Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
This paper describes our virtual fence system for goats. The present invention is a method of controlling goats without visible physical fences and monitoring their condition. Control occurs through affecting goats, using one or more sound signals and electric shocks when they attempt to enter a restricted zone. One of the best Machine Learning (ML) classifications named Support Vector Machines (SVM) is used to observe the condition. A virtual fence boundary can be of any geometrical shape. A smart collar on goats' necks can be detected by using a virtual fence application. Each smart collar consists of a global positioning system (GPS), an XBee communication module, an mp3 player, and an electrical shocker. Stimuli and classification results are presented from on-farm experiments with a goat equipped with smart collar. Using the proposed stimuli methods, we showed that the probability of a goat receiving an electrical stimulus following an audio cue (dog and emergency sounds) was low (20%) and declined over the testing period. Besides, the RBF kernel-based SVM classification model classified lying behavior with an extremely high classification accuracy ( of 1), whilst grazing, running, walking, and standing behaviors were also classified with a high accuracy ( of 0.95, 0.97, 0.81, and 0.8, respectively).
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480636 | PMC |
http://dx.doi.org/10.3390/s19071598 | DOI Listing |
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