scholarly journals Adhesion Force Detection Method Based on the Kalman Filter for Slip Control Purpose

Automatika ◽  
2016 ◽  
Vol 57 (2) ◽  
pp. 405-415 ◽  
Author(s):  
Petr Pichlík ◽  
Jiří Zděnek
Keyword(s):  
Kalman Filter ◽  
Detection Method ◽  
Adhesion Force ◽  
Force Detection ◽  
Slip Control ◽  
Control Purpose

Kalman Filter-based Heavy Hadoop Job Detection Method for Energy Efficient Hybrid Electro-Optical Intra-Data Center Networks

2021 ◽  
Author(s):  
Masaki Murakami ◽  
Nicolas Dubrana ◽  
Yoshihiko Uematsu ◽  
Satoru Okamoto ◽  
Naoaki Yamanaka
Keyword(s):  
Kalman Filter ◽  
Data Center ◽  
Energy Efficient ◽  
Detection Method ◽  
Data Center Networks

The Measurement of Cutting-Force during Compensating the Errors of NC Process

2013 ◽  
Vol 756-759 ◽  
pp. 560-563
Author(s):  
Dong Xi Zheng
Keyword(s):  
Cutting Force ◽  
Detection Method ◽  
Servo Motor ◽  
Force Detection ◽  
Main Portion

Cutting-force error is the main portion of errors in the NC process, and the analysis of the cutting-force error is based on the measurement of cutting force. Introduced the detection method of cutting force, and analyzed the principle of cutting-force detection by detecting the current of servo motor.

Removing Blinks in Video-Oculography

2012 ◽  
Vol 239-240 ◽  
pp. 1165-1168
Author(s):  
Xue Jun Chen ◽  
Chen Hua Zhang
Keyword(s):  
Kalman Filter ◽  
Eye Movement ◽  
Detection Method ◽  
Threshold Level ◽  
Experimental Results ◽  
Video Oculography ◽  
Non Invasive ◽  
Pupil Center ◽  
Simulation And Experiment ◽  
Blink Artifact

Video-oculography (VOG) is a non-invasive detection method used for eye movement. However, during testing, if object blinks, VOG would be difficult to acquire eye movement. A removing blink method based on Kalman Filter was presented. A cubic spline was employed to patch the removed data. Then simulation and experiment were done. The experimental results show that the method well predicts the next state. Compared to a threshold level, it eliminates blink artifact and patches the removed data. The method is a viable means of predicting pupil center for blink in VOG.

scholarly journals Efficient Movement Compensation and Detection Algorithm using Blob Detection and Modified Kalman Filter

2020 ◽  
Vol 9 (5) ◽  
pp. 1187-1193
Keyword(s):  
Kalman Filter ◽  
Video Sequence ◽  
Detection Method ◽  
Detection Algorithm ◽  
Movement Detection ◽  
Blob Detection ◽  
Modified Kalman Filter ◽  
Filtering Technique ◽  
Movement Compensation ◽  
Time Required

In various real time applications such as security and surveillance etc., detection of movement from video sequence is commonly used. In such applications, time required to detect the movement and its accuracy is very crucial. In this paper, an efficient motion compensation and detection algorithm using Blob detection and modified Kalman filter techniques is proposed. The method is mainly based on Kalman filtering technique which is modified to compensate and detect the unwanted movement caused by the camera. Also the shadow effect caused by the variation in the intensity of light and object is removed using thresholding technique. Accuracy of movement detection is improved by implementing the blob detection method. The experimental results obtained from the developed algorithm is compared with few methods existing in the literature for validation.

Sign in / Sign up

Export Citation Format

Share Document