Publications

This paper deals with multi-agent energy management and fault tolerant control of the micro-grid powered by wind farm based on two doubly fed induction generators. The stator flux orientation has used to eliminate the active and reactive power coupling. The proposed control scheme is based on two cascades closed loops. The inner controllers concern the rotor currents. The outer controllers have a parallel configuration with the stator voltage or the stator power control. Switching between these two controllers is realized by the synchronization mechanism. All controllers are designed with Lyapunov approach associated with sliding-mode control, this solution shows good robustness against parameter variations, measurement errors and faults. The global asymptotic stability of the overall system is proven. After that, a Multi-agent energy management was proposed and tested in order to satisfy some objectives and overcome some constraints. The advantages of the wind energy integration associated with multi-agent energy management are: production cost minimization, reduction of the carbon emissions, increasing the energy autonomy and he robustness against weather conditions and faults that may occur during operation. The results confirm the effectiveness of the proposed control.

The purpose of this paper is to propose an improvement strategy based on multi-criteria decision making approaches, including fuzzy analytic hierarchy process (AHP), preference ranking organisation method for enrichment evaluation II (PROMETHEE) and vi\v sekriterijumsko kompromisno rangiranje (VIKOR) for the objective of simplifying and organising the improvement process in complex manufacturing processes. Firstly, the proposed strategy started with the selection of decision makers’, such as company leaders, to determine performance indicators. Then fuzzy AHP is used to quantify the weight of each defined indicators. Finally, the weights carried out from fuzzy AHP approach are used as input in VIKOR and PROMETHE II to rank the operations according to their improvement priority. The results obtained from each outranking method are compared and the best method is determined.

Amniotic band syndrome (ABS) was first described by Montgomery in Montgomery (1832). It is a poorly known congenital malformation due to strangulation of the organs by an amniotic fibrous band. Several parts of the body can be affected: for instance, skull, face, neck, trunk and musculoskeletal system. It generally associates three types of anomalies namely, amputations, deformities, and malformations. There are two genuine theories covering this syndrome; the Intrinsic Theory associating the syndrome to a germline defect and the Purely Mechanical Extrinsic Theory related to the amniotic band. These theories have thoroughly tried to explain the disease and the organ involvement (Goldfarb et al., 2009). In the current study, we report a rare case of an open fracture of both leg bones with amniotic disease in a 10-day-old neonate who underwent surgical treatment. In our case, it is a surgical emergency where we try to explain its physiopathology and show how to operate it. We discuss likewise the appropriateness of using the expressions “leg fracture” and “congenital pseudarthrosis of the leg”. Finally, we describe a revised classification by Hall (1982) and Weinzweig (1994) of ABS incorporating a stage with bone involvement.

Hexavalent chromium (CrVI) compounds are potent toxicants commonly used in numerous industries. Thus, potential toxic effects and health hazards are of high relevance. Selenium (Se) and zinc (Zn) are known for their antioxidant and chemoprotective properties. However, little is known about their protective effects against CrVI-induced renal damage during pregnancy. In this context, the present study aimed to investigate the protective efficacy of these two essential elements against potassium dichromate-induced nephrotoxicity in pregnant Wistar Albino rats. Female rats were divided into control and four treated groups of six each receiving subcutaneously on the 3rd day of pregnancy, K₂Cr₂O₇ (10 mg/kg, s.c. single dose) alone, or in association with Se (0.3 mg/kg, s.c. single dose), ZnCl₂ (20 mg/kg, s.c. single dose) or both of them simultaneously. The nephrotoxic effects were monitored by the evaluation of plasma renal parameters, oxidative stress biomarkers, DNA damage, and renal Cr content. The obtained results showed that K₂Cr₂O₇ disturbed renal biochemical markers, induced oxidative stress and DNA fragmentation in kidney tissues, and altered renal histoarchitecture. The co-administration of Se and/or ZnCl₂ has exhibited pronounced chelative, antioxidant, and genoprotective effects against K₂Cr₂O₇-induced renal damage and attenuated partially the histopathological alterations. These results suggest that Se and Zn can be used as efficient nephroprotective agents against K₂Cr₂O₇-induced toxicity in pregnant Wistar Albino rats.

The aim of this study was to test the influence of nest site characteristics and food supplementation from rubbish dumps on reproductive parameters of white storks breeding in semi-arid habitats. A total of 148 nests were monitored in two colonies of white storks (control colony vs. colony that benefited from high food supply in rubbish dumps) in eastern Algeria over a six-year period (2011–2016) to measure nest characteristics and reproductive parameters (clutch size, number of hatchings, number of fledglings, breeding success). Results showed that pairs breeding at proximity from rubbish dumps had larger clutch sizes (5.1 ± 0.6 vs. 4.6 ± 0.6), hatched more chicks (4.7 ± 0.7 vs. 4.3 ± 0.7) and raised more fledglings (3.0 ± 0.9 vs. 2.6 ± 1.0) than pairs breeding far from rubbish dumps. Results also showed that clutch size was positively related to nest surface area, and that pairs nesting on electricity poles had a lower breeding success than those nesting in trees (48.9 ± 20.4% vs. 64.6 ± 17.6%). Our findings suggest that breeding outputs are strongly related to selective behavior in nest placement and food availability surrounding the nesting site.

This paper proposes a new approach for remaining useful life prediction that combines a bond graph, the Gaussian Mixture Model and similarity techniques to allow the use of both physical knowledge and the data available. The proposed method is based on the identification of relevant variables that carry information on degradation. To this end, the causal properties of the bond graph (BG) are first used to identify the relevant sensors through the fault observability. Then, a second stage of analysis based on statistical metrics is performed to reduce the number of sensors to only the ones carrying useful information for failure prognosis, thus, optimizing the data to be used in the prognosis phase. To generate data in the different system state, a simulator based on the developed BG is used. A Gaussian Mixture Model is then applied on the generated data for fault diagnosis and clustering. The Remaining Useful Life is estimated using a similarity technique. An application on a mechatronic system is considered for highlighting the effectiveness of the proposed approach.

This paper proposes a new approach for remaining useful life prediction that combines a bond graph, the Gaussian Mixture Model and similarity techniques to allow the use of both physical knowledge and the data available. The proposed method is based on the identification of relevant variables that carry information on degradation. To this end, the causal properties of the bond graph (BG) are first used to identify the relevant sensors through the fault observability. Then, a second stage of analysis based on statistical metrics is performed to reduce the number of sensors to only the ones carrying useful information for failure prognosis, thus, optimizing the data to be used in the prognosis phase. To generate data in the different system state, a simulator based on the developed BG is used. A Gaussian Mixture Model is then applied on the generated data for fault diagnosis and clustering. The Remaining Useful Life is estimated using a similarity technique. An application on a mechatronic system is considered for highlighting the effectiveness of the proposed approach.

The grasshopper optimization algorithm is one of the recently population-based optimization techniques inspired by the behaviours of grasshoppers in nature. It is an efficient optimization algorithm and since demonstrates excellent performance in solving continuous problems, but cannot resolve directly binary optimization problems. Many optimization problems have been modelled as binary problems since their decision variables varied in binary space such as feature selection in data classification. The main goal of feature selection is to find a small size subset of feature from a sizeable original set of features that optimize the classification accuracy. In this paper, a new binary variant of the grasshopper optimization algorithm is proposed and used for the feature subset selection problem. This proposed new binary grasshopper optimization algorithm is tested and compared to five well-known swarm-based algorithms used in feature selection problem. All these algorithms are implemented and experimented assessed on twenty data sets with various sizes. The results demonstrated that the proposed approach could outperform the other tested methods.

In the last recent years, a large community of researchers and industrial practitioners has been attracted by combining different prognostics models as such strategy results in boosted accuracy and robust performance compared to the exploitation of single models. The present work is devoted to the investigation of three new fusion schemes for the remaining useful life forecast. These integrated frameworks are based on aggregating a set of Gaussian process regression models thanks to the Induced Ordered Weighted Averaging Operators. The combination procedure is built upon three proposed analytical weighting schemes including exponential, logarithmic and inverse functions. In addition, the uncertainty aspect is supported in this work, where the proposed functions are used to weighted average the variances released from competitive Gaussian process regression models. The training data are transformed into gradient values, which are adopted as new training data instead of the original observations. A lithium-ion battery data set is used as a benchmark to prove the efficiency of the proposed weighting schemes. The obtained results are promising and may provide some guidelines for future advances in performing robust fusion options to accurately estimate the remaining useful life.

In the last recent years, a large community of researchers and industrial practitioners has been attracted by combining different prognostics models as such strategy results in boosted accuracy and robust performance compared to the exploitation of single models. The present work is devoted to the investigation of three new fusion schemes for the remaining useful life forecast. These integrated frameworks are based on aggregating a set of Gaussian process regression models thanks to the Induced Ordered Weighted Averaging Operators. The combination procedure is built upon three proposed analytical weighting schemes including exponential, logarithmic and inverse functions. In addition, the uncertainty aspect is supported in this work, where the proposed functions are used to weighted average the variances released from competitive Gaussian process regression models. The training data are transformed into gradient values, which are adopted as new training data instead of the original observations. A lithium-ion battery data set is used as a benchmark to prove the efficiency of the proposed weighting schemes. The obtained results are promising and may provide some guidelines for future advances in performing robust fusion options to accurately estimate the remaining useful life.

In this article, we propose a new class of search directions based on new kernel function to solve the monotone semidefinite linear complementarity problem by primal-dual interior point algorithm. We show that this algorithm based on this function benefits from the best polynomial complexity, namely O( √ n(log n) 2 log n ). The implementation of the algorithm showed a great improvement concerning the time and the number of iterations.

This paper proposed a new strategy of sinusoidal pulse width modulation (SPWM) technique to control three-phase nine-level switched-capacitor inverter (9LSCI) in grid-connected PV systems. The main advantage of this inverter is high voltage gain, achieved by switching the capacitors in series and parallel to boost up the output voltage using low voltage input. To improve the quality of solar energy for injection into the electrical grid, a rooted tree optimization (RTO) algorithm is used to get optimum values of initial angles of multi carriers SPWM technique, giving the lowest possible values of the total harmonic distortion (THD). The design also can maximize the efficiency of the multi-level inverter by minimizing its size using fewer components and a single DC source and reducing the rate of THD. The higher effectiveness and accuracy of the suggested RTO-SPWM technique was tested and verified in comparison to existing classical SPWM technique from the performance of PV-grid systems that it can effectively reduce the total harmonic distortion to 0.16 %.

This paper proposed a new strategy of sinusoidal pulse width modulation (SPWM) technique to control three-phase nine-level switched-capacitor inverter (9LSCI) in grid-connected PV systems. The main advantage of this inverter is high voltage gain, achieved by switching the capacitors in series and parallel to boost up the output voltage using low voltage input. To improve the quality of solar energy for injection into the electrical grid, a rooted tree optimization (RTO) algorithm is used to get optimum values of initial angles of multi carriers SPWM technique, giving the lowest possible values of the total harmonic distortion (THD). The design also can maximize the efficiency of the multi-level inverter by minimizing its size using fewer components and a single DC source and reducing the rate of THD. The higher effectiveness and accuracy of the suggested RTO-SPWM technique was tested and verified in comparison to existing classical SPWM technique from the performance of PV-grid systems that it can effectively reduce the total harmonic distortion to 0.16 %.

This paper gives and justifies a practical approach for solving fuzzy singular integro-differential equations. First, by using different techniques, we show that solutions to two types of fuzzy singular integro-differential equations exist and are unique: Picard’s theorem for logarithmic kernels and Arzelà–Ascoli theorem for Cauchy ones. Then, utilizing airfoil polynomials, we provide a collocation method to solve the current problems numerically. We also look at the approximate equations’ solutions, and we introduce the concept of error analysis. Using new procedures, we obtain two systems of linear equations. These are the problems to be examined. Eventually, we exhibit the precision of the proposed approach via numerical examples.

Due to the high degree of randomness in the microstructure of real closed-cell foams, many reported numerical models in the literature are not able to capture precisely the local morphological features found in solid foams geometry. This is still the main impediment that restricts the investigation of this novel material and motivates the development of a sophisticated 3D solid model, which describes properly the complex geometry of real closed-cell foams. In this regard, this paper presents an original approach to generate a realistic and accurate 3D computational model of irregular closed-cell foams with relative density control and detailed finite element analysis of their mechanical performance under quasi-static loading up to densification. The solid model is constructed based on spherical particles inflation simulation. It resembles the real foams in terms of local features such as cell walls irregularities and thickness variation. The modeling approach was successfully verified by comparing cell-morphological details of the generated models with those produced experimentally available in the literature and by the high-quality of obtained 3D printed models containing complex shapes and irregular cell wall thickness distribution. The evolution of spherical particles during the inflation process is analyzed based on finite element (FE) simulations. It was found that it can produce varying relative densities of foam due to the gradual decrease in the gap between the inflated particles, this makes the geometrical model of the foam suitable for studying the effect of local morphological characteristics on the mechanical performance of closed-cell foam material. To demonstrate that the compressive performance of the proposed closed-cell foam models can be controlled by relative density, 3D foam models were extracted from different inflation times and then subjected to quasi-static compression tests up to densification using the Abaqus software. The results confirm that the plateau stress can be expressed as a function of foam relative density, its accuracy is validated by comparing it to the closed-cell aluminum foam power law equation existing in the literature. The new design method offers suitable numerical models for AM technology, plenty of experimental works on closed-cell foam can be reduced for engineering applications.

Due to the rapid growth of digital videos and the massive increase in video content, there is an urgent need to develop efficient automatic video content analysis mechanisms for different tasks, namely summarization, retrieval, and classification. In all these applications, one needs to identify shot boundary detection. This paper proposes a novel dual-stage approach for cut transition detection that can withstand certain illumination and motion effects. Firstly, we present a deep neural network model using the pre-trained model combined with long short-term memory LSTM network and the euclidean distance metric. Two parallel pre-trained models sharing the same weights extract the spatial features. Then, these features are fed to the LSTM and the euclidean distance metric to classify the frames into specific categories (similar or not similar). To train the model, we generated a new database containing 5000 frame pairs with two labels (similar, dissimilar) for training and 1000 frame pairs for testing from online videos. Secondly, we adopt the segment selection process to predict the shot boundaries. This preprocessing method can help improve the accuracy and speed of the VSBD algorithm. Then, cut transition detection based on the similarity model is conducted to identify the shot boundaries in the candidate segments. Experimental results on standard databases TRECVid 2001, 2007, and RAI show that the proposed approach achieves better detection rates over the state-of-the-art SBD methods in terms of the F1 score criterion.