This paper deals with the design and optimization of a 1.2 MVA medium-voltage (MV) power electronic traction transformer (PETT) for an AC 15 kV/16.7 Hz railway grid, in which a simple two-stage multi-cell PETT topology consisting of a bidirectional 170 kW, 2.5 kV AC rms to 6 kV DC power factor corrected (PFC) converter stage followed by a bidirectional isolated 46 kHz, 6 kV to 1.5 kV series resonant DC/DC converter for each cell is presented. This paper presents a methodology that maximizes the converter"s efficiency and minimizes the converter"s size and weight. Accordingly, the first stage employs 10 kV SiC MOSFETs based on the integrated Triangular Current Mode (iTCM). The second stage uses 10 kV SiC MOSFETs on the MV-side, 3.3 kV SiC MOSFETs on the LV-side, and a medium frequency (MF) MV transformer operating at 46 kHz. MF transformers offer a way to reduce weight and improve energy efficiency, particularly in electric multiple-unit applications. The MF MV transformer requires power electronic converters, which invert and rectify the voltages and currents at the desired operating frequency. The development of high voltage SiC MOSFETs, which can be used instead of Si IGBTs in PETT topologies, increases the operating frequency without reducing the converter"s efficiency. The designed MV PETT achieves 98.95% efficiency and 0.76 kVA/kg power density.

This work proposes a new modeling framework based on combining graded band-gap (GBG) engineering and metaheuristic optimization to improve the Cd-Free ZnMgO/CIGS solar cell performances. Analytical and numerical calculations are carried out to assess the influence of band-gap profiles of both buffer and active layers on the electronic and optical properties of the studied solar cell. This investigation shows a great improvement of solar cell efficiency by increasing the optoelectronic figures of merit through tuning and optimizing the band-gap profiles and the conduction band offset at the ZnMgO/CIGS interface. Moreover, metaheuristic-based optimization models are developed to optimize the GBG profiles and enhance the optical and electrical performances of the solar cell. In this context, we recorded very satisfactory results, where the optimized design with GBG paradigm offers a high efficiency of 31.88% compared to 23.35% provided by the conventional CdS/CIGS solar cell. Therefore, this study provides a new strategy in enhancing the efficiency of thin-film solar cells by exploiting the graded band-gap engineering combined with metaheuristic optimization approach.

This work proposes a new modeling framework based on combining graded band-gap (GBG) engineering and metaheuristic optimization to improve the Cd-Free ZnMgO/CIGS solar cell performances. Analytical and numerical calculations are carried out to assess the influence of band-gap profiles of both buffer and active layers on the electronic and optical properties of the studied solar cell. This investigation shows a great improvement of solar cell efficiency by increasing the optoelectronic figures of merit through tuning and optimizing the band-gap profiles and the conduction band offset at the ZnMgO/CIGS interface. Moreover, metaheuristic-based optimization models are developed to optimize the GBG profiles and enhance the optical and electrical performances of the solar cell. In this context, we recorded very satisfactory results, where the optimized design with GBG paradigm offers a high efficiency of 31.88% compared to 23.35% provided by the conventional CdS/CIGS solar cell. Therefore, this study provides a new strategy in enhancing the efficiency of thin-film solar cells by exploiting the graded band-gap engineering combined with metaheuristic optimization approach.

This work proposes a new modeling framework based on combining graded band-gap (GBG) engineering and metaheuristic optimization to improve the Cd-Free ZnMgO/CIGS solar cell performances. Analytical and numerical calculations are carried out to assess the influence of band-gap profiles of both buffer and active layers on the electronic and optical properties of the studied solar cell. This investigation shows a great improvement of solar cell efficiency by increasing the optoelectronic figures of merit through tuning and optimizing the band-gap profiles and the conduction band offset at the ZnMgO/CIGS interface. Moreover, metaheuristic-based optimization models are developed to optimize the GBG profiles and enhance the optical and electrical performances of the solar cell. In this context, we recorded very satisfactory results, where the optimized design with GBG paradigm offers a high efficiency of 31.88% compared to 23.35% provided by the conventional CdS/CIGS solar cell. Therefore, this study provides a new strategy in enhancing the efficiency of thin-film solar cells by exploiting the graded band-gap engineering combined with metaheuristic optimization approach.

This work proposes a new modeling framework based on combining graded band-gap (GBG) engineering and metaheuristic optimization to improve the Cd-Free ZnMgO/CIGS solar cell performances. Analytical and numerical calculations are carried out to assess the influence of band-gap profiles of both buffer and active layers on the electronic and optical properties of the studied solar cell. This investigation shows a great improvement of solar cell efficiency by increasing the optoelectronic figures of merit through tuning and optimizing the band-gap profiles and the conduction band offset at the ZnMgO/CIGS interface. Moreover, metaheuristic-based optimization models are developed to optimize the GBG profiles and enhance the optical and electrical performances of the solar cell. In this context, we recorded very satisfactory results, where the optimized design with GBG paradigm offers a high efficiency of 31.88% compared to 23.35% provided by the conventional CdS/CIGS solar cell. Therefore, this study provides a new strategy in enhancing the efficiency of thin-film solar cells by exploiting the graded band-gap engineering combined with metaheuristic optimization approach.

Iris texture is one of the most secure biometric characteristics used for person recognition, where the most significant step in the iris identification process is effective features extraction. Deep Convolutional Neural network models have been achieved massive success in the features extraction field in recent years, but several of these models have tens to hundreds of millions of parameters, which affect the computational time and resources. A lot of systems proposed in the iris recognition field extract features from normalized iris images after applying many pre-processing steps. These steps affect the quality and computational efficiency of these systems; also, occlusion, reflections, blur, and illumination variation affect the quality of features extracted. This paper proposed a new robust approach for iris recognition that locates the iris region based on the YOLOv4-tiny, then it extracts features without using iris images’ pre-processing, which is a delicate task. In addition, we have proposed an effective model that accelerated the feature extraction process by reducing the architecture of the Inception-v3 model. The obtained results on four benchmark datasets validate the robustness of our approach, where we achieved average accuracy rates of 99.91%, 99.60%, 99.91%, and 99.19% on the IITD, CASIA-Iris-V1, CASIA-Iris-Interval, and CASIA-Iris-Thousand datasets, respectively.

Iris texture is one of the most secure biometric characteristics used for person recognition, where the most significant step in the iris identification process is effective features extraction. Deep Convolutional Neural network models have been achieved massive success in the features extraction field in recent years, but several of these models have tens to hundreds of millions of parameters, which affect the computational time and resources. A lot of systems proposed in the iris recognition field extract features from normalized iris images after applying many pre-processing steps. These steps affect the quality and computational efficiency of these systems; also, occlusion, reflections, blur, and illumination variation affect the quality of features extracted. This paper proposed a new robust approach for iris recognition that locates the iris region based on the YOLOv4-tiny, then it extracts features without using iris images’ pre-processing, which is a delicate task. In addition, we have proposed an effective model that accelerated the feature extraction process by reducing the architecture of the Inception-v3 model. The obtained results on four benchmark datasets validate the robustness of our approach, where we achieved average accuracy rates of 99.91%, 99.60%, 99.91%, and 99.19% on the IITD, CASIA-Iris-V1, CASIA-Iris-Interval, and CASIA-Iris-Thousand datasets, respectively.

Thisarticleconcernstheinverse problem of the recoveryof unknown potential coefficient for the Schrödinger equation, in a bounded domain of Rn with non-homogeneous Neumann boundary condition from a time-dependent Dirich let boundary measurement. We prove uniqueness and Lipschitz stability for this inverse problem under certain convexity hypothesis on the geometry of the spatial domain and under weak regularity requirements on the data. The proof is based on aCarleman estimate in [12] for Schrödinger equations and its resulting implication, a continuous observability inequality. Mathematics Subject Classification. 35R30, 35Q40, 49K20.

Thisarticleconcernstheinverse problem of the recoveryof unknown potential coefficient for the Schrödinger equation, in a bounded domain of Rn with non-homogeneous Neumann boundary condition from a time-dependent Dirich let boundary measurement. We prove uniqueness and Lipschitz stability for this inverse problem under certain convexity hypothesis on the geometry of the spatial domain and under weak regularity requirements on the data. The proof is based on aCarleman estimate in [12] for Schrödinger equations and its resulting implication, a continuous observability inequality. Mathematics Subject Classification. 35R30, 35Q40, 49K20.

This research uses the stability radius approach to investigate the robust stability of an infinite-dimensional linear discrete-time system subjected to stochastic perturbations. First, we characterize the stability radius in terms of a Lyapunov equation. These characterizations improve a computational formula for calculating the stability radius. The second goal is to study how state feedback can maximize the stability radius. We characterize the maximum attainable stability radius using an infinite-dimensional discrete-time Riccati equation. Examples are provided to demonstrate the achieved outcomes.

This research uses the stability radius approach to investigate the robust stability of an infinite-dimensional linear discrete-time system subjected to stochastic perturbations. First, we characterize the stability radius in terms of a Lyapunov equation. These characterizations improve a computational formula for calculating the stability radius. The second goal is to study how state feedback can maximize the stability radius. We characterize the maximum attainable stability radius using an infinite-dimensional discrete-time Riccati equation. Examples are provided to demonstrate the achieved outcomes.

This research uses the stability radius approach to investigate the robust stability of an infinite-dimensional linear discrete-time system subjected to stochastic perturbations. First, we characterize the stability radius in terms of a Lyapunov equation. These characterizations improve a computational formula for calculating the stability radius. The second goal is to study how state feedback can maximize the stability radius. We characterize the maximum attainable stability radius using an infinite-dimensional discrete-time Riccati equation. Examples are provided to demonstrate the achieved outcomes.

Most road crashes at work are caused by Driver Behavioral Drift (DBD). This DBD has become a recurring issue on congested road sections. In this context, this study proposes a method called (MASOCU-DBD) which allows to analyze this DBD problem in two steps: assessment of the dynamics of DBD occurrence using a model called BM-NSA and analysis of DCC using a Cost-Benefit Analysis (CBA) weighted by the Analysis Hierarchical Process (AHP). The application of the MASOCU-DBD on a road section of an Algerian city’s entry highlighted the problem of the DBD in terms of its occurrence and uselessness in the studied section. The merit of the proposed method is that it uses multi-criteria analysis tools (AHP and CBA) as well as a mathematical model (BM-NSA) to analyze professional drivers’ behavioral deviations.

Most road crashes at work are caused by Driver Behavioral Drift (DBD). This DBD has become a recurring issue on congested road sections. In this context, this study proposes a method called (MASOCU-DBD) which allows to analyze this DBD problem in two steps: assessment of the dynamics of DBD occurrence using a model called BM-NSA and analysis of DCC using a Cost-Benefit Analysis (CBA) weighted by the Analysis Hierarchical Process (AHP). The application of the MASOCU-DBD on a road section of an Algerian city’s entry highlighted the problem of the DBD in terms of its occurrence and uselessness in the studied section. The merit of the proposed method is that it uses multi-criteria analysis tools (AHP and CBA) as well as a mathematical model (BM-NSA) to analyze professional drivers’ behavioral deviations.

Most road crashes at work are caused by Driver Behavioral Drift (DBD). This DBD has become a recurring issue on congested road sections. In this context, this study proposes a method called (MASOCU-DBD) which allows to analyze this DBD problem in two steps: assessment of the dynamics of DBD occurrence using a model called BM-NSA and analysis of DCC using a Cost-Benefit Analysis (CBA) weighted by the Analysis Hierarchical Process (AHP). The application of the MASOCU-DBD on a road section of an Algerian city’s entry highlighted the problem of the DBD in terms of its occurrence and uselessness in the studied section. The merit of the proposed method is that it uses multi-criteria analysis tools (AHP and CBA) as well as a mathematical model (BM-NSA) to analyze professional drivers’ behavioral deviations.

Most road crashes at work are caused by Driver Behavioral Drift (DBD). This DBD has become a recurring issue on congested road sections. In this context, this study proposes a method called (MASOCU-DBD) which allows to analyze this DBD problem in two steps: assessment of the dynamics of DBD occurrence using a model called BM-NSA and analysis of DCC using a Cost-Benefit Analysis (CBA) weighted by the Analysis Hierarchical Process (AHP). The application of the MASOCU-DBD on a road section of an Algerian city’s entry highlighted the problem of the DBD in terms of its occurrence and uselessness in the studied section. The merit of the proposed method is that it uses multi-criteria analysis tools (AHP and CBA) as well as a mathematical model (BM-NSA) to analyze professional drivers’ behavioral deviations.

Liquefied petroleum gas (LPG) storage fires and explosions occur due to uncontrolled gas leaks and the gradual breakdown of associated safety barriers. By installing an effective safety barrier, these accidents can be greatly reduced. However, this study assesses the probability of failure of emergency safety barriers (ESBs) to help decision makers understand how they can support decisions to reduce the risks associated with LPG storage. In this context, an extension of the event tree analysis is proposed named emergency event tree analysis (EETA). The aim of this paper is to develop an integrated approach that uses interval type-2 fuzzy sets and Analytic Hierarchy Process (AHP) method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers (ESBs). In addition, a case study on the failure probability assessment of the emergency safety barriers of the LPG plant in Algeria based on the proposed methodology is provided and carried out to illustrate its effectiveness and feasibility. The results demonstrated the ability of interval type-2 fuzzy sets and the AHP method to provide highly reliable results and to evaluate the failure probability of emergency safety barriers in emergencies situations. However, the classical event tree analysis (CETA) does not take into account the possibility of assessing the emergency consequences of different accident scenarios. Consequently, it only allows you to estimate the occurrence probability of accident scenarios. The results of this study show that the value of the probability of failure of the emergency safety barriers can be used to estimate the probability of occurrence of emergency consequences under different accident scenarios, improved the reliability and help prioritize emergency improvement measures. The study provides scientific and operational references for analyzing emergency consequences of the various accident scenarios in all fields such as petrochemical, maritime industry, and health occupational.

Liquefied petroleum gas (LPG) storage fires and explosions occur due to uncontrolled gas leaks and the gradual breakdown of associated safety barriers. By installing an effective safety barrier, these accidents can be greatly reduced. However, this study assesses the probability of failure of emergency safety barriers (ESBs) to help decision makers understand how they can support decisions to reduce the risks associated with LPG storage. In this context, an extension of the event tree analysis is proposed named emergency event tree analysis (EETA). The aim of this paper is to develop an integrated approach that uses interval type-2 fuzzy sets and Analytic Hierarchy Process (AHP) method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers (ESBs). In addition, a case study on the failure probability assessment of the emergency safety barriers of the LPG plant in Algeria based on the proposed methodology is provided and carried out to illustrate its effectiveness and feasibility. The results demonstrated the ability of interval type-2 fuzzy sets and the AHP method to provide highly reliable results and to evaluate the failure probability of emergency safety barriers in emergencies situations. However, the classical event tree analysis (CETA) does not take into account the possibility of assessing the emergency consequences of different accident scenarios. Consequently, it only allows you to estimate the occurrence probability of accident scenarios. The results of this study show that the value of the probability of failure of the emergency safety barriers can be used to estimate the probability of occurrence of emergency consequences under different accident scenarios, improved the reliability and help prioritize emergency improvement measures. The study provides scientific and operational references for analyzing emergency consequences of the various accident scenarios in all fields such as petrochemical, maritime industry, and health occupational.

Industrial systems are becoming more sophisticated, and their failure can result in significant losses for the company in terms of production loss, maintenance costs, fines, image loss, etc. Conventional approaches to modeling and evaluating the failure mechanisms of these systems do not consider certain important aspects, such as the interdependencies between failure modes (FMs) with information and data containing uncertainties as they are generally collected from experts’ judgments. These restrictions may lead to improper decision-making. The use of more advanced techniques to model and assess the interdependencies among components’ failures under uncertainties seems to be more than necessary to overcome these deficiencies. It is in this context that the proposed approach fits. It consists of proposing a hybrid multicriteria decision-aking (MCDM) approach that combines several techniques for a better selection of maintenance strategies. Using the failure mode and effects analysis (FMEA) technique, the potential FMs of components, along with their causes and effects, are identified. The relative importance (or weight) of these FMs is determined using the fuzzy simple additive weighing (FSAW) method based on how they affect the system’s goals. The causal relationships between FMs and their final weights are determined by the fuzzy cognitive maps (FCM) method and the nonlinear Hebbian learning and differential evolution (NHL-DE) algorithm. Finally, based on the final FM weights provided by the FCM, the simple additive weighing (SAW) method is used to select the optimal maintenance strategies. The results of applying the proposed approach to an operating compressor lubrication and sealing oil system demonstrate its importance and usefulness in assisting system operators to efficiently allocate the optimal maintenance strategies, considering the strong correlation between FMs and their effects on system performance while accounting for the uncertainties associated with experts’ judgments. These correlation effects have led to changes in the assigned weights of the selected FMs. Specifically, the FM related to the low output of the lube/seal oil pump, which was initially assigned a lower priority, and with the correlation effects has become the first critical FM. This shift in prioritization emphasizes the need to address this particular FM promptly. By focusing on addressing these high-priority FMs, maintenance efforts can be optimized to prevent or mitigate more severe consequences. Among the various maintenance strategies evaluated, it was determined that the combination of condition-based maintenance (CBM) and precision maintenance (PrM) yields the most favorable outcome in terms of mitigating the impact of accidental failures and undesired events on the selected system.

Industrial systems are becoming more sophisticated, and their failure can result in significant losses for the company in terms of production loss, maintenance costs, fines, image loss, etc. Conventional approaches to modeling and evaluating the failure mechanisms of these systems do not consider certain important aspects, such as the interdependencies between failure modes (FMs) with information and data containing uncertainties as they are generally collected from experts’ judgments. These restrictions may lead to improper decision-making. The use of more advanced techniques to model and assess the interdependencies among components’ failures under uncertainties seems to be more than necessary to overcome these deficiencies. It is in this context that the proposed approach fits. It consists of proposing a hybrid multicriteria decision-aking (MCDM) approach that combines several techniques for a better selection of maintenance strategies. Using the failure mode and effects analysis (FMEA) technique, the potential FMs of components, along with their causes and effects, are identified. The relative importance (or weight) of these FMs is determined using the fuzzy simple additive weighing (FSAW) method based on how they affect the system’s goals. The causal relationships between FMs and their final weights are determined by the fuzzy cognitive maps (FCM) method and the nonlinear Hebbian learning and differential evolution (NHL-DE) algorithm. Finally, based on the final FM weights provided by the FCM, the simple additive weighing (SAW) method is used to select the optimal maintenance strategies. The results of applying the proposed approach to an operating compressor lubrication and sealing oil system demonstrate its importance and usefulness in assisting system operators to efficiently allocate the optimal maintenance strategies, considering the strong correlation between FMs and their effects on system performance while accounting for the uncertainties associated with experts’ judgments. These correlation effects have led to changes in the assigned weights of the selected FMs. Specifically, the FM related to the low output of the lube/seal oil pump, which was initially assigned a lower priority, and with the correlation effects has become the first critical FM. This shift in prioritization emphasizes the need to address this particular FM promptly. By focusing on addressing these high-priority FMs, maintenance efforts can be optimized to prevent or mitigate more severe consequences. Among the various maintenance strategies evaluated, it was determined that the combination of condition-based maintenance (CBM) and precision maintenance (PrM) yields the most favorable outcome in terms of mitigating the impact of accidental failures and undesired events on the selected system.