Performance evaluation of multispectral analysis for surface material classification

2002 ◽  
Author(s):  
S.J. Ford ◽  
D.M. McKeown
Keyword(s):  
Performance Evaluation ◽  
Surface Material ◽  
Material Classification ◽  
Multispectral Analysis

Surface Material Classification Using Acceleration Signal

2021 ◽  
pp. 49-58
Author(s):  
Naveeja Sajeevan ◽  
M. Arathi Nair ◽  
R. Aravind Sekhar ◽  
K. G. Sreeni
Keyword(s):  
Surface Material ◽  
Acceleration Signal ◽  
Material Classification

Multimodal Feature-Based Surface Material Classification

2017 ◽  
Vol 10 (2) ◽  
pp. 226-239 ◽  
Author(s):  
Matti Strese ◽  
Clemens Schuwerk ◽  
Albert Iepure ◽  
Eckehard Steinbach
Keyword(s):  
Surface Material ◽  
Material Classification ◽  
Feature Based

scholarly journals Deep Learning for Surface Material Classification Using Haptic and Visual Information

2016 ◽  
Vol 18 (12) ◽  
pp. 2407-2416 ◽  
Author(s):  
Haitian Zheng ◽  
Lu Fang ◽  
Mengqi Ji ◽  
Matti Strese ◽  
Yigitcan Ozer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Visual Information ◽  
Surface Material ◽  
Material Classification

Surface Material Classification Using Landsat-8 OLI Image Acquired in Ulsan by the Different Machine Learning Techniques

2017 ◽  
Vol 865 ◽  
pp. 650-656
Author(s):  
Yun Jae Choung ◽  
Myung Hee Jo
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Machine Learning Techniques ◽  
Landsat 8 ◽  
Surface Material ◽  
Landsat 8 Oli ◽  
Landsat Images ◽  
Material Classification ◽  
Learning Techniques ◽  
The Given

Surface material classification is an important task for the preservation of land properties and the management of land development plans. The use of remotely sensed images is efficient for the surface material classification task without human access. This research aims to select the most appropriate machine learning technique for the surface material classification task using the remotely sensed images. In this research, the three different machine learning techniques (MD (Minimum Distance), MLC (Maximum Likelihood Classification), and SVM (Support Vector Machine)) were applied for surface material classification using the Landsat-8 OLI (Operational Land Imager) image acquired in Ulsan, South Korea, in the following steps. First, the training samples for each land cover in the given Landsat images were selected by manual labor. Next, the different machine learning techniques (MD, MLC, and SVM) were applied on the given Landsat images, respectively, for carrying out the surface material classification tasks. The accuracies of the three land cover classification maps generated by the different techniques were assessed using the ground truths. Finally, accuracy comparison was conducted for selecting the most suitable approach for classifying the various surface materials in Ulsan. The statistical results show that the SVM classifier is superior to the MD and MLC classifiers for carrying out surface material classification using the given Landsat-8 OLI image.

Multimodal Unknown Surface Material Classification and Its Application to Physical Reasoning

2021 ◽  
pp. 1-1
Author(s):  
Junhang Wei ◽  
Cui Shaowei ◽  
Peng Hao ◽  
Jingyi Hu ◽  
Shuo Wang ◽  
...  
Keyword(s):  
Surface Material ◽  
Physical Reasoning ◽  
Material Classification

scholarly journals Development of a multi-roller abrasive Cassava peeling machine

2020 ◽  
Vol 45 (3) ◽  
pp. 15-26
Author(s):  
John Edeh
Keyword(s):  
Mechanical Properties ◽  
Performance Evaluation ◽  
Moisture Content ◽  
Physical And Mechanical Properties ◽  
Throughput Capacity ◽  
Surface Material ◽  
Abrasive Surface ◽  
Engineering Standards ◽  
Operational Factors ◽  
Abrasive Property

A simple to operate, maintain and affordable multi-roller abrasive cassava peeling machine comprising a feed section, peeling chamber, abrasive surface, tuber discharge chute and waste peel chute was designed, fabricated and evaluated. The machine development was based on engineering standards and specification while considering crop, machine and operational factors to include the physical and mechanical properties of cassava tubers, abrasive property of peeling surface, material and speed of the machine. The machine was evaluated for performance with commonly used varieties NR 8082, 8083, 8208, 09/0581 and UMUCAS 38 having moisture content of 63.33%, 64.50%, 65.40%, 63.50% and 84.33% respectively. The performance evaluation results at a roller speed of 360rpm showed peeling efficiency of 91%, throughput capacity of 2.17kg/min, and a proportion by weight of peel of 0.9%. The cassava peeling machine fabricated, eliminated the drudgery, loss of time and tuber flesh encountered in manual peeling with better product quality when compared with the existing machines.

Material analysis techniques using the ion mill

1996 ◽  
Vol 54 ◽  
pp. 1034-1035
Author(s):  
J. P. Benedict ◽  
R. M. Anderson ◽  
S. J. Klepeis
Keyword(s):  
Polished Surface ◽  
Surface Material ◽  
Analysis Techniques ◽  
Material Analysis ◽  
Optical Inspection ◽  
Electron Microscopy Analysis ◽  
Microscopy Analysis ◽  
Argon Ions ◽  
The Impact ◽  
Scanning Electron

Ion mills equipped with flood guns can perform two important functions in material analysis; they can either remove material or deposit material. The ion mill holder shown in Fig. 1 is used to remove material from the polished surface of a sample for further optical inspection or SEM ( Scanning Electron Microscopy ) analysis. The sample is attached to a pohshing stud type SEM mount and placed in the ion mill holder with the polished surface of the sample pointing straight up, as shown in Fig 2. As the holder is rotating in the ion mill, Argon ions from the flood gun are directed down at the top of the sample. The impact of Argon ions against the surface of the sample causes some of the surface material to leave the sample at a material dependent, nonuniform rate. As a result, the polished surface will begin to develop topography during milling as fast sputtering materials leave behind depressions in the polished surface.

Fine Particle Mass Monitoring with Low-Cost Sensors: Corrections and Long-Term Performance Evaluation

2019 ◽  
Author(s):  
Carl Malings ◽  
Rebecca Tanzer ◽  
Aliaksei Hauryliuk ◽  
Provat K. Saha ◽  
Allen L. Robinson ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Fine Particle ◽  
Low Cost ◽  
Particle Mass ◽  
Long Term Performance ◽  
Term Performance
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