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

共 1170 条记录，以下是351-360 订阅

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AI-Enhanced Multi-Dimensional Stochastic Process modeling for QoS Analysis in 6G Networks Under Joint Fading and Dynamic Interference Constraints in $\alpha-\eta-\mu$ Environments

AI-Enhanced Multi-Dimensional Stochastic Process Modeling fo...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International conference on

作者： Deepak Upadhyay Alankrita Aggarwal Pardeep Singh Deepak Suresh Asudani Nookala Venu Aakash Singh Ranswal Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering University Institute of Engineering Apex Institute of Technology UIE Chandigarh University Mohali India School of Computer Science and Engineering Galgotias University Noida India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

In this paper, we investigate AI-enhanced multidimensional stochastic process modelling for QoS analysis in 6G networks subjected to $\alpha-\eta-\mu$ fading and dynamic interference constraints. simulation results and heatmaps are utilized for the investigation of fading parameters such as shape factors and interference power, that impacts network performance. Our experimental findings show that AI-driven models provide markedly superior results in improving a number of QoS metrics, including throughput, latency, and packet loss compared to traditional methods. The research illustrates how AI improves connectivity, and operational stability of a network which adapts in times of fluctuating conditions. Our results demonstrate the ability of AI to preserve QoS while balancing performance and power, thereby providing a perspective for 6G network design and optimization.

关键词： Fading channels 6G mobile communication Adaptation models Analytical models Stochastic processes Packet loss Quality of service Throughput Stability analysis optimization

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Robust Data-driven Model Predictive Control via On-policy Reinforcement Learning for Robot Manipulators

Robust Data-driven Model Predictive Control via On-policy Re...

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Industrial Cyber-Physical Systems (ICPS)

作者： Tianxiang Lu Kunwu Zhang Yang Shi Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

In this paper, a robust data-driven model predictive control (MPC) via on-policy reinforcement learning (RL) is presented for the regulation of constrained robot manipulators subject to both model mismatch and bounded additive disturbances. Based on the Euler-Lagrangian model of the robot manipulator, the model mismatch is characterized by the difference between the system matrix describing the Coriolis and centrifugal torques and its initial approximation. The additive disturbances considered in this work affect the torques applied to the joints. To reduce the complexity of the prediction model adopted by MPC and thus the computational load of solving the online control problem in MPC, the inverse dynamics controller is incorporated into the control framework. The rigid tube-based MPC with adaptive design of the terminal weighting matrix and terminal set is presented to ensure robust constraint satisfaction. The use of the inverse dynamics control policy leads to the selection of the on-policy RL algorithm: Sarsa for designing the policy to update key parameters of the MPC optimization problem. The efficacy of the proposed control framework is validated by a case study using a robot manipulator with two revolute joints in simulation.

关键词： Additives Uncertainty Heuristic algorithms Computational modeling Reinforcement learning Predictive models Prediction algorithms

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Offshore Technology conference, OTC 2025

Offshore Technology Conference, OTC 2025

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Offshore Technology conference, OTC 2025

ISBN: (纸本)9781959025610

The proceedings contain 364 papers. The special focus in this conference is on Offshore Technology. The topics include: An Optimized Universal Spread Mooring System for Utility Scale Floating Offshore Wind Farms;consideration of Creep and Relaxation on the Fatigue Life of Copper Conductors in Dynamic Cables;deformation Analysis of Suction Anchors for Floating Offshore Wind Turbines;Dynamic Kill modeling of Subsea CO2 and CH4 Blowouts: DifferentiatingFactors and their Implications for Offshore Carbon Sequestration;Flow Assurance Challenges of Well Design for CO2 Injection into a DepletedGas Reservoir in Southeast Asia;Paving the Road to Success: The First Advanced Through-Drill PipeFormation Evaluation Run in the UAE to Appraise the Early Jurassic Reservoir Sequence;coupled Drilling optimization Based on Hybrid Physics-Machine Learning Models;finned Suction Piles: Groundbreaking Offshore Experience in Deepwater Clay Soils of Brazil;application of Data-driven Techniques in 3D Soil Characterization for Top-Hole Design;skirt Pile Foundations in Offshore Platforms: Group Effect and Behavior in Shallow Waters;decision-Making Framework for Marine Anchor Selection in Floating Offshore Wind Farms;numerical simulation of Conductor Stability in Deepwater Hydrate-Bearing Formation;Advancing CO2 Gas/Oil Injection at Different IFT Assessment in Carbonated Reservoir Through Innovated Gas Injection Design;Innovative Integration of Geological Concepts in Dynamic Model HistoryMatching for a Complex Offshore Carbonate Reservoir in Abu Dhabi, UAE;flexible Coiled Tubing Implementation for Subsea HydrateRemediation – Campos Basin Field Application;Controlling Joule-Thomson Cooling in Wellhead Equipment in Subsea CO2 Injection Wells;integrated Workflow for Designing a Multi-Zonal Water Injector with Intelligent Well Completion Functionality;Efficient FPSO Redeployment.

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A Dynamic Network simulation Method of Mega-Constellation Networks

A Dynamic Network Simulation Method of Mega-Constellation Ne...

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International conference on Computer and Communications (ICCC)

作者： Xin Xu Zhixiang Gao Jun Cai Dongming Bian Institute of Communication Engineering Army Engineering University of PLA Nanjing China

Mega-constellation networks have attracted great attention in recent years. The main difficulty of the research lies in the lack of packet-level simulation tools. Toward this end, this paper discusses the problem of topology construction and packet-level dynamic simulation for mega-constellation networks. A data-driven fixed timestep dynamic simulation method is proposed. This method combines the characteristics of the fixed timestep static simulation and the data-driven dynamic simulation. It takes into account the timeliness and simulation efficiency simultaneously. Then a local-state congestion avoidance routing method is presented. It is evaluated by using the proposed simulation method. simulation results show that the data-driven fixed timestep dynamic simulation method can accurately and effectively capture the topology dynamics and path changes of mega-constellations. It is a promising tool for the quantitative evaluation of the dynamic behavior and performance of mega-constellation networks. In addition, the optimized routing method can avoid congestion and greatly improve the performance of mega-constellation networks with low network local-state awareness overhead.

关键词： Network topology simulation Computational modeling Routing Topology Behavioral sciences optimization

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Architecture of Digital Twin for Automating Waste and Recycling Material Sorting Process

Architecture of Digital Twin for Automating Waste and Recycl...

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International conference on Emerging Technologies and Factory Automation (ETFA)

作者： Shreyas Parbat Imam Iqbal Isabell Viedt Leon Urbas Chair of Process Control Systems & Process Systems Engineering Group Technische Universität Dresden Dresden Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Waste and recycling material sorting is crucial for reducing environmental impact and promoting resource recovery. However, its complexity poses significant challenges, necessitating the development of effective sorting processes. Manual operation of these plants can be less efficient than automated systems. The first step toward automation is utilizing a Digital Twin, which combines fundamental principles and data-driven insights into the waste sorting process. To achieve this, the equipment involved in waste sorting plants can be analyzed in detail, focusing on operational settings and their impact on overall efficiency. Initially, a steady-state model of the plant is developed, followed by the implementation of advanced strategies like model predictive control. The model can be rigorously tested and refined using a case study on German post-consumer waste. The architecture of the Digital Twin, comprising various building blocks such as the modeling and simulation block, is being developed to transition away from manual operations. This Digital Twin aims to enhance sorting efficiency through offline and online optimization of operational set points, leading to a more sustainable and resource-efficient future. Through simulations and real-time data integration, a Digital Twin for the waste and recycling material process can aid with process design, fine-tuning, and plant automation.

关键词： Process design Predictive models Real-time systems Digital twins Recycling Steady-state optimization Manufacturing automation Sorting Predictive control

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Demand Response Management of Grid-Connected Nano-Grid Using Data driven Algorithms

Demand Response Management of Grid-Connected Nano-Grid Using...

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Frontiers of Information Technology (FIT)

作者： Ruqayya Manzoor Shakil R Sheikh Muhammad Faisal Department of Electrical Engineering Air University Islamabad Pakistan Smart Green Solutions LLP Islamabad Pakistan

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

The use of renewable energy resources (RERs) has drawn more attention in recent decades and extensive research has been conducted to determine how best to integrate them into the electrical infrastructure to maximize their benefits. Managing demand response (DR), which entails adjusting the burden to match the availability of RERs, is one of the most difficult aspects of integrating RERs. This paper proposes a DR optimization technique based on real-time prices (RTP) for wind and solar-powered DC residences. The proposed strategy utilizes machine learning (ML) techniques including Decision Tree (DT) Regressors, Random Forest (RF), and Linear Regression (LR) on a multi-source, renewable energy sources inclusive dataset to maximize profit via load shifting. A novel approach to tune the hyperparameters of ML algorithms with an Antlion optimizer is used in this research to enhance their efficacy. Our study compares the proposed RTP-based dynamic pricing technique to conventional fixed pricing strategies to determine its effectiveness. The simulation results indicate that the proposed method outperforms conventional fixed pricing strategies.

关键词： Radio frequency Renewable energy sources Heuristic algorithms simulation Pricing Wind power generation Demand response Solar power generation Regression tree analysis Load modeling

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Study on Air Arc simulation of Molded Case Circuit Breaker Using Circuit Model

Study on Air Arc Simulation of Molded Case Circuit Breaker U...

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International conference on Electric Power Equipment - Switching Technology (ICEPE-ST)

作者： K. Kim H. Heo J. S. Lee J. Ryu C. Y. Bae LS ELECTRIC Co. Ltd. Republic of Korea

ISBN: (数字)9798350388947

ISBN: (纸本)9798350388954

A molded case circuit breaker (MCCB) is a critical low-voltage switching device widely used to protect electrical circuits from fault currents by interrupting the flow of excessive current. During the switching operation of an MCCB, the movement of the electrode initiates the formation of an electrical arc. This arc is a plasma that is driven into the arc chamber, where its behavior plays a pivotal role in determining the overall effectiveness of the current interruption process. The process of extinguishing the arc, which is crucial for reducing the current to zero, involves increasing the voltage across the arc plasma. As a result, the shape and behavior of the arc become key factors in defining the electrical characteristics of the MCCB during its switching operation. In this study, a detailed simulation approach is employed to model the arc plasma phenomenon within the MCCB. By simulating the arc's behavior, including its shape and movement, the study aims to predict the resulting arc voltage. This prediction is essential for understanding and improving the switching performance of the MCCB, as the arc voltage directly influences the device's ability to effectively interrupt the fault current. To further enhance the accuracy of the simulation, a circuit model is integrated into the analysis. This combined approach allows for the observation of the current limiting effect, which is a crucial performance characteristic of MCCB. The results of the simulation are then validated by comparing them with experimental data obtained from controlled tests. This comparison demonstrates a strong agreement between the simulated and experimental results, confirming the reliability of the simulation model in predicting the behavior of arc plasma and the associated voltage during the switching operation of the MCCB. The findings of this study contribute valuable insights into the optimization of MCCB design and performance, providing a foundation for further improvements in the

关键词： Performance evaluation Analytical models Accuracy Current measurement Circuits Plasmas Integrated circuit modeling Protection Switching circuits Testing

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Data-driven two-stage stochastic programming with marginal data 21

Data-driven two-stage stochastic programming with marginal d...

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Proceedings of the Winter simulation conference

作者： Ke Ren Hoda Bidkhori University of Pittsburgh

We present new methodologies to solve data-driven two-stage stochastic optimization when only the marginal data are available. We propose a novel data-driven distributionally robust framework that only uses the available marginal data. The proposed model is distinguished from the traditional techniques of solving missing data in that it conducts an integrated analysis of missing data and optimization problems, whereas classical methods conduct separate analyses by first recovering the missing data and then finding the optimal solutions. On the theoretical side, we show that our model produces risk-averse solutions and guarantees finite sample performance. Empirical experiments are conducted on two applications based on synthetic data and real-world data. We validate the proposed finite sample guarantee and show that the proposed approach achieves better out-of-sample performance and higher reliability than the classical data imputation-based approach.

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Fish weight prediction using empirical and data-driven models in aquaculture systems

Fish weight prediction using empirical and data-driven model...

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Control Technology and Applications (CCTA),

作者： Xiao Chen Ibrahima N’Doye Fahad Aljehani Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Science and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Saudi Arabia Environmental Science and Engineering Program Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Saudi Arabia National Institute for Research in Digital Science and Technology (INRIA) Paris-Saclay France

ISBN: (数字)9798350370942

ISBN: (纸本)9798350370959

Predicting fish growth trajectory is crucial in achieving better control for the growing phase of the fish in aquaculture systems. However, fish biomass sensors that directly measure the fish’s weight are currently unavailable. Additionally, the fish growth model that relies on the fish weight is affected by environmental and feeding management factors, raising the difficulty of predicting the weight. This paper proposes comparative empirical and data-driven models for fish weight prediction. The empirical fish growth model relies directly on the growth ratio, an essential component for optimizing fish growth, while the data-driven model is based on long short-term memory (LSTM). Since the exact daily food ratio, proportional to the body weight of a growing fish, cannot be prescribed, we optimize the growth ratio of the empirical model in a sliding window framework based on the previous growth periods and then predict the fish weights a few days ahead through a nonlinear exponential regression approach. The LSTM method uses a normal distribution to generate multiple datasets over the experimental fish weight range and interpolate these data to provide a good prediction. To this end, we propose a weighted sum optimization method that combines the individual cost of the LSTM and empirical methods to achieve better short-term prediction performance. The simulation results demonstrate the effectiveness of the proposed models in predicting the fish weight and show that the combined weighted sum approach reduces the testing errors by 61.3% and 70.9% compared to the empirical and LSTM methods, respectively. Furthermore, an increasing specific growth rate (SGR) for both LSTM and combined models was observed in the short-term predictions.

关键词： Weight measurement Costs Biological system modeling simulation Predictive models Fish Trajectory

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Data-driven optimization of Lead-Free Perovskite Solar Cell Architecture Using Machine Learning and SCAPS simulations

Data-Driven Optimization of Lead-Free Perovskite Solar Cell ...

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International conference on Electrical and Computer Engineering (ICECE)

作者： Md. Arifur Rahman Mohammad Jahangir Alam Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9798331531782

ISBN: (纸本)9798331507923

Significant advancements have been made in the study, research, and optimization of electrical devices and materials using machine learning (ML) models. Data regression and prediction provide for accurate estimates of device performance across a variety of factors by utilizing machine learning algorithms and input information. This study utilizes an ML algorithm to analyze the impact of 9 parameters on the output characteristics of methylammonium tin halide perovskite (CH 3 NH 3 SnX 3 ) solar cells. The perovskite device structure proposed here is FTO/ETL/CH3NH3SnX3/HTL/Au. Distinct parameters like doping levels, defect densities, and thickness of the diverse materials are analyzed to understand their impacts on device performance. A dataset of 2565 points was created using validated numerical simulations for training and testing the model. The PCE variation across the two metal halides was investigated, yielding a maximum PCE of 24.31% for CH 3 NH 3 SnBr 3 and $27.37 \%$ for CH 3 NH 3 SnI 3 . Using the SCAPS-1D simulation, the effects of several HTLs are examined. The device’s PCE rose from $27.37 \%$ to $39.68 \%$ by adjusting intrinsic material factors like absorber layer thickness, HTL thickness, doping, and defect concentration. A trained ML model further predicts PSC performance metrics with 91% accuracy. Consequently, the Al/FTO/PCBM/CH3NH3SnI3/V2O5/Au configuration is identified as the most suitable for manufacturing highly efficient photovoltaic devices.

关键词： Training Accuracy Machine learning algorithms Photovoltaic cells Machine learning Doping Tin Perovskites Semiconductor process modeling optimization

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