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An optimized Graphene/4H-SiC/Graphene MSM UV-photodetector operating in a wide range of temperature
摘要： In this paper, .an accurate analytical model has been developed to optimize the performance of an Interdigitated Graphene Electrode/p-silicon carbide (IGE/p-4H-SiC) Metal semiconductor fitness function for the multi objective optimization (MOGA) approach. The optimized sensitivity and speed performances was executed. Our results confirm the excellent ability of the suggested Graphene electrode system to decrease the unwanted shadowing effect. A responsivity of 238 μA/W was obtained under 325-nm illumination compared to the 16.7 μA/W for the conventional Cr-Pd/p-SiC PD. A photocurrent to- dark-current ratio (PDCR) of 5.75 × 105 at 300K and 270 at 500K was distinguished. The response time was found to be around 14 μs at 300K and 54.5 μs at 500K. Furthermore, the developed model serves as a fitness function to recognize the IGE formalism pattern which permits the enhancement of the performance of the proposed Gr/4H-SiC IE MSM PD using MOGA-based technique. The achieved results indicate that the suggested design methodology not only permits to realize a superior compromise amid responsivity and response time, but also shed light on the proposed device’s ruggedness under high temperature conditions. This opens the way to realize ultra-sensitive, high-speed SiC optoelectronic devices for extremely high temperature applications.
关键词： Analytical Model,UV photodetector,Graphene,MOGA approach,4H-SiC,interdigitated electrodes
更新于2025-09-23 15:19:57
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Optimization of process parameters in laser transmission welding for food packaging applications
摘要： Plastics' joining is used widely in food processing applications for the packaging of increasingly diverse food products. Laser transmission welding is an attractive proposition for such applications as it can significantly reduce tooling costs and potential downtime at product changeovers. In order to fulfil this promise in an industrial environment, an effective means of process parameter prediction is required. In this paper, goal driven optimization is conducted, utilizing numerical simulations as the basis for the prediction of optimal process parameters for the laser transmission welding of polyethylene film to a polypropylene substrate. A key consideration of the optimization process is the requirement for specific, pre-defined bonded track widths.
关键词： MOGA,Laser,Finite Element,Optimization,Response Surface
更新于2025-09-19 17:13:59
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A Pareto-optimal evolutionary approach of image encryption using coupled map lattice and DNA
摘要： Evolutionary algorithms are generally a suitable approach for optimization problems, having more than one conflicting objectives. For many complicated engineering optimization problems, multi-objective formulations are treated as realistic models. The paper presents and implements a Pareto-optimal image encryption algorithm that uses coupled map lattice (CML) chaos function and deoxyribonucleic acid (DNA) combination to encrypt an image. The discussed work uses multi-objective genetic algorithm (MOGA) to get the optimized results. The proposed two-step algorithm uses pseudo-random number generators, the chaotic method CML and DNA to create an initial population of DNA masks in its initial stage. The MOGA is applied in the second stage to obtain the best mask for encrypting the given plain image. The focus is on the generation of Pareto fronts by using the Pareto generation method of multi-objective optimization. The paper evaluates the performance of the implemented work using standard metrics like key sensitivity, secret key space, number of pixel change rate, unified average changed intensity, entropy, histogram and correlation coefficient. It also discusses the impact of using a genetic algorithm that uses more than one fitness function as the objective for encrypting images.
关键词： Pareto optimization,Image encryption,MOGA,DNA,CML,Chaos
更新于2025-09-12 10:27:22
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Optimal Hybrid Neuro-fuzzy Based Controller Using MOGA for Photovoltaic (PV) Battery Charging System
摘要： This paper proposes an optimal hybrid neuro-fuzzy/fuzzy controller based on maximum power point tracking (MPPT) technique and voltage regulation for photovoltaic lead-acid battery charging system through the constant current and constant voltage (CC-CV) charge, denoted by NFC-CC/FLC-CV. The parameter optimization of NFC and FLC, including rule selection, based on multi-objective genetic algorithm (MOGA) is applied to the NFC-CC design to improve the tracking accuracy while reducing complexity. By means of genetic optimization, the number of fuzzy rules can be greatly reduced by 50%. In addition, GA is applied to the FLC-CV design to increase voltage regulation (VR) accuracy. After satisfying the stability condition through the solutions determined by MOGA and GA, the performances of controllers under rapidly-changing weather are evaluated tradeoff by several creteria, including transient response, stabilized accuracy, charging time, and energy utilization and charging ef?ciency. As results, the proposed controller outperforms the other existing controllers with the fastest rise time without overshoot, the highest MPPT and VR accuracy with negligible oscillations, a 12-23% reduction in charging time, and an increase of 5-15% and 1-6% in energy utilization and charging ef?ciency. Furthermore, it provides superior results in terms of computational complexity by achieving the minimum number of multiplications and system parameters, and high reliability with the lowest Akaike information criterion (AIC).
关键词： MPPT,neuro-fuzzy,Fuzzy logic,MOGA,photovoltaic charging system
更新于2025-09-10 09:29:36