This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR) and the nonlinear autoregressive neural network with exogenous inputs (NARX), respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

A stochastic numerical analysis based on hybrid NAR-RBFs networks nonlinear SITR model for novel COVID-19 dynamics.

Incremental face recognition for large-scale social network services

Smart city surveillance systems are the battery operated light weight Internet of Things (IoT) devices. In such devices, automatic face recognition requires a low powered memory efficient visual computing system. For these real time applications in smart cities, efficient visual recognition systems are need of the hour. In this manuscript, efficient fast subspace decomposition over Chi Square transformation is proposed for IoT based on smart city surveillance systems. The proposed technique extracts the features for visual recognition using local binary pattern histogram. The redundant features are discarded by applying the fast subspace decomposition over the Gaussian distributed Local Binary Pattern (LBP) features. This redundancy is major contributor to memory and time consumption for battery based surveillance systems. The proposed technique is suitable for all visual recognition applications deployed in IoT based surveillance devices due to higher dimension reduction. The validation of proposed technique is proved on the basis of well-known databases. The technique shows significant results for all databases when implemented on Raspberry Pi. A comparison of the proposed technique with already existing/reported techniques for the similar applications has been provided. Least error rate is achieved by the proposed technique with maximum feature reduction in minimum time for all the standard databases. Therefore, the proposed algorithm is useful for real time visual recognition for smart city surveillance.

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An efficient framework using visual recognition for IoT based smart city surveillance

As air descends the intake shaft, its infrastructure, lining and the strata will emit heat during the night when the intake air is cool and, on the contrary, will absorb heat during the day when the temperature of the air becomes greater than that of the strata. This cyclic phenomenon, also known as the “thermal damping effect” will continue throughout the year reducing the effect of surface air temperature variation. The objective of this paper is to quantify the thermal damping effect in vertical underground airways. A nonlinear autoregressive time series with external input (NARX) algorithm was used as a novel method to predict the dry-bulb temperature (Td) at the bottom of intake shafts as a function of surface air temperature. Analyses demonstrated that the artificial neural network (ANN) model could accurately predict the temperature at the bottom of a shaft. Furthermore, an attempt was made to quantify typical “damping coefficient” for both production and ventilation shafts through simple linear regression models. Comparisons between the collected climatic data and the regression-based predictions show that a simple linear regression model provides an acceptable accuracy when predicting the Td at the bottom of intake shafts.

Quantifying the thermal damping effect in underground vertical shafts using the nonlinear autoregressive with external input (NARX) algorithm

In this paper, a new face recognition system based on Haar wavelet transform (HWT) and Principal Component Analysis (PCA) using Levenberg-Marquardt backpropagation (LMBP) neural network is presented. The image face is preprocessed and detected. The Haar wavelet is used to form the coefficient matrix for the detected face. The image feature vector is obtained by computing PCA for the coefficient matrix of DWT. A comparison between the proposed recognition system using DWT, PCA and Discrete Cosine Transform (DCT) is also made. The experimental results indicated that the image faces can be recognized by the proposed face recognition system effectively.

Saudi Arabia

Papers

Face Recognition Based on Haar Wavelet Transform and Principal Component Analysis via Levenberg-Marquardt Backpropagation Neural Network