Accuracy Improvement When Determining Copper( II ) Ions With Zincon at Acidic Condition

2020 ◽  
Vol 41 (12) ◽  
pp. 1206-1210
Author(s):  
Yesol Lee ◽  
Ra Yun Kim ◽  
In‐Yong Eom
Keyword(s):  
Acidic Condition ◽  
Accuracy Improvement

Accuracy Improvement of Terminal Voltage Self-coupled Laser Distance Sensor

2020 ◽  
Vol 140 (11) ◽  
pp. 1264-1269
Author(s):  
Tatsuya Ohba ◽  
Daisuke Mizushima ◽  
Keishiro Goshima ◽  
Norio Tsuda ◽  
Jun Yamada
Keyword(s):  
Accuracy Improvement ◽  
Terminal Voltage ◽  
Laser Distance Sensor ◽  
Distance Sensor

A Study of a Next Day Electric Load Curve Forecasting Method and its Accuracy Improvement

2014 ◽  
Vol 134 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Hisatomo Miyata ◽  
Kazutoshi Miyashita ◽  
Takayuki Endo ◽  
Yuichi Shimasaki ◽  
Tatsuya Iizaka ◽  
...  
Keyword(s):  
Electric Load ◽  
Load Curve ◽  
Accuracy Improvement ◽  
Forecasting Method

scholarly journals A Driver’s Visual Attention Prediction Using Optical Flow

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3722
Author(s):  
Byeongkeun Kang ◽  
Yeejin Lee
Keyword(s):  
Visual Attention ◽  
Optical Flow ◽  
Prediction Models ◽  
Apparent Movement ◽  
Image Sequences ◽  
Motion Information ◽  
Accuracy Improvement ◽  
Flow Maps ◽  
Current State ◽  
Motion Features

Motion in videos refers to the pattern of the apparent movement of objects, surfaces, and edges over image sequences caused by the relative movement between a camera and a scene. Motion, as well as scene appearance, are essential features to estimate a driver’s visual attention allocation in computer vision. However, the fact that motion can be a crucial factor in a driver’s attention estimation has not been thoroughly studied in the literature, although driver’s attention prediction models focusing on scene appearance have been well studied. Therefore, in this work, we investigate the usefulness of motion information in estimating a driver’s visual attention. To analyze the effectiveness of motion information, we develop a deep neural network framework that provides attention locations and attention levels using optical flow maps, which represent the movements of contents in videos. We validate the performance of the proposed motion-based prediction model by comparing it to the performance of the current state-of-art prediction models using RGB frames. The experimental results for a real-world dataset confirm our hypothesis that motion plays a role in prediction accuracy improvement, and there is a margin for accuracy improvement by using motion features.

Sign in / Sign up

Export Citation Format

Share Document