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.

This paper presents a state-of-the-art of recent research work analyzing the requirements of Industry 4.0, particularly related to the competences issue. Over the last few years, the fourth industrial revolution has attracted researchers worldwide to find suitable solutions. However, there are still many gaps related to the Industry 4.0, particularly related to the humans competences issue. Among the many challenges facing companies in this paradigm, one of the most important is the qualification of employees with the necessary skills to succeed in a transformed work environment. To cope with knowledge and competence challenges related to new technologies and processes of Industry 4.0, new strategic approaches for holistic human resource management are needed in manufacturing companies. The main objective of the presented research is to investigate the importance of employee competences, key to the development of Industry 4.0

The era of Industry 4.0 has already begun, however, several improvements should be achieved concerning this revolution. Data mining is one of the modest and efficient tools. Based on a specific query entered in Scopus, related to Industry 4.0, data mining (DM) and logistics, selected documents were studied and analyzed. A brief background of Industry 4.0 and DM are presented. A generic analysis showed that the attentiveness for the cited subject area by countries, universities, authors and especially companies and manufacturers increased through the years. Content analysis reveals that the improvement in quality of the technologies used in manufacturing was noticed, concluding that DM would give Industry 4.0 a leap forward, yet research is dealing with several challenges.

Despite technological advancements, chemical and process industries are still prone to accidents due to their complexity and hazardous installations. These accidents lead to significant losses that represent economic losses and most importantly human losses. Risk management is one of the appropriate tools to guarantee the safe operations of these plants. Risk analysis is an important part of risk management, it consists of different methods such as Fault tree, Bow-tie, and Bayesian network. The latter has been widely applied for risk analysis purposes due to its flexible and dynamic structure. Bayesian networks approaches have shown a significant increase in their application as shown by in the publication in this field. This paper summarizes the result of a literature review performed on Bayesian network approaches adopted to conduct risk assessments, safety and risk analyses. Different application domains are analysed (i.e. accident modelling, maintenance area, fault diagnosis) in chemical and process industries from the year 2006 to 2018. Furthermore, the advantages of different types of Bayesian networks are presented.

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.

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 paper we analyze a problem of inventory management in a multi-levels multi-products distribution network with three echelon, the studied system consists of a central warehouse and three distribution centers identified by their location zones where each center is connected to a wholesaler group that serve the retailers of his region, which in turn feeds the customers of the regions located in the Algerian territory. The aim of this study is to apply a collaboration between the different actors of the same level in a form of an exchange of products, the exchange can occurs only when the actual demand is being received, in order to study the impact of product exchanges in the distribution networks and its influence on the total costs of the logistics chain from the central warehouse to the delivery to the final customer.