- 标题
- 摘要
- 关键词
- 实验方案
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Machine-learning assisted prediction of spectral power distribution for full-spectrum white light-emitting diode
摘要： The full-spectrum white light-emitting diode (LED) emits light with a broad wavelength range by mixing all lights from multiple LED chips and phosphors. Thus, it has great potentials to be used in healthy lighting, high resolution displays, plant lighting with higher color rendering index close to sunlight and higher color fidelity index. The spectral power distribution (SPD) of light source, representing its light quality, is always dynamically controlled by complex electrical and thermal loadings when the light source operates under usage conditions. Therefore, a dynamic prediction of SPD for the full-spectrum white LED has become a hot but challenging research topic in the high quality lighting design and application. This paper proposes a dynamic SPD prediction method for the full-spectrum white LED by integrating the SPD decomposition approach with the artificial neural network (ANN) based machine learning method. Firstly, the continuous SPDs of a full-spectrum white LED driven by an electrical-thermal loading matrix are discretized by the multi-peak fitting with Gaussian model as the relevant spectral characteristic parameters. Then, the Back Propagation (BP) and Genetic Algorithm-Back Propagation (GA-BP) NNs are proposed to predict the spectral characteristic parameters of LEDs operated under any usage conditions. Finally, the dynamically predicted spectral characteristic parameters are used to reconstruct the SPDs. The results show that: (1) The spectral characteristic parameters obtained by fitting with the Gaussian model can be used to represent the emission lights from multiple chips and phosphors in a full-spectrum white LED; (2) The prediction errors of both BP NN and GA-BP NN can be controlled at low level, that is to say, our proposed method can achieve a highly accurate SPD dynamic prediction for the full-spectrum white LED when it operates under different operation mission profiles.
关键词： Machine learning,Spectral power distribution,Genetic algorithm,Full-spectrum white LED,BP neural network
更新于2025-09-16 10:30:52
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Key Parameter Identification and Optimization of Photovoltaic Power Plants Based on Genetic Algorithm
摘要： As the penetration rate of the photovoltaic power continues to grow, its impact on the stability of the power system becomes more considerable ever than before. However, due to the relatively low accuracy of the parameters, the traditional electromagnetic transient simulation used to assess the impact is biased. Therefore, it is of great importance to perform key parameter identification and optimization on a solar power plant containing many photovoltaic panels, which can avoid the problem of combination explosion. In this paper, a scheme of key parameter identification is proposed. Then, an optimization method based on genetic algorithm is also established to improve the accuracy. Simulation tests validate the effectiveness of the proposed method.
关键词： genetic algorithm,parameter identification,photovoltaic power plants,optimization
更新于2025-09-16 10:30:52
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Multi-Objective Optimization of Cutting Parameters during Laser Cutting of Titanium Alloy Sheet using Hybrid approach of Genetic Algorithm and Multiple Regression Analysis
摘要： Now a day’s advance engineering materials are playing a key role in the field of aeronautics, defense and medical. Titanium and its alloys are one of the advanced engineering materials that have great demand in the various fields of industries due to its greater behavior and better mechanical properties. These applications require precise and better quality cuts which may not be obtained by conventional machining processes due to the unfavourable properties of Ti and its alloys. This problem may be minimized by using laser cutting process. In this study, the Advanced 300 W Nd:YAG Laser cutting system has been applied for the cutting of titanium alloy sheet. The point of current research is to optimize kerf width and kerf deviation, simultaneously during the laser cutting of Titanium alloy sheet (Grade 5).The multiple regression analysis has been applied for developing the second order regression models of kerf width and kerf deviation. The optimization technique genetic algorithm has been applied for the multi-objective optimization of the developed models. The comparison results show an improvement of 29.78% for kerf width and 95% for kerf deviation, respectively. The overall percentage improvement of 27.39% has been found by considering the equal importance of both quality characteristics. The parametric effects on quality characteristic have been also discussed.
关键词： Multiple Regression Analysis,Kerf Deviation,Laser cutting,Hybrid Approach,Kerf Width,Genetic Algorithm
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - The Role of Second Life Li-ion Batteries in Avoiding Generation Curtailment in Utility-scale Wind + Solar Parks in Brazil
摘要： An intelligent hybrid Taguchi-genetic algorithm (IHTGA) is used to optimize bearing offsets and shaft alignment in a marine vessel propulsion system. The objectives are to minimize normal shaft stress and shear force. The constraints are permissible reaction force, bearing stress, shear force, and bending moment in the shaft thrust ?ange under cold and hot operating conditions. Accurate alignment of the shaft for a main propulsion system is important for ensuring the safe operation of a vessel. To obtain a set of acceptable forces and stresses for the bearings and shaft under operating conditions, the optimal bearing offsets must be determined. Instead of the time-consuming classical local search methods with some trial-and-error procedures used in most shipyards to optimize bearing offsets, this paper used IHTGA. The proposed IHTGA performs Taguchi method between the crossover operation of the conventional GA. Incorporating the systematic reasoning ability of Taguchi method in the crossover operation enables intelligent selection of genes used to achieve crossover, which enhances the performance of the IHTGA in terms of robustness, statistical performance, and convergence speed. A penalty function method is performed using the ?tness function as a pseudo-objective function comprising a linear combination of design objectives and constraints. A ?nite-element method is also used to determine the reaction forces and stresses in the bearings and to determine normal stresses, bending moments, and shear forces in the shaft. Computational experiments in a 2200 TEU container vessel show that the results obtained by the proposed IHTGA are signi?cantly better than those obtained by the conventional local search methods with some trial-and-error procedures.
关键词： genetic algorithm,shaft alignment,Marine vessel propulsion system,bearing offsets,optimal design
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE XXVI International Conference on Electronics, Electrical Engineering and Computing (INTERCON) - Lima, Peru (2019.8.12-2019.8.14)] 2019 IEEE XXVI International Conference on Electronics, Electrical Engineering and Computing (INTERCON) - A photovoltaic multi-functional converter with multi-resonant controller coefficients improved by a genetic algorithm
摘要： The present work deals with the simulation of a photovoltaic distributed generation system at the micro generation level, using a DC-AC power converter responsible for injecting active power into the grid and acting as a shunt active power filter, mitigating the harmonics of local nonlinear loads. For this, the system has a reference current generator that employs an algorithm based on the synchronous reference frame. The current reference has a distorted portion, corresponding to the distortion power of the nonlinear load added to the active portion to be injected to the grid. A PI plus multi-resonant controller are inserted to reproduce correctly the current reference. In order to enhance stability and waveform quality a genetic algorithm is adopted to find the best gains for the controllers. Simulations throught MatLab/Simulink? are presented to confirm the feasibility of the proposal.
关键词： PI+multi-resonant,Genetic Algorithm,Inverter,Active filter
更新于2025-09-16 10:30:52
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An Optimal Genetic Algorithm for Fatigue Life Control of Medium Carbon Steel in Laser Hardening Process
摘要： This study proposes a genetic algorithm-optimized model for the control of the fatigue life of AISI 1040 steel components after a high-power diode laser hardening process. First, the effect of the process parameters, i.e., laser power and scan speed, on the fatigue life of the components after the laser treatment was evaluated by using a rotating bending machine. Then, in light of the experimental findings, the optimization model was developed and tested in order to find the best regression model able to fit the experimental data in terms of the number of cycles until failure. The laser treatment was found to significantly increase the fatigue life of the irradiated samples, thus revealing its suitability for industrial applications. Finally, the application of the proposed genetic algorithm-based method led to the definition of an optimal regression model which was able to replicate the experimental trend very accurately, with a mean error of about 6%, which is comparable to the standard deviation associated with the process variability.
关键词： genetic algorithm,laser hardening,diode laser,fatigue life
更新于2025-09-16 10:30:52
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Adjustable Optical Path Length Compact Spherical Mirrors Multipass Cell Optimized with Genetic Algorithm
摘要： Numerous practical applications, including health and environmental protection, need compact and high sensitivity gas sensors. This work reports an experimental investigation on a compact multipass cell (MPC) designed and optimized with a genetic algorithm (GA) with long, adjustable optical path length (OPL) using only inexpensive spherical mirrors. A four-mirror based MPC with the GA developed here, offers great design flexibility in comparison to a two-mirror solution. As an example, a stable 24 m OPL was reached within only 80 cc volume. Moreover, by changing the mirror positions various stable OPLs can be achieved in a controllable way. The presented MPC consists of four mirrors in a configuration similar to a bow-tie. As a result, the symmetry between the mirrors is broken (mirrors are not parallel to each other) and an astigmatic spot pattern with a high fill factor is obtained. Additionally, the use of the folded optical path geometry causes higher compactness in contrast to an astigmatic mirror-based MPC. Compared to other dense-pattern or folded spherical mirrors MPCs reported previously, longer OPL in the same volume can be obtained. In order to accurately calculate the line-sphere intersection points and reflection angles based on algorithms reported in custom ray tracing software was developed. It also allows to determine the optimal MPC configuration with specified design constraints (mirror diameters, their focal lengths and desirable OPL) by using a GA. To verify the simulation results, we assembled a MPC with four 1” in diameter, 25 mm focal length mirrors mounted in kinematic holders and fixed them to an aluminum base. By changing the angle and distance between the mirrors, different MPC configurations were tested. Several OPLs ranging from 4.5 m to 28 m were achieved with the GA. The longest OPL, with sufficient output beam quality for such mirrors, was 24 m. In order to prove the agreement between the simulation and experiment, a 16 m and 24 m OPL configurations were prepared and the time-of-flight inside the MPC was measured by injecting a 10 ns pulse laser into the cavity. The first pulse (registered at 0 ns delay in Fig. 1c) corresponds to light partially reflected from the optical plate situated near the MPC input, whereas the second one arrives from the output of MPC. By measuring the time delay between both pulses, the actual OPL was calculated. The experimentally obtained OPLs were 16.11 m and 23.88 m, which is in good agreement with the simulated values of 16.1 m and 23.82 m respectively. In conclusion, we present a compact, four-mirror MPC, designed and optimized with a GA in several OPL variants. Then two of them were experimentally verified through time-of-flight measurement inside the MPC.
关键词： optical path length,genetic algorithm,spherical mirrors,multipass cell,gas sensors
更新于2025-09-12 10:27:22
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Optimal Scheduling of Residential Home Appliances by Considering Energy Storage and Stochastically Modelled Photovoltaics in a Grid Exchange Environment Using Hybrid Grey Wolf Genetic Algorithm Optimizer
摘要： The transformation of a conventional power system to a smart grid has been underway over the last few decades. A smart grid provides opportunities to integrate smart homes with renewable energy resources (RERs). Moreover, it encourages the residential consumers to regulate their home energy consumption in an effective way that suits their lifestyle and it also helps to preserve the environment. Keeping in mind the techno-economic reasons for household energy management, active participation of consumers in grid operations is necessary for peak reduction, valley filling, strategic load conservation, and growth. In this context, this paper presents an efficient home energy management system (HEMS) for consumer appliance scheduling in the presence of an energy storage system and photovoltaic generation with the intention to reduce the energy consumption cost determined by the service provider. To study the benefits of a home-to-grid (H2G) energy exchange in HEMS, photovoltaic generation is stochastically modelled by considering an energy storage system. The prime consideration of this paper is to propose a hybrid optimization approach based on heuristic techniques, grey wolf optimization, and a genetic algorithm termed a hybrid grey wolf genetic algorithm to model HEMS for residential consumers with the objectives to reduce energy consumption cost and the peak-to-average ratio. The effectiveness of the proposed scheme is validated through simulations performed for a residential consumer with several domestic appliances and their scheduling preferences by considering real-time pricing and critical peak-pricing tariff signals. Results related to the reduction in the peak-to-average ratio and energy cost demonstrate that the proposed hybrid optimization technique performs well in comparison with different meta-heuristic techniques available in the literature. The findings of the proposed methodology can further be used to calculate the impact of different demand response signals on the operation and reliability of a power system.
关键词： energy storage system,home-to-grid energy exchange,hybrid grey wolf genetic algorithm,home energy management system,photovoltaic generation and smart grid
更新于2025-09-12 10:27:22
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Optimization of building fenestration and shading for climate-based daylight performance using the coupled genetic algorithm and simulated annealing optimization methods
摘要： Various metaheuristic optimization methods have been applied in high-performance glazing and shading system design due to the complexity and nonlinear impact of the fenestration characteristics on the daylighting performance. However, the optimal solutions found by different optimization methods may vary because of the stochastic nature and the configurations of the optimization methods. This paper studied the improvements in the reliability, consistency and robustness of the genetic algorithm (GA) using hybridization with simulated annealing (SA) considering different cooling strategies in the SA, including the initial temperature and state transitions for each temperature, which controls the comprehensiveness and the convergence acceleration of the search by SA. Analysis of the reliability, consistency and robustness of the optimization methods based on the mean values and the variances of the objective function values of the best cases found in different methods revealed that there is a significant difference between the hybrid GA/SA with higher temperature and GA, where hybrid algorithm performed better than the GA.
关键词： Optimization,Genetic algorithm,Building fenestration,Hybrid evolutionary algorithms,Daylighting,Simulated annealing
更新于2025-09-12 10:27:22
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Experimental study of Hole Taper in Laser Trepan Drilling of Nickel Based Super alloy Sheet
摘要： This research article reports the optimum laser drilling input parameter for getting minimum hole taper and experimentally investigates the behaviour of hole taper in selected laser drilling input parameters on Inconel -718 sheet. Inconel -718 is nickel based super alloy, has diverse application in the field of manufacturing industries, including aerospace, aircraft, automotive, medical equipments, food service equipments and many others. The material is well suited for applications requiring high strength in temperature ranges from cryogenic up to 1400°F. Inconel-718 also exhibits excellent tensile and impact strength. The conventional drilling process faces difficulties to drill quality and precise holes in advanced materials due to its better mechanical properties. Making geometrical better hole is major concerned with conventional drilling process. With the help of Laser drilling process, a geometrically and dimensionally improved hole may be produced. The geometry of hole can be made further better if operating the Laser system at optimum parameters level. In this paper the effects of laser input parameters on hole taper have been investigated and optimal value of input parameters for reduced hole taper has been suggested. The experiments have been conducted by varying one parameter at a time. The experimental data are used to develop the multi regression model for hole taper. A reliable multi regression model is developed for hole taper and modern optimization tool, Genetic algorithm (GA) is used for optimization of the kerf taper. The optimal value of studied laser input parameters such as assist gas pressure, laser Current, stand-off distance, and cutting speed (Trepanning speed) have been suggested for getting lower value of hole taper. Finally, the effects of each laser input parameter of the kerf taper has been discussed.
关键词： Hole Taper,Laser Trapan Drilling,Inconel-718,Regression model,Genetic Algorithm
更新于2025-09-12 10:27:22