26 research outputs found
A Hybrid Neural Network and Simulated Annealing Approach to the Unit Commitment Problem
No full textIn this paper, the authors present an approach combining the feedforward neural network and the simulated annealing method to solve unit commitment, a mixed integer combinatorial optimisation problem in power system. The artificial neural network (ANN) is used to determine the discrete variables corresponding to the state of each unit at each time interval. The simulated annealing method is used to generate the continuous variables corresponding to the power output of each unit and the production cost. The type of neural network used in this method is a multi-layer perceptron trained by the back-propagation algorithm. A set of load profiles as inputs and the corresponding unit-commitment schedules as outputs (satisfying the minimum up-down, spinning reserve and crew constraints) are utilized to train the network. A method to generate the training patterns is also presented. The experimental result demonstrates that the proposed approach can solve unit commitment in a reduced computational time with an optimum generation schedule
A new geometric programming formulation for a reliability problem
No full textA new formulation for the problem of system reliability optimization when constrained by some linear constraints is presented in this paper. The formulation provided is easily adaptable to geometric programming form. The problem is further reduced to that of an optimization of an unconstrained objective function with variables one less than the number of constraints, when its dual is defined. It is amply demonstrated through this paper that the formulation and approach of this paper are simpler than earlier attempts described elsewhere. An example is also given
Reliability optimization of a series system with active and standby redundancy
No full textThis paper presents a method of maximizing reliability of a series system subject to multiple constraints. The use of both active series parallel and standby redundancy is considered to increase the reliability of the system. A flexible tree search method has been used in this paper which is found to be an efficient method of solution, as it requires only simple mathematical computations. A three stage system is used for illustration
Optical properties of InAsBi and optimal designs of lattice-matched and strain-balanced III-V semiconductor superlattices
Get PDFabstract: The optical properties of bulk InAs[subscript 0.936]Bi[subscript 0.064] grown by molecular beam epitaxy on a (100)-oriented GaSb substrate are measured using spectroscopic ellipsometry. The index of refraction and absorption coefficient are measured over photon energies ranging from 44 meV to 4.4 eV and are used to identify the room temperature bandgap energy of bulk InAs[subscript 0.936]Bi[subscript 0.064] as 60.6 meV. The bandgap of InAsBi is expressed as a function of Bi mole fraction using the band anticrossing model and a characteristic coupling strength of 1.529 eV between the Bi impurity state and the InAs valence band. These results are programmed into a software tool that calculates the miniband structure of semiconductor superlattices and identifies optimal designs in terms of maximizing the electron-hole wavefunction overlap as a function of transition energy. These functionalities are demonstrated by mapping the design spaces of lattice-matched GaSb/InAs[subscript 0.911]Sb[subscript 0.089] and GaSb/InAs[subscript 0.932]Bi[subscript 0.068] and strain-balanced InAs/InAsSb, InAs/GaInSb, and InAs/InAsBi superlattices on GaSb. The absorption properties of each of these material systems are directly compared by relating the wavefunction overlap square to the absorption coefficient of each optimized design. Optimal design criteria are provided for key detector wavelengths for each superlattice system. The optimal design mid-wave infrared InAs/InAsSb superlattice is grown using molecular beam epitaxy, and its optical properties are evaluated using spectroscopic ellipsometry and photoluminescence spectroscopy.This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. The following article appeared in Journal of Applied Physics and may be found at http://aip.scitation.org/doi/10.1063/1.4953027
Multi-objective optimization of electro-discharge machining (EDM) parameter for sustainable machining
No full textIn the present investigation, experiments have been planned using L9 orthogonal array to obtain optimal level combination of input process parameters such as current, pulse on time and voltage. A multi-objective optimization technique using Vikor Index has been used to optimize simultaneously the material removal rate, tool wear rate, surface roughness and radial overcut .The effects of various input process parameters on the overall performance of EDM have been studied using ANOVA Technique. Further, a mathematical correlation has been established between various input parameters with the individual output parameters using a multiple linear regression Analysis
