A Terrain Classification Method for POLSAR Images Based on Modified Scattering Parameters

2019 ◽  
pp. 149-153
Author(s):  
Shuang Zhang ◽  
Lu Wang ◽  
Xiangchuan Yu ◽  
Bo Chen
Keyword(s):  
Classification Method ◽  
Terrain Classification ◽  
Scattering Parameters

scholarly journals Adaptive online terrain classification method for mobile robot based on vibration signals

2021 ◽  
Vol 18 (6) ◽  
pp. 172988142110620
Author(s):  
Mingming Wang ◽  
Liming Ye ◽  
Xiaoyun Sun
Keyword(s):  
Mobile Robot ◽  
Vibration Signal ◽  
Classification Model ◽  
Classification Method ◽  
Terrain Classification ◽  
Vibration Signals ◽  
Time Frequency ◽  
Combined Features ◽  
Different Dimensions ◽  
The Time Domain

To improve the accuracy of terrain classification during mobile robot operation, an adaptive online terrain classification method based on vibration signals is proposed. First, the time domain and the combined features of the time, frequency, and time–frequency domains in the original vibration signal are extracted. These are adopted as the input of the random forest algorithm to generate classification models with different dimensions. Then, by judging the relationship between the current speed of the mobile robot and its critical speed, the classification model of different dimensions is adaptively selected for online classification. Offline and online experiments are conducted for four different terrains. The experimental results show that the proposed method can effectively avoid the self-vibration interference caused by an increase in the robot’s moving speed and achieve higher terrain classification accuracy.

An Automatic Classification Method for Involuntary and Two Types of Voluntary Blinks

2016 ◽  
Vol 136 (9) ◽  
pp. 1350-1358 ◽  
Author(s):  
Hironobu Sato ◽  
Kiyohiko Abe ◽  
Shoichi Ohi ◽  
Minoru Ohyama
Keyword(s):  
Automatic Classification ◽  
Classification Method

Bone Density Classification Method for Robotic Drilling System

2013 ◽  
Vol 133 (3) ◽  
pp. 328-334 ◽  
Author(s):  
Koyo Yu ◽  
Yuki Saito ◽  
Yusuke Kasahara ◽  
Hiromasa Kawana ◽  
Shin Usuda ◽  
...  
Keyword(s):  
Bone Density ◽  
Classification Method ◽  
Robotic Drilling ◽  
Drilling System

Experimental investigation and modelling of light scattering in a-Si:H solar cells deposited on glass/ZnO:Al substrates

2002 ◽  
Vol 715 ◽  
Author(s):  
J. Krc ◽  
M. Zeman ◽  
O. Kluth ◽  
F. Smole ◽  
M. Topic
Keyword(s):  
Surface Roughness ◽  
Experimental Investigation ◽  
Solar Cells ◽  
Angular Distribution ◽  
Light Scattering ◽  
Optical Model ◽  
Distribution Functions ◽  
Interface Roughness ◽  
Scattering Parameters ◽  
Good Agreement

AbstractThe descriptive scattering parameters, haze and angular distribution functions of textured ZnO:Al transparent conductive oxides with different surface roughness are measured. An approach to determine the scattering parameters of all internal interfaces in p-i-n a-Si:H solar cells deposited on the glass/ZnO:Al substrates is presented. Using the determined scattering parameters as the input parameters of the optical model, a good agreement between the measured and simulated quantum efficiencies of the p-i-n a-Si:H solar cells with different interface roughness is achieved.

Sử dụng phương pháp thang điểm có trọng số đánh giá tổng hợp tài nguyên khí hậu cho du lịch, nghỉ dưỡng (Tại một số trung tâm du lịch ở Việt Nam)

2008 ◽  
Vol 30 (4) ◽  
pp. 356-362
Author(s):  
Nguyễn Khanh Vân
Keyword(s):  
Classification Method ◽  
Viet Nam ◽  
Bioclimatic Conditions

Using the weighted classification method to evaluate bioclimatic conditions for tourism and concevalescence (in some tourism centers of Vietnam)

Single Image Dehazing by Predicting Atmospheric Scattering Parameters

2020 ◽  
Vol 2020 (1) ◽  
pp. 74-77
Author(s):  
Simone Bianco ◽  
Luigi Celona ◽  
Flavio Piccoli
Keyword(s):  
Reference Image ◽  
Single Image ◽  
Scattering Parameters ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Local Structures ◽  
Full Reference ◽  
Reconstruction Quality ◽  
Single Image Dehazing

In this work we propose a method for single image dehazing that exploits a physical model to recover the haze-free image by estimating the atmospheric scattering parameters. Cycle consistency is used to further improve the reconstruction quality of local structures and objects in the scene as well. Experimental results on four real and synthetic hazy image datasets show the effectiveness of the proposed method in terms of two commonly used full-reference image quality metrics.

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