A study on the limitations of evolutionary computation and other bio-inspired approaches for integer factorization

Integer Factorization is a vital number theoretic problem frequently finding application in public-key cryptography like RSA encryption systems, and other areas like Fourier transform algorithm. The problem is computationally intractable because it is a one-way mathematical function. Due to its computational infeasibility, it is extremely hard to find the prime factors of a semi prime number generated from two randomly chosen similar sized prime numbers. There has been a recently growing interest in the community with regards to evolutionary computation and other alternative approaches to solving this problem as an optimization task. However, the results still seem to be very rudimentary in nature and there\u27s much work to be done. This paper emphasizes on such approaches and presents a critic study in details. The paper puts forth criticism and ideas in this aspect

Stability prediction of a natural and man-made slope using various machine learning algorithms

In this paper, an attempt has been made to implement various machine learning techniques to predict the factor of safety of a natural residual soil slope and a man-made overburden mine dump slope using several physical and geometrical parameters of the respective slopes. As the stability predictions of a slope, whether natural or man-made, is very complex and time-consuming, several machine learning-based algorithms like Support Vector Regressor, Artificial Neural Network, Random Forest, Gradient Boosting and Extreme Gradient Boost were selected for modelling. The results derived from the models were compared with those achieved from numerical analysis. Moreover, various performance indices such as coefficient of determination, variance account for, root mean square error, learning rate and residual error were employed to evaluate the predictive performance of the developed models. The results indicate an excellent prediction performance and ease of interpretation of tree-based algorithms like Random Forest, Gradient Boosting and Extreme Gradient Boost than linear models like Support Vector Regressor and Neural Network-based algorithm for both the slope types. The Support Vector Regressor has the least while Extreme Gradient Boost has the highest predictive performance. Also, it was observed that the efficiency of various machine learning models to predict the factor of safety was found to be superior in the case of man-made dump slope than natural residual soil slope. © 2022 Elsevier Lt

A Figure of Merit for Selection of the Best Family of SiC Power MOSFETs

This paper proposes a criterion to select the best family of commercial SiC power metal–oxide–semiconductor field-effect transistors (MOSFETs) that provides the highest quality and reliability. Applying a recently published integrated-charge method, a newly proposed figure of merit is correlated to the density of near-interface traps that degrade the quality and reliability of SiC MOSFETs. The applicability of the proposed figure of merit is experimentally demonstrated with the most widely used and commercially available planar and trench MOSFETs from different manufacturers

Modeling Power GaN-HEMTs Using Standard MOSFET Equations and Parameters in SPICE

The device library in the standard circuit simulator (SPICE) lacks a gallium nitride based high-electron-mobility-transistor (GaN-HEMT) model, required for the design and verification of power-electronic circuits. This paper shows that GaN-HEMTs can be modeled by selected equations from the standard MOSFET LEVEL 3 model in SPICE. A method is proposed for the extraction of SPICE parameters in these equations. The selected equations and the proposed parameter-extraction method are verified with measured static and dynamic characteristics of commercial GaN-HEMTs. Furthermore, a double pulse test is performed in LTSpice and compared to its manufacturer model to demonstrate the effectiveness of the MOSFET LEVEL 3 model. The advantage of the proposed approach to use the MOSFET LEVEL 3 model, in comparison to the alternative behavioral-based model provided by some manufacturers, is that users can apply the proposed method to adjust the parameters of the MOSFET LEVEL 3 model for the case of manufacturers who do not provide SPICE models for their HEMTs

Modeling Power GaN-HEMTs Using Standard MOSFET Equations and Parameters in SPICE

The device library in the standard circuit simulator (SPICE) lacks a gallium nitride based high-electron-mobility-transistor (GaN-HEMT) model, required for the design and verification of power-electronic circuits. This paper shows that GaN-HEMTs can be modeled by selected equations from the standard MOSFET LEVEL 3 model in SPICE. A method is proposed for the extraction of SPICE parameters in these equations. The selected equations and the proposed parameter-extraction method are verified with measured static and dynamic characteristics of commercial GaN-HEMTs. Furthermore, a double pulse test is performed in LTSpice and compared to its manufacturer model to demonstrate the effectiveness of the MOSFET LEVEL 3 model. The advantage of the proposed approach to use the MOSFET LEVEL 3 model, in comparison to the alternative behavioral-based model provided by some manufacturers, is that users can apply the proposed method to adjust the parameters of the MOSFET LEVEL 3 model for the case of manufacturers who do not provide SPICE models for their HEMTs

A Method for Selection of Power MOSFETs to Minimize Power Dissipation

A balance between static and dynamic losses of a power MOSFET is always desirable for accomplishing the maximum efficiency for a specific power converter. The standard semiconductor theory suggests that a minimum power dissipation in a MOSFET can be achieved by selecting a specific device active area. However, for power circuit designers, the active device area is unknown given that only datasheet parameters are available. Hence, in this paper, we propose a simple method, based on semiconductor theory, to select optimum power MOSFET from a family of MOSFETs using only datasheet parameters. By applying this optimization method to the specific power supply circuit under development, power engineers can select the best transistors to yield lowest power losses for the systems under development