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Modelling the flow behaviour, recrystallisation and crystallographic texture in hot deformed Fe-30wt%Ni Austenite
Abstract: The present work describes a hybrid modelling approach developed for predicting the flow behaviour, recrystallisation characteristics and crystallographic texture evolution in a Fe-30wt%Ni austenitic model alloy subjected to hot plane strain compression. A series of compression tests were performed at temperatures between 850 and 1050ÂșC and strain rates between 0.1 and 10 s-1. The evolution of grain structure, crystallographic texture and dislocation substructure was characterised in detail for a deformation temperature of 950ÂșC and strain rates of 0.1 and 10 s-1, using electron backscatter diffraction and transmission electron microscopy. The hybrid modelling method utilises a combination of empirical, physically-based and neuro-fuzzy models. The flow stress is described as a function of the applied variables of strain rate and temperature using an empirical model. The recrystallisation behaviour is predicted from the measured microstructural state variables of internal dislocation density, subgrain size and misorientation between subgrains using a physically-based model. The texture evolution is modelled using artificial neural networks
A Hybrid Intelligent Early Warning System for Predicting Economic Crises: The Case of China
This paper combines artificial neural networks (ANN), fuzzy optimization and time-series econometric models in one unified framework to form a hybrid intelligent early warning system (EWS) for predicting economic crises. Using quarterly data on 12 macroeconomic and financial variables for the Chinese economy during 1999 and 2008, the paper finds that the hybrid model possesses strong predictive power and the likelihood of economic crises in China during 2009 and 2010 remains high.Computational intelligence; artificial neural networks; fuzzy optimization; early warning system; economic crises
Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning
A novel hybrid data-driven approach is developed for forecasting power system
parameters with the goal of increasing the efficiency of short-term forecasting
studies for non-stationary time-series. The proposed approach is based on mode
decomposition and a feature analysis of initial retrospective data using the
Hilbert-Huang transform and machine learning algorithms. The random forests and
gradient boosting trees learning techniques were examined. The decision tree
techniques were used to rank the importance of variables employed in the
forecasting models. The Mean Decrease Gini index is employed as an impurity
function. The resulting hybrid forecasting models employ the radial basis
function neural network and support vector regression. Apart from introduction
and references the paper is organized as follows. The section 2 presents the
background and the review of several approaches for short-term forecasting of
power system parameters. In the third section a hybrid machine learning-based
algorithm using Hilbert-Huang transform is developed for short-term forecasting
of power system parameters. Fourth section describes the decision tree learning
algorithms used for the issue of variables importance. Finally in section six
the experimental results in the following electric power problems are
presented: active power flow forecasting, electricity price forecasting and for
the wind speed and direction forecasting
From Property Companies to Real Estate Investment Trusts: The Impact of Economic and Property Factors in the UK Commercial Property Returns
This study investigates cross-sectionally the impact of economic and property factors on the returns of UK property companies and real estate investment trusts. By applying structural time-series modelling and the Kalman Filter to obtain unexpected changes or innovations in selected economic and property variables it was found for the sample period analysed that economic and property variables influence commercial property returns in the UK. It was also found that by converting into REITS property companies quickly acquired hybrid features of securitised and property backed assets.Keywords: REITS, commercial, property returns, innovations, Kalman Filter, crosssection, panel data, innovations, unexpected changes.
Plug-in Hybrid Electric Vehicle Energy Management with Clutch Engagement Control via Continuous-Discrete Reinforcement Learning
Energy management strategy (EMS) is a key technology for plug-in hybrid
electric vehicles (PHEVs). The energy management of certain series-parallel
PHEVs involves the control of continuous variables, such as engine torque, and
discrete variables, such as clutch engagement/disengagement. We establish a
control-oriented model for a series-parallel plug-in hybrid system with clutch
engagement control from the perspective of mixed-integer programming.
Subsequently, we design an EMS based on continuous-discrete reinforcement
learning (CDRL), which enables simultaneous output of continuous and discrete
variables. During training, we introduce state-of-charge (SOC) randomization to
ensure that the hybrid system exhibits optimal energy-saving performance in
both high and low SOC. Finally, the effectiveness of the proposed CDRL strategy
is verified by comparing EMS based on charge-depleting charge-sustaining
(CD-CS) with rule-based clutch engagement control, and Dynamic Programming
(DP). The simulation results show that, under a high SOC, the CDRL strategy
proposed in this paper can improve energy efficiency by 8.3% compared to CD-CS,
and the energy consumption is just 6.6% higher than the global optimum based on
DP, while under a low SOC, the numbers are 4.1% and 3.9%, respectively
Time series estimates of the US new Keynesian Phillips curve with structural breaks
This paper uses recent US data to estimate the new Keynesian Phillips curve (NKPC) with three modifications. Firstly, the variables in the NKPC are found to be nonstationary. Therefore, it is estimated with the time series methods and the cointegrating equations are tested for structural breaks. Secondly, inflationary expectations are proxied with the survey data. Thirdly, unlike in the hybrid NKPC, the effects of the lagged inflation rates are introduced into the dynamic adjustment equations. This offers an opportunity to estimate these dynamic effects with a more general specification instead of the restricted partial adjustment mechanism underlying the hybrid NKPC. Our NKPC, with these changes, is consistent with its underlying micro foundations and forward looking expectations. The results of our NKPC can explain the dynamics of the US inflation rate as well as any other alternative model.US New Keynesian Phillips Curve, Forward looking expectations, Survey data, Wage share, Cointegration
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