Compensation method of FOG temperature drift with improved support vector machine

2018 ◽  
Vol 47 (5) ◽  
pp. 522003
Author(s):  
吴军伟 Wu Junwei ◽  
缪玲娟 Miao Lingjuan ◽  
李福胜 Li Fusheng ◽  
沈 军 Shen Jun
Keyword(s):  
Support Vector Machine ◽  
Compensation Method ◽  
Support Vector ◽  
Temperature Drift ◽  
Improved Support Vector Machine

scholarly journals Temperature drift modeling and compensation of micro-electro-mechanical system gyroscope based on improved support vector machine algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 155014772090819
Author(s):  
Xinwang Wang ◽  
Huiliang Cao
Keyword(s):  
Support Vector Machine ◽  
Mechanical System ◽  
Micro Electro Mechanical System ◽  
Support Vector ◽  
Temperature Drift ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Noise Characteristics ◽  
Improved Support Vector Machine

This article suggested two methods to compensate for the temperature drift of the micro-electro-mechanical system gyroscopes, which are support vector machine method and C-means support vector machine. The output of X axis which was ranged from −40°C to 60°C based on the micro-electro-mechanical system gyroscope is reduced and analyzed in this article. The results showed the correctness of the two methods. The final results indicate that when the temperature is ranged from −40°C to 60°C, the factor of B is reduced from 0.424 [Formula: see text] to 0.02194 [Formula: see text], and when the temperature is ranged from 60°C to −40°C, the factor of B is reduced from 0.1056 [Formula: see text] to 0.0329 [Formula: see text], and the temperature drift trend and noise characteristics are improved clearly.

scholarly journals Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Kun Zhang ◽  
Minrui Fei ◽  
Xin Li ◽  
Huiyu Zhou
Keyword(s):  
Support Vector Machine ◽  
Mean Shift ◽  
Least Square ◽  
Support Vector ◽  
Svm Classifier ◽  
Least Square Estimation ◽  
Unstructured Environments ◽  
Novel Approach ◽  
Traditional Approaches ◽  
Improved Support Vector Machine

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

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