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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121-130 订阅

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AN optimization FRAMEWORK AND TOOL FOR CONTEXT-SENSITIVE SOLAR-driven DESIGN USING CELLULAR AUTOMATA (SDCA)

AN OPTIMIZATION FRAMEWORK AND TOOL FOR CONTEXT-SENSITIVE SOL...

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70th Annual modeling and simulation conference (ANNSIM)

作者： Luitjohan, Seth Ashayeri, Mehdi Abbasabadi, Narj Es Southern Illinois Univ Carbondale Sch Architecture 875 South Normal Ave Carbondale IL 62901 USA Univ Texas Arlington Sch Architecture 601 South Nedderman Dr Arlington TX 76019 USA

ISBN: (数字)9781713852889

ISBN: (纸本)9781713852889

This article develops a framework and computational tool that integrates cellular automata (CA) as a generative design (GD) system into the solar radiation incident ( SRI) simulation. It automates generating and optimizing urban building form considering the surrounding morphological context. The proposed framework is tested for designing the building form located at dense urban districts at multiple locations where abundant shadows are cast on the target building from neighboring structures. Findings indicate that this framework can automatically produce forms with increased solar energy gain by buildings in urban environments for suggestive use in the early-stage design. Comparison studies upon the equal-volume control forms indicate that the proposed framework can maximize SRI on building surfaces for renewable energy generation applications through form generations by up to 24%. The proposed framework and toolkit aid architects in building form-finding with increased solar-energy harvesting through optimizing context-sensitive exploration at early-stage design.

关键词： Generative Design Cellular Automata Solar Radiation Form Finding Context-sensitive

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2nd International conference on Futuristic Advancements in Materials, Manufacturing, and Thermal Sciences, ICFAMMT 2024

2nd International Conference on Futuristic Advancements in M...

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2nd International conference on Futuristic Advancements in Materials, Manufacturing, and Thermal Sciences, ICFAMMT 2024

ISBN: (纸本)9789819756209

The proceedings contain 32 papers. The special focus in this conference is on Futuristic Advancements in Materials, Manufacturing, and Thermal Sciences,. The topics include: Design and Development of Welding Fixture on Release Welding Machine;advancements in Design of Semi-Active Suspension Control System During Pre- and Post-Covid-19—A Review of Research;Design of Flexural Bearings in Experimental Analysis and PID Control of a Voice Coil Actuator;design and optimization of Railway Power Axle System for Structural Safety;design and Analysis of Rotary Slag Skimmer Machine;developing and Implementing Vision-Based Production Lines for Detecting and Removing Defective Components;designing a Piezo-Actuated Four-Bar Motion Amplification Mechanism for Enhanced Compliance;geometrical Aspects of Snow Sinkage for Robotic Application;finite Element Analysis of a Cable-driven Robotic Hand Exoskeleton;envisioning the Future of Robotics Sensors: Innovations and Prospects;Home Automation System with Multiple Control Access Using IoT and RTC Module;dynamic Analysis of Underactuated Soft Robotic Gripper for Space Applications;EMG-Controlled Upper Arm Exoskeleton Powered by Pneumatic Artificial Muscle;self-Operated Optimized Design of an Automated Seed-Sowing Robot;Fault Diagnosis in a Centrifugal Pump Using MODWPT and SVMA;methodology for Wall Thickness Validation with Stress Analysis of ClO2 Generator Piping System;A Comparative Study of Live Load for Bridge Deck with Custom Fighter Aircraft Loading and IRC Standard Loading for Design of Elevated Taxiway;modeling and simulation of Self-stabilizing Platform for Industrial Application;enhancing the Thermal Performance of a Solar Air Heater by Incorporating Artificial Roughness to the Absorber Plate;topology optimization of Wind Turbine Structural Components.

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DYNAMO-GRASP: DYNAMics-aware optimization for GRASP Point Detection in Suction Grippers 7

DYNAMO-GRASP: DYNAMics-aware Optimization for GRASP Point De...

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7th conference on Robot Learning (CoRL)

作者： Yang, Boling Atar, Soofiyan Grotz, Markus Boots, Byron Smith, Joshua R. Univ Washington Seattle WA 98195 USA

In this research, we introduce a novel approach to the challenge of suction grasp point detection. Our method, exploiting the strengths of physics-based simulation and data-driven modeling, accounts for object dynamics during the grasping process, markedly enhancing the robot's capability to handle previously unseen objects and scenarios in real-world settings. We benchmark DYNAMO-GRASP against established approaches via comprehensive evaluations in both simulated and real-world environments. DYNAMO-GRASP delivers improved grasping performance with greater consistency in both simulated and real-world settings. Remarkably, in real-world tests with challenging scenarios, our method demonstrates a success rate improvement of up to 48% over SOTA methods. Demonstrating a strong ability to adapt to complex and unexpected object dynamics, our method offers robust generalization to real-world challenges. The results of this research set the stage for more reliable and resilient robotic manipulation in intricate real-world situations. Experiment videos, dataset, model, and code are available at: https://***/view/dynamo-grasp.(1)

关键词： Suction Grasping Manipulation Deep Learning Vision

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optimization of thermoplastic composites process parameters based on robotic fiber placement 11

Optimization of thermoplastic composites process parameters ...

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11th Global conference on Polymer and Composite Materials, PCM 2024

作者： Liu, Mingyu Fu, Hongya Zhao, Zhiyuan Jin, Ziang Han, Zhenyu Sun, Shouzheng School of Mechatronics Engineering Harbin Institute of Technology Harbin China Structural Processing Division Shanghai Aerospace Precision Machinery Institute Shanghai China

In robot-driven composite manufacturing, managing the temperature field is crucial for determining the strength of the molding. This paper presents a process simulation analysis of the temperature field during the robot placement of thermoplastic composite materials on curved surfaces. The paper establishes an optimized temperature field model for the lay-up process on curved surfaces. Innovatively considering the influence of different plys on the heating temperature, this model can calculate the optimal heating temperature parameters for various ply layers. A two-dimensional heat transfer model is created using APDL command flow in ANSYS, and the mesh is partitioned. Transient thermal analysis is then conducted utilizing the "birth and death" element technique. Analyzing the effects of mold temperature, robot placement speed, and heating temperature on the temperature field of the first ply helps to understand their impact on multiple plys in the placement of curved surfaces. This analysis provides insights into how these factors influence the overall process. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Thermal modeling

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A Deep Reinforcement Learning Algorithm to Project Portfolio Selection Problem 5

A Deep Reinforcement Learning Algorithm to Project Portfolio...

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5th International Communication Engineering and Cloud Computing conference, CECCC 2023

作者： Chen, Hang Dou, Yajie Chen, Ziyi Jia, Qingyang Zhang, Mengyu Qiu, Biaobiao College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350344752

Project construction and development are an important part of the development of any large organization. The most critical issue for decision-makers is how to select the right projects from the large number of candidate projects for construction and rational arrangement, that is, project portfolio selection and scheduling problem (PPSS). PPSP is a typical NP-hard problem. As the size of the problem grows, the effectiveness and practicality of traditional exact mathematical programming methods is greatly reduced due to the high time cost. Therefore, in this paper, we design a deep reinforcement learning method to solve this problem. We validate the algorithm on several datasets and compare it with metaheuristics for combinatorial optimization problems to verify the effectiveness of our algorithm. © 2023 IEEE.

关键词： Combinatorial optimization

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Deep Learning-driven Acceleration of Stochastic Gradient Methods for Well Location optimization Under Uncertainty

Deep Learning-Driven Acceleration of Stochastic Gradient Met...

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2024 SPE Annual Technical conference and Exhibition, ATCE 2024

作者： Eltahan, Esmail Sepehrnoori, Kamy Alpak, Faruk Omer The University of Texas at Austin AustinTX United States Shell International Exploration and Production Inc HoustonTX United States

ISBN: (纸本)9781959025375

We have developed the deep-learning-accelerated-gradient (DLAG) algorithm, a novel solution for well location optimization (WLO) problems that leverages data collected from the explored parameter space to accelerate optimization. During optimization, we collect data to train a deep neural network (NN), creating a map from well-location parameters to the objective function. We utilize the analytical gradient of the NN, resulting in an effective search direction while saving the computational cost associated with stochastic-gradient perturbations. Our method features a novel NN architecture, the Spatial Pairwise Interaction Network (SPINet) with independent and contextual neural pathways (NPs), designed to capture the primary well characteristics, and its complex interactions with the neighboring wells. For the contextual NP, we explore using the popular Attention mechanism and simpler mechanism with weight-sharing Multilayer Perceptron (MLP) layers. To evaluate the architectures, we designed the Bird Ensemble (BE) test problem which resembles the structure of the WLO problem. The comparison between DL architectures reveals that the NP with a weight-sharing mechanism has superior performance in terms of MSE error and gradient accuracy. The weight-sharing structure allows the model to efficiently model relationships with shared parameters while maintaining invariance to input permutations. For the test function, utilizing DLAG drastically improves computational efficiency, reducing the number of function evaluations required to achieve the same level of optimization by one to two orders of magnitude. Following validation of this test problem, we successfully applied our method to optimizing locations of injection and production wells in the Egg reservoir model. To alleviate random artifacts and the inevitable possibility of encountering local minima, we conducted 20 iterations of the WLO problems both with and without DLAG. The results reveal that, on average, our DLAG

关键词： Stochastic systems

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2023 IEEE Latin American Electron Devices conference, LAEDC 2023

2023 IEEE Latin American Electron Devices Conference, LAEDC ...

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2023 IEEE Latin American Electron Devices conference, LAEDC 2023

ISBN: (纸本)9798350311907

The proceedings contain 40 papers. The topics discussed include: implementation of a ROS node for roaming between aps for an autonomous mobile robot;a general toolkit for advanced semiconductor transistors: from simulation to machine learning;modeling high frequency response of nanometer soi devices using Monte Carlo transient technique;improving the performance of photovoltaic laser power converters using automatic global optimization techniques;experimental characterization of a thermoelectric generator system;digital twin of electrical motorcycle battery charger as ac load in a microgrid based on renewable energy;development of simplified lumbar spine mechanism implemented with tendon-driven motion;a new microstrip directional filter configuration composed by hybrid-mode resonators;effect of the thermal annealing temperature on the luminescent and morphological properties of silicon rich oxide bilayer structures;simulation and operational evaluation of distributed storage devices connected to a direct current distribution nanogrid;a proposed STEM program to make institutions more inclusive for people with visual and physical disabilities in Panama;and development of drop-on-demand inkjet process for the fabrication of thin-film printed devices.

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Data-based modeling and simulation of Denso Robotic Arm 11

Data-based Modeling and Simulation of Denso Robotic Arm

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11th IEEE Data driven Control and Learning Systems conference, DDCLS 2022

作者： Shoufu, Liu Na, Dong Kesen, He Xiaoming, Mai Tianjin University School of Electrical and Information Engineering Tianjin300072 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Robotic arms now play an important role in various fields and have more and more extensive applications. However, robotic arm systems are complex nonlinear systems with multiple inputs and outputs. Its parameters have great uncertainty, strong coupling, internal friction and external disturbance, which makes it difficult to construct an accurate dynamic model. Therefore, a data-driven modeling method of the Denso robotic arm is proposed based on the BP neural network in this paper. It's effectiveness is proved through numerical simulations. © 2022 IEEE.

关键词： Robotic arms

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Model-based and Koopman-based predictive control: a braking control systems comparison 3

Model-based and Koopman-based predictive control: a braking ...

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3rd modeling, Estimation and Control conference (MECC)

作者： Sassella, Andrea Breschi, Valentina Korda, Milan Formentin, Simone Politecn Milan Dipartimento Elett Informat & Bioingn DEIB Piazza Leonardo da Vinci 32 I-20133 Milan Italy Eindhoven Univ Technol Eindhoven Netherlands CNRS LAAS F-31400 Toulouse France Czech Tech Univ Fac Elect Engn Prague 16626 Czech Republic

ISBN: (纸本)9781713872344

Anti-locking Braking systems are crucial safety systems in modern vehicles. In this work, we investigate the possibility to use Model Predictive Control (MPC) for braking systems by considering three different models identified from data. Specifically, we consider two models, whose structure and the identification procedure are driven by physics principles, and a third black-box modeling approach that relies on Koopman theory. By comparing the effectiveness of the three resulting MPC schemes in a high-fidelity simulation environment, we show that Koopman-based MPC can generally be a viable solution for the design of braking controllers, which might not be the case of nonlinear MPC or approximated scheme like the second one we test. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Koopman control Braking control Nonlinear predictive control Model predictive and optimization-based control Nonlinear and optimal automotive control

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2023 21st International conference on Advanced Robotics, ICAR 2023

2023 21st International Conference on Advanced Robotics, ICA...

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21st International conference on Advanced Robotics, ICAR 2023

ISBN: (纸本)9798350342291

The proceedings contain 96 papers. The topics discussed include: early and accurate detection of tomato leaf diseases using TomFormer;evaluating visual-selective visual-inertial odometry: an end-to-end multi-modal pose estimation framework for underwater environments;path planning for autonomous inland vessels in complex harbor environments;modeling, optimization, and musculoskeletal simulation of elbow-wrist exosuit;learning complicated manipulation skills via deterministic policy with limited demonstrations;visual tracking nonlinear model predictive control method for autonomous wind turbine inspection;a deep reinforcement learning decision-making approach for adaptive cruise control in autonomous vehicles;subgoal-driven navigation in dynamic environments using attention-based deep reinforcement learning;and fast and accurate tactile object recognition using a random convolutional kernel transform.

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