technique that ensures high fidelity in capturing the intricate dynamics of power converters. The validation of our approach is underscored by the use of an advanced simulation model that not only embodies key non-linearities and parasitic components but is also under closed-loop control with a PI compensator. This adds a layer of complexity and realism to our model, illustrating its potential for real-world application. By demonstrating the improved performance of digital twins in power electronics through this refined delay optimization process, our study opens new avenues for future research and application in the field.

关键词： NARX digital twin Blackbox model Boost converter closed-loop control MAI

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AutoOPT: Data Generation and optimization for Digital Twin Network (DTN) 16

AutoOPT: Data Generation and Optimization for Digital Twin N...

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IEEE 16th International conference on Cloud Computing (IEEE CLOUD)

作者： Li, Mei Zhou, Cheng Lu, Lu Zhang, Yan Sun, Tao China Mobile Res Inst Beijing Peoples R China Univ Oslo Dept Informat Oslo Norway

ISBN: (纸本)9798350304817

Traditional network management solutions can not easily meet the requirements of new applications (such as AR/VR, cloud gaming, and vehicular networks) at a reasonable cost. Digital Twin Network (DTN) builds real-time mirrors of physical networks, which can enhance the simulation, optimization, verification, and control capabilities that physical networks lack. AI models have been used to model complex network environments, which helps build real-time, lightweight, and high-precision DTN. This paper proposes a data generation and optimization method for data-driven DTN AI modeling through Data-Centric AI called AutoOPT. First, AutoOPT generates data in small networks based on scale-independent indicators, which helps the model generalize to large networks. Then, AutoOPT automatically filters out highquality data through seed sample selection and incremental optimization, which helps the model be effectively trained to enhance accuracy and generalization. We apply AutoOPT to the DTN performance modeling scenario and test on GNNet Challenge datasets. The experimental results show that AutoOPT is more cost-efficient than the winning solutions of the challenge but can obtain similar results.

关键词： digital twin network (DTN) data generation data optimization data-centric AI network performance

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Dynamic Energy Management and Carbon Footprint Mitigation using AI and IoT in Residential Communities 2

Dynamic Energy Management and Carbon Footprint Mitigation us...

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2nd International conference on Inventive Computing and Informatics (ICICI)

作者： Raj, J. Relin Francis Karthikeyan, U. Krishnan, R. Santhana Rajkumar, G. Vinoth Kuruvilla, Sincy Elezebeth Sakthidevi, I Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Commun Engn Chennai Tamil Nadu India SRM Inst Sci & Technol Fac Engn & Technol Sch Comp Coll Engn & TechnolDept Comp Technol Chennai Tamil Nadu India SCAD Coll Engn & Technol Dept Elect & Commun & Engn Cheranmahadevi India JP Coll Engn Dept Elect & Commun Engn Thenkasi India RajaRajeswari Coll Engn Dept Elect & Elect Engn Bengaluru India Adhiyamaan Coll Engn Dept Informat Technol Hosur India

ISBN: (纸本)9798350373301;9798350373295

Dynamic energy management and carbon footprint mitigation in residential communities are crucial for achieving sustainability goals. This research presents a novel algorithm, termed EnerGAIN, which integrates Bayesian optimization and Random Forests within an AI and IoT framework. EnerGAIN aims to optimize energy consumption patterns while reducing carbon emissions in residential settings. simulation analyses were conducted to evaluate the performance of EnerGAIN compared to existing algorithms. Key simulation metrics including energy efficiency, carbon footprint reduction, and predictive accuracy were utilized for comparison. Results demonstrate that EnerGAIN outperforms existing algorithms in terms of energy optimization and carbon footprint reduction, showcasing its effectiveness in promoting sustainable energy practices in residential communities. This research contributes to the advancement of AI and IoT-driven solutions for dynamic energy management and carbon footprint mitigation, offering promising avenues for fostering environmentally conscious behaviors in residential settings.

关键词： Deep Learning Energy Efficiency Energy modeling Hierarchical Framework optimization Smart Buildings Deep Belief Networks Multi-scale Analysis

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