Two-Stage Optimization Method for Energy Loss Minimization in Microgrid Based on Smart Power Management Scheme of PHEVs

2016 ◽  
Vol 7 (3) ◽  
pp. 1268-1276 ◽  
Author(s):  
Hamed Nafisi ◽  
Seyed Mohammad Mousavi Agah ◽  
Hossien Askarian Abyaneh ◽  
Mehrdad Abedi
Keyword(s):  
Energy Loss ◽  
Power Management ◽  
Optimization Method ◽  
Loss Minimization ◽  
Smart Power ◽  
Two Stage ◽  
Management Scheme

scholarly journals A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Alejandra Ríos ◽  
Eusebio E. Hernández ◽  
S. Ivvan Valdez
Keyword(s):  
Genetic Algorithm ◽  
Performance Metrics ◽  
Tracking Error ◽  
Parallel Manipulators ◽  
Optimization Method ◽  
Statistical Hypothesis ◽  
Two Stage ◽  
Multi Objective ◽  
Second Stage ◽  
Conflicting Objectives

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

A Two-Stage Optimization Method for the Stencil Printing Process Based on Neural Network and Response Surface Method

2010 ◽  
Vol 156-157 ◽  
pp. 10-17 ◽  
Author(s):  
Er Shun Pan ◽  
Yao Jin ◽  
Zhao Mei ◽  
Ying Wang
Keyword(s):  
Neural Network ◽  
Response Surface ◽  
Response Surface Method ◽  
Model Fitting ◽  
Optimization Method ◽  
Small Sample ◽  
Two Stage ◽  
Stencil Printing ◽  
Printing Process ◽  
Surface Method

A stencil printing process (SPP) optimization problem is studied in this paper. Due to the limitation that neural network requires a large number of samples for the accurate model fitting, a two-stage SPP optimization method is proposed. The design interval can be reduced with small sample by using neural network. In this reduced design interval , response surface method is adopted to obtain the accurate mathematical SPP model. The concept of confidence level is introduced to make the proposed model robust. An interactive method is used to solve the model. The proposed method is compared with the one-stage optimization method and the results show that the proposed method achieves a better performance on each objective.

scholarly journals A novel two-stage optimization method for beam–plate structure design

2016 ◽  
Vol 8 (11) ◽  
pp. 168781401667956 ◽  
Author(s):  
Kai Li ◽  
Yunlong Wang ◽  
Yan Lin ◽  
Wei Xu ◽  
Manting Liu
Keyword(s):  
Optimization Method ◽  
Structure Design ◽  
Two Stage ◽  
Plate Structure

A Low-Power High-Linear Time-to-Digital Converter for Measuring R-R Intervals in ECG Signal Optimized by Neural Network and TLBO Algorithm

2018 ◽  
Vol 28 (02) ◽  
pp. 1950021
Author(s):  
B. Ghanavati ◽  
E. Abiri ◽  
M. R. Salehi ◽  
N. Azhdari
Keyword(s):  
Neural Network ◽  
Dynamic Range ◽  
Linear Time ◽  
Optimization Method ◽  
Cmos Technology ◽  
Time Interval ◽  
Digital Converter ◽  
Two Stage ◽  
Time To Digital Converter ◽  
Tlbo Algorithm

In this paper, a two-stage time interpolation time-to-digital converter (TDC) is proposed to achieve adequate resolution and wide dynamic range for measuring R-R intervals in QRS detection. The architecture is based on a coarse counter and a couple of two-stage interpolator circuit in order to improve the conversion linearity. The proposed TDC is modeled with the neural network, while the teacher–learner-based optimization algorithm (TLBO) is used to optimize the integral nonlinearity (INL) of the proposed TDC. The proposed optimization method shows a characteristic close to the ideal output of the TDC behavior over a wide input range. Using the achieved results of the TLBO algorithm simulation results using CADENCE VIRTUOSO and standard 180[Formula: see text]nm CMOS technology shows 1.2[Formula: see text]s dynamic range, 100[Formula: see text]ns resolution, 0.19[Formula: see text]mW power consumption and area of 0.16[Formula: see text]mm2. The proposed circuit can find application in biomedical engineering systems and other fields where long and accurate time interval measurement is needed.

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