Scale classification of internal and external properties of metallic powders and composite materials based on them

1990 ◽  
Vol 29 (7) ◽  
pp. 552-558
Author(s):  
M. M. Khvorov ◽  
T. M. Shvets
Keyword(s):  
Composite Materials ◽  
Metallic Powders ◽  
Scale Classification

scholarly journals Evaluation of seabed mapping methods for fine-scale classification of extremely shallow benthic habitats – Application to the Venice Lagoon, Italy

2016 ◽  
Vol 170 ◽  
pp. 45-60 ◽  
Author(s):  
G. Montereale Gavazzi ◽  
F. Madricardo ◽  
L. Janowski ◽  
A. Kruss ◽  
P. Blondel ◽  
...  
Keyword(s):  
Venice Lagoon ◽  
Fine Scale ◽  
Benthic Habitats ◽  
Seabed Mapping ◽  
Scale Classification

scholarly journals Hierarchy concepts: classification and preparation strategies for zeolite containing materials with hierarchical porosity

2016 ◽  
Vol 45 (12) ◽  
pp. 3353-3376 ◽  
Author(s):  
Wilhelm Schwieger ◽  
Albert Gonche Machoke ◽  
Tobias Weissenberger ◽  
Amer Inayat ◽  
Thangaraj Selvam ◽  
...  
Keyword(s):  
Composite Materials ◽  
Hierarchically Porous ◽  
Hierarchical Porosity ◽  
Broad Overview

Starting from a basic classification of “hierarchical porosity” this review gives a broad overview of preparation routes towards hierarchically porous all-zeolite and zeolite containing composite materials.

Application of processing fiber-plastic composite washing

2021 ◽  
Vol 2021 (23) ◽  
pp. 214-224
Author(s):  
Artur Onyshchenko ◽  
◽  
Mykola Garkusha ◽  
Оlena Deli ◽  
◽  
...  
Keyword(s):  
Composite Materials ◽  
Operating Conditions ◽  
Structural Elements ◽  
Washing Machine ◽  
Normative Documents ◽  
Plastic Composite ◽  
Fiber Plastic ◽  
Fiberglass Composite ◽  
Composite Bridge

Introduction. Innovative, new materials are increasingly used in transport construction, among which composite materials are becoming widespread.Small bridges and elements of large bridges, such as roadway slabs, pavements, railings, composite reinforcement, reinforcement elements, are made of composite materials.Recently, the use of polymer composite materials for the manufacture of lightly loaded structural elements of transport structures, such as lighting poles, drainage trays, railings.Much attention should be paid to the fiberglass composite railing, which has a number of advantages over traditional metal fencing. Unfortunately, at present there are no clearly defined in Ukraine regulations on fiberglass composite fencing, so this topic is relevant and necessary for the transport industry.Problem Statement. From the literature analysis it is established that the railings of highways and sidewalks are in difficult operating conditions, are constantly exposed to aggressive environments - water, chemicals, salts.Goal. Increasing the durability of the railing by using new materials.Results. The analysis of production of a fiberglass profile is carried out. On the basis of the conducted researches the general requirements to a protection of fiberglass composite washing machine are established. On the basis of the current normative documents the classification of a protection on a place of installation, type of filling of a skeleton, a method of fastening of risers is developed. The paper presents the main parameters and dimensions of the fence. Material requirements are set. Methods of control of a protection with establishment of a technique of test of a protection on resistance to action of horizontal and vertical loadings are developed. Recommendations on installation and installation of a protection of fiberglass composite washing machine are offered.Conclusions. The research results were used in the development of technical conditions for the protection of fiberglass composite washing machine.Keywords: road, composite, bridge, fencing, profile, fiberglass, artificial construction

Damage Classification of Composites Using Machine Learning

2019 ◽  
Author(s):  
Shweta Dabetwar ◽  
Stephen Ekwaro-Osire ◽  
João Paulo Dias
Keyword(s):  
Machine Learning ◽  
Composite Materials ◽  
Random Forest ◽  
Condition Monitoring ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Damage Classification ◽  
Combining Data ◽  
Ultrasonic Measurements

Abstract Composite materials have tremendous and ever-increasing applications in complex engineering systems; thus, it is important to develop non-destructive and efficient condition monitoring methods to improve damage prediction, thereby avoiding catastrophic failures and reducing standby time. Nondestructive condition monitoring techniques when combined with machine learning applications can contribute towards the stated improvements. Thus, the research question taken into consideration for this paper is “Can machine learning techniques provide efficient damage classification of composite materials to improve condition monitoring using features extracted from acousto-ultrasonic measurements?” In order to answer this question, acoustic-ultrasonic signals in Carbon Fiber Reinforced Polymer (CFRP) composites for distinct damage levels were taken from NASA Ames prognostics data repository. Statistical condition indicators of the signals were used as features to train and test four traditional machine learning algorithms such as K-nearest neighbors, support vector machine, Decision Tree and Random Forest, and their performance was compared and discussed. Results showed higher accuracy for Random Forest with a strong dependency on the feature extraction/selection techniques employed. By combining data analysis from acoustic-ultrasonic measurements in composite materials with machine learning tools, this work contributes to the development of intelligent damage classification algorithms that can be applied to advanced online diagnostics and health management strategies of composite materials, operating under more complex working conditions.

Large-scale classification of major depressive disorder via distributed Lasso

2017 ◽  
Author(s):  
Dajiang Zhu ◽  
Qingyang Li ◽  
Brandalyn C. Riedel ◽  
Neda Jahanshad ◽  
Derrek P. Hibar ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Large Scale ◽  
Major Depressive ◽  
Scale Classification

scholarly journals Multi-scale classification of fluvial architecture: An example from the Palaeocene-Eocene Bighorn Basin, Wyoming

Sedimentology ◽  
2017 ◽  
Vol 64 (6) ◽  
pp. 1572-1596 ◽  
Author(s):  
Amanda Owen ◽  
Alena Ebinghaus ◽  
Adrian J. Hartley ◽  
Maurício G. M. Santos ◽  
Gary S. Weissmann
Keyword(s):  
Bighorn Basin ◽  
Multi Scale ◽  
Fluvial Architecture ◽  
Scale Classification

Large-scale classification of traffic signs under real-world conditions

2012 ◽  
Author(s):  
Lykele Hazelhoff ◽  
Ivo Creusen ◽  
Dennis van de Wouw ◽  
Peter H. N. de With
Keyword(s):  
Real World ◽  
Large Scale ◽  
Traffic Signs ◽  
Scale Classification

Multi-scale classification of disease using structural MRI and wavelet transform

NeuroImage ◽  
2012 ◽  
Vol 62 (1) ◽  
pp. 48-58 ◽  
Author(s):  
Kerstin Hackmack ◽  
Friedemann Paul ◽  
Martin Weygandt ◽  
Carsten Allefeld ◽  
John-Dylan Haynes
Keyword(s):  
Wavelet Transform ◽  
Structural Mri ◽  
Multi Scale ◽  
Scale Classification
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