scholarly journals Model-Based Manipulation of Linear Flexible Objects: Task Automation in Simulation and Real World

Machines ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 46
Author(s):  
Peng Chang ◽  
Taşkın Padır
Keyword(s):  
Real World ◽  
Geometric Model ◽  
Model Parameters ◽  
Power Cable ◽  
Deformable Objects ◽  
Model Based ◽  
Revolute Joints ◽  
New Strategy ◽  
Flexible Objects ◽  
3D Geometric Model

Manipulation of deformable objects is a desired skill in making robots ubiquitous in manufacturing, service, healthcare, and security. Common deformable objects (e.g., wires, clothes, bed sheets, etc.) are significantly more difficult to model than rigid objects. In this research, we contribute to the model-based manipulation of linear flexible objects such as cables. We propose a 3D geometric model of the linear flexible object that is subject to gravity and a physical model with multiple links connected by revolute joints and identified model parameters. These models enable task automation in manipulating linear flexible objects both in simulation and real world. To bridge the gap between simulation and real world and build a close-to-reality simulation of flexible objects, we propose a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim). We demonstrate the feasibility of our approach by completing the Plug Task used in the 2015 DARPA Robotics Challenge Finals both in simulation and real world, which involves unplugging a power cable from one socket and plugging it into another. Numerical experiments are implemented to validate our approach.

Complex Network Evolution Model Based on Node Attraction

2014 ◽  
Vol 596 ◽  
pp. 843-846
Author(s):  
Rui Sun
Keyword(s):  
Complex Network ◽  
Real World ◽  
Simulation Analysis ◽  
Network Evolution ◽  
Evolution Process ◽  
Statistical Characteristics ◽  
Model Parameters ◽  
Model Based ◽  
Attraction Model ◽  
Theory Research

This paper studied the evolution law of the real-world networks, and then proposed a complex network model based on node attraction with tunable parameters in order to solve the problems existing in BA model and the original node attraction model. The model considered the effects of preferential attachments by the changes of degree and node attraction in the evolution process of networks. Theory research and simulation analysis show that we can more flexible adjust the evolution process of network through adjusting model parameters, therefore make it more accord with the network topology and statistical characteristics of real-world networks.

scholarly journals The Bottleneck Simulator: A Model-Based Deep Reinforcement Learning Approach

2020 ◽  
Vol 69 ◽  
pp. 571-612
Author(s):  
Iulian Vlad Serban ◽  
Chinnadhurai Sankar ◽  
Michael Pieper ◽  
Joelle Pineau ◽  
Yoshua Bengio
Keyword(s):  
Reinforcement Learning ◽  
Language Processing ◽  
Real World ◽  
Response Selection ◽  
Space Structure ◽  
Model Parameters ◽  
Transition Model ◽  
Model Estimation ◽  
Model Based ◽  
Adventure Game

Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to real-world problems is their lack of data-efficiency. To this end, we propose the Bottleneck Simulator: a model-based reinforcement learning method which combines a learned, factorized transition model of the environment with rollout simulations to learn an effective policy from few examples. The learned transition model employs an abstract, discrete (bottleneck) state, which increases sample efficiency by reducing the number of model parameters and by exploiting structural properties of the environment. We provide a mathematical analysis of the Bottleneck Simulator in terms of fixed points of the learned policy, which reveals how performance is affected by four distinct sources of error: an error related to the abstract space structure, an error related to the transition model estimation variance, an error related to the transition model estimation bias, and an error related to the transition model class bias. Finally, we evaluate the Bottleneck Simulator on two natural language processing tasks: a text adventure game and a real-world, complex dialogue response selection task. On both tasks, the Bottleneck Simulator yields excellent performance beating competing approaches.

scholarly journals Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

2019 ◽  
Vol 147 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
Yuchu Zhao ◽  
Zhengyu Liu ◽  
Fei Zheng ◽  
Yishuai Jin
Keyword(s):  
Data Assimilation ◽  
Parameter Optimization ◽  
Real World ◽  
Climate Models ◽  
Coupled Model ◽  
Prediction Skill ◽  
Model Parameters ◽  
Real World Data ◽  
The Real ◽  
Advection Term

Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

Hoist Sheave Structure Optimization and Lightweight

2015 ◽  
Vol 741 ◽  
pp. 133-137
Author(s):  
Xian Zhao Jia ◽  
Yong Fei Wang
Keyword(s):  
Working Condition ◽  
Geometric Model ◽  
Structure Optimization ◽  
Element Model ◽  
Stiffened Plate ◽  
Optimal Result ◽  
Ansys Software ◽  
Table Analysis ◽  
3D Geometric Model ◽  
Relational Table

To ensure wheel body of the hoisting sheave strength and stability condition. For the purpose of wheel body lightweighting. There are two schemes to reduce body weight.Reduce the spokes at the same time increase the ring stiffened plate, and reduce the spokes at the same time change the spokes width and thickness.The wheel body was established based on Pro/E 3D geometric model. Import the mesh in the Workbench of ANSYS software for finite element model. Statics analysis to select the optimized scheme. Establish a hoisting sheave wheel body under the actual working condition of widening the width - deformation - wheel weight relational table. Analysis to lightweight at the same time ensure that stiffness of wheel,then it can obtaine the optimal result.

scholarly journals A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 1: Pre-Processing of Scenario Based Flood Characteristics for the Current State of Land Use

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 102
Author(s):  
Frauke Kachholz ◽  
Jens Tränckner
Keyword(s):  
Land Use ◽  
River Flow ◽  
Land Use Changes ◽  
Small Rivers ◽  
Small Scale ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Model Based ◽  
Current State ◽  
The Impact

Land use changes influence the water balance and often increase surface runoff. The resulting impacts on river flow, water level, and flood should be identified beforehand in the phase of spatial planning. In two consecutive papers, we develop a model-based decision support system for quantifying the hydrological and stream hydraulic impacts of land use changes. Part 1 presents the semi-automatic set-up of physically based hydrological and hydraulic models on the basis of geodata analysis for the current state. Appropriate hydrological model parameters for ungauged catchments are derived by a transfer from a calibrated model. In the regarded lowland river basins, parameters of surface and groundwater inflow turned out to be particularly important. While the calibration delivers very good to good model results for flow (Evol =2.4%, R = 0.84, NSE = 0.84), the model performance is good to satisfactory (Evol = −9.6%, R = 0.88, NSE = 0.59) in a different river system parametrized with the transfer procedure. After transferring the concept to a larger area with various small rivers, the current state is analyzed by running simulations based on statistical rainfall scenarios. Results include watercourse section-specific capacities and excess volumes in case of flooding. The developed approach can relatively quickly generate physically reliable and spatially high-resolution results. Part 2 builds on the data generated in part 1 and presents the subsequent approach to assess hydrologic/hydrodynamic impacts of potential land use changes.

A Theoretical Analysis of CO2 Absorption in Sun Versus Shade Leaves

1978 ◽  
Vol 100 (1) ◽  
pp. 20-24 ◽  
Author(s):  
R. H. Rand
Keyword(s):  
Experimental Data ◽  
Quantitative Estimate ◽  
Geometric Model ◽  
Model Parameters ◽  
Co2 Absorption ◽  
Temperature Model ◽  
Spongy Mesophyll ◽  
One Dimensional ◽  
Mesophyll Tissue ◽  
Shade Leaves

A one-dimensional, steady-state, constant temperature model of diffusion and absorption of CO2 in the intercellular air spaces of a leaf is presented. The model includes two geometrically distinct regions of the leaf interior, corresponding to palisade and spongy mesophyll tissue, respectively. Sun, shade, and intermediate light leaves are modeled by varying the thicknesses of these two regions. Values of the geometric model parameters are obtained by comparing geometric properties of the model with experimental data of other investigators found from dissection of real leaves. The model provides a quantitative estimate of the extent to which the concentration of gaseous CO2 varies locally within the leaf interior.

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