The main purpose of this study was to compare and evaluate the performance of two multicriteria models for landslide susceptibility assessment in Constantine, north-east of Algeria. The landslide susceptibility maps were produced using the analytic hierarchy process (AHP) and Fuzzy AHP (FAHP) via twelve landslides conditioning factors, including the slope gradient, lithology, land cover, distance from drainage network, distance from the roads, distance from faults, topographic wetness index, stream power index, slope curvature, Normalized Difference Vegetation Index, slope aspect and elevation. In this study, the mentioned models were used to derive the weighting value of the conditioning factors. For the validation process of these models, the receiver operating characteristic analysis, and the area under the curve (AUC) were applied by comparing the obtained results to The landslide inventory map which prepared using the archives of scientific publications, reports of local authorities, and field survey as well as analyzing satellite imagery. According to the AUC values, the FAHP model had the highest value (0.908) followed by the AHP model (0.777). As a result, the FAHP model is more consistent and accurate than the AHP in this case study. The outcome of this paper may be useful for landslide susceptibility assessment and land use management.

The main purpose of this study was to compare and evaluate the performance of two multicriteria models for landslide susceptibility assessment in Constantine, north-east of Algeria. The landslide susceptibility maps were produced using the analytic hierarchy process (AHP) and Fuzzy AHP (FAHP) via twelve landslides conditioning factors, including the slope gradient, lithology, land cover, distance from drainage network, distance from the roads, distance from faults, topographic wetness index, stream power index, slope curvature, Normalized Difference Vegetation Index, slope aspect and elevation. In this study, the mentioned models were used to derive the weighting value of the conditioning factors. For the validation process of these models, the receiver operating characteristic analysis, and the area under the curve (AUC) were applied by comparing the obtained results to The landslide inventory map which prepared using the archives of scientific publications, reports of local authorities, and field survey as well as analyzing satellite imagery. According to the AUC values, the FAHP model had the highest value (0.908) followed by the AHP model (0.777). As a result, the FAHP model is more consistent and accurate than the AHP in this case study. The outcome of this paper may be useful for landslide susceptibility assessment and land use management.

Using first-principles calculations, we study the structural, electronic and optical properties of the monolayer, bilayer and trilayer germanium monosulfide GeS. The results reveal an indirect semiconducting band gap for the monolayer and trilayer GeS, whereas the gap is direct for the bilayer GeS. Both the generalized gradient approximation and the screened hybrid functionals assess a decrease in band energy as the number of layers is improved. Furthermore, due to the high buckling of lattice structures, the optical spectra show significant degree of anisotropy. The number of layers engineers key optical parameters including the refractive index, the reflectivity absorption and provides the layered GeS with excellent absorption in the low energy region, namely the visible and UV range of the electromagnetic spectrum. Accordingly, 2D hexagonal GeS few-layers can be used as a highly promising material in the optoelectronic, ultraviolet optical nanodevices and photovoltaics.

Using first-principles calculations, we study the structural, electronic and optical properties of the monolayer, bilayer and trilayer germanium monosulfide GeS. The results reveal an indirect semiconducting band gap for the monolayer and trilayer GeS, whereas the gap is direct for the bilayer GeS. Both the generalized gradient approximation and the screened hybrid functionals assess a decrease in band energy as the number of layers is improved. Furthermore, due to the high buckling of lattice structures, the optical spectra show significant degree of anisotropy. The number of layers engineers key optical parameters including the refractive index, the reflectivity absorption and provides the layered GeS with excellent absorption in the low energy region, namely the visible and UV range of the electromagnetic spectrum. Accordingly, 2D hexagonal GeS few-layers can be used as a highly promising material in the optoelectronic, ultraviolet optical nanodevices and photovoltaics.

Using first-principles calculations, we study the structural, electronic and optical properties of the monolayer, bilayer and trilayer germanium monosulfide GeS. The results reveal an indirect semiconducting band gap for the monolayer and trilayer GeS, whereas the gap is direct for the bilayer GeS. Both the generalized gradient approximation and the screened hybrid functionals assess a decrease in band energy as the number of layers is improved. Furthermore, due to the high buckling of lattice structures, the optical spectra show significant degree of anisotropy. The number of layers engineers key optical parameters including the refractive index, the reflectivity absorption and provides the layered GeS with excellent absorption in the low energy region, namely the visible and UV range of the electromagnetic spectrum. Accordingly, 2D hexagonal GeS few-layers can be used as a highly promising material in the optoelectronic, ultraviolet optical nanodevices and photovoltaics.

Using first-principles calculations, we study the structural, electronic and optical properties of the monolayer, bilayer and trilayer germanium monosulfide GeS. The results reveal an indirect semiconducting band gap for the monolayer and trilayer GeS, whereas the gap is direct for the bilayer GeS. Both the generalized gradient approximation and the screened hybrid functionals assess a decrease in band energy as the number of layers is improved. Furthermore, due to the high buckling of lattice structures, the optical spectra show significant degree of anisotropy. The number of layers engineers key optical parameters including the refractive index, the reflectivity absorption and provides the layered GeS with excellent absorption in the low energy region, namely the visible and UV range of the electromagnetic spectrum. Accordingly, 2D hexagonal GeS few-layers can be used as a highly promising material in the optoelectronic, ultraviolet optical nanodevices and photovoltaics.

Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long–short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path.

Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long–short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path.

Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long–short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path.

In this paper, a new approach being different from the concept of DTC and IFOC for a robust torque control design for induction motor is addressed. The design is based on the framework of singularly perturbed system theory and linear varying parameter systems. In these systems, the rotor flux is considered to be a time-varying parameter in order to guarantee a robust torque control with LPV flux observer with respect to the speed and resistance variations. In fact, this observer is designed to estimate the rotor flux as well as an MRAS observer is introduced to estimate the mechanical speed and rotor resistance. The main feature of this proposed structure is the enhancement of robustness with flux, speed and rotor resistance variation. This improvement leads to a considerable decrease of the torque ripples and ensures the stability for the entire operating range. The obtained simulations and experimental results are used to validate the effectiveness of the proposed control strategy.

In this paper, a new approach being different from the concept of DTC and IFOC for a robust torque control design for induction motor is addressed. The design is based on the framework of singularly perturbed system theory and linear varying parameter systems. In these systems, the rotor flux is considered to be a time-varying parameter in order to guarantee a robust torque control with LPV flux observer with respect to the speed and resistance variations. In fact, this observer is designed to estimate the rotor flux as well as an MRAS observer is introduced to estimate the mechanical speed and rotor resistance. The main feature of this proposed structure is the enhancement of robustness with flux, speed and rotor resistance variation. This improvement leads to a considerable decrease of the torque ripples and ensures the stability for the entire operating range. The obtained simulations and experimental results are used to validate the effectiveness of the proposed control strategy.

In this paper, a new approach being different from the concept of DTC and IFOC for a robust torque control design for induction motor is addressed. The design is based on the framework of singularly perturbed system theory and linear varying parameter systems. In these systems, the rotor flux is considered to be a time-varying parameter in order to guarantee a robust torque control with LPV flux observer with respect to the speed and resistance variations. In fact, this observer is designed to estimate the rotor flux as well as an MRAS observer is introduced to estimate the mechanical speed and rotor resistance. The main feature of this proposed structure is the enhancement of robustness with flux, speed and rotor resistance variation. This improvement leads to a considerable decrease of the torque ripples and ensures the stability for the entire operating range. The obtained simulations and experimental results are used to validate the effectiveness of the proposed control strategy.

In this paper, a new approach being different from the concept of DTC and IFOC for a robust torque control design for induction motor is addressed. The design is based on the framework of singularly perturbed system theory and linear varying parameter systems. In these systems, the rotor flux is considered to be a time-varying parameter in order to guarantee a robust torque control with LPV flux observer with respect to the speed and resistance variations. In fact, this observer is designed to estimate the rotor flux as well as an MRAS observer is introduced to estimate the mechanical speed and rotor resistance. The main feature of this proposed structure is the enhancement of robustness with flux, speed and rotor resistance variation. This improvement leads to a considerable decrease of the torque ripples and ensures the stability for the entire operating range. The obtained simulations and experimental results are used to validate the effectiveness of the proposed control strategy.

In this paper, a new approach being different from the concept of DTC and IFOC for a robust torque control design for induction motor is addressed. The design is based on the framework of singularly perturbed system theory and linear varying parameter systems. In these systems, the rotor flux is considered to be a time-varying parameter in order to guarantee a robust torque control with LPV flux observer with respect to the speed and resistance variations. In fact, this observer is designed to estimate the rotor flux as well as an MRAS observer is introduced to estimate the mechanical speed and rotor resistance. The main feature of this proposed structure is the enhancement of robustness with flux, speed and rotor resistance variation. This improvement leads to a considerable decrease of the torque ripples and ensures the stability for the entire operating range. The obtained simulations and experimental results are used to validate the effectiveness of the proposed control strategy.

Tunnel FET is one of the promising devices advocated as a replacement of conventional MOSFET to be used for low power applications. Temperature is an important factor affecting the performance of circuits or system, so temperature associated reliability issues of double gate Tunnel FET and its impact on essential circuit design components have been addressed here. The temperature reliability investigation is based on double gate Tunnel FET, containing Si_1-xGe _x /Si, source/channel and HfO₂ high-k gate dielectric material. During investigation, it has been found that at high temperature application range ~ 300 K - to - 600 K,the Tunnel FET device design parameters exhibit weak temperature dependency with switching current (I_ON), while the off-state current (I_OFF) is slightly varying ~10⁻¹⁷A/μm-to-10⁻¹⁰A/μm. In addition, the impact of temperature on various device design element such as V_TH(i.e.,switching voltage),on-current (I_ON), off-current (I_OFF), switching ratio (I_ON/I_OFF) and average subthreshold slope (i.e., SS_avg), ambipolar current (I_AMB) have been done in this research work.The essential circuit design components for digital and analog/RF applications, such as current amplification factor(g_m) and its derivative (g_m’),the C-V components of device design, Cgg, Cgd and C_gs, cut - off frequency (ƒ_T) and gain band width (GBW) product have deeply investigated. In conclusion, the obtained results show that the designed double gate Tunnel FET device configuration and its circuit design components are suitable for ultra-low power circuit,system applications and reliable for hazardous temperature environment.

Tunnel FET is one of the promising devices advocated as a replacement of conventional MOSFET to be used for low power applications. Temperature is an important factor affecting the performance of circuits or system, so temperature associated reliability issues of double gate Tunnel FET and its impact on essential circuit design components have been addressed here. The temperature reliability investigation is based on double gate Tunnel FET, containing Si_1-xGe _x /Si, source/channel and HfO₂ high-k gate dielectric material. During investigation, it has been found that at high temperature application range ~ 300 K - to - 600 K,the Tunnel FET device design parameters exhibit weak temperature dependency with switching current (I_ON), while the off-state current (I_OFF) is slightly varying ~10⁻¹⁷A/μm-to-10⁻¹⁰A/μm. In addition, the impact of temperature on various device design element such as V_TH(i.e.,switching voltage),on-current (I_ON), off-current (I_OFF), switching ratio (I_ON/I_OFF) and average subthreshold slope (i.e., SS_avg), ambipolar current (I_AMB) have been done in this research work.The essential circuit design components for digital and analog/RF applications, such as current amplification factor(g_m) and its derivative (g_m’),the C-V components of device design, Cgg, Cgd and C_gs, cut - off frequency (ƒ_T) and gain band width (GBW) product have deeply investigated. In conclusion, the obtained results show that the designed double gate Tunnel FET device configuration and its circuit design components are suitable for ultra-low power circuit,system applications and reliable for hazardous temperature environment.

Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.

Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.

Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.

Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.