scholarly journals Real-Time Probabilistic Data Fusion for Large-Scale IoT Applications

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 10015-10027 ◽  
Author(s):  
Adnan Akbar ◽  
George Kousiouris ◽  
Haris Pervaiz ◽  
Juan Sancho ◽  
Paula Ta-Shma ◽  
...  
Keyword(s):  
Data Fusion ◽  
Real Time ◽  
Large Scale ◽  
Probabilistic Data ◽  
Iot Applications

scholarly journals Large-Scale Real-Time Semantic Processing Framework for Internet of Things

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Xi Chen ◽  
Huajun Chen ◽  
Ningyu Zhang ◽  
Jue Huang ◽  
Wen Zhang
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Semantic Processing ◽  
Large Scale ◽  
Sensor Data ◽  
Sensor Technology ◽  
Semantic Web Technologies ◽  
Computing Platform ◽  
Iot Applications ◽  
Processing Framework

Nowadays, the advanced sensor technology with cloud computing and big data is generating large-scale heterogeneous and real-time IOT (Internet of Things) data. To make full use of the data, development and deploy of ubiquitous IOT-based applications in various aspects of our daily life are quite urgent. However, the characteristics of IOT sensor data, including heterogeneity, variety, volume, and real time, bring many challenges to effectively process the sensor data. The Semantic Web technologies are viewed as a key for the development of IOT. While most of the existing efforts are mainly focused on the modeling, annotation, and representation of IOT data, there has been little work focusing on the background processing of large-scale streaming IOT data. In the paper, we present a large-scale real-time semantic processing framework and implement an elastic distributed streaming engine for IOT applications. The proposed engine efficiently captures and models different scenarios for all kinds of IOT applications based on popular distributed computing platform SPARK. Based on the engine, a typical use case on home environment monitoring is given to illustrate the efficiency of our engine. The results show that our system can scale for large number of sensor streams with different types of IOT applications.

Real-Time Well Construction Process Inference through Probabilistic Data Fusion

2018 ◽  
Author(s):  
Sylvain Chambon ◽  
Sai Venkatakrishnan ◽  
Mohammad Khairi Hamzah ◽  
Jim Belaskie ◽  
Yingwei Yu
Keyword(s):  
Data Fusion ◽  
Real Time ◽  
Construction Process ◽  
Probabilistic Data

A data fusion method to improve winter PM10 concentration predictions in Budapest based on the CAMS air quality models

2020 ◽  
Author(s):  
Adrienn Varga-Balogh ◽  
Ádám Leelőssy ◽  
István Lagzi ◽  
Róbert Mészáros
Keyword(s):  
Air Quality ◽  
Data Fusion ◽  
Real Time ◽  
Large Scale ◽  
Urban Traffic ◽  
Pm10 Concentration ◽  
Fusion Method ◽  
Concentration Data ◽  
Model Predictions ◽  
Urban Monitoring

<div> <p><span>Winter air pollution in Budapest is a major environmental issue, caused by an interaction of residential heating, urban traffic and large-scale transport. I</span><span>ncreasing public and political demand are</span><span> present to achieve </span><span>more accurate air quality predictions to support both real-time public health measures and long-term mitigation policies.  </span><span>A</span><span>tmospheric chemistry and transport models of the Copernicus Atmospheric Monitoring Service (CAMS) provide </span><span>near-real-time </span><span>air quality forecasts for Europe</span><span>. The validation of these model predictions for Budapest showed that although large-scale processes are well captured, the complex interaction of large-scale plumes with significant and highly variable local residential emissions leads to the underestimation of winter PM10 concentrations. Furthermore, CAMS models are not expected to fully predict the non-representative concentrations at specific urban monitoring locations, which, on the other hand, serve as the legal basis of all public policies and measures. Therefore, obtaining a relationship between monitoring site observations and CAMS model predictions is of primary importance.</span> </p> </div><div> <p><span>In this study, we used observed PM10 concentration data from 12 air quality monitoring sites within Budapest, as well as 24-hour predictions from 7 of the 9 CAMS models to </span><span>produce an optimal linear combination of models that best matched, in terms of RMSE, the observed time series. A zero-degree term to correct the model bias was also applied. The applied data fusion method was cross-validated on urban monitoring sites not used in fitting the model, and found to improve PM10 forecast validation statistics compared to the pointwise model median (CAMS ensemble) as well as each of the 7 single models. The presented fusion of CAMS models can therefore provide an improved prediction of PM10 concentrations at urban monitoring sites in Budapest. </span> </p> </div>

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.

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data
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