Using Evolutionary Algorithms for Hyperparameter Tuning and Network Reduction Techniques to Classify Core Porosity Classes Based on Petrographical Descriptions

2019 ◽  
pp. 750-757
Author(s):  
Tommy Liu ◽  
Jo Plested
Keyword(s):  
Evolutionary Algorithms ◽  
Network Reduction ◽  
Reduction Techniques

scholarly journals Transient stability enhancement using thyristor controlled series compensator

2019 ◽  
Vol 9 (2) ◽  
pp. 884 ◽  
Author(s):  
Surender Reddy Salkuti
Keyword(s):  
Power System ◽  
Transient Stability ◽  
Stability Margin ◽  
Stable Operation ◽  
Step Method ◽  
Network Reduction ◽  
Reduction Techniques ◽  
Linear Differential ◽  
Thyristor Controlled Series Compensator ◽  
Bus System

Stability is an important issue which determines the stable operation of power system. At present, the most practical available method of transient stability analysis is time domain simulation, in which the non-linear differential equations are solved by step by step method or network reduction techniques. In this paper, FACTS devices are used  in the existing system for effective utilization of existing transmission resources. In this paper, the studies have been carried out in order to improve the transient stability of 5 bus system, and Western System Coordinating Council (WSCC) 9 bus system with fixed compensation on various lines, and the optimal location has been investigated for better results. To improve the transient stability margin further, a Thyristor Controlled Series Compensator (TCSC) has been used, and the results shows the effectiveness of the application of TCSC in improving the transient stability of power system.

scholarly journals Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 465
Author(s):  
Katharina Baum ◽  
Jagath C. Rajapakse ◽  
Francisco Azuaje
Keyword(s):  
Molecular Mechanisms ◽  
Network Connectivity ◽  
Biological Data ◽  
Global Network ◽  
Molecular Networks ◽  
Network Reduction ◽  
Cancer Data ◽  
Reduction Techniques ◽  
Biomolecular Networks ◽  
Block Models

Background: Biological entities such as genes, promoters, mRNA, metabolites or proteins do not act alone, but in concert in their network context. Modules, i.e., groups of nodes with similar topological properties in these networks characterize important biological functions of the underlying biomolecular system. Edges in such molecular networks represent regulatory and physical interactions, and comparing them between conditions provides valuable information on differential molecular mechanisms. However, biological data is inherently noisy and network reduction techniques can propagate errors particularly to the level of edges. We aim to improve the analysis of networks of biological molecules by deriving modules together with edge relevance estimations that are based on global network characteristics. Methods: The key challenge we address here is investigating the capability of stochastic block models (SBMs) for representing and analyzing different types of biomolecular networks. Fitting them to SBMs both delivers modules of the networks and enables the derivation of edge confidence scores, and it has not yet been investigated for analyzing biomolecular networks. We apply SBM-based analysis independently to three correlation-based networks of breast cancer data originating from high-throughput measurements of different molecular layers: either transcriptomics, proteomics, or metabolomics. The networks were reduced by thresholding for correlation significance or by requirements on scale-freeness.  Results and discussion: We find that the networks are best represented by the hierarchical version of the SBM, and many of the predicted blocks have a biologically and phenotypically relevant functional annotation. The edge confidence scores are overall in concordance with the biological evidence given by the measurements. We conclude that biomolecular networks can be appropriately represented and analyzed by fitting SBMs. As the SBM-derived edge confidence scores are based on global network connectivity characteristics and potential hierarchies within the biomolecular networks are considered, they could be used as additional, integrated features in network-based data comparisons.

scholarly journals Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 465
Author(s):  
Katharina Baum ◽  
Jagath C. Rajapakse ◽  
Francisco Azuaje
Keyword(s):  
Molecular Mechanisms ◽  
Network Connectivity ◽  
Biological Data ◽  
Global Network ◽  
Molecular Networks ◽  
Network Reduction ◽  
Cancer Data ◽  
Reduction Techniques ◽  
Biomolecular Networks ◽  
Block Models

Background: Biological entities such as genes, promoters, mRNA, metabolites or proteins do not act alone, but in concert in their network context. Modules, i.e., groups of nodes with similar topological properties in these networks characterize important biological functions of the underlying biomolecular system. Edges in such molecular networks represent regulatory and physical interactions, and comparing them between conditions provides valuable information on differential molecular mechanisms. However, biological data is inherently noisy and network reduction techniques can propagate errors particularly to the level of edges. We aim to improve the analysis of networks of biological molecules by deriving modules together with edge relevance estimations that are based on global network characteristics.  Methods: We propose to fit the networks to stochastic block models (SBM), a method that has not yet been investigated for the analysis of biomolecular networks. This procedure both delivers modules of the networks and enables the derivation of edge confidence scores. We apply it to correlation-based networks of breast cancer data originating from high-throughput measurements of diverse molecular layers such as transcriptomics, proteomics, and metabolomics. The networks were reduced by thresholding for correlation significance or by requirements on scale-freeness.  Results and discussion: We find that the networks are best represented by the hierarchical version of the SBM, and many of the predicted blocks have a biological meaning according to functional annotation. The edge confidence scores are overall in concordance with the biological evidence given by the measurements. As they are based on global network connectivity characteristics and potential hierarchies within the biomolecular networks are taken into account, they could be used as additional, integrated features in network-based data comparisons. Their tight relationship to edge existence probabilities can be exploited to predict missing or spurious edges in order to improve the network representation of the underlying biological system.

Catalogue reduction techniques

1978 ◽  
Vol 48 ◽  
pp. 389-390 ◽  
Author(s):  
Chr. de Vegt
Keyword(s):  
Adjustment Process ◽  
Proper Motions ◽  
Measuring Process ◽  
Aerial Photogrammetry ◽  
Reduction Techniques ◽  
Observational Technique ◽  
The Common ◽  
Astrometric Data

AbstractReduction techniques as applied to astrometric data material tend to split up traditionally into at least two different classes according to the observational technique used, namely transit circle observations and photographic observations. Although it is not realized fully in practice at present, the application of a blockadjustment technique for all kind of catalogue reductions is suggested. The term blockadjustment shall denote in this context the common adjustment of the principal unknowns which are the positions, proper motions and certain reduction parameters modelling the systematic properties of the observational process. Especially for old epoch catalogue data we frequently meet the situation that no independent detailed information on the telescope properties and other instrumental parameters, describing for example the measuring process, is available from special calibration observations or measurements; therefore the adjustment process should be highly self-calibrating, that means: all necessary information has to be extracted from the catalogue data themselves. Successful applications of this concept have been made already in the field of aerial photogrammetry.

Opponent Center-Surround Contrast: Colour to Grey Conversion

2020 ◽  
Vol 2020 (1) ◽  
pp. 105-108
Author(s):  
Ali Alsam
Keyword(s):  
Evolutionary Algorithms ◽  
Three Dimensional ◽  
The State ◽  
Colour Space ◽  
Vision Science ◽  
State Of Art ◽  
Over Time

Vision is the science that informs us about the biological and evolutionary algorithms that our eyes, opticnerves and brains have chosen over time to see. This article is an attempt to solve the problem of colour to grey conversion, by borrowing ideas from vision science. We introduce an algorithm that measures contrast along the opponent colour directions and use the results to combine a three dimensional colour space into a grey. The results indicate that the proposed algorithm competes with the state of art algorithms.

PVT-Biomass-Battery systems optimized with evolutionary algorithms for small and highly shaded environments

2016 ◽  
pp. 568-573
Author(s):  
J.A. Fernández Fernández ◽  
P. González-Rodelas ◽  
E. Alameda-Hernández
Keyword(s):  
Evolutionary Algorithms ◽  
Battery Systems
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