Support Vector Machine Based Approach for State Estimation of Iraqi Super Grid Network

2008 ◽  
Author(s):  
Afaneen A. Abod ◽  
Abdullah H. Abdullah ◽  
Mohammed K. Abd
Keyword(s):  
Support Vector Machine ◽  
State Estimation ◽  
Support Vector ◽  
Grid Network

Support Vector Machine State Estimation

2019 ◽  
Vol 33 (6) ◽  
pp. 517-530 ◽  
Author(s):  
Vedran Kirinčić ◽  
Ervin Čeperić ◽  
Saša Vlahinić ◽  
Jonatan Lerga
Keyword(s):  
Support Vector Machine ◽  
State Estimation ◽  
Support Vector

scholarly journals Transient thermal characteristic analysis and charging state estimation of lithium batteries for Automated Guided Vehicle during discharge

2019 ◽  
Vol 23 (5 Part A) ◽  
pp. 2731-2739
Author(s):  
Chunyuan Pian ◽  
Junfeng Liu ◽  
Hongzhi Zhao ◽  
Liwei Zhang
Keyword(s):  
Support Vector Machine ◽  
State Estimation ◽  
Lithium Batteries ◽  
Lithium Battery ◽  
Health Management ◽  
Thermal Characteristic ◽  
Support Vector ◽  
Automated Guided Vehicle ◽  
Discharge Process ◽  
Characteristic Analysis

The lithium batteries and their health management for automated guided vehicle power supply system are studied in depth in this paper. First, the transient heat generation for the discharge process of a lithium battery will cause it to work in an unhealthy state and non-linear conditions, seriously affecting the life expectancy. The thermal behavior for lithium battery discharge is studied in depth, and a reliable thermal model is constructed to provide a theoretical basis for designing a lithium battery health management system. Secondly, the accurate and reliable residual state estimation of the lithium battery cannot only provide visualized battery residual capacity, but also reflect the aging status of the lithium battery and other related information, and is one of the important functions to ensure the healthy operation of the lithium battery pack. A new support vector machine is proposed on account of the analysis of the equivalent circuit model of lithium battery, which combines genetic algorithm with particle swarm optimization to enhance the parameters of hybrid kernel function, to analyze accurately the charging status. Finally, the state-of-charge simulation of lithium batteries with variable current discharge is conducted, which proves that the support vector machine algorithm proposed in this paper can accurately judge the charging state of lithium batteries.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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