Storage Monitoring and Forecasting at the ERDC MSRC

2006 ◽  
Author(s):  
T. Moncrief ◽  
D. Sanders
Keyword(s):  
Monitoring And Forecasting

Diagnostics of Track Infrastructure as Part of the Digitisation of Russian Railways

2020 ◽  
Vol 64 (188) ◽  
pp. 149-160
Author(s):  
Janusz Poliński
Keyword(s):  
Diagnostic Tools ◽  
Gradual Transition ◽  
Technical Diagnostics ◽  
Strategic Development ◽  
Railway Infrastructure ◽  
Monitoring And Forecasting ◽  
Infrastructure Maintenance ◽  
Result Analysis ◽  
Railway Maintenance ◽  
Condition Diagnosis

Technical diagnostics is an integral part of the railway maintenance process. Through timely maintenance, in addition to ensuring the safety, functional and technical reliability of the infrastructure, maintenance costs are reduced and downtime losses, due to failures or premature repair requests, are eliminated or reduced. The track infrastructure diagnostic tools have evolved. This is related to, among others, the miniaturisation of instruments, reading accuracy during motion, as well as upgraded measurement automation and result analysis. Currently, data obtained from multifunctional diagnostic tools is the basis for the developed Russian railway infrastructure maintenance and operation digital model. The strategic development of mobile diagnostic labs is the gradual transition to solutions with advanced digital analysis, supported by artificial intelligence, monitoring and forecasting. The article presents the development of mobile labs for the railroad infrastructure condition diagnosis up to the current solutions, in which measurements take place without human intervention and the obtained information is transmitted in real time to the analysis and decision centres. Keywords: rail transport, measuring wagons, digitisation of railways, Russian railways

Monitoring and forecasting hazards from a slow growing lava dome using aerial imagery, tri-stereo Pleiades-1A/B imagery and PDC numerical simulation

2021 ◽  
Vol 564 ◽  
pp. 116906
Author(s):  
Yves Moussallam ◽  
Talfan Barnie ◽  
Álvaro Amigo ◽  
Karim Kelfoun ◽  
Felipe Flores ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Lava Dome ◽  
Aerial Imagery ◽  
Monitoring And Forecasting ◽  
Slow Growing

scholarly journals Estimating, monitoring, and forecasting COVID-19 epidemics: a spatiotemporal approach applied to NYC data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vinicius V. L. Albani ◽  
Roberto M. Velho ◽  
Jorge P. Zubelli
Keyword(s):  
New York ◽  
New York City ◽  
Census Data ◽  
Age Group ◽  
Age Classes ◽  
Basic Model ◽  
Monitoring And Forecasting ◽  
Group Sex ◽  
Age Range ◽  
Spatial Locations

AbstractWe propose a susceptible-exposed-infective-recovered-type (SEIR-type) meta-population model to simulate and monitor the (COVID-19) epidemic evolution. The basic model consists of seven categories, namely, susceptible (S), exposed (E), three infective classes, recovered (R), and deceased (D). We define these categories for n age and sex groups in m different spatial locations. Therefore, the resulting model contains all epidemiological classes for each age group, sex, and location. The mixing between them is accomplished by means of time-dependent infection rate matrices. The model is calibrated with the curve of daily new infections in New York City and its boroughs, including census data, and the proportions of infections, hospitalizations, and deaths for each age range. We finally obtain a model that matches the reported curves and predicts accurate infection information for different locations and age classes.

scholarly journals Ensuring food security in Kazakhstan in the context of the EAEU

2021 ◽  
Vol 3 ◽  
pp. 24-30
Author(s):  
A. E. Bedelbayeva ◽  
◽  
A. K. Sharipov ◽  
Zh. S. Dossumova ◽  
◽  
...  
Keyword(s):  
Food Security ◽  
Food Products ◽  
Threshold Value ◽  
Member States ◽  
Qualified Personnel ◽  
Goods Market ◽  
Monitoring And Forecasting ◽  
Food Market ◽  
The Republic ◽  
Agroindustrial Complex

The aim is to study the problems of ensuring food security of the Republic of Kazakhstan in the conditions of the Eurasian Economic Union. Methods - monographic, economic-statistical, abstract-logical. Results - it is shown that each of the EAEU member States has identified priority areas and mechanisms for sustainable development. The main economic indicators of agroindustrial complex, indicating a significant increase in the volume of agricultural production in comparison with the previous time periodare considered. The level of per capita consumption of basic food products of the Union member States has been justified, which characterizes the economic and physical availability of food and confirms a favorable situation in this area. The regions producing the largest volumes of food products in monetary terms have been identified; sectors that provide more than half of all production in the countryare identified. Despite the positive trends, problems that have a significant impact on the growth of production potential of Kazakhstanare identified. An insufficient level of consumption of certain types of products in the republic is noted. Conclusions - in order to ensure the collective food security of the EAEU member States, measures aimed to develop cooperation and integration in the AIC, to create a favorable environment for increasing the competitiveness of the industry, marketing of agricultural products and foodhave been proposed. In agricultural policy, it is important to take into account the main risks and threats to sustainable economic development: exceeding the threshold value of imports; price imbalances in goods market; shortage of qualified personnel; underdevelopment of the system of monitoring and forecasting indicators of agri-food market.

scholarly journals AI powered IoT based Real-Time Air Pollution Monitoring and Forecasting

2021 ◽  
Vol 2115 (1) ◽  
pp. 012016
Author(s):  
Geetha Mani ◽  
Joshi Kumar Viswanadhapalli ◽  
P Sriramalakshmi
Keyword(s):  
Air Pollution ◽  
Air Pollutants ◽  
Moving Average ◽  
Pollution Monitoring ◽  
Model Parameters ◽  
Air Pollution Monitoring ◽  
Moving Average Model ◽  
Monitoring And Forecasting ◽  
Life On Earth ◽  
Auto Regression

Abstract Air is one of the most fundamental constituents for the sustenance of life on earth. The consumption of non-renewable energy sources and industrial parameters steadily increases air pollution. These factors affect the welfare and prosperity of life on earth; therefore, the nature of Air Quality in our environment needs to be monitored continuously. This paper presents the execution and plan of Internet-of-Things (IoT) based Air Pollution Monitoring and Forecasting utilising Artificial Intelligent (AI) methods. Also, Online Dashboard was created for real-time monitoring of Air pollutants (both live and forecasted data) through ‘firebase’ from the Google cloud server. The air pollutants like Carbon Mono Oxide (CO), Ammonia (NH3), and Ozone (O3) layer information are collected from IoT-based sensor nodes in Vijayawada Region. Time Series modelling techniques like the Naive Bayes Model, Auto Regression Model (AR), Auto Regression Moving Average Model (ARMA), and Auto-Regression Integrating Moving Average Model (ARIMA) used to forecast the individual air pollutants aforementioned. The data collected from the IoT sensor node with a time frame is fed as input features for training the model, and optimised model parameters are obtained. The obtained model parameters are again verified with new unseen data for time. The performances of various Time Series models are validated with the help of performance indices like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The machine learning algorithm flashed in Raspberry Pi-3. It acts as an edge computing device. The current air pollutants data and forecasted data are monitored for the next 4 hours through an online dashboard created in an open-source firebase from Google cloud service.

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