Feature Optimization to Recognize Telugu Handwritten Characters by Implementing DE and PSO Techniques

2017 ◽  
pp. 397-405 ◽  
Author(s):  
T. R. Vijaya Lakshmi ◽  
Panyam Narahari Sastry ◽  
T. V. Rajinikanth
Keyword(s):  
Feature Optimization

Point spread function based feature optimization and fusion for underwater objet recognition

2013 ◽  
Vol 26 (11) ◽  
pp. 944-952 ◽  
Author(s):  
Huibin Wang ◽  
Rong Zhang ◽  
Zhe Chen ◽  
Lizhong Xu ◽  
Jie Shen
Keyword(s):  
Point Spread Function ◽  
Point Spread ◽  
Spread Function ◽  
Feature Optimization

scholarly journals Windturbine blade fault diagnosis based on MFCC feature optimization strategy

2021 ◽  
Vol 675 (1) ◽  
pp. 012074
Author(s):  
Yuxing Wang ◽  
Jianxin Tan ◽  
Xiaoliang Qin ◽  
Zhanfei Hu ◽  
Yanwei Jing ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Optimization Strategy ◽  
Feature Optimization

Monocular Pseudo-LiDAR 3D Object Detection Method Based on Confidence and Feature optimization

2021 ◽  
Author(s):  
Jianlong Zhang ◽  
Guangzu Fang ◽  
Bin Wang ◽  
Chen Chen ◽  
Xinyu Guo ◽  
...  
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
3D Object ◽  
Feature Optimization ◽  
3D Object Detection

Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI

2021 ◽  
pp. 2150030
Author(s):  
Jing Jin ◽  
Hua Fang ◽  
Ian Daly ◽  
Ruocheng Xiao ◽  
Yangyang Miao ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Classification Performance ◽  
Minimum Covariance Determinant ◽  
Fisher Score ◽  
Practical Applications ◽  
Computer Interfaces ◽  
Highly Sensitive ◽  
Feature Optimization ◽  
The Common ◽  
Model Training

The common spatial patterns (CSP) algorithm is one of the most frequently used and effective spatial filtering methods for extracting relevant features for use in motor imagery brain–computer interfaces (MI-BCIs). However, the inherent defect of the traditional CSP algorithm is that it is highly sensitive to potential outliers, which adversely affects its performance in practical applications. In this work, we propose a novel feature optimization and outlier detection method for the CSP algorithm. Specifically, we use the minimum covariance determinant (MCD) to detect and remove outliers in the dataset, then we use the Fisher score to evaluate and select features. In addition, in order to prevent the emergence of new outliers, we propose an iterative minimum covariance determinant (IMCD) algorithm. We evaluate our proposed algorithm in terms of iteration times, classification accuracy and feature distribution using two BCI competition datasets. The experimental results show that the average classification performance of our proposed method is 12% and 22.9% higher than that of the traditional CSP method in two datasets ([Formula: see text]), and our proposed method obtains better performance in comparison with other competing methods. The results show that our method improves the performance of MI-BCI systems.

Joint Feature Optimization and Fusion for Compressed Action Recognition

2021 ◽  
pp. 1-1
Author(s):  
Hanhui Li ◽  
Xudong Jiang ◽  
Boliang Guan ◽  
Raymond Rui Ming Tan ◽  
Ruomei Wang ◽  
...  
Keyword(s):  
Action Recognition ◽  
Feature Optimization

sEMG Feature Optimization Strategy for Finger Grip Force Estimation

2020 ◽  
pp. 184-194
Author(s):  
Changcheng Wu ◽  
Qingqing Cao ◽  
Fei Fei ◽  
Dehua Yang ◽  
Baoguo Xu ◽  
...  
Keyword(s):  
Grip Force ◽  
Optimization Strategy ◽  
Force Estimation ◽  
Feature Optimization

Airport Runway Area Detection Based on Multi-Feature Optimization in PolSAR Images

2018 ◽  
Author(s):  
Ping Han ◽  
Can Zou ◽  
Binbin Han ◽  
Qingyan Shi ◽  
Xiaoguang Lu ◽  
...  
Keyword(s):  
Feature Optimization ◽  
Airport Runway
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