Human Mobility Estimation Following Massive Disaster Using Filtering Approach

2016 ◽  
Vol 11 (2) ◽  
pp. 217-224 ◽  
Author(s):  
Akihito Sudo ◽  
◽  
Takehiro Kashiyama ◽  
Takahiro Yabe ◽  
Hiroshi Kanasugi ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Mobile Phones ◽  
Large Scale ◽  
Population Distribution ◽  
Human Mobility ◽  
Time Estimation ◽  
Observation Data ◽  
Real Time Estimation ◽  
Using Data

Real-time estimation of people distribution immediately after a disaster is directly related to disaster reduction and is also highly beneficial in society. Recently, traffic estimation research has been actively performed using data assimilation techniques for observation data obtained from mobile phones. However, there has been no research on data assimilation technique using real-time gridded aggregated observation data obtained from mobile phones, which are available and can be used to estimate population flow and distribution in a metropolitan area during a large-scale disaster. In this research, population distribution in an urban area during a disaster was estimated using gridded aggregated observation data obtained from mobile phones, using particle filter. The experimental results indicated that the particle filters enabled high-precision real-time estimation in the Kanto district.

Data assimilation for real-time estimation of hydraulic states and unmeasured perturbations in a 1D hydrodynamic model

2011 ◽  
Vol 81 (10) ◽  
pp. 2201-2214 ◽  
Author(s):  
Nelly Jean-Baptiste ◽  
Pierre-Olivier Malaterre ◽  
Christophe Dorée ◽  
Jacques Sau
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Hydrodynamic Model ◽  
Time Estimation ◽  
Real Time Estimation

A machine for real-time estimation of the tribological characteristics of materials

2020 ◽  
Vol 86 (4) ◽  
pp. 61-65
Author(s):  
M. V. Abramchuk ◽  
R. V. Pechenko ◽  
K. A. Nuzhdin ◽  
V. M. Musalimov
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
Tribological Characteristics ◽  
Continuous Processing ◽  
Friction Machine ◽  
Dynamic Friction Coefficient ◽  
Displacement Sensors ◽  
Frictional Interaction ◽  
Real Time Estimation ◽  
Automated Quality Control

A reciprocating friction machine Tribal-T intended for automated quality control of the rubbing surfaces of tribopairs is described. The distinctive feature of the machine consists in implementation of the forced relative motion due to the frictional interaction of the rubbing surfaces fixed on the drive and conjugate platforms. Continuous processing of the signals from displacement sensors is carried out under conditions of continuous recording of mutual displacements of loaded tribopairs using classical approaches of the theory of automatic control to identify the tribological characteristics. The machine provides consistent visual real time monitoring of the parameters. The MATLAB based computer technologies are actively used in data processing. The calculated tribological characteristics of materials, i.e., the dynamic friction coefficient, damping coefficient and measure of the surface roughness, are presented. The tests revealed that a Tribal-T reciprocating friction machine is effective for real-time study of the aforementioned tribological characteristics of materials and can be used for monitoring of the condition of tribo-nodes of machines and mechanisms.

Onboard Real-time Estimation of Mean Orbital Elements with Atmospheric Drag

2013 ◽  
Vol 39 (10) ◽  
pp. 1722
Author(s):  
Zhao-Wei SUN ◽  
Wei-Chao ZHONG ◽  
Shi-Jie ZHANG ◽  
Jian ZHANG
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
Atmospheric Drag ◽  
Orbital Elements ◽  
Real Time Estimation

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.

Soft sensor for real-time estimation of tablet potency in continuous direct compression manufacturing operation

2021 ◽  
Vol 602 ◽  
pp. 120624
Author(s):  
Reza Kamyar ◽  
David Lauri Pla ◽  
Anas Husain ◽  
Giuseppe Cogoni ◽  
Zilong Wang
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
Soft Sensor ◽  
Direct Compression ◽  
Real Time Estimation

scholarly journals Real-Time Estimation of Microgrid Inertia and Damping Constant

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ujjwol Tamrakar ◽  
David A. Copp ◽  
Tu Nguyen ◽  
Timothy M. Hansen ◽  
Reinaldo Tonkoski
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
Real Time Estimation

scholarly journals Joint structure for the real-time estimation and control of automotive dry clutch engagement

2018 ◽  
Vol 51 (15) ◽  
pp. 1062-1067 ◽  
Author(s):  
Mojtaba Sharifzadeh ◽  
Mario Pisaturo ◽  
Arash Farnam ◽  
Adolfo Senatore
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
The Real ◽  
Joint Structure ◽  
Clutch Engagement ◽  
Real Time Estimation ◽  
Dry Clutch ◽  
Estimation And Control ◽  
And Control
Sign in / Sign up

Export Citation Format

Share Document