VIIRS Nighttime Light Data for Income Estimation at Local Level

The aim of the paper is to develop a model for the real-time estimation of local level income data by combining machine learning, Earth Observation, and Geographic Information System. More exactly, we estimated the income per capita by help of a machine learning model for 46 cities with more than 50,000 inhabitants, based on the National Polar-orbiting Partnership–Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime satellite images from 2012–2018. For the automation of calculation, a new ModelBuilder type tool was developed within the ArcGIS software called EO-Incity (Earth Observation–Income city). The sum of light (SOL) data extracted by means of the EO-Incity tool and the observed income data were integrated in an algorithm within the MATLAB software in order to calculate a transfer equation and the average error. The results achieved were subsequently reintegrated in EO-Incity and used for the estimation of the income value at local level. The regression analyses highlighted a stable and strong relationship between SOL and income for the analyzed cities. The EO-Incity tool and the machine learning model proved to be efficient in the real-time estimation of the income at local level. When integrated in the information systems specific for smart cities, they can serve as a support for decision-making in order to fight poverty and reduce social inequalities.

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

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2018.09.053 ◽

2018 ◽

Vol 51 (15) ◽

pp. 1062-1067 ◽

Cited By ~ 2

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

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Adaptive Scheme for the Real-Time Estimation of Tire-Road Friction Coefficient and Vehicle Velocity

IEEE/ASME Transactions on Mechatronics ◽

10.1109/tmech.2017.2704606 ◽

2017 ◽

Vol 22 (4) ◽

pp. 1508-1518 ◽

Cited By ~ 25

Author(s):

Kyoungseok Han ◽

Eunjae Lee ◽

Mooryong Choi ◽

Seibum B. Choi

Keyword(s):

Friction Coefficient ◽

Real Time ◽

Time Estimation ◽

Adaptive Scheme ◽

The Real ◽

Vehicle Velocity ◽

Road Friction ◽

Real Time Estimation ◽

Road Friction Coefficient

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A Technique for the Real-Time Estimation of Air-Fuel Ratio Using Molecular Weight Ratios

10.4271/900260 ◽

1990 ◽

Cited By ~ 12

Author(s):

Ronald S. Patrick ◽

J. David Powell

Keyword(s):

Molecular Weight ◽

Real Time ◽

Time Estimation ◽

The Real ◽

Fuel Ratio ◽

Real Time Estimation

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Real-Time Estimation of Eye Movement Condition Using a Deep Learning Model

10.1007/978-3-030-90963-5_11 ◽

2021 ◽

pp. 132-143

Author(s):

Akihiro Sugiura ◽

Yoshiki Itazu ◽

Kunihiko Tanaka ◽

Hiroki Takada

Keyword(s):

Deep Learning ◽

Real Time ◽

Eye Movement ◽

Time Estimation ◽

Learning Model ◽

Movement Condition ◽

Real Time Estimation ◽

Deep Learning Model

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Application of Machine Learning Model on Streaming Health Data Event in Real-Time to Predict Health Status Using Spark

2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT) ◽

10.1109/isaect.2018.8618860 ◽

2018 ◽

Cited By ~ 4

Author(s):

Abderrahmane Ed-daoudy ◽

Khalil Maalmi

Keyword(s):

Machine Learning ◽

Health Status ◽

Real Time ◽

Learning Model ◽

Health Data ◽

Machine Learning Model

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Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries

American Journal of Obstetrics and Gynecology ◽

10.1016/j.ajog.2020.05.025 ◽

2020 ◽

Vol 223 (3) ◽

pp. 437.e1-437.e15

Author(s):

Joshua Guedalia ◽

Michal Lipschuetz ◽

Michal Novoselsky-Persky ◽

Sarah M. Cohen ◽

Amihai Rottenstreich ◽

...

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Real Time ◽

Learning Model ◽

Time Data ◽

Machine Learning Model ◽

Vaginal Deliveries ◽

Real Time Data

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Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction

Journal of Diabetes Science and Technology ◽

10.1177/1932296820922622 ◽

2020 ◽

pp. 193229682092262

Author(s):

Darpit Dave ◽

Daniel J. DeSalvo ◽

Balakrishna Haridas ◽

Siripoom McKay ◽

Akhil Shenoy ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Learning Model ◽

Machine Learning Model ◽

Feature Based

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Modeling Technique of Prosthetic Heart Valves

Journal of Biomechanical Engineering ◽

10.1115/1.3138463 ◽

1984 ◽

Vol 106 (1) ◽

pp. 83-88 ◽

Cited By ~ 7

Author(s):

T. Kitamura ◽

T. Kijima ◽

H. Akashi

Keyword(s):

Real Time ◽

Heart Valves ◽

Time Estimation ◽

Modeling Technique ◽

Computational Time ◽

Prosthetic Heart Valves ◽

The Real ◽

Hemodynamic State ◽

Real Time Estimation

This paper demonstrates a modeling technique of prosthetic heart valves. In the modeling, a pumping cycle is divided into four phases, in which the state of the valve and flow is different. The pressure-flow relation across the valve is formulated separately in each phase. This technique is developed to build a mathematical model used in the real time estimation of the hemodynamic state under artificial heart pumping. The model built by this technique is simple enough for saving the computational time in the real time estimation. The model is described by the first-order ordinary differential equation with 12 parameters. These parameters can be uniquely determined beforehand from in-vitro experimental data. It is shown that the model can adapt, with sufficient accuracy, to a change in the practical pumping condition and the viscosity of the fluid in their practical range, and is also demonstrated that the estimated backflow volume by model agrees closely with the actual one.

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