The aim of economic optimal experiment design is precisely to plan an experiment in such a way that the expected loss of optimality in the optimization is minimized.
Example results show that the product of multiple correlation characteristics of manufacturing cost and quality loss of tolerance optimization design model has certain validity, and compared with the traditional this tolerance design having great superiority.
Given two disjoint sets of arbitrary objects X = (x1, x2,..., x n ) and Y = (y1, y2,..., y m ) with n ≥ m without loss of generality, the optimization problem can be defined as: (1).
With no loss of generality, any optimization problems for which GA aims to solve can be converted to posterior mode searches in Bayesian context.
Simulations of photovoltaic devices provide a promising tool for the exploration of internal loss mechanisms and assessment of optimization potentials.
The results show that the proposed approach can give more reasonable solutions than the existing approaches when both quality loss and the reliability of optimization results are important issues.
According to amount of power loss reduction, the priority of optimization cases is proposed as follows which can be used by utilities to plan their networks optimally:(1) optimal network reconfiguration and capacitor placement simultaneously; (2) first, network reconfiguration and then capacitor placement; (3) first, capacitor placement and then network reconfiguration.
The purpose of the automated energy monitoring system with a converged computing model are to improve the energy efficiency of enterprises, reduction of energy losses, optimization of energy consumption.
For the two models the optimization algorithm found 21,968 (loss-of-signal) and 23,121 (interference) valid parameter sets.
This paper discusses and evaluates simulated photovoltaic power output and battery state of charge profiles, using estimated climate data and electricity load profiles for the Altiplanic highland location of Patacamaya in Bolivia to determine the loss of load probability as optimization parameter.
