CASIA OpenIR

浏览/检索结果: 共12条,第1-10条 帮助

限定条件        
已选(0)清除 条数/页:   排序方式:
A Wide Learning Approach for Interpretable Feature Recommendation for 1-D Sensor Data in IoT Analytics 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 6, 页码: 800-811
作者:  Snehasis Banerjee;  Tanushyam Chattopadhyay;  Utpal Garain
浏览  |  Adobe PDF(891Kb)  |  收藏  |  浏览/下载:248/66  |  提交时间:2021/02/22
Feature engineering  sensor data analysis  Internet of things (IoT) analytics  interpretable learning  automation.  
An Integrated MCI Detection Framework Based on Spectral-temporal Analysis 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 6, 页码: 786-799
作者:  Jiao Yin;  Jinli Cao;  Siuly Siuly;  Hua Wang
Adobe PDF(2203Kb)  |  收藏  |  浏览/下载:188/52  |  提交时间:2021/02/22
Electroencephalogram (EEG)  dementia early detection  mild cognitive impairment (MCI)  stationary wavelet transformation (SWT)  support vector machine (SVM).  
An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 6, 页码: 737-747
作者:  Siuly Siuly;  Varun Bajaj;  Abdulkadir Sengur;  Yanchun Zhang
Adobe PDF(4171Kb)  |  收藏  |  浏览/下载:174/57  |  提交时间:2021/02/22
Electroencephalogram (EEG)  alcoholism  optimum allocation technique  feature extraction  decision table.  
Predictive Control Based on Fuzzy Supervisor for PWARX Hybrid Model 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 683-695
作者:  Olfa Yahya;  Zeineb Lassoued;  Kamel Abderrahim
浏览  |  Adobe PDF(2179Kb)  |  收藏  |  浏览/下载:101/58  |  提交时间:2021/02/22
Nonlinear control  hybrid systems  mixed logical dynamic (MLD) model  predictive control  fuzzy supervisor.  
A Hybrid Time Frequency Response and Fuzzy Decision Tree for Non-stationary Signal Analysis and Pattern Recognition 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 3, 页码: 398-412
作者:  N. R. Nayak;  P. K. Dash;  R. Bisoi
浏览  |  Adobe PDF(1576Kb)  |  收藏  |  浏览/下载:163/53  |  提交时间:2021/02/22
Non-stationary signals  sparse S-transform (SST)  scaling method  fuzzy decision tree  pattern classification.  
Dual-modal Physiological Feature Fusion-based Sleep Recognition Using CFS and RF Algorithm 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 3, 页码: 286-296
作者:  Bing-Tao Zhang;  Xiao-Peng Wang;  Yu Shen
浏览  |  Adobe PDF(847Kb)  |  收藏  |  浏览/下载:190/50  |  提交时间:2021/02/22
Feature fusion  mild difficulty in falling asleep (MDFA)  decision support tool  sleep issues  optimal feature set.  
An Approach to Reducing Input Parameter Volume for Fault Classifiers 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 2, 页码: 199-212
作者:  Ann Smith;  Fengshou Gu;  Andrew D. Ball
浏览  |  Adobe PDF(1085Kb)  |  收藏  |  浏览/下载:163/70  |  提交时间:2021/02/22
Fault diagnosis  classification  variable clustering  data compression  big data.  
A Survey of the Research Status of Pedestrian Dead Reckoning Systems Based on Inertial Sensors 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 1, 页码: 65-83
作者:  Yuan Wu;  Hai-Bing Zhu;  Qing-Xiu Du;  Shu-Ming Tang
浏览  |  Adobe PDF(1248Kb)  |  收藏  |  浏览/下载:283/86  |  提交时间:2021/02/22
Inertial measurement unit (IMU)  pedestrian dead-reckoning  indoor navigation  technical route  general framework.  
Effective Crowd Anomaly Detection Through Spatio-temporal Texture Analysis 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 1, 页码: 27-39
作者:  Yu Hao;  Zhi-Jie Xu;  Ying Liu;  Jing Wang;  Jiu-Lun Fan
浏览  |  Adobe PDF(1521Kb)  |  收藏  |  浏览/下载:236/67  |  提交时间:2021/02/22
Crowd behavior  spatial-temporal texture  gray level co-occurrence matrix  information entropy.  
Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 1, 页码: 16-26
作者:  Tian-Xiang Zhang;  Jin-Ya Su;  Cun-Jia Liu;  Wen-Hua Chen
浏览  |  Adobe PDF(1123Kb)  |  收藏  |  浏览/下载:202/59  |  提交时间:2021/02/22
Sentinel-2A  remote sensing  image classification  supervised learning  precision agriculture.