Loading Subspace Selection for Multidimensional Characterization Tests via Computational Experiments

2010 ◽  
Author(s):  
A. P. Iliopoulos ◽  
J. G. Michopoulos
Keyword(s):  
Strain State ◽  
Material Testing ◽  
High Dimensional ◽  
Virtual Experiments ◽  
Selection For ◽  
Subspace Selection ◽  
Strain Space ◽  
Systematic Program

To pursue characterization of composite materials, contemporary automated material testing machines are programmed to follow loading paths in multidimensional spaces. A computational methodology for selecting the best loading subspace among all those possible is formulated and presented in this paper. The criterion for subspace selection employed is based on the assessment of which among the possible subspaces generates the richest set of strain-states as compared to those of the union of all possible 4D loading spaces. A systematic program of simulation sequences of virtual experiments is presented and the concept of strain state cloud (SSC) is introduced as a high dimensional volumetric histogram describing the frequency of appearance of each strain state within the corresponding strain space. Comparison of the SSCs for each of the fifteen 4D subspaces relative to the full 6D space allows a ranking classification of each subspace. Based on this ranking we select the three top cases as being those considered for actual testing.

The Mechanical Properties of Soils with Uneven Grain-Size Distribution

2014 ◽  
Vol 1073-1076 ◽  
pp. 1689-1696
Author(s):  
Zaven Ter-Martirosyan ◽  
Anatoly Mirnyy ◽  
Armen Ter-Martirosyan
Keyword(s):  
Mechanical Properties ◽  
Laboratory Tests ◽  
Granulometric Composition ◽  
Strain State ◽  
Analytic Solutions ◽  
Diameter Ratio ◽  
Selection For ◽  
Stress States ◽  
The Impact

This issue deals with peculiarities of stress-strain state forming in a representative volume of inhomogeneous soil. Analytic solutions for describing such stress states and obtaining equivalent mechanical values for such massive are given. Basing on the performed triaxial laboratory tests the impact of diameter ratio, percentage, and contact between particles on mechanical properties of a mixture is estimated. As a conclusion some recommendations on using the research results in practical geotechnical engineering are given. The classification of inhomogeneous soils, based on granulometric data, allowing to estimate mechanical properties is presented, as a method of granulometric composition, humidity and density selection for artificial foundations.

scholarly journals K-means clustering based filter feature selection on high dimensional data

2016 ◽  
Vol 2 (1) ◽  
pp. 38 ◽  
Author(s):  
Dewi Pramudi Ismi ◽  
Shireen Panchoo ◽  
Murinto Murinto
Keyword(s):  
Feature Selection ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Economic Activities ◽  
Label Information ◽  
Data Points ◽  
Selection For ◽  
Computation Load ◽  
High Dimensional Datasets

With hundreds or thousands of features in high dimensional data, computational workload is challenging. In classification process, features which do not contribute significantly to prediction of classes, add to the computational workload. Therefore the aim of this paper is to use feature selection to decrease the computation load by reducing the size of high dimensional data. Selecting subsets of features which represent all features were used. Hence the process is two-fold; discarding irrelevant data and choosing one feature that representing a number of redundant features. There have been many studies regarding feature selection, for example backward feature selection and forward feature selection. In this study, a k-means clustering based feature selection is proposed. It is assumed that redundant features are located in the same cluster, whereas irrelevant features do not belong to any clusters. In this research, two different high dimensional datasets are used: 1) the Human Activity Recognition Using Smartphones (HAR) Dataset, containing 7352 data points each of 561 features and 2) the National Classification of Economic Activities Dataset, which contains 1080 data points each of 857 features. Both datasets provide class label information of each data point. Our experiment shows that k-means clustering based feature selection can be performed to produce subset of features. The latter returns more than 80% accuracy of classification result.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

scholarly journals Characterization of M dwarfs using optical mid-resolution spectra for exploration of small exoplanets

2020 ◽  
Author(s):  
Yohei Koizumi ◽  
Masayuki Kuzuhara ◽  
Masashi Omiya ◽  
Teruyuki Hirano ◽  
John Wisniewski ◽  
...  
Keyword(s):  
Sample Selection ◽  
Optical Spectra ◽  
Average Error ◽  
Spectral Energy ◽  
M Dwarfs ◽  
Energy Distributions ◽  
Fitting Procedures ◽  
Optical Region ◽  
Selection For

Abstract We present the optical spectra of 338 nearby M dwarfs, and compute their spectral types, effective temperatures (Teff), and radii. Our spectra were obtained using several optical spectrometers with spectral resolutions that range from 1200 to 10000. As many as 97% of the observed M-type dwarfs have a spectral type of M3–M6, with a typical error of 0.4 subtype, among which the spectral types M4–M5 are the most common. We infer the Teff of our sample by fitting our spectra with theoretical spectra from the PHOENIX model. Our inferred Teff is calibrated with the optical spectra of M dwarfs whose Teff have been well determined with the calibrations that are supported by previous interferometric observations. Our fitting procedures utilize the VO absorption band (7320–7570 Å) and the optical region (5000–8000 Å), yielding typical errors of 128 K (VO band) and 85 K (optical region). We also determine the radii of our sample from their spectral energy distributions. We find most of our sample stars have radii of <0.6 R⊙, with the average error being 3%. Our catalog enables efficient sample selection for exoplanet surveys around nearby M-type dwarfs.

scholarly journals Morphological and Ultrastructural Characterization of Hemocytes in an Insect Model, the Hematophagous Dipetalogaster maxima (Hemiptera: Reduviidae)

Insects ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 640
Author(s):  
Natalia R. Moyetta ◽  
Fabián O. Ramos ◽  
Jimena Leyria ◽  
Lilián E. Canavoso ◽  
Leonardo L. Fruttero
Keyword(s):  
Cell Biology ◽  
Cell Types ◽  
Transmission Electron ◽  
Ultrastructural Characterization ◽  
Current Classification ◽  
Contrast Microscopy ◽  
First Time ◽  
Controversial Aspect

Hemocytes, the cells present in the hemolymph of insects and other invertebrates, perform several physiological functions, including innate immunity. The current classification of hemocyte types is based mostly on morphological features; however, divergences have emerged among specialists in triatomines, the insect vectors of Chagas’ disease (Hemiptera: Reduviidae). Here, we have combined technical approaches in order to characterize the hemocytes from fifth instar nymphs of the triatomine Dipetalogaster maxima. Moreover, in this work we describe, for the first time, the ultrastructural features of D. maxima hemocytes. Using phase contrast microscopy of fresh preparations, five hemocyte populations were identified and further characterized by immunofluorescence, flow cytometry and transmission electron microscopy. The plasmatocytes and the granulocytes were the most abundant cell types, although prohemocytes, adipohemocytes and oenocytes were also found. This work sheds light on a controversial aspect of triatomine cell biology and physiology setting the basis for future in-depth studies directed to address hemocyte classification using non-microscopy-based markers.

Using class-based feature selection for the classification of hyperspectral data

2011 ◽  
Vol 32 (15) ◽  
pp. 4311-4326 ◽  
Author(s):  
Yasser Maghsoudi ◽  
Mohammad Javad Valadan Zoej ◽  
Michael Collins
Keyword(s):  
Feature Selection ◽  
Hyperspectral Data ◽  
Selection For
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