Real-time turning rate estimation in urban networks using floating car data

2021 ◽  
Vol 133 ◽  
pp. 103457
Author(s):  
Omid Mousavizadeh ◽  
Mehdi Keyvan-Ekbatani ◽  
Tom M. Logan
Keyword(s):  
Real Time ◽  
Urban Networks ◽  
Rate Estimation ◽  
Floating Car Data

scholarly journals Experimental Verification of Real-Time Flow-Rate Estimations in a Tilting-Ladle-Type Automatic Pouring Machine

2021 ◽  
Vol 11 (15) ◽  
pp. 6701
Author(s):  
Yuta Sueki ◽  
Yoshiyuki Noda
Keyword(s):  
Real Time ◽  
Molten Metal ◽  
Flow Rate ◽  
Kalman Filters ◽  
Estimation Method ◽  
Estimation Methods ◽  
Liquid Volume ◽  
Model Parameters ◽  
Rate Estimation ◽  
Time Flow

This paper discusses a real-time flow-rate estimation method for a tilting-ladle-type automatic pouring machine used in the casting industry. In most pouring machines, molten metal is poured into a mold by tilting the ladle. Precise pouring is required to improve productivity and ensure a safe pouring process. To achieve precise pouring, it is important to control the flow rate of the liquid outflow from the ladle. However, due to the high temperature of molten metal, directly measuring the flow rate to devise flow-rate feedback control is difficult. To solve this problem, specific flow-rate estimation methods have been developed. In the previous study by present authors, a simplified flow-rate estimation method was proposed, in which Kalman filters were decentralized to motor systems and the pouring process for implementing into the industrial controller of an automatic pouring machine used a complicatedly shaped ladle. The effectiveness of this flow rate estimation was verified in the experiment with the ideal condition. In the present study, the appropriateness of the real-time flow-rate estimation by decentralization of Kalman filters is verified by comparing it with two other types of existing real-time flow-rate estimations, i.e., time derivatives of the weight of the outflow liquid measured by the load cell and the liquid volume in the ladle measured by a visible camera. We especially confirmed the estimation errors of the candidate real-time flow-rate estimations in the experiments with the uncertainty of the model parameters. These flow-rate estimation methods were applied to a laboratory-type automatic pouring machine to verify their performance.

Real-time Neonatal Respiratory Rate Estimation using a Pressure-Sensitive Mat

2018 ◽  
Author(s):  
Amente Bekele ◽  
Shermeen Nizami ◽  
Yasmina Souley Dosso ◽  
Cheryl Aubertin ◽  
Kim Greenwood ◽  
...  
Keyword(s):  
Respiratory Rate ◽  
Real Time ◽  
Rate Estimation ◽  
Pressure Sensitive

scholarly journals A Novel Real-Time Human Heart Rate Estimation Method for Noncontact Vital Sign Radar Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 88689-88699
Author(s):  
Yipeng Ding ◽  
Xiali Yu ◽  
Chengxi Lei ◽  
Yinhua Sun ◽  
Xuemei Xu ◽  
...  
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Vital Sign ◽  
Human Heart ◽  
Estimation Method ◽  
Radar Detection ◽  
Rate Estimation ◽  
Heart Rate Estimation

An enhanced weight-based real-time map matching algorithm for complex urban networks

2019 ◽  
Vol 534 ◽  
pp. 122318 ◽  
Author(s):  
Mujun He ◽  
Linjiang Zheng ◽  
Wei Cao ◽  
Jing Huang ◽  
Xu Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Map Matching ◽  
Matching Algorithm ◽  
Urban Networks ◽  
Time Map

Hybrid method based on particle filter and NARX for real-time flow rate estimation in multi-product pipelines

2020 ◽  
Vol 88 ◽  
pp. 19-31 ◽  
Author(s):  
Lei He ◽  
Kai Wen ◽  
Changchun Wu ◽  
Jing Gong ◽  
Xie Ping
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Flow Rate ◽  
Hybrid Method ◽  
Rate Estimation ◽  
Time Flow

scholarly journals FLOATING CAR DATA (FCD) FOR MOBILITY APPLICATIONS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1517-1523
Author(s):  
A. Ajmar ◽  
E. Arco ◽  
P. Boccardo ◽  
F. Perez
Keyword(s):  
Real Time ◽  
Geographical Distribution ◽  
Traffic Control ◽  
Traffic Analysis ◽  
Video Observation ◽  
Vehicle Type ◽  
Traffic Jams ◽  
Floating Car Data ◽  
Vehicle Density ◽  
Physical Sensors

<p><strong>Abstract.</strong> Floating car data (FCD) is becoming more and more relevant for mobility domain applications, overcoming issues derived by the use of physical sensors (e.g. inductive loops, video observation, infrared and laser vehicle detection etc.), such as limited geographical distribution, measure inhomogeneities, limited or null coverage of minor roads. An increasing number of vehicles are equipped with devices capable of acquiring GPS positions and other data, transmitted in almost real-time to traffic control centres. Based on FCD data, several traffic analysis in support to mobility services can be performed: vehicle density, speed, origin-destination matrices, different patterns in function of vehicle type. If currently the representativeness of FCD can be considered an issue, current growing trend in FCD penetration should naturally overcome this issue. FCD are also higher sensitive to traffic events (e.g. traffic jams) than model-based approaches.</p>

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