scholarly journals How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 907 ◽  
Author(s):  
Gustavo Furquim ◽  
Geraldo Filho ◽  
Roozbeh Jalali ◽  
Gustavo Pessin ◽  
Richard Pazzi ◽  
...  
Keyword(s):  
Fault Tolerance ◽  
Real Data ◽  
Flash Floods

scholarly journals Towards DC Energy Efficient Homes

2021 ◽  
Vol 11 (13) ◽  
pp. 6005
Author(s):  
Daniel Villanueva ◽  
Moisés Cordeiro-Costas ◽  
Andrés E. Feijóo-Lorenzo ◽  
Antonio Fernández-Otero ◽  
Edelmiro Miguez-García
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicles ◽  
Energy Efficient ◽  
Electricity Consumption ◽  
Real Data ◽  
Mean Values ◽  
Led Lighting ◽  
Shed Light

The aim of this paper is to shed light on the question regarding whether the integration of an electric battery as a part of a domestic installation may increase its energy efficiency in comparison with a conventional case. When a battery is included in such an installation, two types of electrical conversion must be considered, i.e., AC/DC and DC/AC, and hence the corresponding losses due to these converters must not be forgotten when performing the analysis. The efficiency of the whole system can be increased if one of the mentioned converters is avoided or simply when its dimensioning is reduced. Possible ways to achieve this goal can be: to use electric vehicles as DC suppliers, the use of as many DC home devices as possible, and LED lighting or charging devices based on renewables. With all this in mind, several scenarios are proposed here in order to have a look at all possibilities concerning AC and DC powering. With the aim of checking these scenarios using real data, a case study is analyzed by operating with electricity consumption mean values.

scholarly journals Spread of Epidemic Disease on Edge-Weighted Graphs from a Database: A Case Study of COVID-19

2021 ◽  
Vol 18 (9) ◽  
pp. 4432
Author(s):  
Ronald Manríquez ◽  
Camilo Guerrero-Nancuante ◽  
Felipe Martínez ◽  
Carla Taramasco
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Preventive Measures ◽  
Weighted Graph ◽  
Real Data ◽  
Sir Model ◽  
Weighted Graphs ◽  
Epidemic Disease ◽  
The Real

The understanding of infectious diseases is a priority in the field of public health. This has generated the inclusion of several disciplines and tools that allow for analyzing the dissemination of infectious diseases. The aim of this manuscript is to model the spreading of a disease in a population that is registered in a database. From this database, we obtain an edge-weighted graph. The spreading was modeled with the classic SIR model. The model proposed with edge-weighted graph allows for identifying the most important variables in the dissemination of epidemics. Moreover, a deterministic approximation is provided. With database COVID-19 from a city in Chile, we analyzed our model with relationship variables between people. We obtained a graph with 3866 vertices and 6,841,470 edges. We fitted the curve of the real data and we have done some simulations on the obtained graph. Our model is adjusted to the spread of the disease. The model proposed with edge-weighted graph allows for identifying the most important variables in the dissemination of epidemics, in this case with real data of COVID-19. This valuable information allows us to also include/understand the networks of dissemination of epidemics diseases as well as the implementation of preventive measures of public health. These findings are important in COVID-19’s pandemic context.

scholarly journals Macroscopic and Multi-Scale Models for Multi-Class Vehicular Dynamics with Uneven Space Occupancy: A Case Study

Axioms ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 102
Author(s):  
Maya Briani ◽  
Emiliano Cristiani ◽  
Paolo Ranut
Keyword(s):  
Road Network ◽  
Real Data ◽  
Second Order ◽  
Heavy Vehicles ◽  
First Order ◽  
Multi Scale ◽  
Stop And Go ◽  
Scale Models ◽  
Road Space

In this paper, we propose two models describing the dynamics of heavy and light vehicles on a road network, taking into account the interactions between the two classes. The models are tailored for two-lane highways where heavy vehicles cannot overtake. This means that heavy vehicles cannot saturate the whole road space, while light vehicles can. In these conditions, the creeping phenomenon can appear, i.e., one class of vehicles can proceed even if the other class has reached the maximal density. The first model we propose couples two first-order macroscopic LWR models, while the second model couples a second-order microscopic follow-the-leader model with a first-order macroscopic LWR model. Numerical results show that both models are able to catch some second-order (inertial) phenomena such as stop and go waves. Models are calibrated by means of real data measured by fixed sensors placed along the A4 Italian highway Trieste–Venice and its branches, provided by Autovie Venete S.p.A.

scholarly journals Exploring Geostatistical Modeling and VisualizationTechniques of Uncertainties for Categorical Spatial Data

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Carlos A. Felgueiras ◽  
Jussara O. Ortiz ◽  
Eduardo C. G. Camargo ◽  
Laércio M. Namikawa ◽  
Thales S. Körting
Keyword(s):  
Spatial Data ◽  
Three Dimensional ◽  
Real Data ◽  
Geographic Region ◽  
Geostatistical Modeling ◽  
Probability Maps ◽  
Uncertainty Model ◽  
Probability Criterion ◽  
Sample Points

This article presents and analyzes the indicator geostatistical modeling and some visualization techniques of uncertainty models for categorical spatial attributes. A set of sample points of some categorical attribute is used as input information. The indicator approach requires a transformation of sample points on fields of indicator samples according to the classes of interest. Experimental and theoretical semivariograms of the indicator fields are defined representing the spatial variation of the indicator information. The indicator fields, along with their semivariograms, are used to determine the uncertainty model, the conditioned probability distribution function, of the attribute at any location inside the geographic region delimited by the samples. The probability functions are considered for producing prediction and probability maps based on the maximum class probability criterion. These maps can be visualized using different techniques. In this work, it is considered individual visualization of the predicted and probability maps and a combination of them. The predicted maps can also be visualized with or without constraints related to the uncertainty probabilities. The combined visualizations are based on three-dimensional (3D) planar projection and on the Red-Green-Blue to Intensity-Hue-Saturation (RGB-IHS) fusion transformation techniques. The methodology of this article is illustrated by a case study with real data, a sample set of soil textures observed in an experimental farm located in the region of São Carlos city in São Paulo State, Brazil. The resulting maps of this case study are presented and the advantages and the drawbacks of the visualization options are analyzed and discussed.

scholarly journals The interconnection exchange and complex systems properties in power grid network

2021 ◽  
Vol 336 ◽  
pp. 05020
Author(s):  
Piotr Hadaj ◽  
Marek Nowak ◽  
Dominik Strzałka
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Real Data ◽  
Electrical Energy ◽  
Significant Drop ◽  
Average Efficiency ◽  
Grid Network ◽  
System Operator ◽  
Energy Network

A case study based on the real data obtained from the Polish PSE System Operator of the highest voltages electrical energy network is shown. The data about the interconnection exchange and some complex networks (graphs) parameters were examined, after the removal of selected nodes. This allowed to test selected network parameters and to show that the breakdown of only three nodes in this network can cause significant drop of its average efficiency.

scholarly journals Implementation of a standard work routine using Lean Manufacturing tools: A case Study

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Diego Michael Cornelius dos Santos ◽  
Bruna Karine dos Santos ◽  
César Gabriel dos Santos
Keyword(s):  
Continuous Improvement ◽  
Lean Manufacturing ◽  
Assembly Line ◽  
Real Data ◽  
Production Methods ◽  
Standard Work ◽  
Technological Advances ◽  
Work Routine ◽  
Constant Improvement

Abstract: Due to technological advances, trade politicies and society's consumption patterns, competitiveness among companies has increased considerably, requiring practices that provide a constant improvement in production indicators and product quality. In this context, the use of Toyota Production System tools, also known as Lean Manufacturing, have a fundamental role in the elimination of waste and continuous improvement of industrial production levels. Thus, this work aims to implement a standardized work routine among employees working in a market of parts in an Agricultural Machinery industry, which lacks production methods. To represent this situation, real data were used, which correspond to the needs of the assembly line, and which served as the basis for the analysis and implementation of a new work routine. The results obtained enabled the creation of a standardized work routine, which was obtained by balancing activities between operators and eliminating activities that did not add value to the product.

Multi-Criteria Selection of Wind Turbine Using TOPSIS Approach

2020 ◽  
Author(s):  
Shafiqur Rehman ◽  
Salman A. Khan ◽  
Luai M. Alhems
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Selection Process ◽  
Geographical Location ◽  
Real Data ◽  
Maximum Energy ◽  
Decision Criteria ◽  
Topsis Approach ◽  
Selection Of

Abstract The recent revolution in the use of renewable energy worldwide has opened many dimensions of research and development for sustainable energy. In this context, the use of wind energy has received notable attention. One critical decision in the development of a wind farm is the selection of the most appropriate turbine compatible with the characteristics of the geographical location under consideration in order to harness maximum energy. This selection process considers multiple decision criteria which are often in conflict with each other, as improving one criterion negatively affects one or more other criteria. Therefore, it is desired to find a tradeoff solution where all selection criteria are simultaneously optimized to the best possible level. This paper proposes a TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) based approach for multi-criteria selection of wind turbine. Three decision criteria, namely, hub height, wind speed, and net capacity factor are used in the decision process. A case study is shown on real data collected from the Aljouf region located at an altitude of 753 meters above sea level in the northern part of Saudi Arabia. Seventeen turbines with rated capacities ranging from 1.5 GW to 3 GW from various manufacturers are evaluated. Results indicate that Vestas V110 turned out to be the most appropriate turbine for the underlying site.

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