scholarly journals Parallel Motion Simulation of Large-Scale Real-Time Crowd in a Hierarchical Environmental Model

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Xin Wang ◽  
Jianhua Zhang ◽  
Massimo Scalia
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Simulation Method ◽  
Crowd Simulation ◽  
Motion Path ◽  
Motion Simulation ◽  
Environmental Model ◽  
Parallel Methods ◽  
Parallel Motion ◽  
Methods Numerical

This paper presents a parallel real-time crowd simulation method based on a hierarchical environmental model. A dynamical model of the complex environment should be constructed to simulate the state transition and propagation of individual motions. By modeling of a virtual environment where virtual crowds reside, we employ different parallel methods on a topological layer, a path layer and a perceptual layer. We propose a parallel motion path matching method based on the path layer and a parallel crowd simulation method based on the perceptual layer. The large-scale real-time crowd simulation becomes possible with these methods. Numerical experiments are carried out to demonstrate the methods and results.

A real-time control method-based simulation for high-speed trains on large-scale rail network

2017 ◽  
Vol 28 (10) ◽  
pp. 1750126 ◽  
Author(s):  
Yutong Liu ◽  
Chengxuan Cao ◽  
Yaling Zhou ◽  
Ziyan Feng
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
High Speed ◽  
Large Scale ◽  
Simulation Method ◽  
Real Time Control ◽  
Rail Network ◽  
Time Control ◽  
High Speed Trains ◽  
Rail Traffic

In this paper, an improved real-time control model based on the discrete-time method is constructed to control and simulate the movement of high-speed trains on large-scale rail network. The constraints of acceleration and deceleration are introduced in this model, and a more reasonable definition of the minimal headway is also presented. Considering the complicated rail traffic environment in practice, we propose a set of sound operational strategies to excellently control traffic flow on rail network under various conditions. Several simulation experiments with different parameter combinations are conducted to verify the effectiveness of the control simulation method. The experimental results are similar to realistic environment and some characteristics of rail traffic flow are also investigated, especially the impact of stochastic disturbances and the minimal headway on the rail traffic flow on large-scale rail network, which can better assist dispatchers in analysis and decision-making. Meanwhile, experimental results also demonstrate that the proposed control simulation method can be in real-time control of traffic flow for high-speed trains not only on the simple rail line, but also on the complicated large-scale network such as China’s high-speed rail network and serve as a tool of simulating the traffic flow on large-scale rail network to study the characteristics of rail traffic flow.

A Real-Time Simulation Method for Large-Scale 3D Clouds

2010 ◽  
Vol 22 (4) ◽  
pp. 662-669 ◽  
Author(s):  
Wei Ren ◽  
Xiaohui Liang ◽  
Shang Ma ◽  
Xukun Shen
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Simulation Method ◽  
Real Time Simulation ◽  
Time Simulation

A Practical Real-Time Motion Cueing Algorithm for Crane Simulator

2014 ◽  
Vol 532 ◽  
pp. 280-284
Author(s):  
Ze Zhu ◽  
Han Bin Xiao ◽  
Guo Xian Wang ◽  
Kan Hu
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Computing Time ◽  
Reference Value ◽  
Simulation Models ◽  
Field Application ◽  
Operating Conditions ◽  
Motion Simulation ◽  
Time Motion ◽  
Motion Cueing

This paper addresses motion simulation issues involved in a crane simulator with a 6-Degree-Of-Freedom platform. The crane dynamical models in terms of a range of motion equations are built for the operator cab in various main operating conditions. The ways to make the motion to be reproduced are investigated and the corresponding real-time motion cueing algorithm is proposed based on a special curve-merging approach. The algorithm is of simplified form and the needed computing time is very short, which makes it very practicable. The crane simulation system with this algorithm being adopted has been successfully developed and put into practice. The field application showed that the algorithm is able to ensure achievement of satisfied motion cueing effects. The proposed motion simulation models and the corresponding algorithm will provide significant reference value for developing similar motion simulation systems for all types of large-scale cranes or construction machinery.

Real-time digital co-simulation method of smart grid for integrating large-scale demand response resources

2017 ◽  
Vol 2017 (1) ◽  
pp. 1949-1953 ◽  
Author(s):  
Jie Song ◽  
Songhan Jiang ◽  
Peichao Zhang ◽  
Jian Zhou
Keyword(s):  
Smart Grid ◽  
Real Time ◽  
Demand Response ◽  
Large Scale ◽  
Simulation Method

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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