Combined evidence model to enhance pavement condition prediction from highly uncertain sensor data

2022 ◽  
Vol 217 ◽  
pp. 108031
Author(s):  
William Seites-Rundlett ◽  
Mohammad Z. Bashar ◽  
Cristina Torres-Machi ◽  
Ross B. Corotis
Keyword(s):  
Sensor Data ◽  
Pavement Condition ◽  
Evidence Model

The Vehicle Adjustable Suspension System Based on Data Mining

2013 ◽  
Vol 416-417 ◽  
pp. 790-795
Author(s):  
Gu Xiong Li ◽  
Kai Huang ◽  
Kan Feng Huang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Bp Neural Network ◽  
Ride Comfort ◽  
Suspension System ◽  
Experimental Results ◽  
Sensor Data ◽  
Pavement Condition ◽  
Good Outcome ◽  
Control Accuracy

One being developed vehicle adjustable suspension system, need to predict pavement condition then automatically adjust each suspension height, in order to ensure control accuracy and ride comfort. This paper proposed a method using BP neural network to predict the vehicle height sensor data of each wheel suspension. The experimental results show that, the proposed algorithm is practical and reliable, and good outcome have been achieved in the application of instruction carriage.

Auditory visualization of landmine detector sensor data

2009 ◽  
Author(s):  
Bradley M. Davis ◽  
Woodrow W. Winchester ◽  
Jason D. Zedlitz
Keyword(s):  
Sensor Data

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

VALIDITY OF GARBER MODEL IN PREDICTING PAVEMENT CONDITION INDEX OF FLEXIBLE PAVEMENT IN KERBALA CITY

2018 ◽  
Vol 09 (02) ◽  
pp. 139-151
Author(s):  
Hussein Ewadh ◽  
◽  
Raid Almuhanna ◽  
Saja Alasadi ◽  
◽  
...  
Keyword(s):  
Condition Index ◽  
Flexible Pavement ◽  
Pavement Condition ◽  
Pavement Condition Index

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

Analysis of Space Sensor Data.

1994 ◽  
Author(s):  
Andrew J. Mazzella ◽  
Delorey Jr. ◽  
Dennis E.
Keyword(s):  
Sensor Data
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