Mix and match overlay optimization strategy for advanced lithography tools (193i and EUV)

2012 ◽  
Author(s):  
David Laidler ◽  
Koen D'havé ◽  
Jan Hermans ◽  
Shaunee Cheng
Keyword(s):  
Optimization Strategy ◽  
Mix And Match

Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

2020 ◽  
pp. 1-11
Author(s):  
Wenjuan Ma ◽  
Xuesi Zhao ◽  
Yuxiu Guo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Education Reform ◽  
Traditional Education ◽  
Machine Learning Algorithms ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Fish School ◽  
Tlbo Algorithm ◽  
Neural Network System

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

Simulation Tool for Assignment Models: SIMASI

2014 ◽  
Vol 13 (8) ◽  
pp. 4723-4728
Author(s):  
Pratiksha Saxena ◽  
Smt. Anjali
Keyword(s):  
Optimization Procedure ◽  
Simulation Tool ◽  
Optimization Strategy ◽  
Performance Measurements ◽  
Integrated Simulation ◽  
Assignment Model ◽  
Classical Models ◽  
Simulation Results ◽  
User Friendly ◽  
Assignment Models

In this paper, an integrated simulation optimization model for the assignment problems is developed. An effective algorithm is developed to evaluate and analyze the back-end stored simulation results. This paper proposes simulation tool SIMASI (Simulation of assignment models) to simulate assignment models. SIMASI is a tool which simulates and computes the results of different assignment models. This tool is programmed in DOT.NET and is based on analytical approach to guide optimization strategy. Objective of this paper is to provide a user friendly simulation tool which gives optimized assignment model results. Simulation is carried out by providing the required values of matrix for resource and destination requirements and result is stored in the database for further comparison and study. Result is obtained in terms of the performance measurements of classical models of assignment system. This simulation tool is interfaced with an optimization procedure based on classical models of assignment system. The simulation results are obtained and analyzed rigorously with the help of numerical examples. 

Hybrid optimization strategy beyond local optima in aerospace panel designs

AIAA Journal ◽  
1999 ◽  
Vol 37 ◽  
pp. 588-593
Author(s):  
K. L. Chan ◽  
David Kennedy ◽  
Fred W. Williams
Keyword(s):  
Hybrid Optimization ◽  
Optimization Strategy ◽  
Local Optima

scholarly journals Vibrionaceae core, shell and cloud genes are non-randomly distributed on Chr 1: An hypothesis that links the genomic location of genes with their intracellular placement

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Cecilie Bækkedal Sonnenberg ◽  
Tim Kahlke ◽  
Peik Haugen
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Fast Growth ◽  
Gene Copy ◽  
Distribution Data ◽  
Optimization Strategy ◽  
Gene Distribution ◽  
Fast Growing ◽  
Genomic Location ◽  
Growing Conditions

Abstract Background The genome of Vibrionaceae bacteria, which consists of two circular chromosomes, is replicated in a highly ordered fashion. In fast-growing bacteria, multifork replication results in higher gene copy numbers and increased expression of genes located close to the origin of replication of Chr 1 (ori1). This is believed to be a growth optimization strategy to satisfy the high demand of essential growth factors during fast growth. The relationship between ori1-proximate growth-related genes and gene expression during fast growth has been investigated by many researchers. However, it remains unclear which other gene categories that are present close to ori1 and if expression of all ori1-proximate genes is increased during fast growth, or if expression is selectively elevated for certain gene categories. Results We calculated the pangenome of all complete genomes from the Vibrionaceae family and mapped the four pangene categories, core, softcore, shell and cloud, to their chromosomal positions. This revealed that core and softcore genes were found heavily biased towards ori1, while shell genes were overrepresented at the opposite part of Chr 1 (i.e., close to ter1). RNA-seq of Aliivibrio salmonicida and Vibrio natriegens showed global gene expression patterns that consistently correlated with chromosomal distance to ori1. Despite a biased gene distribution pattern, all pangene categories contributed to a skewed expression pattern at fast-growing conditions, whereas at slow-growing conditions, softcore, shell and cloud genes were responsible for elevated expression. Conclusion The pangene categories were non-randomly organized on Chr 1, with an overrepresentation of core and softcore genes around ori1, and overrepresentation of shell and cloud genes around ter1. Furthermore, we mapped our gene distribution data on to the intracellular positioning of chromatin described for V. cholerae, and found that core/softcore and shell/cloud genes appear enriched at two spatially separated intracellular regions. Based on these observations, we hypothesize that there is a link between the genomic location of genes and their cellular placement.

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