nonlinear autoregressive
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Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 615
Author(s):  
Alessandro Bile ◽  
Hamed Tari ◽  
Andreas Grinde ◽  
Francesca Frasca ◽  
Anna Maria Siani ◽  
...  

The environmental microclimatic characteristics are often subject to fluctuations of considerable importance, which can cause irreparable damage to art works. We explored the applicability of Artificial Intelligence (AI) techniques to the Cultural Heritage area, with the aim of predicting short-term microclimatic values based on data collected at Rosenborg Castle (Copenhagen), housing the Royal Danish Collection. Specifically, this study applied the NAR (Nonlinear Autoregressive) and NARX (Nonlinear Autoregressive with Exogenous) models to the Rosenborg microclimate time series. Even if the two models were applied to small datasets, they have shown a good adaptive capacity predicting short-time future values. This work explores the use of AI in very short forecasting of microclimate variables in museums as a potential tool for decision-support systems to limit the climate-induced damages of artworks within the scope of their preventive conservation. The proposed model could be a useful support tool for the management of the museums.


Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3302
Author(s):  
Naveed Ishtiaq Chaudhary ◽  
Muhammad Asif Zahoor Raja ◽  
Zeshan Aslam Khan ◽  
Khalid Mehmood Cheema ◽  
Ahmad H. Milyani

Recently, a quasi-fractional order gradient descent (QFGD) algorithm was proposed and successfully applied to solve system identification problem. The QFGD suffers from the overparameterization problem and results in estimating the redundant parameters instead of identifying only the actual parameters of the system. This study develops a novel hierarchical QFDS (HQFGD) algorithm by introducing the concepts of hierarchical identification principle and key term separation idea. The proposed HQFGD is effectively applied to solve the parameter estimation problem of input nonlinear autoregressive with exogeneous noise (INARX) system. A detailed investigation about the performance of HQFGD is conducted under different disturbance conditions considering different fractional orders and learning rate variations. The simulation results validate the better performance of the HQFGD over the standard counterpart in terms of estimation accuracy, convergence speed and robustness.


Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8420
Author(s):  
Muhammad Mohsin Khan ◽  
Peter W. Tse ◽  
Amy J.C. Trappey

Smart remaining useful life (RUL) prognosis methods for condition-based maintenance (CBM) of engineering equipment are getting high popularity nowadays. Current RUL prediction models in the literature are developed with an ideal database, i.e., a combination of a huge “run to failure” and “run to prior failure” data. However, in real-world, run to failure data for rotary machines is difficult to exist since periodic maintenance is continuously practiced to the running machines in industry, to save any production downtime. In such a situation, the maintenance staff only have run to prior failure data of an in operation machine for implementing CBM. In this study, a unique strategy for the RUL prediction of two identical and in-process slurry pumps, having only real-time run to prior failure data, is proposed. The obtained vibration signals from slurry pumps were utilized for generating degradation trends while a hybrid nonlinear autoregressive (NAR)-LSTM-BiLSTM model was developed for RUL prediction. The core of the developed strategy was the usage of the NAR prediction results as the “path to be followed” for the designed LSTM-BiLSTM model. The proposed methodology was also applied on publically available NASA’s C-MAPSS dataset for validating its applicability, and in return, satisfactory results were achieved.


2021 ◽  
Author(s):  
Malihe Ashena ◽  
Ghazal Shahpari

Abstract Over the last few years, economic uncertainty has become a global concern. Not only has its impact on economic activities, but there are pieces of evidence that show uncertainty can be the reason for CO2 emissions. It is also expected that the economic policy uncertainty may decrease or delay economic production, which may lead to a reduction in carbon emissions. Furthermore, uncertainty may decrease friendly environment policies and budgets, which cause increase in carbon emissions. Thus, there may be an asymmetric relationship between economic uncertainty and the amount of CO2 emissions. This study investigates the effects of economic policy uncertainty and economic activity on carbon emission applying a Nonlinear Autoregressive Distributive Lag (NARDL) cointegration approach in Iran between 1971 and 2018. Findings show that both policy uncertainty and economic growth contribute to CO2 emissions. The negative and positive shocks of GDP and uncertainty index on CO2 emissions in both the short-run and long-run are significant. It can be concluded that there is an asymmetric effect of economic production on CO2 emissions in Iran. The results of analyzing asymmetric effects of economic uncertainty show a symmetric relationship between uncertainty index and CO2 emissions. In a way that a shock in uncertainty index lowers carbon emission. To sum up, since uncertainty may affect the analysis of carbon emissions incorrectly, some environmental policies such as allocating a budget for R&D on clean energy, and environmental taxes must be implemented.


2021 ◽  
Author(s):  
Fulufhelo Nemavhola ◽  
Harry Ngwangwa

The modelling of tendon behaviour during failure stages is nonlinear and heavily random. However, the understanding of its behavior during such stages, and development of models that can give an accurate prediction of its behavior during failure can provide a means for developing effective tendon therapies. This study is aimed at demonstrating the capability of an artificial neural network in the modelling of failure phases in tendons. A nonlinear autoregressive with exogenous inputs network is applied to three different tensile test data of the human supraspinatus tendons. Owing to data scarcity, the network was trained using two different test data which were randomly sampled and divided into 50%, 25% and 25% proportions for training, validation and preliminary testing. The third test data were used for the final testing phase. The procedure was cyclically performed for each of the results that have been presented in this study. The neural network predictions are presented as curves fitted over actual test results with corresponding error plots. The results indicate that the network is able to accurately predict the failure behaviour of these tendons with correlations of above 99 % for all tests. This is an excellent and very promising result in the light of the difficulties that most deterministic mechanistic models encounter in the modelling of soft tissue failure behaviour. With further development of this technique, sports and exercise physicians would enhance knowledge in mechanisms of tendon failure and be able to devise more injury preventive strategies.


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