scholarly journals Classifying and analysis of random composites using structural sums feature vector

2019 ◽  
Vol 475 (2225) ◽  
pp. 20180698 ◽  
Author(s):  
Wojciech Nawalaniec
Keyword(s):  
Materials Science ◽  
Feature Vector ◽  
Effective Conductivity ◽  
Feature Space ◽  
Two Dimensional ◽  
Learning Tools ◽  
Classification Problems ◽  
Random Composites ◽  
Random Structures ◽  
Computational Materials

The main goal of this paper is to present the application of structural sums, mathematical objects originating from the computational materials science, in construction of a feature space vector of two-dimensional random composites simulated by distributions of non-overlapping discs on the plane. Construction of the feature vector enables the immediate application of machine learning tools and data analysis techniques to random structures. In order to present the accuracy and the potential of structural sums as geometry descriptors, we apply them to classification problems comprising composites with circular inclusions as well as composites with shapes formed by discs. As an application, we perform the analysis of different models of composites in order to formulate the irregularity measure of random structures. We also visualize the relationship between the effective conductivity of two-dimensional composites and the geometry of inclusions.

scholarly journals Effective Conductivity of Densely Packed Disks and Energy of Graphs

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2161
Author(s):  
Wojciech Nawalaniec ◽  
Katarzyna Necka ◽  
Vladimir Mityushev
Keyword(s):  
Effective Conductivity ◽  
Periodic Structures ◽  
Voronoi Tessellation ◽  
Two Dimensional ◽  
Random Composites ◽  
Geometrical Information

The theory of structural approximations is extended to two-dimensional double periodic structures and applied to determination of the effective conductivity of densely packed disks. Statistical simulations of non-overlapping disks with the different degrees of clusterization are considered. The obtained results shows that the distribution of inclusions in a composite, as an amount of geometrical information, remains in the discrete corresponding Voronoi tessellation, hence, precisely determines the effective conductivity for random composites.

scholarly journals A branch & bound algorithm to determine optimal bivariate splits for oblique decision tree induction

2021 ◽  
Author(s):  
Ferdinand Bollwein ◽  
Stephan Westphal
Keyword(s):  
Decision Tree ◽  
Feature Space ◽  
Classification Problems ◽  
Decision Tree Induction ◽  
Single Attribute ◽  
Global Optimal ◽  
The Individual ◽  
Tree Building ◽  
Very High ◽  
Multiclass Classification Problems

AbstractUnivariate decision tree induction methods for multiclass classification problems such as CART, C4.5 and ID3 continue to be very popular in the context of machine learning due to their major benefit of being easy to interpret. However, as these trees only consider a single attribute per node, they often get quite large which lowers their explanatory value. Oblique decision tree building algorithms, which divide the feature space by multidimensional hyperplanes, often produce much smaller trees but the individual splits are hard to interpret. Moreover, the effort of finding optimal oblique splits is very high such that heuristics have to be applied to determine local optimal solutions. In this work, we introduce an effective branch and bound procedure to determine global optimal bivariate oblique splits for concave impurity measures. Decision trees based on these bivariate oblique splits remain fairly interpretable due to the restriction to two attributes per split. The resulting trees are significantly smaller and more accurate than their univariate counterparts due to their ability of adapting better to the underlying data and capturing interactions of attribute pairs. Moreover, our evaluation shows that our algorithm even outperforms algorithms based on heuristically obtained multivariate oblique splits despite the fact that we are focusing on two attributes only.

Emotion recognition using time–frequency ridges of EEG signals based on multivariate synchrosqueezing transform

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ahmet Mert ◽  
Hasan Huseyin Celik
Keyword(s):  
Emotion Recognition ◽  
Feature Vector ◽  
Dimensional Space ◽  
Computational Cost ◽  
Feature Space ◽  
Maximum Energy ◽  
Machine Learning Algorithms ◽  
Eeg Signals ◽  
Time Frequency ◽  
Synchrosqueezing Transform

Abstract The feasibility of using time–frequency (TF) ridges estimation is investigated on multi-channel electroencephalogram (EEG) signals for emotional recognition. Without decreasing accuracy rate of the valence/arousal recognition, the informative component extraction with low computational cost will be examined using multivariate ridge estimation. The advanced TF representation technique called multivariate synchrosqueezing transform (MSST) is used to obtain well-localized components of multi-channel EEG signals. Maximum-energy components in the 2D TF distribution are determined using TF-ridges estimation to extract instantaneous frequency and instantaneous amplitude, respectively. The statistical values of the estimated ridges are used as a feature vector to the inputs of machine learning algorithms. Thus, component information in multi-channel EEG signals can be captured and compressed into low dimensional space for emotion recognition. Mean and variance values of the five maximum-energy ridges in the MSST based TF distribution are adopted as feature vector. Properties of five TF-ridges in frequency and energy plane (e.g., mean frequency, frequency deviation, mean energy, and energy deviation over time) are computed to obtain 20-dimensional feature space. The proposed method is performed on the DEAP emotional EEG recordings for benchmarking, and the recognition rates are yielded up to 71.55, and 70.02% for high/low arousal, and high/low valence, respectively.

scholarly journals Two-dimensional Fourier Transform Technique for analysing Random Structures

Nature ◽  
1961 ◽  
Vol 190 (4774) ◽  
pp. 431-431
Author(s):  
DOUGLAS M. C. MACEWAN ◽  
H. H. SUTHERLAND
Keyword(s):  
Fourier Transform ◽  
Two Dimensional ◽  
Random Structures

scholarly journals Two-dimensional based hybrid materials for photocatalytic conversion of carbon dioxide into hydrocarbon fuels: A mini review

2021 ◽  
Vol 22 (1) ◽  
pp. 132-140
Author(s):  
Kannan Karthik ◽  
Devi Radhika ◽  
D. Gnanasangeetha ◽  
K. Gurushankar ◽  
Md Enamul Hoque
Keyword(s):  
Carbon Dioxide ◽  
Metal Oxide ◽  
Hybrid Materials ◽  
Environmental Sustainability ◽  
Materials Science ◽  
Two Dimensional ◽  
Carbon Dioxide Conversion ◽  
Two Dimensional Materials ◽  
The Impact ◽  
Material Composite

Carbon dioxide conversion to chemicals and fuels based on two-dimensional based hybrid materials will present a thorough discussion of the physics, chemistry, and electrochemical science behind the new and important area of materials science, energy, and environmental sustainability. The tremendous opportunities for two-dimensional based hybrid materials in the photocatalytic carbon dioxide conversion field come up from their huge number of applications. In the carbon dioxide conversion field, nanostructured metal oxide with a two-dimensional material composite system must meet assured design and functional criteria, as well as electrical and mechanical properties. The whole content of the proposed review is anticipated to build on what has been learned in elementary courses about synthesizing two-dimensional nanomaterials, metal oxide with composites, carbon dioxide conversion requirements, uses of two-dimensional materials with nanocomposites in carbon dioxide conversion as well as fuels and the major mechanisms involved during each application. The impact of hybrid materials and synergistic composite mixtures which are used extensively or show promising outcomes in the photocatalytic carbon dioxide conversion field will also be discussed.

Mentoring Undergraduate Students in Computational Materials Research

2015 ◽  
Vol 1762 ◽  
Author(s):  
Jie Zou
Keyword(s):  
Undergraduate Students ◽  
Experimental Research ◽  
Materials Science ◽  
Computational Materials Science ◽  
Undergraduate Institutions ◽  
Materials Research ◽  
Computational Research ◽  
Computational Materials ◽  
Eastern Illinois University ◽  
Physics Department

ABSTRACTComputation has become an increasingly important tool in materials science. Compared to experimental research, which requires facilities that are often beyond the financial capability of primarily-undergraduate institutions, computation provides a more affordable approach. In the Physics Department at Eastern Illinois University (EIU), students have opportunities to participate in computational materials research. In this paper, I will discuss our approach to involving undergraduate students in this area. Specifically, I will discuss (i) how to prepare undergraduate students for computational research, (ii) how to motivate and recruit students to participate in computational research, and (iii) how to select and design undergraduate projects in computational materials science. Suggestions on how similar approaches can be implemented at other institutions are also given.

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