Optical burst switching (OBS) has become one of the best and widely used optical networking techniques. It offers more efficient bandwidth usage than optical packet switching (OPS) and optical circuit switching (OCS).However, it undergoes more attacks than other techniques and the Classical security approach cannot solve its security problem. Therefore, a new security approach based on machine learning and cloud computing is proposed in this article. We used the Google Colab platform to apply Support Vector Machine (SVM) and Extreme Learning Machine (ELM)to Burst Header Packet (BHP) flooding attack on Optical Burst Switching (OBS) Network Data Set.

Industry 4.0 is a tsunami that will invade the whole world. The real challenge of the future factories requires a high degree of reliability both in machinery and equipment. Thereupon, shifting the rudder towards new trends is an inevitable obligation in this fourth industrial revolution where the maintenance system has radically changed to a new one called predictive maintenance 4.0 (PdM 4.0). This latter is used to avoid predicted problems of machines and increase their lifespan taking into account that if machines have not any predicted problem, they will never be checked. However, in order to get successful prediction of any kind of problems, minimizing energy and resources consumption along with saving costs, this PdM 4.0 needs many new emerging technologies such as the internet of things infrastructure, collection and distribution of data from different smart sensors, analyzing/interpreting a huge amount of data using machine/deep learning…etc. This paper is devoted to present the industry 4.0 and its specific technologies used to ameliorate the existing predictive maintenance strategy. An example is given via a web platform to get a clear idea of how PdM 4.0 is applied in smart factories.

Le Groupe Sonatrach est le géant algérien de l’industrie pétro-gazière. Sa force réside dans sa capacité à être un Groupe intégré dans l’ensemble de la chaîne de valeurs (depuis l’exploration en passant par la production jusqu’à la commercialisation). Ses installations onshore, qui sont considérées comme des systèmes sociotechniques complexes, souffrent des problèmes de vieillissement matérialisés par la dégradation des performances de ces installations. Cette thèse de doctorat a pour objet d’étudier ce problème de vieillissement dans le but de le maîtriser. S’intégrant dans ce contexte et après avoir rappelé le phénomène du vieillissement ainsi que les approches qui le gouverne, une proposition d’une approche de maîrise du vieillissement à base d’indicateurs est proposée dans un premier temps et dans un second temps une étude critique du référentiel "Gestion des Modifications" du Groupe Sonatrach est également présentée.

The common used goodeness-of-fit tests are based on the empirical distributions functions (EDF) where distances between empirical and theoretical hypothesized distributions are compared to critical values. The aim of this paper is to provide for different sample sizes, tables of goodness-of-fit critical values of modified Kolmogorov-Smirnov statistic Dn,��, Anderson-Darling statistic A², Cramer-Von Mises statistic W2,�2, Liao and Shimokawa statistic L_n, and Watson statistic U² for the competing risks model of Bertholon which is used to describe the reliability of real systems where failure times can have different risks and in medical studies to characterize the survival time of patients who can have risks of death from different causes. The power of these statistics is studied using some alternatives such as the exponential, the inverse Weibull, the exponentiated Weibull and the exponentiated exponential distributions. All the computation are carried out by using matlab software and Monte Carlo method.

This article is devoted to the study of the performance of the double star cage asynchronous generator (GASDE) in isolated site. The control system consists of a GASDE connected to a dc bus and a load at the output of two PWM control rectifiers. A comparative study between the conventional control technique and the adapted control based on the introduction of the SVM- PI-fuzzy and a new flux estimator (virtual stator flux) in order to improve the quality of energy and to attenuate the harmonic of the current.

Association rules are the specific data mining methods aiming to discover explicit relations between the different attributes in a large dataset. However, in reality, several datasets may contain both numeric and categorical attributes. Recently, many meta-heuristic algorithms that mimic the nature are developed for solving continuous problems. This article proposes a new algorithm, DCSA-QAR, for mining quantitative association rules based on crow search algorithm (CSA). To accomplish this, new operators are defined to increase the ability to explore the searching space and ensure the transition from the continuous to the discrete version of CSA. Moreover, a new discretization algorithm is adopted for numerical attributes taking into account dependencies probably that exist between attributes. Finally, to evaluate the performance, DCSA-QAR is compared with particle swarm optimization and mono and multi-objective evolutionary approaches for mining association rules. The results obtained over real-world datasets show the outstanding performance of DCSA-QAR in terms of quality measures.

This paper presents a teaching-learning-based optimization algorithm for discrete large-scale multi-objective problems (DLM-TLBO). Unlike the previous variants, the learning strategy used by each individual and the acquired knowledge are defined based on its level. The proposed approach is used to solve a bi-objective object clustering task (B-OCT) in a swarm robotic system, as a case study. The simple robots have as mission the gathering of a number of objects distributed randomly, while respecting two objectives: maximizing the clustering quality, and minimizing the energy consumed by these robots. The simulation results of the proposed algorithm are compared to those obtained by the well-known algorithm NSGA-II. The results show the superiority of the proposed DLM-TLBO in terms of the quality of the obtained Pareto front approximation and convergence speed.

The direct control power (DPC) of the of the double feed induction generator (DFIG) using conventional controllers based on PI regulators is characterized by poor results: Robustness properties are not guaranteed in the face of parametric uncertainties and strong ripple of the powers. From the best evoked control techniques presented in this field to overcome these drawbacks, we will study some improvement variants such as the use of The second order sliding mode control (SOSMC) developed on the basis of the super twisting torsion algorithm (STA) associated with the fuzzy logic control to obtain (FSOSMC) in order to obtain acceptable performance. Finally, the effectiveness of the planned control system is studied using Matlab/Simulink. The proposed method that not only reduces power ripples, but also improves driving dynamics by making it less sensitive to parameter uncertainty.

On one hand, there are many proposed intrusion detection systems (IDSs) in the literature. On the other hand, many studies try to deduce the important features that can best detect attacks. This paper presents a new and an easy-to-implement approach to intrusion detection, named distance sum-based k-nearest neighbors (DS-kNN), which is an improved version of k-NN classifier. Given a data sample to classify, DS-kNN computes the distance sum of the k-nearest neighbors of the data sample in each of the possible classes of the dataset. Then, the data sample is assigned to the class having the smallest sum. The experimental results show that the DS-kNN classifier performs better than the original k-NN algorithm in terms of accuracy, detection rate, false positive, and attacks classification. The authors mainly compare DS-kNN to CANN, but also to SVM, S-NDAE, and DBN. The obtained results also show that the approach is very competitive.

In this paper, we propose an implementation of a new technique of power maximization using a photovoltaic system emulator. The PV system design and its performance evaluation test before installation would be both costly and time-consuming. To overcome this problem the use of an emulator adds more performance and efficiency in the laboratory. Also, by measuring the voltage and current from the PV emulator the characteristic I-V and P-V are extract.The need to consider the measure power state is strongly nonlinear distribution curve with noise. For that reason, to establish and to detect the power value, measurement equations and dynamic equations proposed MPPT control strategy based on Kalman filter algorithm. The correctness and effectiveness of the strategy is verified by simulation and experiment. This algorithm was experimentally implemented. Data acquisition and control system were implemented using dSPACE1103. The results show that the Kalman filter MPPT work accurately and successfully under the change of solar irradiation.

In this paper a dual direct torque control (DDTC) strategy with second-order sliding mode control (SOSMC) controller of the doubly fed Induction motor (DFIM) is presented in order to overcome some drawback such as ripples in torque, flux and to improve dual direct torque control (DDTC) performance toward the electrical parameters variations. This control strategy used in the doubly fed induction machine supplied, coupled by two voltage source inverters in rotor and stator sides witches are linked to two switching tables in order to determined the rotor and stator flux vector control. This controller based on super-twisting algorithm (STA). Comparative results between a classical controller (PI) and the proposed controller can prove the very satisfactory performance and robustness of this new controller.

 

In this paper, we have carried out an experimental study of the detection of top rail surface cracks. Firstly, we have highlighted the inability to inspect the entire rail head surface by a single sensor with a single scan. To overcome this inspection inability, we have proposed a multisensor system composed of three differential probes arranged within a specific configuration. The yielded results showed the efficiency and the robustness of the proposed configuration in the detection of cracks regardless its size, orientation and location.

The analysis of the failure due to the effect of the propagation of normal and reversed faults with different angles of inclination and by sliding through the Ourkiss dam isstudied numerically. Mainly at the end of construction and at the highest water level, for this purpose the non-linear finite difference method is used considering four fault angles of inclination, activated at the center of the base of the embankment.

The results of the study show that the shear stress values increase with the increase of the vertical base displacement imposed in both conditions of the dam state, and this for both normal and overturned faults.

The present work concerns the numerical investigation of reinforced concrete frame buildings containing masonry infill panel under seismic loading that are widely used even in high seismicity areas. In seismic zones, these frames with masonry infill panels are generally considered as higher earthquake risk buildings. As a result there is a growing need to evaluate their level of seismic performance. The numerical modelling of infilled frames structures is a complex task, as they exhibit highly nonlinear inelastic behaviour, due to the interaction of the masonry infill panel and the surrounding frame. The available modelling approaches for masonry infill can be grouped into two principal types; Micro models and Macro models. A two dimensional model of the structure is used to carry out non-linear static analysis. Beams and columns are modelled as non-linear with lumped plasticity where the hinges are concentrated at both ends of the beams and the columns. This study is based on structures with design and detailing characteristics typical of Algerian construction model. In this regard, a non-linear pushover analysis has been conducted on three considered structures, of two, four and eight stories. Each structure is analysed as a bare frame and with two different infill configurations (totally infilled, and partially infilled). The main results that can be obtained from a pushover analysis are the capacity curves and the distribution of plastic hinges in structures. The addition of infill walls results in an increase in both the rigidity and strength of the structures. The results indicate that the presence of non-structural masonry infills can significantly modify the seismic response of reinforced concrete "frames". The initial rigidity and strength of the fully filled frame are considerably improved and the patterns of the hinges are influenced by structural elements type depending on the dynamic characteristics of the structures.

The infill walls are usually considered as nonstructural elements and, thus, are not taken into account in analytical models. However, numerous researches have shown that they can significantly affect the seismic response of the structures. The aim of the present study is to examine the role of masonry infill on the damage response of steel frame without and with various types of openings systems subjected to nonlinear static analysis and nonlinear time history analysis. For the purposes of the above investigation, a comprehensive assessment is conducted using twelve typical types of steel frame without masonry, with full masonry and with different heights and widths of openings. The results revealed that the influence of the successive earthquake phenomenon on the structural damage is larger for the infill buildings compared to the bare structures. Furthermore, when buildings with masonry infill are analyzed for seismic sequences, it is of great importance to account for the orientation of the seismic motion. The nonlinear static response indicated that the opening area has an influence on the maximal strength, the ductility and the initial rigidity of these frames. But the shape of the opening will not influence the global behavior. Then, the nonlinear time history analysis indicates that the global displacement is greatly decreased and even the behavior of the curve is affected by the earthquake intensity when opening is considered.

In this work, the performance of an eccentric annular finned tube heat exchanger under natural convection conditions has been investigated numerically. The objective of the study is to analyse the effect of eccentric coefficient on the rate heat flux as a function of the fin material, diameter, spacing and thicknesses. To perform the numerical simulations, 3D ANSYS FLUENT computational fluid dynamics has been used. The study has been conducted in laminar flow across the single annular finned tube with Rayleigh numbers within of (4 × 10⁴−7 × 10⁴). It is concluded that the eccentricity effect appears better in high thermal conductivity materials with small fin diameter. Regardless of the fin eccentricity, thick fins produce the best heat transfer.

This paper aims to evaluate the effect of poultry droppings waste on the different life stage of Eisenia fetida earthworm to protect them from hazardous doses. Adults, juveniles and cocoons were exposed during 90 days to increased doses of poultry droppings (0, 10, 20, 50 and 100 g), added to 250 g of culture substrate. The biological parameters, like mortality, body length, fresh biomass, and cocoons hatching were affected by the organic waste doses and the exposure time. Both poultry droppings doses 10 g (4%) and 20 g (8%) were the less toxic to the cocoons hatching and to the adults' and juveniles' growth. While the two doses, 50 g (20%) and 100 g (40%), had a negative impact on the cocoon hatchability and a toxic effect on the juveniles and adults. Otherwise, the poultry droppings dose 100 g was lethal for the all life stage of E. fetida.

Owing to the comeback of small-scale models, this paper presents results of an experimental study based on the effect of underground circular voids on strip footing placed on the edge of a cohesionless slope and subjected to eccentric loads. The bearing capacity-settlement relationship of footing on the slope and impact of diverse variables are expressed using dimensionless parameters such as the top vertical distance of the void from the base of footing, horizontal space linking the void-footing centre, and load eccentricity. The results verified that the stability of strip footing is influenced by the underground void, as well as the critical depth between the soil and top layer of the void. The critical horizontal distance between the void and the centre was also affected by the underground void. Furthermore, the results also verified that the influence of the void appeared insignificant when it was positioned at a depth or eccentricity equal to twice the width of footing.    

Recently and due to the impressive growth in the amounts of transmitted data over the heterogeneous sensor networks and the emerged related technologies especially the Internet of Things in which the number of the connected devices and the data consumption are remarkably growing, big data has emerged as a widely recognized trend and is increasingly being talked about. The term big data is not only about the volume of data, but also refers to the high speed of transmission and the wide variety of information that is difficult to collect, store and process using the available classical technologies. Although the generated data by the individual sensors may not appear to be significant, all the data generated through the many sensors in the connected sensor networks are able to produce large volumes of data. Big data management imposes additional constraints on the wireless sensor networks and especially on the data aggregation process, which represents one of the essential paradigms in wireless sensor networks. Data aggregation process can represent a solution to the problem of big data by allowing data from different sources to be combined to eliminate the redundant ones and consequently reduce the amounts of data and the consumption of the available resources in the network. The main objective of this work is to propose a new approach for supporting big data in the data aggregation process in heterogeneous wireless sensor networks. The proposed approach aims to reduce the data aggregation cost in terms of energy consumption by balancing the data loads on the heterogeneous nodes. The proposal is improved by integrating the feedback control closed loop to reinforce the balance of the data aggregation load on the nodes, maintaining therefore an optimal delay and aggregation time.

Juvenile Idiopathic Arthritis (JIA) is a group of chronic heterogenous disorders that manifests as joint inflammation in patients aged <16 years. Globally, approximately 3 million children and young adults are suffering from JIA with prevalence rates consistently higher in girls. The region of Africa and Middle East constitute a diverse group of ethnicities, socioeconomic conditions, and climates which influence the prevalence of JIA. There are only a few studies published on epidemiology of JIA in the region. There is an evident paucity of adequate and latest data from the region. This review summarizes the available data on the prevalence of JIA and its subtypes in Africa and Middle East and discusses unmet needs for patients in this region. A total of 8 journal publications were identified concerning epidemiology and 42 articles describing JIA subtypes from Africa and Middle East were included. The prevalence of JIA in Africa and Middle East was observed to be towards the lower range of the global estimate. We observed that the most prevalent subtype in the region was oligoarticular arthritis. The incidence of uveitis and anti-nuclear antibody (ANA) positivity were found to be lower as compared to the incidence from other regions. There is a huge unmet medical need in the region for reliable epidemiological data, disease awareness, having regional and local treatment guidelines and timely diagnosis. Paucity of the pediatric rheumatologists and economic disparities also contribute to the challenges regarding the management of JIA.