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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261-270 订阅

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Data-driven Uncertainty Revenue modeling for Computation Resource Allocation in Recommendation Systems 24

Data-Driven Uncertainty Revenue Modeling for Computation Res...

引用

Proceedings of the Winter simulation conference

作者： Feixue Liu Yuqing Miao Yun Ye Peisong Wang Xinglu Liu Kai Zhang Wai Kin (Victor) Chan Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen P.R. CHINA and Institute of Data and Information Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen P.R. CHINA Ant Group Shanghai P.R. CHINA Institute of Data and Information Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen P.R. CHINA

ISBN: (纸本)9798331534202

In recent years, as the resource consumption of computation-intensive recommendation systems (RS) significantly increased, and the supply of large-scale resources encountered bottleneck, computation resource allocation for improving computing efficiency grabbed the attention of the industry. The simulation of revenue is a focal point in the allocation problem. However, due to the complex engineering architecture of RS, no existing research proposes a simulation model that addresses the relationship between resource allocation strategies and benefits. This paper, based on real data from Alipay's advertising RS and integrating queuing theory, models the relationship from resource allocation decisions to revenue considering exposure randomness of traffic. We further merge allocation tasks with capacity planning (CP) to establish a two-stage joint optimization model and use the revenue model above as the objective. The proposed model outperforms the baseline with 1.9% in revenue, and represents flexible adaptation of exposure rate, providing insights for the simulation of industrial RS.

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optimization of Hemorrhagic Stroke Strategy Based on Random Forest and Apriori Algorithm 24

Optimization of Hemorrhagic Stroke Strategy Based on Random ...

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Proceedings of the 2024 4th International conference on Computational modeling, simulation and Data Analysis

作者： Tao Lin Yu Wan Dejin Ma Yiheng Zhang Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798400711831

After a hemorrhagic stroke, patients may experience increased hematoma expansion and changes in edema volume. Timely and effective prognosis diagnosis and evaluation can improve the quality of life and survival rate of patients. This article is based on a data-driven approach, integrating imaging features and clinical patient information, and using random forest and Apriori algorithms for analysis, diagnosis, prediction, and evaluation. This provides strong support for clinical decision-making. The research results indicate that the model can effectively predict the probability of hematoma enlargement in patients and evaluate the influence of key factors such as edema volume.

关键词： Apriori Algorithm

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Investigation and optimization of Pre-Loading Strategy in Parent Well for Fracture driven Interaction Mitigation

Investigation and Optimization of Pre-Loading Strategy in Pa...

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2023 SPE Hydraulic Fracturing Technology conference and Exhibition, HFTC 2023

作者： Wu, Ruiting Ramsaran, Keith Cai, Yuzhe Madara, Ben Chen, Yuguang Singh, Amit Prouhet, Jeremy Banki, Reza Lannen, Christopher Rijken, Peggy Liang, Baosheng Chevron Corporation

ISBN: (纸本)9781613998731

Inter-well Fracture driven Interaction (FDI), aka Frac Hit, has gained visibility as more high intensity events (pressure increase in parent wells). Severe production performance impact (parent and child wells) are observed in depleted and tightly spaced wells in unconventional reservoirs. Various strategies, including water pre-loading in parent wells, adjusting well completion design of child wells, optimizing development plans, etc., have been tried in the industry to mitigate the negative impact of FDIs on production performance. However, mixed results have been observed in some pilot tests utilizing a water pre-loading mitigation strategy. Hence, it is critical to understand the driving mechanisms and key parameters impacting pre-loading performance in order to quantify the expected benefit of pre-loading in different well scenarios and optimize the pre-loading design. A novel integrated and coupled geomechanics / fluid flow / fracturing propagation modeling workflow is applied to simulate the FDI (including pre-loading) on the pad scale. The workflow is capable of simulating the entire life cycle of the FDI interaction, (a) stimulation of parent wells, (b) production of parent well, (c) pre-loading of the parent well, (d) stimulation of the child wells, and (e) production forecasting of both parent and child wells. The workflow includes calibration with field observations of fracture geometry, production performance, pre-loading pressure behavior, etc. Geomechanics and reservoir simulation coupling captures the stress change due to the depletion at the parent well and re-pressurizing from pre-loading. The workflow was applied to one pre-loading pilot test in the Permian Basin. The goal of the study is to identify the key parameters that contribute to the pre-loading success and optimize the pre-loading design. History match of parent and child wells production performance using the integrated and coupled geomechanics / fluid flow / fracturing propagation model

关键词： Stress analysis

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Solving Nonlinear Equations to Generate Fractals via Discrete Varying-Parameter Complex-Valued Dynamics

Solving Nonlinear Equations to Generate Fractals via Discret...

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Data-driven optimization of Complex Systems (DOCS), International conference on

作者： Qichen Peng Yamei Luo Zhijun Zhang School of Automation Xi'an University of Posts and Telecommunications Xi'an China School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

In this paper, the discrete complex-valued dynamics is used to solve nonlinear equations to generate fractals. This new class of fractals, called Zhang fractals, are different from the famous Newton Iteration. By introducing exponential functions in the basis of the iteration of Zhang fractals, generated fractals have wider dynamic range due to rapid growth or decrease of values during iterations. This great character also increases the diversity of fractals since it may lead to different iterative paths and results, which will result in multiple possible fractal mor-phologies. Moreover, the using of different activation functions will further improve the diversity of generated fractal. Actually, the selection and adjustment of activation functions provide more flexibility and creativity for the generation of fractals. A new way of fractal generation is opened up by introducing a exponential function in the traditional discrete complex-valued dynamics. The generation of fractals can be applied to many fields such as art and natural phenomena, and is of great significance to understanding the complex structure of nature.

关键词： Adaptation models Nonlinear equations Art Biological system modeling simulation Turning Fractals Mathematical models Iterative methods optimization

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Deep Learning-driven Design of Plasmonic Optical Coloring Filters

Deep Learning-Driven Design of Plasmonic Optical Coloring Fi...

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Novel Intelligent and Leading Emerging Sciences conference (NILES)

作者： Pansee S. Elessawy Mai O. Sallam Yasser M. El-Batawy Nouran M. Ali Nanoelectronics Integrated Systems Center Nile University Giza Egypt Engineering Mathematics and Physics Department Faculty of Engineering Cairo University Giza Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

Plasmonic Coloring Filters can be dynamically tuned by manipulating the geometric parameters of meta-materials, such as unit cell size, shape, and arrangement. In the presented work, the impact of the unit cell dimensions of the plasmonic color tunable filter on the transmitted color is investigated. A deep neural network model is implemented to efficiently predict transmission coefficients across various dimensional configurations and to predict the optimal one, enabling the rapid optimization of design parameters for desired wavelengths. The model's high accuracy significantly reduces both the simulation time and the computational resources.

关键词： Optical filters Filters Accuracy Shape Computational modeling Color Artificial neural networks Predictive models Plasmons optimization

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A Programmatic Metamodel-driven modeling Paradigm for MCNP

A Programmatic Metamodel-Driven Modeling Paradigm for MCNP

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2022 International conference on Physics of Reactors, PHYSOR 2022

作者： Kowal, Peter J. Blake, Camden E. Dominesey, Kurt A. Ji, Wei Rensselaer Polytechnic Institute 110 8th Street TroyNY12180 United States

ISBN: (纸本)9780894487873

MCNP is a widely used and well validated Monte Carlo radiation transport code applicable for a plethora of reactor physics problems. Despite being a powerful simulation tool, MCNP provides no programmatic interface through which to automate execution, transform models, or support user-defined logic and a bstractions. To address this need, we have developed an intuitive yet full-featured MCNP Application Program Interface (API) in Python, named mcnpy. Moreover, to guarantee our reading, writing, and modeling capabilities remain self-consistent (and to render the huge scope of the MCNP API manageable) we have adopted a strategy of model-driven software development described in previous work. As mcnpy is primarily a Python wrapper around a codebase automatically generated through this strategy, this ensures mcnpy too remains remains consistent with our MCNP (meta)model and maintainable. With mcnpy, users can freely read, write, and modify any syntactically valid MCNP input file regardless of its origin. These capabilities allow users to automate complicated tasks like nonlinear optimization and model translation. As examples, this work demonstrates the use of mcnpy to find the critical radius of a plutonium sphere and to translate a 9,000+ line MCNP input file into a corresponding OpenMC model. © 2022 Proceedings of the International conference on Physics of Reactors, PHYSOR 2022. All Rights Reserved.

关键词： API MCNP mcnpy Metamodel Python

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Design, optimization and Analysis of a Novel Compliant Guiding Mechanism for Piezo-driven Vibration Microinjection

Design, Optimization and Analysis of a Novel Compliant Guidi...

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International conference on Advanced Robotics and Mechatronics (ICARM)

作者： Yitong Li Tingting Ye Jie Ling Xiaohui Xiao Zhao Feng School of Power and Mechanical Engineering Wuhan University Wuhan China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong SAR China College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

Cell microinjection, as a significant tool of biomedical research, calls for advanced equipment with high efficiency, high success rate, high reliability, and repeatability. With the intent of providing a useful manipulator for robotic cell microinjection, this paper presents a novel complaint guiding mechanism for piezo-driven vibration cell microinjection. Mechanism design, kinematic modeling, parametric optimization, and simulation of the designed guiding mechanism are introduced in detail. The compliant guiding mechanism has a symmetrical and parallel connection structure with lumped compliance at its double-notch right-circle flexure hinges. Aiming at avoiding the resonance of the mechanism at actuation frequency, the response surface algorithm is utilized to optimize the dominant structural parameters of the designed guiding mechanism based on kinematic modeling with the pseudo-rigid-body method. The finite element simulation results show that the optimized compliant mechanism has the advantages of high off-axis stiffness and ideal working performance at the actuation frequency of 15 kHz, where the output amplitude of in-plane and out-of-plane lateral vibrations are diminished to 40% and 25% of the input lateral vibration amplitude respectively.

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Manifold optimization for Data driven Reduced-Order modeling*

Manifold Optimization for Data Driven Reduced-Order Modeling...

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Annual Allerton conference on Communication, Control, and Computing

作者： Rachel Newton Zhe Du Laura Balzano Peter Seiler Department of Electrical Engineering and Computer Science University of Michigan

The focus of this paper is on data-driven reduced-order modeling. We assume a high fidelity, discrete-time model is available for simulation. The simulator allows state and output trajectories to be collected for any specified initial condition and input signal. An optimal reduced-order model (ROM) requires: (i) the selection of a lower dimensional subspace for the state of the ROM, and (ii) an optimal reduced-order state-space model (evolving on the lower dimensional subspace). A common heuristic is to: (i) select the lower-order subspace using proper orthogonal decomposition (POD), and (ii) use least-squares to fit the reduced-order state-space model on the POD subspace. We demonstrate the potential deficiencies of this heuristic via two simple examples. In order to address these deficiencies, we propose a novel method to optimize the choice of subspace using the Grassmann manifold. Finally, we show that our proposed manifold optimization empirically outperforms the POD heuristic on the two motivating examples and a planar wind farm model.

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Multi-UAV Placement for Integrated Access and Backhauling Using LLM-driven optimization

Multi-UAV Placement for Integrated Access and Backhauling Us...

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2025 IEEE Wireless Communications and Networking conference, WCNC 2025

作者： Wang, Yuhui Farooq, Junaid Ghazzai, Hakim Setti, Gianluca University of Michigan-Dearborn Department of Electrical and Computer Engineering DearbornMI48126 United States King Abdullah University of Science & Technology Computer Electrical and Mathematical Sciences Division Saudi Arabia

ISBN: (纸本)9798350368369

Unmanned aerial vehicles (UAVs) can enhance wireless access by dynamically positioning themselves closer to users while maintaining a backhaul connection to cellular base stations. In scenarios where users are geographically dispersed, multiple UAVs can be orchestrated to establish multi-hop integrated access and backhaul (IAB) connections. Traditionally, finding optimal UAV placement has required computationally demanding methods, such as combinatorial optimization or reinforcement learning, which are often impractical for real-time applications due to their complexity and training requirements. Additionally, UAV operators may lack the capability to solve complex optimization problems during live operations. This paper presents a novel framework that leverages large language models (LLMs) for optimizing the placement of multiple UAVs through iterative structured prompting. The proposed method achieves near-optimal solutions in significantly fewer iterations compared to traditional methods, making it suitable for real-time deployment without extensive mathematical modeling. simulation result demonstrate that the proposed method achieves scores over 82% of the theoretical optimal solution while reducing computational time from hours to minutes compared to the baseline deep Q network approach, ensuring robust network connectivity and service quality. The LLM-driven framework simplifies problem-solving for UAV network operators, paving the way for its application in more complex real-world scenarios. © 2025 IEEE.

关键词： Drones

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Data-driven Geometric Programming for System-Level Performance optimization

Data-Driven Geometric Programming for System-Level Performan...

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International conference on Information and Communication Technology Convergence (ICTC)

作者： Soihem Gonmei Junhwan Lee Taesoo Kwon Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul South Korea Electronics and Telecommunications Research Institute Daejeon South Korea

More than often, the output of a comprehensive network-wide performance modeling is a non-linear and non-convex function of the input data. To estimate the non-linear relationship of such a procedure, this paper employs a data-driven methodology. By formulating the non-linear objective as a geometric program, we leverage Levenberg-Marquardt algorithm to fit a convex log-sum-exponential function to data obtained through system-level simulation. Focusing on the downlink energy efficiency in mmWave cellular networks, we formulate a convex optimization problem and numerically obtain the optimal BS density and transmit power.

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