Nowadays, the integration of solar power into the electrical grids is vital to increase energy efficiency and profitability. Effective usage of the instable solar production of photovoltaic (PV) systems necessitates trustworthy forecasting information. Actually, this addition can gives an ameliorated service quality if the solar radiation variation can be forecasted accurately. In this paper, we propose a new forecasting approach that integrates Autoregressive Moving Average (ARMA) and Genetic algorithms (GA) to make benefit of both of them in order to forecast Global Horizontal Irradiance (GHI) component. The proposed approach is compared with the standard ARMA model. The experimental results show that, the proposed approach outperforms the classical ARMA models in terms of mean absolute percentage error (MAPE), root mean squared error (RMSE) coefficient of determination (R)2 and the normalized mean squared error (NMSE).

In this work a new data-driven approach for Remaining Useful Life estimation of aircraft engines is developed. The proposed approach is a regularized Single Hidden Layer Feedforward Neural network (SLFN) with incremental constructive enhancements. The training rules of this algorithm are inspired form different Extreme Learning Machine (ELM) variants. Particle Swarm Optimization (PSO) algorithm is integrated to enhance tracking ability of the best regularization parameter to reduce the norm of the tuned weights. The proposed approach is evaluated using C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset and compared to its other derivatives and proved its accuracy. C-MAPSS software has revisions in military and civil applications. In this paper, the military version of its application is the used one.

In this work a new data-driven compression approach is presented. The compression algorithm is an autoencoder trained with an improved On-line sequential Extreme Learning Machine (OS-ELM). First, a dynamic adaptation of the training algorithm towards the newly coming data is achieved by integrating an updated selection strategy (USS) and dynamic forgetting function (DDF). Second, Singular Value Decomposition (SVD) is involved to enhance hidden layer representation via sparse mapping. This new developed autoencoder (ASOS- AE) is compared with the ordinary OS-ELM autoencoder (OS-AE) and proved its accuracy in CMAPSS dataset (Commercial Modular Aero-Propulsion System Simulation). The C-MAPSS software has revisions in civil and military applications. In the present work we used the military version of its applications.

In this paper, a new length changeable extreme learning machine is proposed. The aim of the proposed method is to improve the learning performances of a Single hidden layer feedforward neural network (SLFN) under rich dynamic imbalanced data. Particle Swarm Optimization (PSO) is involved for hyper-parameters tuning and updating during incremental learning. The algorithm is evaluated using a subset from C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset of gas turbine engine and compared to its derivatives. The results prove that the new algorithm has a better learning attitude. The toolbox that contains the developed algorithms of this comparative study is publicly available.

The main objective of this works is to study and improve the performances of the Single hidden Layer Feedforward Neural network (SLFN) for the application of Remaining Useful Life (RUL) prediction of aircraft engines. The most common problems in SLFNs based old training algorithms such as backpropagation are time consuming, over-fitting and the appropriate network architecture identification. In this paper a new incremental constructive learning algorithm based on Extreme Learning Machine algorithm is proposed for founding the appropriate architecture of a neural network under less computational costs. The aim of the proposed training approach is to study its maximum capabilities during RUL prediction by reducing over-fitting and human intervention. The performances of the proposed approach which are evaluated on C-MAPPS dataset and compared with its original variant from the literature. Experimental results proved that the new algorithm outperforms the old one in many metrics evaluations.

Wireless sensors networks are used in several fields of application, in most applications, knowing the location of the sensors nodes is necessary. According to the distribution of the calculations on sensors nodes, the localization techniques are classified into two techniques: centralized and decentralized (distributed). Distributed localization techniques can be classified into two categories: range free and range based. DV-HOP is a range free simple localization algorithm, but it has low localization accuracy, despite the improvements of the proposed DV-Hop algorithms. Maintaining a balance between a high location accuracy and good stability and simplicity is always a challenge. In this article, we have focused our study on the localization algorithm range free DV-HOP, the study is based on the accuracy error which is the most important factor in the localization. To better study the DV-HOP algorithm, we implemented DV-HOP using MATLAB. The purpose of this article is to present Range Free localization techniques and open up a perspective of proposing new localization techniques in wireless sensors networks.

This study addresses the problem of inventory management in a multi-level retailers' network of the company Lit Mag that manufactures and distributes mattresses. Its distribution network constitutes a central warehouse and several distribution centers located in Algerian cities, and each center delivers vendors to the area and each vendor is connected to set of customers. The network studied is composed of several levels: the starting point is the warehouse, the first level of the distributors, the second level for sellers and the last level for customers. each retailer in the network handles its order locally and independently of other retailers. In this work, we propose a collaboration between the different actors of the same level, by the exchange of products between them, in order to satisfy the demand of a seller by the residual stock of another neighbor seller and this is applied to all stages of the network. The purpose of this study in to see the impact of product exchanges in the distribution networks and their influence on the total costs of the logistics chain from the central warehouse to the final customer