scholarly journals Interpreting the socio-technical interactions within a wind damage–artificial neural network model for community resilience

2020 ◽  
Vol 7 (11) ◽  
pp. 200922
Author(s):  
Stephanie F. Pilkington ◽  
Hussam N. Mahmoud
Keyword(s):  
Spatial Distribution ◽  
Graph Theory ◽  
Structural Damage ◽  
Black Box ◽  
Wind Pressure ◽  
Wind Damage ◽  
Wind Loading ◽  
Physical Damage ◽  
Artificial Neural ◽  
Wind Events

The use of machine learning has grown in popularity in various disciplines. Despite the popularity, the apparent ‘black box’ nature of such tools continues to be an area of concern. In this article, we attempt to unravel the complexity of this black box by exploring the use of artificial neural networks (ANNs), coupled with graph theory, to model and interpret the spatial distribution of building damage from extreme wind events at a community level. Structural wind damage is a topic that is mostly well understood for how wind pressure translates to extreme loading on a structure, how debris can affect that loading and how specific social characteristics contribute to the overall population vulnerability. While these themes are widely accepted, they have proven difficult to model in a cohesive manner, which has led primarily to physical damage models considering wind loading only as it relates to structural capacity. We take advantage of this modelling difficulty to reflect on two different ANN models for predicting the spatial distribution of structural damage due to wind loading. Through graph theory analysis, we study the internal patterns of the apparent black box of artificial intelligence of the models and show that social parameters are key to predict structural damage.

scholarly journals Update article: applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events

2021 ◽  
Vol 8 (12) ◽  
Author(s):  
Stephanie F. Pilkington ◽  
Hussam Mahmoud
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Graph Theory ◽  
Traditional Approach ◽  
Social Variables ◽  
Neural Network Models ◽  
Damage Models ◽  
Companion Article ◽  
Artificial Neural ◽  
Wind Events

In a companion article, previously published in Royal Society Open Science , the authors used graph theory to evaluate artificial neural network models for potential social and building variables interactions contributing to building wind damage. The results promisingly highlighted the importance of social variables in modelling damage as opposed to the traditional approach of solely considering the physical characteristics of a building. Within this update article, the same methods are used to evaluate two different artificial neural networks for modelling building repair and/or rebuild (recovery) time. By contrast to the damage models, the recovery models (RMs) consider (A) primarily social variables and then (B) introduce structural variables. These two models are then evaluated using centrality and shortest path concepts of graph theory as well as validated against data from the 2011 Joplin tornado. The results of this analysis do not show the same distinctions as were found in the analysis of the damage models from the companion article. The overarching lack of discernible and consistent differences in the RMs suggests that social variables that drive damage are not necessarily contributors to recovery. The differences also serve to reinforce that machine learning methods are best used when the contributing variables are already well understood.

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

2018 ◽  
Vol 5 (1) ◽  
pp. 41-46
Author(s):  
Rosalina Rosalina ◽  
Hendra Jayanto
Keyword(s):  
Neural Network ◽  
Graph Theory ◽  
Wavelet Transform ◽  
Stock Market ◽  
Discrete Wavelet Transform ◽  
Stock Price ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Stock Market Forecasting ◽  
Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

The Extreme Wind Events in the Ross Island Region of Antarctica

2016 ◽  
Vol 31 (3) ◽  
pp. 985-1000 ◽  
Author(s):  
Nicholas J. Weber ◽  
Matthew A. Lazzara ◽  
Linda M. Keller ◽  
John J. Cassano
Keyword(s):  
Structural Damage ◽  
Ross Sea ◽  
Enso Event ◽  
Central Pacific ◽  
Island Region ◽  
Back Trajectories ◽  
Extreme Wind ◽  
Mcmurdo Station ◽  
Wind Events ◽  
Upper Level

Abstract Numerous incidents of structural damage at the U.S. Antarctic Program’s (USAP) McMurdo Station due to extreme wind events (EWEs) have been reported over the past decade. Utilizing nearly 20 yr (~1992–2013) of University of Wisconsin automatic weather station (AWS) data from three different stations in the Ross Island region (Pegasus North, Pegasus South, and Willie Field), statistical analysis shows no significant trends in EWE frequency, intensity, or duration. EWEs more frequently occur during the transition seasons. To assess the dynamical environment of these EWEs, Antarctic Mesoscale Prediction System (AMPS) forecast back trajectories are computed and analyzed in conjunction with several other AMPS fields for the strongest events at McMurdo Station. The synoptic analysis reveals that McMurdo Station EWEs are nearly always associated with strong southerly flow due to an approaching Ross Sea cyclone and an upper-level trough around Cape Adare. A Ross Ice Shelf air stream (RAS) environment is created with enhanced barrier winds along the Transantarctic Mountains, downslope winds in the lee of the glaciers and local topography, and a tip jet effect around Ross Island. The position and intensity of these Ross Sea cyclones are most influenced by the occurrence of a central Pacific ENSO event, which causes the upper-level trough to move westward. An approaching surface cyclone would then be in position to trigger an event, depending on how the wind direction and speed impinges on the complex topography around McMurdo Station.

scholarly journals Feasibility of Black-Box Time Domain Modeling of Single-Phase Photovoltaic Inverters Using Artificial Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2118
Author(s):  
Elias Kaufhold ◽  
Simon Grandl ◽  
Jan Meyer ◽  
Peter Schegner
Keyword(s):  
Time Domain ◽  
Single Phase ◽  
Low Voltage ◽  
Black Box ◽  
Steady States ◽  
Domain Modeling ◽  
Dynamic Simulations ◽  
Time Domain Modeling ◽  
Artificial Neural ◽  
Grid Side

This paper introduces a new black-box approach for time domain modeling of commercially available single-phase photovoltaic (PV) inverters in low voltage networks. An artificial neural network is used as a nonlinear autoregressive exogenous model to represent the steady state behavior as well as dynamic changes of the PV inverter in the frequency range up to 2 kHz. The data for the training and the validation are generated by laboratory measurements of a commercially available inverter for low power applications, i.e., 4.6 kW. The state of the art modeling approaches are explained and the constraints are addressed. The appropriate set of data for training is proposed and the results show the suitability of the trained network as a black-box model in time domain. Such models are required, i.e., for dynamic simulations since they are able to represent the transition between two steady states, which is not possible with classical frequency-domain models (i.e., Norton models). The demonstrated results show that the trained model is able to represent the transition between two steady states and furthermore reflect the frequency coupling characteristic of the grid-side current.

scholarly journals Real-Time Anticipation and Prevention of Hot Spots by Monitoring the Dynamic Conductance of Photovoltaic Panels

2021 ◽  
Author(s):  
William Lamb ◽  
Dallon Asnes ◽  
Jonathan Kupfer ◽  
Emma Lickey ◽  
Jeremy Bakken ◽  
...  
Keyword(s):  
Real Time ◽  
Hot Spots ◽  
Structural Damage ◽  
Reverse Bias ◽  
Control Method ◽  
Physical Damage ◽  
Operating Current ◽  
A Cell ◽  
Real Time Simulations ◽  
Dynamic Conductance

<div>Hot spotting in photovoltaic (PV) panels causes physical damage, power loss, reduced lifetime reliability, and increased manufacturing costs. The problem arises routinely in defect-free standard panels; any string of cells that receives uneven illumination can develop hot spots, and the temperature rise often exceeds 100°C in conventional silicon panels despite on-panel bypass diodes, the standard mitigation technique. Bypass diodes limit the power dissipated in a cell subjected to reverse bias, but they do not prevent hot spots from forming. An alternative control method has been suggested by Kernahan [1] that senses in real time the dynamic conductance |dI/dV| of a string of cells and adjusts its operating current so that a partially shaded cell is never forced into reverse bias. We start by exploring the behavior of individual illuminated PV cells when externally forced into reverse bias. We observe that cells can suffer significant heating and structural damage, with desoldering of cell-tabbing and discolorations on the front cell surface. Then we test PV panels and confirm Kernahan’s proposed panel-level solution that anticipates and prevents hot spots in real time. Simulations of cells and panels confirm our experimental observations and provide insights into both the operation of Kernahan’s method and panel performance.</div>

Wind Loading on a Pyramidal Roof Structure

1985 ◽  
Vol 1 (2) ◽  
pp. 105-110 ◽  
Author(s):  
A. J. Dutt
Keyword(s):  
Wind Tunnel ◽  
Peripheral Region ◽  
Wind Pressure ◽  
Scale Model ◽  
Wind Loading ◽  
Wind Tunnel Experiments ◽  
Wind Tunnel Tests ◽  
Roof Structure ◽  
Inner Region ◽  
The Church

This paper deals with the investigation of wind loading on the pyramidal roof structure of the Church of St Michael in Newton, Wirral, Cheshire, England, by wind tunnel tests on a 1/48 scale model. The roof of the model was flat in the peripheral region of the building while in the inner region there was a grouping of four pyramidal roofs. Wind tunnel experiments were carried out; wind pressure distribution and contours of wind pressure on all surfaces of the pyramid roofs were determined for four principal wind directions. The average suctions on the roof were evaluated. The highest point suction encountered was — 4q whilst the maximum average suction on the roof was —0·86q. The results obtained from wind tunnel tests were used for the design of pyramidal roof structures and roof coverings for which localised high suctions were very significant.

scholarly journals Optimization of a Steam Reforming Plant Modeled with Artificial Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1923
Author(s):  
Eduardo G. Pardo ◽  
Jaime Blanco-Linares ◽  
David Velázquez ◽  
Francisco Serradilla
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steam Reforming ◽  
Real Data ◽  
Memetic Algorithms ◽  
Black Box ◽  
Optimization Techniques ◽  
Ann Model ◽  
The Real ◽  
Artificial Neural

The objective of this research is to improve the hydrogen production and total profit of a real Steam Reforming plant. Given the impossibility of tuning the real factory to optimize its operation, we propose modelling the plant using Artificial Neural Networks (ANNs). Particularly, we combine a set of independent ANNs into a single model. Each ANN uses different sets of inputs depending on the physical processes simulated. The model is then optimized as a black-box system using metaheuristics (Genetic and Memetic Algorithms). We demonstrate that the proposed ANN model presents a high correlation between the real output and the predicted one. Additionally, the performance of the proposed optimization techniques has been validated by the engineers of the plant, who reported a significant increase in the benefit that was obtained after optimization. Furthermore, this approach has been favorably compared with the results that were provided by a general black-box solver. All methods were tested over real data that were provided by the factory.

scholarly journals Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 749 ◽  
Author(s):  
Jorge E. Jiménez-Hornero ◽  
Inés María Santos-Dueñas ◽  
Isidoro García-García
Keyword(s):  
Neural Networks ◽  
Acetic Acid ◽  
Artificial Neural Networks ◽  
First Principles ◽  
Goodness Of Fit ◽  
Black Box ◽  
Operating Conditions ◽  
Box Models ◽  
Artificial Neural ◽  
Black Box Models

Modelling techniques allow certain processes to be characterized and optimized without the need for experimentation. One of the crucial steps in vinegar production is the biotransformation of ethanol into acetic acid by acetic bacteria. This step has been extensively studied by using two predictive models: first-principles models and black-box models. The fact that first-principles models are less accurate than black-box models under extreme bacterial growth conditions suggests that the kinetic equations used by the former, and hence their goodness of fit, can be further improved. By contrast, black-box models predict acetic acid production accurately enough under virtually any operating conditions. In this work, we trained black-box models based on Artificial Neural Networks (ANNs) of the multilayer perceptron (MLP) type and containing a single hidden layer to model acetification. The small number of data typically available for a bioprocess makes it rather difficult to identify the most suitable type of ANN architecture in terms of indices such as the mean square error (MSE). This places ANN methodology at a disadvantage against alternative techniques and, especially, polynomial modelling.

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